Tag: sentiment analysis

Natural Language Processing (NLP) Techniques & Examples

Posted on by Emily Louise Spencer

Data is key to understanding what customers want and need. But sifting through mountains of data and analyzing it can prove a daunting undertaking. That’s where advanced AI tools come in. In this article, we’ll discuss natural language processing techniques (NLP) and share examples of their application, examining how they can drive your growth.

The AI revolution is coming. Today, 35% of companies report using AI in their business, an increase of four percent from 2021. And an additional 42% report that they are exploring ways to begin using AI. 

No matter where you are in terms of readiness to begin adopting artificial intelligence and machine learning in your company, it’s to your organization’s benefit to learn about these emerging technologies and understand how you might be able to apply them in order to improve business outcomes. 

Natural language processing, or NLP for short, is the perfect place to start. 

It’s a powerful application of machine learning technology that can be used in a wide variety of industries for countless applications to help with everything from streamlining business processes to boosting efficiency to improving e-commerce customer experience and brand loyalty.

In this article, we’ll dive into everything you need to know about natural language processing including: 

  • What it is.
  • Its advantages.
  • Relevant techniques.
  • Applications.
  • And, finally, real-world examples.

Let’s start from the top.

What is natural language processing?

Natural language processing is a branch of artificial intelligence that aims to help computers to understand human language input in the form of text or speech. 

NLP combines multiple disciplines, including computation linguistics, machine learning, deep learning, and statistics. 

These technologies work together to essentially give computer software the ability to process and understand human language in the way that another human could, including its meaning, intent, and sentiment. 

NLP technology is used in a variety of applications including:

  • Digital assistants such as Siri.
  • Speech-to-text dictation software.
  • Voice-operated GPS systems.
  • Customer service chatbots.
  • Predictive text.
  • Digital voicemail.
  • Autocorrect.
  • Search autocomplete.
  • Email filters.

Additionally, companies are increasingly using NLP to create enterprise solutions that help businesses simplify processes, increase productivity, and streamline operations.

The benefits of employing natural language processing

It’s standard these days for companies to collect, store, process, and analyze large quantities of numerical data in order to generate valuable insights that can improve results. 

Natural language processing opens up and empowers businesses to make smarter decisions that are based on larger sets of data. Further, this collection and analysis process happens quickly, especially compared to traditional methods.

For this reason, natural language processing has a number of relevant advantages. 

When working with so much data, you’ll be able to generate insights to improve customer experience with the launch of new products.

On top of that, using NLP helps businesses become more efficient by automating work processes that require reviewing or analyzing texts. This frees up employees to work on other needle-moving tasks.

Taken together, you’re bound to see improved productivity, reduced costs, and an uplift in revenue.

The top techniques used in NLP

NLP is a rich field requiring the use of a number of different techniques in order to successfully process and understand human language. Below, we review and define a selection of the techniques commonly used in NLP technology. 

Tokenization 

Also called word segmentation, tokenization is one of the simplest and most important techniques involved in NLP. 

It’s a crucial preprocessing step in which a long string of text is broken down into smaller units called tokens. Tokens include words, characters, and subwords. They are the building blocks of natural language processing, and most NLP models process raw text on the token level.

An example from Medium of how a simple phrase can be broken down into tokens.

Stemming & lemmatization

After tokenization, the next preprocessing step is either stemming or lemmatization. These techniques generate the root word from the different existing variations of a word. 

For example, the root word “stick” can be written in many different variations, like:

  • Stick
  • Stuck
  • Sticker
  • Sticking 
  • Sticks
  • Unstick

Stemming and lemmatization are two different ways to try to identify a root word. Stemming works by removing the end of a word. This NLP  technique may or may not work depending on the word. For example, it would work on “sticks,” but not “unstick” or “stuck.” 

Lemmatization is a more sophisticated technique that uses morphological analysis to find the base form of a word, also called a lemma. 

The difference between how stemming and lemmatization work is illustrated in this image from itnext, using different forms of the word “change.”

Morphological segmentation

Morphological segmentation is the process of splitting words into the morphemes that make them up. A morpheme is the smallest unit of language that carries meaning. Some words such as “table” and “lamp” only contain one morpheme. 

But other words can contain multiple morphemes. For example, the word “sunrise” contains two morphemes: sun and rise. Like stemming and lemmatization, morphological segmentation can help preprocess input text. 

John Hopkins shows morphological segmentation by breaking the word “unachievability” into its morphemes.

Stop words removal

Stop words removal is another preprocessing step of NLP that removes filler words to allow the AI to focus on words that hold meaning. This includes conjunctions such as “and” and “because,” as well as prepositions such as “under” and “in.” 

By removing these unhelpful words, NLP systems are left with less data to process, allowing them to work more efficiently. It isn’t a necessary step of every NLP use case, but it can help with things such as text classification. 

Examples from geeksforgeeks of what short phrases look like with the stop words removed.

Text classification

Text classification is an umbrella term for any technique used to organize large quantities of raw text data. Sentiment analysis, topic modeling, and keyword extraction are all different types of text classification. And we’ll talk about them shortly.

Text classification essentially takes unstructured text data and structures it, preparing it for further analysis. It can be used on nearly every text type and help with a number of different organization and categorization applications. 

In this way, text classification is an essential part of natural language processing, used to help with everything from detecting spam to monitoring brand sentiment. 

Some possible applications of text classification include:

  • Grouping product reviews into categories based on sentiment.
  • Flagging customer emails as more or less urgent.
  • Organizing content by topic.

Sentiment analysis

Sentiment analysis, also known as emotion AI or opinion mining, is the process of analyzing text to determine whether it is generally positive, negative, or neutral. 

As one of the most important NLP techniques for text classification, sentiment analysis is commonly used for applications such as analyzing user-generated content. It can be used on a variety of text types, including reviews, comments, tweets, and articles. 

The Revuze platform employs sentiment analysis to understand how customers feel about various aspects of products. This allows companies to gain insights about consumers’ needs in real-time, and act accordingly to improve overall CX.

In this example from the Revuze platform, you can see how customers rate different aspects of the product.

Topic modeling

Topic modeling is a technique that scans documents to find themes and patterns within them, clustering related expressions and word groupings as a way to tag the set. 

It’s an unsupervised machine learning process, meaning that it doesn’t require the documents it is processing to have previously been categorized by humans. 

A sample NLP workflow from Frontiersin demonstrates how Input text is proprocessed before undergoing topic modeling, which breaks it into several topics. 

Keyword extraction

Keyword extraction is a technique that skims a document, ignoring the filler words and honing in on the important keywords. It is used to automatically extract the most frequently used and essential words and phrases from a document, helping to summarize it and identify what it’s about. 

This is highly useful for any situation in which you want to identify a topic of interest in a textual dataset, such as whether there is a problem that comes up again and again in customer emails. 

Text summarization

This NLP technique summarizes a text in a coherent way, and it’s great for extracting useful information from a source. While a human would have to read an entire document in order to write an accurate summary of it, which takes quite a bit of time, automatic text summarization can do it much more quickly.

There are two types of text summarization:

  • Extraction-based – This technique pulls key phrases and words from the document to make a summary without changing the original text.
  • Abstraction-based – This technique creates new phrases and sentences based on the original document, essentially paraphrasing it.

An example from the Microsoft tech community of how the two types of text summarization work.

Parsing

Parsing is the process of figuring out the grammatical structure of a sentence, determining which words belong together as phrases and which are the subject or object of a verb. This NLP technique offers additional context about a text in order to help with processing and analyzing it accurately. 

This is how parsing might work on a short sentence.

Named entity recognition

Named entity recognition (NER) is a type of information extraction that locates and tags “named entities” with predefined keywords such as names, locations, dates, events, and more. 

In addition to tagging a document with keywords, NER also keeps track of how many times a named entity is mentioned in a given dataset. NER is similar to keyword extraction, but the extracted keywords are put into predefined categories.

NER can be used to identify how often a certain term or topic is mentioned in a given data set. For example, it might be used to identify that a certain issue, tagged as a word like “slow” or “expensive,” comes up again and again in customer reviews. 

A sample by Shaip of how named entity recognition works. 

TF-IDF

TD-IDF, which stands for term frequency-inverse document frequency, is a statistical technique that determines the relevance of a word to one document in a collection of documents. It works by looking at two metrics: the number of times a word appears in a given document and the number of times the same word appears in a set of documents. 

If a word is common in every document, it won’t receive a high score, even if it appears many times. But if a word frequently repeats in one document while rarely appearing in the rest of the documents in a set, it will rank high, suggesting it is highly relevant to that one document in particular. 

Natural language processing applications 

NLP is a quickly developing technology with many different applications for organizations of every kind. Some of the different ways a business can benefit from NLP include:

  • Machine translation – Using NLP, computers can translate large amounts of text from a target to a source language, which can be used for customer support, data mining, and even publishing multilingual content.
  • Information retrieval – NLP can be used to quickly access and retrieve information based on a user’s query from text repositories such as file servers, databases, and the internet.
  • Sentiment analysis – This NLP technique can be used to monitor brand and product sentiment to help with customer service and product sentiment, among other applications.
  • Information extracting – This process, which includes retrieving information from unstructured data and extracting it into structured, editable formats, can be used for business intelligence, including competitive intelligence.
  • Question answering – Question answering uses NLP to give an answer to a question asked in natural human language and can be used for chatbots and customer support.

Natural language processing examples

Here are just a few more concrete examples of ways an organization might apply NLP to its business processes.

NLP in ChatGPT

One of the most popular recent applications of NLP technology is ChatGPT, the trending AI chatbot that’s probably all over your social media feeds. ChatGPT is fueled by NLP technology, using a multi-layer transformer network to generate human-like written responses to inquiries submitted in natural human language. ChatGPT uses unsupervised learning, which means it can generate responses without being told what the correct answer is. 

ChatGPT is an exciting step forward in the application of NLP technology for businesses and individuals alike, with many saying it can rival even Google. Possible uses for ChatGPT include customer service, translation, summarization, and even content writing. 

NLP for customer experience analytics

Using NLP for social listening and customer review analysis can lead to tremendous insight into what customers are thinking and saying about a brand and its products. With sentiment analysis and text classification, companies can:

  • Understand general sentiment about the brand – Does the public feel positively or negatively about us? 
  • Identify what customers like and dislike about a service or product.
  • Learn what new products customers might be interested in.
  • Know which products to scale and which to pull back on.
  • Discover insights that can be used to improve customer experience and boost customer satisfaction. 

For example, let’s say spicy chocolate brand Shock-O just released a new Popping Jalapeno Chocolate and wants to get a sense of whether or not customers like it. Shock-O can use an NLP-powered tool to analyze customer sentiment and learn what people are saying about the Popping Jalapeno Chocolate, whether they speak about it positively or negatively, and what themes come up again and again in reviews of this product. 

All of this information can then be used to determine whether to continue producing Popping Jalapeno Chocolate, whether to increase or decrease its production of it, whether to make it spicier or less spicy, etc. 

NLP for customer service

90% of customers believe that it is essential or very important to receive an immediate response when they have a question. Yet human customer service representatives are limited in availability and bandwidth. 

This is just one reason why NLP-powered chatbots are growing in popularity. By being able to properly understand and analyze customer inquiries, chatbots can offer the necessary answers to questions, helping to improve customer satisfaction while cutting down on agents’ workload.

NLP can also be used to process and analyze customer service surveys and tickets in order to better understand what issues customers are having, what they’re happy with, what they’re unhappy with and more. All of this serves as crucial data for boosting customer happiness, which will, in turn, increase customer retention and improve word-of-mouth.

NLP for recruitment

HR professionals spend countless hours reviewing resumes in order to identify suitable candidates. NLP can make this process much more efficient by taking over the screening process and analyzing resumes for certain keywords. 

For example, you might set up an NLP system to flag any resume that uses the word “Python” or “leadership” for a human to review later on.

This can increase the likelihood of finding strong candidates, helping an organization fill open positions more quickly and with better talent. What’s more, it can also free up HR professionals’ time to focus on tasks that require more strategic thinking.

Conclusion

The idea that data has important insights to offer companies has been widely accepted, leading businesses to invest in various business intelligence technologies in order to improve their processes and offerings. 

But if your organization is only mining numerical data, you’re missing out on a wealth of valuable information to be found in unstructured human language-based data. 

Natural language processing is a powerful technology allowing text and words to be analyzed as efficiently as numbers can. By learning about and investing in NLP, you’ll be able to achieve a number of desirable outcomes, including streamlining processes, improving brand reputation and loyalty, and ultimately boosting revenue.

The next step would be taking these actionable insights and using them to further drive CX with e-commerce personalization.

 

How Brand Equity Can Positively Impact Your Business and Drive Growth

Posted on by Emily Louise Spencer

Brand equity is the ability to be recognized and acknowledged as more than simply another face in the crowd. Some brands have it, and even fewer know how to build it. With time and effort, you can learn how to become a master of brand equity, similar to giants like Apple & Microsoft. Your Journey Starts Here.

Brand equity is a great tool to have in today’s ever-changing competitive markets. 

The main benefit of having strong brand equity is that consumers will continue considering your products even when the cost is high. 

Consumers perceive them as having innate value or quality solely because they associate it with your brand.

Being the top brand whenever consumers think of your market sector is the ideal position, but it’s not quite that straightforward. 

I’m sure you’ve heard of the Pepsi vs. Coca-Cola, Apple vs. Microsoft feuds, etc. No one side can claim to truly be at the top of the market, despite all having strong brand equity.

Still, it’s a great position to be in. 

In this guide, we’ll take you through the steps of creating strong brand equity, allowing you to dominate the conversation. 

Let’s begin with the basics.

What is brand equity?

The definition of brand equity is a brand’s perceived value according to consumers. It can also be defined as the level of positive feelings that consumers have about a brand when compared to others in the same market space.

For example, if you order a rum and coke at a bar, you might be asked if Pepsi is okay. Some would answer yes, some no — that’s brand equity. If you buy a new gaming console and are dead set on having a PlayStation? You guessed it, that’s brand equity once again.

Some brands even dominate the market to the point where their name becomes the commonly used word for the item they produce. 

Coca-Cola and Sellotape, for example, have become synonymous with their markets, despite being only one among dozens of brands. That’s strong brand equity at work.

If you have strong brand equity, you have a dedicated customer base and the option to charge premium prices. 

When launching a new product, you’re guaranteed to get customers’ interest no matter what it is. 

That said, you can’t coast by on brand equity alone. You must ensure your products are still top-quality and are within the market’s expectations. Microsoft learned that the hard way with Windows Vista and even Sony with the $600 PlayStation 3.

Keller’s brand equity model (aka brand equity pyramid model)

It’s worth mentioning the Keller Brand Equity model here. We won’t cover it in too much detail as that would be an article in and of itself, but let’s go over it to give you a general idea.

Keller’s brand equity pyramid model states that to gain strong brand equity, you need to shape the way your customers think and feel about you. 

This starts at the base level with establishing your brand identity, then works its way up the pyramid by asking questions about what your brand might want to achieve.

It’s a step-by-step process that makes thinking about how you plan to position yourself and what feelings you want to evoke in your customers. 

Each stage contains crucial components that evoke brand loyalty, so be sure to give it a look if you want to build your brand up to the next level.

The impact of brand equity on customer interactions

Now that you know what brand equity is, you might be asking yourself – “is it worth it?”

It’s true that building brand equity is a long and difficult process, but the results are well worth it. 

Let’s take a look at some of the most tangible benefits, ones that you can point to when an investor asks why you’re putting so much effort into building your brand’s equity.

Customer spending

Brand equity impacts customer spending in two main ways. 

First of all, if you have a high brand equity you can charge more for a product than you otherwise might. In fact, it’s often expected of you to do that. So much so consumers will become suspicious of a product line if you don’t. 

When was the last time you saw a new iPhone going for less than $1,000? It would seem suspicious if it did, right? That’s Apple’s brand equity at work.

The second way in which customer spending is impacted is in making decisions about what to buy, especially in cases where a customer has little knowledge of the products in that market sector.

When a not technologically aligned parent decides to buy their child a simple phone for calls and texts, they’re left to rely on what little they know about a brand’s reputation.

What do they pick? An obscure and niche phone with specific uses? Or a well-known brand such as Apple or Samsung that they’ve probably heard of in passing? Probably the latter, right?

That’s brand equity in action. 

Customer loyalty & advocacy

I’m sure you’ve come across a friend or acquaintance who buys only from a specific brand and won’t accept replacements. I mean, what other laptop could replace my trusty Macbook?

I’ve grown to love it and how it functions so much, that buying another Macbook when it’s time to replace it is a no-brainer. And there are millions like me.

That’s the epitome of customer loyalty, which is different from customer retention (coming up in a few paragraphs).

Customer advocacy is when that loyalty is taken one step further. 

In essence, the customer becomes someone who will promote your brand to their friends and acquaintances, sometimes to the point of convincing them to switch brands.

Brand equity is of great help here. Not only do customers have a much easier time advocating for a brand that is well known, but the actual process of loyalty can be sped up tremendously.

Customer loyalty relies on great experiences, that’s true, but the opinions of others also matter. A HubSpot study on the topic found that 81% of consumers would rely on referrals from friends and family to choose & try a brand over an advertisement. This means that you’ll likely need a recommendation simply to get on the customer loyalty ladder in the first place!

Having strong brand equity means that people are more open to trusting you from the get-go, which makes climbing that ladder from customer to loyal customer to brand advocate that much quicker.

Customer retention

Your customer retention rates are one of the key metrics that help your business keep going. After all, if your customers leave unsatisfied and don’t return it’ll hurt your performance in the long run. A mere 5% increase in your retention rates can bring up to a 25% increase in profits!

Your churn rate, or the rate at which you lose customers over time, is another measure that’s similar to customer retention, just in the opposite direction.

It’s calculated by taking the number of customers who stopped interacting with you over a set period of time and dividing it by the number of customers you had at the start of the time period, then converting it into a percentage.

If your churn rate is high, your customer retention rate is low. Churn is often a more useful metric to look at than retention since it’s more directly comparable over different periods of time.

So, what does brand equity have to do with customer retention? After all, retention rates are solely about customer experience, right? Well, not entirely.

Research has shown that customers care about more than simply their experiences with you, with 80% being willing to change brands based on “a company’s social responsibility, inclusiveness, and/or environmental impact.” How does news on these topics spread? Via brand equity of course.

An apt metaphor to describe this would be meeting someone for the first time. Consider what would happen in the following circumstances.

You meet someone who is clearly in a bad mood, is rude to you, and snaps over minor things. You’d feel insulted, maybe even a little scared. You mark this person in your brain as bad news, and won’t want to deal with them again.

You then tell people that you know about this encounter, and how you felt. They have met this person before and reassure you that they aren’t normally like this, that it must have been a bad day or something similar. 

From this, you decide to revise your opinion, and the next interaction you have with them is great! Clearly, it was just an off day and they’re not normally like this. 

Having strong brand equity keeps customers coming back to you, even when they’ve had one bad experience. 

It’s a sense of trust that the consumer population as a whole has with you, which means that individuals are willing to give you another shot even when they didn’t like what you had to offer the first time around. 

Brand equity’s impact on your internal workings

Brand equity doesn’t just impact your dealings with customers, rather it shapes the very way your business will operate. 

There are plenty of strategies and tactics that big brands with strong brand equity can use that smaller, less well-known ones cannot. 

One example that springs to mind is the TV show Rick and Morty, which premiered its third season completely unannounced back in 2017. 

Any other television show would spend time hyping up a new release, using advertisements, press releases, and other means to keep the buzz going. 

Rick and Morty’s strong brand equity meant that it didn’t need to do that in order to keep viewers engaged.

There are more internal benefits to having strong brand equity than pickles and portals. Here are a few.

Stock prices

Stock prices are a great indicator of how your business is doing. Of course, this is only applicable if you actually have them up for sale, but let’s go over them briefly anyway. If this isn’t relevant to you, feel free to skip to the next section.

Strong brand equity will increase your stock prices, as it brings with it the expectation that the brand will continue to perform well. This in turn can also increase your brand equity in a feedback loop, though there is a limit to it.

So, why are stock prices important? Well, they’re an indicator of how well your brand is doing in its market, as well as a status symbol that can open doors to you that would otherwise be closed.

Higher stock prices are also attractive to investors who will continue to put funding into your brand if they think it’s going to give them a good return on investment.

Easy expansion of product lines

Creating new product lines is never easy, however with brand equity you can make the process a bit smoother.

Imagine a completely unknown business releasing a new line of soda drinks. They’re unusual flavors that haven’t really been tried before, and overall the public seems uncertain. Would you buy that drink, or would you avoid it for your regular soda?

Now, let’s flip the circumstances. Let’s say that Coca-Cola releases lots of new and unusual flavors. You know the brand, and know what they usually make is considered good quality, so you’re more likely than not to try it out at least once.

There is actually a great real-world example of this with Walkers, the UK-based potato chip company that regularly comes out with absurd flavors such as Breakfast, Fish & Chips, and even Squirrel! That’s not a joke, they actually did this.

Thanks to their strong brand equity, Walkers have been able to turn their experimental product lines into a game of sorts, with the most popular limited-time flavor being kept and turned into a regular product. 

Not only did the public do their research for them, but they actively engaged with their product testing and expansion. 

Imagine a no-name brand releasing these flavors, they’d likely be considered a joke. That’s what brand equity is truly capable of doing! 

Greater influence on the market as a whole

Strong brand names bring with them a sense of dominance. 

With strong brand equity, you’ll be able to negotiate with others from a position of power rather than equal footing or from a position of weakness.

With this position at the negotiating table comes opportunity. Partnerships, sponsorship deals, and collaborations, all these are possible only if you have a strong bargaining position. 

You also open yourself up to greater investment potential and maybe even get better deals from your suppliers once you’ve made a name for yourself.

Five ways of measuring brand equity

Alas, measuring brand equity isn’t straightforward. There are many factors to consider, and which one you put weight on will depend on your business model, industry, etc. 

Further, brand equity isn’t something you can measure in cold numbers. Still, there are a few tried and tested brand equity analytics you can use. We’ve laid out five of them below for your consideration.

Competitive analysis

Competitive metrics set you up against your competitors and see how you’re doing compared to them. 

It’s a more aggressive form of analysis that takes their marketing campaigns and yours, sees their results, and tells you how well you’re doing in comparison. If your competitors are lagging, that means you’re leading, and vice-versa.

Other factors you can look at to compare brands include relative customer sentiment, acquisition rates, social media engagement, etc. 

Remember though, just because your competitors are below now doesn’t mean you can relax. They’ll be looking for ways to improve just as you are, and if you stop to watch, you’ll be left behind!

Financial data

Another metric you can use to measure brand equity is financial data. 

Market share, profits, revenues, prices – these all tie into how well your brand is doing, since more brand equity correlates with more customers. Compare these to those of previous years or quarters, and you’ll be able to measure brand equity data over time.

Customer lifetime value is another strong indicator. 

Essentially it’s the value that a customer brings to you during the entirety of their total interactions with you. 

CLV = Average purchase price Average purchase rate Average customer lifetime

Strong brand equity correlates to higher CLV since loyal customers will bring in more revenue for you overall. Conversely, if you need to keep re-attracting customers, it might end up lowering their overall value to you since acquiring a customer is more costly than keeping a current one.

Also worth mentioning is the cost of acquiring new customers, which is a huge indicator of brand equity.

If said cost is high, it means that it takes a lot of incentive for a consumer to switch from a competing brand to yours, meaning your brand equity is low, and you need to work on your image.

Brand awareness

Brand awareness is another abstract quality that’s hard to measure, but nevertheless, it’s very valuable when you’re looking at your brand equity. 

To put it simply, if consumers don’t know about you, then they won’t buy from you. Further, if they know of you only vaguely, you won’t be their first thought when looking for a product.

Having high brand awareness means that you’re synonymous with the market you’re in – like the examples of Coca-Cola and Sellotape mentioned earlier. 

Being so well known comes with certain risks to your brand, as you lose copyright on any name that becomes the commonly used term for an item, but it’s a definite sign that you’re well up there in people’s minds. 

Coca-Cola managed to retain its trademark since the commonly used term is the nickname Coke. Sellotape, However, lost theirs when the term was deemed genericized enough.

Ways you can measure brand awareness include:

  • Surveys.
  • Store traffic.
  • Search volume.
  • Google search rankings.

These aren’t the end all be all, but they’re a good start. You can also look to social media for hints, but this information will be highly polarized due to the nature of such spaces. 

After all, when would you be more likely to post on social media? After a routine, bog-standard experience, or one that was absolutely awful? 

Customer sentiment

Customer sentiment is about feelings, specifically customers’ feelings towards a particular product or brand, depending how you measure it. 

Customer sentiment is a measure of how strong the emotions associated with your brand are, and how positive or negative they are. 

It’s especially important in today’s markets, as 86% of customers are willing to spend more after a positive experience with a brand.

Generally, customer sentiment is generated by surveys or similar methods, asking customers to rank their experiences based on how they felt about their interactions with you. 

However, it can also be found by scraping review data with sentiment analysis or analyzing social media chatter. 

It’s not straightforward at all to measure customer sentiment, and you may need to use specialized platform like Sentimate to analyze the data for you.

Brand audits

Something to consider when you’re analyzing your brand’s equity is what’s the total value of the brand itself, or what it contributes to the business simply by existing. 

There are a myriad of factors you can measure when doing this, but depending on who you are and what you do some will be more important than others.

In general, things to consider when auditing your brand are:

  • The cost to build the brand. How much money did you pump into advertising, trademarks, etc.?
  • The market value of the brand, or how much value it brings to stamp it on a product. Can you charge more for a branded product compared to a generic equivalent?
  • The income value of the brand, or how much money it brings in by making customers aware of your products. Can you launch a new product and expect high sales, or would you need to put funds into advertisement?

How to build & develop brand equity (with examples)

Brand equity develops in two distinct ways. 

Firstly, there’s the way in which awareness about a particular brand can spread over time from person to person naturally. This is often overlooked as a method of building brand equity as it is a slow process but nevertheless is important.

The second way is to build it yourself, taking action to increase your brand’s visibility, reputation, and relationships with consumers. 

We’ve outlined below the processes by which these two methods take place, as well as how you might go about beginning the latter.

How brand equity develops organically

Brand equity is something you’d ideally want to craft, however, it’s also something that can develop naturally over time. 

Back in the 1950s, for example, they didn’t have the knowledge we do on how brands can build equity for themselves, yet Ford was still considered a top-tier manufacturer of cars. 

This happened because information can spread organically from person to person by word of mouth, which increases brand equity without any input from the brand itself. 

Let’s take a look at the process by which this happens.

Awareness

In the first stage, a consumer becomes aware of your brand’s existence. This can be via spotting products on shelves, seeing advertisements, or simply by word of mouth.

They will have no immediate opinion on them beyond what others might have told them and their immediate gut response to anything of your brand’s image they’ve seen.

At this stage, it’s not likely that a person will buy from you, but a small number of them might do so. If they do, they skip the next step and go straight to the third one.

Recognition

Next, the person in question will come across your brand again. This time they’ll recognize it, and it won’t be completely unknown to them. 

Their prior experience with your brand will add to the current one, forming an opinion. 

This is where good advertisement comes into play, as many potential customers simply gloss over a brand at this stage if it doesn’t catch their eye, forget about it, and do not progress further along in the process.

Trial

In the third step of the process, a consumer will feel comfortable enough with your brand to test one of your products. This might come after coming into contact with your brand just a few times, or it may take longer.

The important part of this step is that the person takes the leap from consumer to customer. They’ve invested money into you, and their opinions will be highly polarized by their experiences with your product or service. 

If the customer likes what you have to offer, it’s likely that they’ll come back. If they don’t, they won’t, and might even badmouth you. This fact is why businesses will often advertise their generic products more, leaving the more niche ones aside as fewer customers would prefer those as their first experience with the brand.

Preference

Next, a customer who has had good experiences with your brand will begin to prefer you to others in the market. This step absolutely requires that you get the previous one right, with most potential advocates straying from the track at this point. 

It’s not enough to simply be good, you see, you have to be better than their previous brand in order to convince them to prefer you. It’s been shown time and again that humans are creatures of habit, and won’t change their habits unless given an incentive to.

In this case, that incentive is a better experience than your competitors provide.

Loyalty

When a customer has had repeated good experiences with a brand, they will not only prefer it but begin to recommend it to others. 

After all, wouldn’t you want your friends to have a good time just like you did?

It’s at this stage that a customer can be considered an advocate for your brand. They will spread information on you to another person, who will then begin this whole process all over again as they’ve just become aware of it.

Advocates don’t just help spread awareness either, their efforts can be seen at every step of the brand equity process. 

  • If you’re aware of a brand but haven’t yet tried it out, someone recommending them to you might convince you to give them a go.
  • If you’ve tried out a brand, but haven’t committed to them, the opinions of others might help sway you.
  • If you’ve tried out a brand, and had a single bad experience, hearing about the good experiences of others might convince you to give them another try.
  • If you have a preference for a brand but aren’t comfortable talking about them to others, seeing another person do so might put you at ease.

Keep in mind, however, that not everybody finishes these steps. Some may simply prefer not to air their opinions so openly, others might simply be stubbornly stuck to their current brands. That’s okay though, not everyone needs to be an advocate in order to spread brand equity!

Building brand equity yourself

Brand equity spreads organically, though this is a slow process. In order to speed things up, there are several things that you can do in order to increase your brand equity artificially.

These factors really dive into the why and how of your brand. Consumers want brands that stand for something, that have a purpose and a meaning behind them. 

You need to have more tangible business goals than simply “be successful and make money”, and they need to be ones that consumers can relate to in order to truly create brand equity.

The sections below aren’t steps per say, but rather overarching guidelines that you should always keep in mind when attempting to build your brand equity. There’s no point at which you can say you’re finished, you should instead be constantly analyzing your brand and the world around it.

Understanding your brand’s drive

The purpose of your brand needs to be clear in order to build strong brand equity. If you take a look at the most prominent brands today, you’ll find that they put their purpose and drive at the forefront of their communications.

That’s not to say that they all have the same messages or goals. Each brand has its own unique approach, meaning you can’t simply copy someone else’s drive if you want to set yourself up as unique.

So, what kind of messages are there? Let’s take a look at two prominent examples in the tech industry – Apple and Microsoft.

Apple

Apple’s stated purpose is to stretch the limits of technology, to create things that no one else can. To that end, they portray themselves as providers of future technology.

Apple’s advertising tends to focus on the brand itself, more than the products, which has allowed them to break away from their initial focus on computers and into phones, tablets, and even TVs.

Overall, Apple’s strategy has been to present itself via dazzling and simplified displays that cling to people’s minds. It’s certainly worked, with their advertisements being some of the most memorable and creative in recent years.

Microsoft

In contrast to Apple, Microsoft portrays itself as reliable, down-to-earth, and hard-working. In other words, similar to your average working Joe. Instead of being a futuristic, out-of-this-world brand that dazzles you, they stick to the practical aspects.

Microsoft positions itself as the good old reliable company that will never let you down, one that keeps working people in mind. 

While certainly less exciting than Apple’s dazzling displays there’s no denying that the straightforward, practical-centered message resonates with a lot of consumers worldwide, resulting in Microsoft’s systems being the most used by far.

Of course, a brand’s drive can change over time.  Markets change, technology evolves, and the needs and desires of consumers change too. A business that aims to provide dial-up internet service would find it extremely difficult to attract customers today, for instance, despite it being a fairly attractive, low-cost option just 20 years ago.

Developing your brand’s message

When you’re creating messages that consumers will encounter, it’s important to make sure that they’ll find them appealing and interesting in order to further engage with you.

In other words, it’s not just what you say, but how you say it too.

The key element of your brand’s message is taking your drive and translating it into real-world problems that consumers face. Specifics and details are extremely important, as consumers are put off by vague wording and ill-defined tones.

So, how do you find out what consumers would relate to? In one word, data.

  • Consumer opinion surveys can tell you directly what worries them.
  • Search traffic is a great indicator of what topics are growing in importance.
  • Social media is a goldmine of opinion data and is searchable and segmentable.
  • Reviews and ratings of similar products or services to yours can give insight into consumer desires.

One thing to keep in mind is that deciding your message isn’t something that you do once and then stick to. As times change, you need to change too, and altering your message in order ot appeal to consumers more is standard practise for most brands.

Driving awareness of your brand

Being aware of a band means more than acknowledging its existence. You want customers to understand both what you stand for and how you plan to uphold your values.

Awareness comes with long-term strategies, and taking actions that align with your values. It’s a trust factor, one which will only come after you’ve demonstrated your commitment to upholding the values you’ve stated.

The most important thing you can do with your awareness strategies is to be consistent. Consumers connect the most with brands that they can form emotional bonds with, which onloy happens if that brand is consistent in its ways. You’ll get more out of long-term, loyal customers than you ever would by simply partaking in one-and-done sales.

In short, focus on the broader future of your brand instead of simply the next transaction. While you might profit in the short term, you’ll lose out in the long run.

Maintaining consistency & transparency

Once you’ve established your brand, don’t change it unless you have to.

This might seem completely opposed to everything we’ve spoken about in the previous few sections but bear with us here.

When we say keep your brand the same, what we mean is the personality and tone behind your brand needs to remain consistent in order for customers to continue to relate to you.

While there have been a few instances of brands radically altering their image in order to refresh themselves – see Savage Wendys – it’s generally better to maintain your image.

If you do pivot, make sure to stay consistent. Wendy’s has been roasting ordinary people and antagonizing their competition on Twitter for over half a decade now, and has become something of a sensation.

The customers that you retain tend to do so because they relate to you and your brand. If you wipe the slate clean, you’ll have to re-acquire loyalty from them all over again.

Sometimes, newer isn’t always better. Then again, that’s up to you to decide.

Customer experience

Customers are at the heart of brand equity. News can travel faster than ever in the age of the internet, and bad news always seems to spread the fastest.

The solution? Simply providing a good customer experience.

Brands aren’t defined just by what they do anymore, they’re also known for how they do it. Unless being rude to your customers is part of your appeal, and yes, there are actually businesses that do this, you need to put great customer experience at the heart of your brand.

Social media is a great place to let customers air their praises and grievances to you. By taking note of the former you can continue to provide great experiences in the future, and by responding to the latter you’ll be potentially turning a negative into a positive. Almost all brands, even smaller, local ones, have some kind of social media presence.

An often overlooked way to gain insight into what kind of experience your customers want with you is simply to ask them. While it’s not always possible to get real-time feedback, asking your customers how you did at the end of each interaction can get you detailed information on how your strategies are working.

At the end of the day, the customer is king – at least when it comes to brand equity, anyway.

Real-life examples of building brand equity

All this talk of brand equity sounds very impressive, but you might be wondering – if it’s all hypothetical, nothing guaranteed, what’s the point of it? 

Well, we’ve gathered below some real-life examples of how brand equity was built, as well as the lessons you can learn from them.

Maggi

You might know Maggi as a provider of cheap, filling instant noodle snacks. What you might not know is that they were banned in India in 2015, after regulators determined that their products weren’t as free of MSG as they claimed, and even contained lead!

The validity of these tests was later called into question, but you’d expect there to be some damage to their reputation … right?

Despite the fact that these noodles were banned in the entire country of India for almost six months, and that production had been halted during this time, there was still an enormous demand from the Indian population for their one-pot snacks.

So, why is this?

Well, Maggi’s success was in adapting to the culture present in India. In quite a few nations, offering noodles as an alternative to rice would be seen as sensible, however in India, the idea of “rice for dinner” is so ingrained (no pun intended) that they needed to try a different strategy.

Instead, they advertised their noodles as an afternoon snack, something that could be made and eaten quickly by those in a rush – for instance, parents who needed to feed their children quickly after school.

In essence, Maggi offered itself as an “in-between” option and did so with great success. The convenience of their products meant that even after a scandal that halted sales for six months, many households still returned to consuming them almost immediately.

The lesson here? Adapt yourself to the demands of the market you find yourself in.

Netflix

Netflix is a huge success story when it comes to brand equity. Once they were nothing but another video rental company, now they’re synonymous with online streaming services. 

They’ve even entered our casual vocabulary as a verb … to Netflix and chill. 😉

Netflix was able to build its brand equity by being one of the first organizations out there to expand into what it’s now known for – streaming services.

In fact, I’d bet that a few of you reading this don’t even know that it did anything else before streaming.

Netflix was in the right place at the right time to begin the streaming revolution, launching its platform in 2008. It may not have been the first streaming service, but it was definitely the first major one.

Why was this the case? Well, they were already established as a video rental company at the time. 

With the rise of the internet, Netflix saw that they had an opportunity to expand their services. Eventually, as their streaming service gained momentum, they turned it into their primary source of revenue.

Today, Netflix no longer offers video & DVD rentals.

The lesson here? Adapt your brand’s strategy and identity to changing times.

Conclusion

Hopefully, after reading this guide you’ll know a little more about brand equity – what it is, how it’s grown, and how it’s maintained.

Brand equity requires knowing your brand, and knowing what your brand’s greater purpose in the world is. That’s a big question to ask, and a lot of brands can’t even boast of having one.

By having a purpose, a message, and the means to spread awareness of these, you can propel your brand to great heights. People naturally seek purpose in life and align themselves with those brands that hold values they can understand and empathize with. 

It’s not entirely out there to say that these purposes sometimes matter more to them than the products & services that these brands provide. 

Take a deep breath, and ask yourself – what is our brand’s purpose? What can we do to make sure this purpose is fulfilled? Do that, and you’re on the right track to having brand equity for yourself.

How Competitive Intelligence Lets You Stay Ahead of Your Rivals

Posted on by Emily Louise Spencer

Businesses don’t exist in a bubble. Competitors always try new tactics to gain market share, leaving you behind. That’s why performing competitive intelligence isn’t optional these days but a must. This guide will walk you through the details of competitive intelligence, from basic definitions to actionable strategies explaining how to perform it and gain the upper hand.

 

Imagine, if you will, that you’re a baseball player who’s up to bat.

You’ve no idea what pitch the pitcher is going to throw.

You’ve got no clue where the catchers are planning to move to.

All you can do is make your best guess, right?

Well, not exactly. Each pitcher has their preferred throws, and there’s a good chance they’ll use one that they think you’re weak to. If you prepare for such a pitch, you’re more likely to score a home run ????. 

In a way, the world of business is just like the game of baseball. Being able to predict what’s going to happen next will give you a huge advantage. You’ll be able to see your competitors’ latest moves to try and squeeze you out of the market and counteract them.

That, quite simply, is competitive intelligence. 

What is competitive intelligence?

Competitive intelligence is data collection and analysis by your organization on its competitors. This is done using openly-available sources, such as:

  • Press releases.
  • Patent filings.
  • Whitepapers. 
  • And more, which we’ll cover later on.

Public companies are much easier to gather data on, as they are lawfully required to publish their quarterly earnings. Private companies are a little bit more difficult to examine, but there are methods you can use. Legal ones, that is.

Competitive intelligence vs. industrial spying

When we say competitive intelligence, you might think that you’ll be aiming right for the target and getting ahold of your competitors’ strategies directly. 

This is not actually the case.

You see, competitive intelligence is done legally. 

Every business has a right to keep its inner workings a secret, especially concerning its information. Stealing that information is highly illegal as it would involve either breach of contract or hacking into their servers.

Information gathered illegally can, of course, be used to make decisions, but that’s not competitive intelligence – it’s industrial espionage, which we strongly discourage.

How do you get competitive intelligence?

Competitive intelligence has many sources.

Which ones you turn to will largely depend on your intentions when performing your research. Nevertheless, all of them can provide valuable data if analyzed correctly.

Blog content

The content that your competitors write about on their blogs is a gold mine of information. Not only does it tell you what type of consumer they’re hoping to attract, but how they’re hoping to attract them.

Just like our blog, you’ll find a variety of topics based around the general market that your rivals are in being written and posted. Some of them might seem to have little relevance, but when you analyze them fully, you’ll find ways in which they link back to your competitors’ game plans.

We wouldn’t be writing about competitive intelligence here at Revuze if our software couldn’t help in that regard, now would we? Our AI can help you obtain competitive intelligence in real-time, so feel free to book a demo if you’d like to learn more.

Social media

Social media often has a news component for businesses, whereby they keep their stakeholders updated with their new and ongoing dealings.

Thanks to this, they’re prime information sources. New product launches, new deals, and customer responses, you can find a lot of data on these platforms.

You might think that a lot of social media information can only be used in reactive responses rather than proactively, and in a sense, you’re both right and wrong. 

You can only respond to each individual piece of information, however, they may reveal a trend that shows your competition’s ongoing strategies.

Take, for instance, the example below from Nestle.

Taken individually, each advertisement gives off a different message. The first one showcases the benefits of plant-based food, the second promotes involving children in your cooking.

When looked at together, they tell a different story. Nestle is promoting healthy eating, presumably because they want to boost sales of their healthy eating product lines. This is a bold new direction for a company known mostly for its chocolate and confectionary sales.

Reviews & feedback

Whenever your rivals launch new products or services, the first thing you should look at is their reviews. 

Not only will they give you insight into what your competition is trying to achieve, but they’ll tell you the audience’s response to it.

There’s no use creating the perfect egg scrambler if your customers want their eggs fried, after all.

Reviews are useful for existing products too, with any changes reflected in the feedback they receive. 

Job boards

While not the most intuitive of places to look, job boards can nevertheless be a valuable source of information.

Imagine for a second that your main rival has suddenly posted a lot of job openings in their R&D department. This is pretty unusual since they typically have a low personnel turnover rate.

So what’s going on? Well, there are several options:

  • They’re looking to expand their R&D department permanently.
  • They’re starting an experimental project which they aren’t sure will succeed.
  • They’re feeling behind on their research and want to hire more staff to catch up.

In the case of the first option, the job listings would be permanent posts. In the case of the other two, they’d likely be temporary. 

The second option might also be listed as “temporary with a possibility of becoming permanent.”

Just like that, simply by keeping an eye on the job boards, you’ve gained valuable information on what your rival is up to.

Financial statements

As mentioned previously, public companies have to release their financial statements quarterly. With this data in hand, you can see:

  • Where they’re earning the most money, in essence, what areas they rely on.
  • Where they’re earning more money than last quarter, therefore growing their operations.
  • Where they’re investing their money, what they’re buying, and what they’re not.
  • Which strategies of theirs are working, and which aren’t.

Press releases

Business news is absolutely full of valuable information. There will be announcements about new products, new hires, expansion moves, and more.

Some businesses even have a News section on their websites or apps, which coalesces all this information in one place.

Local information

If you’re part of an industry that has brick-and-mortar stores, you can look at the local information surrounding said stores in order to gather information.

What times do your competitors open and close? What area of the town or city are they located in? Do they have a decent amount of floor space dedicated to customers? Do they keep their stock on hand in a back room or a nearby warehouse?

All these little details can tell you what might and might not work in your own stores. Of course, copying your rivals exactly isn’t ideal as they might be constrained by circumstances and not able to operate as they would ideally, but it’s a start.

Legal papers

You can look at legal papers filed by your competitors as they are typically publicly viewable. 

For instance, a planning permission request for a large industrial building would indicate that your competitor plans to build a new manufacturing plant or similar establishment.

This tells you that your competitor is trying to expand their manufacturing processes, and if it’s in a new location, you can assume that they’re trying to expand into a new market.

Another great source of information is patent filings. These will tell you not only what your competition is doing but what they plan to make legally exclusive. 

It’s sometimes said that there’s not an original thought under the sun that someone hasn’t already had at one point. That’s true in R&D as well. 

You might have to end up abandoning your projects or adjusting them not to break copyright law if your competitors patent what you’ve been working on.

Industry conferences

Conferences are similar to news reports, except more detailed. They’re designed to showcase all of the best aspects of a business to others within their industry, whether that’s to attract new customers or collect new talent.

Conferences are often the most detailed sources of information on this list, and that’s for one simple reason. You can actually talk to participants and get their opinions and perspectives.

All news, legal papers, and social network content are heavily moderated by those who release the information. When talking to someone face to face, you can often get far more information.

The downside is the unorganized nature of gathering data at conferences. You also have to factor in the ability to get to the conference, accommodation, etc. 

So are conferences worth it to attend? It’s up to you, but we’d say definitely. The rise of virtual conferences is also making attendance easier, so keep an eye out for these opportunities.

What are the goals of competitive intelligence?

So, you might be thinking, why use competitive intelligence? 

If it’s all indirect information, is it really going to give me an accurate picture of what my competition is up to?

Competitive intelligence is akin to following footprints in the sand. You might not be there to see the person walk it, but you can see which direction they’re going in, and when they shift or double back on themselves.

You can infer a great deal from indirect information. If you want an example from history, look no further than when Kodak accidentally discovered the Manhattan Project, simply by noticing that they were getting reports of radioactively-contaminated film in the area around the test site.

Anticipating your competitor’s moves

“Know thy enemy, and know thyself,” said Sun Tzu in the Art of War.

While not quite a battle between armies, business is no less competitive. If you know your rivals’ moves before they begin, you will be able to counter them with great effect.

Let’s say that both you and your competitor are in the e-commerce business. 

You know from experience that your competitor has put on a Black Friday sale every year, thus you can anticipate that they’ll do the same this year and develop a strategy that accounts for this.

There’s also the option to take a look at their past sales, as well as current hot items, which will give you a clue as to which products they might put on sale this year. If you know which categories they plan to target, you can sidestep or counteract them.

All in all, the best way to counter an opponent’s strategy is to never let it get off the ground in the first place.

Driving your innovation

You always need innovation in order to keep your business fresh and on top of things. This goes doubly so for those dealing with the ecommerce market, where trends and habits change faster than anywhere else.

Competitive intelligence can uncover what it is that your competition has, but more importantly, it can also reveal what they lack and what consumers are after. No one can cover all bases, after all, and by keeping an eye on new opportunities, you can really get ahead in the game.

Keeping track of market conditions

Markets change, and you need to change with them in order to survive.

One of the greatest examples of decision-making in business is Netflix, which in 2007 launched a streaming service alongside its video rental one. This proved a huge hit and allowed Netflix to become the giant that it is today.

Netflix saw which way the wind was blowing as music consumers turned more and more to digital media and decided to follow it. It allowed them to survive a major upheaval in the industry, which many, including Blockbuster, did not. 

Netflix’s changes showcase a great example of strategic intelligence in action. Video rental was still popular back in 2007, after all, and they could simply have stuck to it. 

Instead, they took account of trends that showed a shift towards online streaming and were able to adapt and weather the storm. 

Blockbuster, on the other hand, continued to have faith in their traditional methods and failed to take into account the long-term implications. There’s only one Blockbuster left in the US now, and for a good reason.

Growing your brand to customer expectations

Times change, and so do people. Nobody would say that applying the same business tactics that worked in the 1950s is appropriate in today’s world, but few realize just how fast expectations can change in the age of the internet.

This is something that e-commerce-based businesses especially need to be aware of, what with the internet being an ever-evolving pace. 

Payment options, fonts and colors, images – especially if you try to use internet humor to sell your brand – links and plugins, all these can have a massive effect on your ability to sell. 

If you use unusual plugins, for instance, you’ll turn off most customers who won’t want to install one to use your store. 

If you’re given the option between two sites, one of which requires you to download extra features to use while the other is free of such inconveniences, which would you choose to use?

Links can also break or be archived over time, meaning what worked perfectly one day won’t the next. Relying on other websites to grant information is a tricky business.

If a certain brand that you’re displaying prominently has bad press, you may want to decrease its visibility on your store lest you get a bad reputation by association. Conversely, you can increase the visibility of other brands that have a surge in popularity.

Competitive intelligence will help here, telling you what customers expect from you and what you need to do in order to meet those expectations. 

A good example of growth meeting customer expectations is Amazon’s adoption of Venmo as a payment option, allowing those who don’t want to use their bank accounts directly a more secure way to pay.

Credit and debit card fraud has been on the rise over the past few years, making more and more consumers wary of using their cards online. 

When using Venmo, your bank information is never directly given to the vendor, meaning that phishing attacks or breaches in Amazon’s security are unlikely to put consumer data at risk.

The COVID-19 pandemic showed just how fast brands needed to be able to change to survive, with plenty of businesses not making it through 2020 intact. 

While not exactly a typical event where markets are concerned, it does nevertheless highlight how much circumstances can change in short periods of time.

Knowing your position within the market

Another great example of where competitive intelligence can help you is in finding your market position. 

You see, it isn’t always obvious from sales figures and other such indirect data exactly where you stand nor how you are perceived by the consumer base.

Data sources such as reviews, ratings, etc., can give you an insight into the consumers’ minds and how they perceive your brand, letting you assess your standing. 

Are you seen as a first-pick, top-rate seller? Are you just a reliable alternative when the usual options aren’t available? Or are you seen as someone niche who fulfills specific demands that only a few consumers would have?

By using this information and comparing it to your strategies, you can see if you’ve achieved your targeted market position. If you have, great! If not, you’ll gain insight into why this is the case and how you might resolve that in the future. 

Remember, Spotify was able to alter its position from merely a simple music player to a content creation platform in just a few years, proving that anything is possible if you have the right intelligence.

Staying ahead of your rivals

Let’s go back to the baseball metaphor. Your biggest rival has started upping their game by purchasing new players specifically to beat you. 

You can’t stop them from doing this as the people selling the players aren’t going to stop just because you ask nicely. So, what can you do?

Well, there are several ways in which you can undermine them, all above board.

For starters, you can either coach your team in strategies that counteract theirs, or you can hire new players yourself to counteract their changes. 

If one pitcher they obtain is known for their difficult-to-hit fastballs, you can hire a hitter who’s known for being able to knock them out of the park.

Secondly, you can put in bids yourself. 

Not only can you stop your rival from obtaining certain players that way, but by entering into bidding wars with them, you can potentially limit the number of players they can buy. 

An organization only has so much money to throw around, after all.

Following on from that, if you take a look at their team and the players they’re placing bids on, you might be able to see dazzling combinations that would work very well together and disrupt them. 

For instance, if you split up a pitcher-catcher combination that is known for doing great together, you can disrupt their seamless play by having their players have to adjust to each other.

In short, just because your competition plans for something to happen doesn’t mean that they will be able to pull it off.

Helping decision making

Finally, we get to the main point that underlies most use of competitive intelligence, that it helps you decide what to do next.

All of the previous examples we’ve mentioned today allow you to make better choices, ultimately helping you make data-driven decisions that will elevate you in the market. 

The more information you have, the better a decision you will be able to make.

If the British had known that Washington planned to cross the Delaware, they wouldn’t have simply let him slip through their fingers. 

Knowledge is power and some would say knowing your competitors is a superpower.

Types of competitive intelligence

Now that we’ve mentioned why you want to use competitive intelligence, it’s time to take a look at what it is in more depth. 

We’ll start by defining the two types, strategic and tactical.

Strategic intelligence

Strategic intelligence is all about long-term thinking. It’s information that could affect the business’ direction over long periods of time. 

This type of intelligence isn’t something that you need to worry about right away. However, you shouldn’t let yourself be lulled into a false sense of security.

Some examples of strategic intelligence include how consumers use the internet, for instance, which social media sites they use, which plugins are considered essential, and which browsers are in favor. 

Tactical intelligence

Tactical intelligence deals with things that alter your plans in the short term.

This type of intelligence demands an immediate response if you’re going to act on it, and in some cases, you may have to act without having the complete picture. 

That said, you shouldn’t let that stop you from making a decision. 

In business, it’s often best to make a decision with the information that you currently have rather than miss the opportunity.

Examples of tactical intelligence include new product launches by competitors, new government regulations being introduced, new stores being opened in your area, and disrupted supply chains due to road accidents.

How do you perform competitive intelligence analysis?

If you’ve read this far, you’re probably convinced that competitive intelligence is the right type of analysis for you. 

So, how do you go about performing it? We’ve laid out the seven crucial steps below that will assist you in undertaking your analysis.

Decide what you want to achieve

The first step, as with many a project, is to determine what exactly it is that you want to achieve with this analysis.

Do you want to know your position within the market? Perhaps you’d like more information on what your rivals are up to? Or maybe you’d like to scour the market for a gap that you can fill?

Your aims will determine everything, from your information sources to your analysis methods. If you don’t make a firm decision here, you’ll be floundering about during the entire process.

Remember, your business will likely have several different markets that it stretches into. By segmenting your analysis into different sections for each market, you’ll be able to collect data that’s relevant to each without muddying the waters.

The narrower your field, the more precise your information will be.

Identify your competitors

Once you know what you aim to achieve, you can then identify your competitors. These are those businesses who are:

  • Within your market.
  • Selling similar products.
  • Aiming to appeal to the same audience.
  • Able to take away your customers.

This is a fairly straightforward step in most cases since you can simply look at who is selling similar products or services to you. The more similar these are, the more they are a direct competitor.

Determine the data you need to collect

Now that you know what information you want to find, and who you want to find it on, it’s time to determine what data will showcase that information.

Are you looking for information on new products that your rivals are launching? Look to reviews, your rivals’ websites, and unboxing videos.

Do you want to know what your competition is up to with their advertising? You need to look at their campaigns and observe the trends.

You can be more specific too, splitting advertisement into online and offline, email and social media, etc. Remember, the more segmented the data, the more precisely you can guess your opponents’ moves.

Find your data sources

Once you’ve determined the data you want to collect, you can pick your data sources. 

Products? Find their reviews, look at their product showcases, and check out the product’s sentiment via sentiment analysis. 

You can even go so far as to buy one of their products for more direct comparison and testing, and maybe find a way to get an edge that way.

Pricing strategies? Take a look at price trends offered by Amazon, at past offers and pamphlets, and compare them to your own.

SEO tools such as Semrush and Ahrefs will give you detailed information on a variety of topics, as they’ll easily be able to show you which keywords your competitors are aiming to rank strongly in. 

If you know where they’re trying to appear in Google searches, you’ll know what kind of customers they’re aiming to attract.

Analyze your data

Next up is data analysis. There are plenty of methods to choose from, some involving software and some involving doing the number crunching yourself. 

Some examples include but aren’t limited to:

  • Porter’s Five Forces.
  • Driving Forces Analysis.
  • Product Life Cycles.
  • Porters Four Corners.
  • SWOT Analysis.

Remember, while computers might be all the rage, they’re a little lacking when it comes to the emotional side of things. 

Sometimes you can simply look at the information you’re presented with and come to a better conclusion than a computer could ever reach.

Convert your analysis into plans of action

Now that you’ve gained your information, it’s time to convert it into plans of action. Or rather, factor it into your existing plans to make them better.

To take the previous baseball metaphor, you need to adjust your stance once you’ve deduced what pitch you’re about to face. In the case of business, however, the pitch is anything that can alter the ideal outcome of your plans.

The direction that you take is extremely dependent on your situation, so for the most part it’s up to you. Your intuition and experience can be excellent tools here, allowing you to see solutions that wouldn’t ordinarily be obvious.

Repeat, repeat, repeat

Competitive intelligence isn’t just a one-and-done type of deal. Your strategies change, so why shouldn’t your competitors do the same? 

Competitive intelligence should be a regular habit that you indulge in, not a one-off project. 

After all, markets are volatile, consumer expectations change, and you need to be aware of all of this if you want to get ahead in the game.

When should you perform competitive intelligence analysis?

As we’ve previously mentioned, competitive intelligence analysis is something that’s ideally done whenever possible. 

However, there are a few specific times in your business’s life cycle that you definitely need to be performing it.

Let’s take a look at some of the best points in time when you can be undertaking this.

When you’re starting out

Analyzing your competition should be one of the first things you do when you’re planning to start a business. Knowing who you’re up against, what they do, and how they do it is crucial to staying competitive.

Understanding the industry you’re in will tell you a great deal about how you should operate. 

Is the market saturated with brands that seem indistinguishable? You’ll need to stand out by having a feature that’s unique. 

Shopify, for instance, saw the world of ecommerce and decided to create a platform for small businesses to create their own customized online stores easily, rather than having to rely on pre-existing platforms that would limit their design abilities.

Your investors will want to know about the surrounding market too since it gives them an insight into how well you’re likely to perform and how risky an investment you are. 

Very few businesses can get off the ground without funding, so this is a top priority.

When you’re developing & launching a new product

William Henry Perkin isn’t likely a name that you know, since he lived in the 1800s. In short, he’s best known for creating the first cheap purple dye, which until those times had been limited to the extremely rich due to the cost of its production.

He also created a red dye in 1869. However, another company beat him to the punch in patenting it by just a single day! 

This showcases why you need to perform competitive intelligence both when developing and launching a new product – your rivals might be thinking along similar lines and get their product out before yours, at which point you may have to abandon the entire project due to patenting.

When you’re considering a change in market strategy

Change comes to us all, whether we like it or not. A crucial part of planning for change is analyzing the current market in order to assess whether or not your planned changes will be effective.

One of the most important parts of this is analyzing your competitors in order to make sure they’re not planning the same as you. 

After all, it’s no good to plan a pivot that makes you stand out only for your competition to take the same direction. 

When you’re seeing a drop in physical business activity

When business stagnates, there isn’t always an obvious reason why meaning you need to look deeper to find the source of your problems.

Of course, foot traffic and physical sales can decrease for reasons other than your competition, but by analyzing them, you can see whether they are stealing your customers or if there’s a bigger problem that you all are facing.

Are you all seeing a drop in foot traffic, but only within a specific area of your city? It’s probably a transportation issue that prevents customers from being able to reach you easily.

Maybe some of your competitors are seeing a boom, whereas others are seeing a bust also. There will be some correlating reasons as to why specific businesses are more attractive right now compared to others, and you will be able to find them.

Maybe everyone sees a drop in business activity. This points to a drop in consumer confidence, indicating that you should be prepared for them to spend less and spend only on products they really desire.

All this information adds up to an edge that lets you see where consumers are engaging with businesses and, more importantly, why. This enables you to adjust your approach and meet their expectations.

When you’re seeing a drop in online traffic

Getting hits on your website is crucial to ecommerce, and a big part of this is search engine optimization to account for organic searches, which are making up a larger and larger share of online traffic. 

In essence, hitting the right keywords within your website and meta information makes sure that you’re at the top of the list when it comes to Google searches in your particular field.

The problem with this is two-fold. 

First, the inner workings of search engine rankings are secret and will naturally change over time. 

Secondly, the searches that consumers perform will change over time as their needs and wants evolve.

Both of these factors add up to one single outcome – you need to keep on top of your optimization to stand out. 

If you see a drop in traffic, you likely need to redo your metadata to upstage your competition.

Hitting a home run with competitive intelligence

With this complete guide on competitive intelligence, you should be able to get the edge your need over competitors.

You’ll understand what competitors are after, their plans, and indirectly – what customers want.

The journey doesn’t end here, though. Once you have customers’ attention, you need to provide them with a show they won’t forget. Check out our ecommerce customer experience guide to get up to speed on the topic.

Sentiment Analysis For Brand Building

Posted on by Emily Louise Spencer

They love me… they love me not. 

It’s a question most people ask themselves about loved ones. But what about asking this question from the position of a CEO or a data analyst? 

When you do that, you’re conducting sentiment analysis, albeit without stripping a flower of its petals.

When building your brand, one of the most important things you can do is read your audience. 

How people feel about your product is imperative to its success. And understanding the nuances of these feelings will help you get a leg up over your competitors.

It’s not just about a general “they love my product; they love it don’t.” It extends to minor details that make up your products or services and how you present them. 

If there are things that rub customers the wrong way, keeping on top of them is key to success. 

What customers want isn’t always obvious and consistent. If something works in one place and time, there’s no guarantee it’ll work in another. This is certainly true in trendy industries like fashion, where there’s an emphasis on culture and everything changes quickly.

So how do you keep on top of consumer perception and your response to it? Especially in the internet age, where social media posts and website reviews are published every few minutes. There’s simply too much data to analyze manually.

That’s where sentiment analysis comes in.

What is sentiment analysis?

Sentiment analysis, also known as “opinion mining,” is the automated process of analyzing a text and interpreting the sentiments behind it. 

Through machine learning and text analytics, algorithms can classify statements as positive, negative, and neutral.

Companies and brands often use this process as a strategy to manage large amounts of data coming from Yelp, Twitter, Amazon, you name it. 

This data allows businesses to learn more about customers’ feelings for their products and competitors’ offerings.

How sentiment analysis works

Sentiment analysis relies on an AI engine powered by machine learning (ML) and natural language processing (NLP) to extract information.

Machine learning allows the software to learn independently and become more accurate at predicting the outcome of analysis without being programmed for that explicit scenario. Essentially, it allows the software to “learn” from past examples to improve itself over time.

NLP analyzes human language and the meaning behind it. This covers text segmentation, grammatical analysis, and terminology extraction.

Which algorithms are used for sentiment analysis?

ML and NLP are tools to help the sentiment analysis algorithm produce the final results. There are three types of algorithms that are usually deployed:

  • Rule-based – This is the basic and easy approach to implement. It’s based on manually pre-defined rules, helping the system analyze the text it reads. The drawbacks are clear, with having to rely on manual inputs that take plenty of resources and aren’t able to evolve automatically.
  • Automatic – This is the advanced approach, using both NPL and ML. The system is first fed with thousands of expressions that are pre-defined as either negative, neutral, or positive. This is the “training” stage. Then, with its newfound knowledge, it can venture into its “prediction” stage, understand new terms and classify them appropriately.
    There is a downside here, though. The algorithm is bound to make some mistakes, and it’s often hard to pinpoint exactly why this happened.
  • Hybrid – It’s the best of both worlds and the most effective algorithm. This approach Enjoys the high accuracy of the rule-based algorithm while running through new terms and expressions in the blink of an eye.

With these sophisticated algorithms in place, the sentiment analysis tool can go over the endless text and score it based on negative, neutral, or positive sentiment.

How sentiment analysis works
How sentiment analysis works

Further, when dealing with customer experience, it can also break down the text to topics such as:

  • Product quality.
  • Speed of service.
  • Ease of communication.
  • And more.

Let’s look at a couple of examples to understand it better.

Due to the large variety of cordless vacuum cleaners and the breadth of functions, people will often turn to customer reviews and see something like this.

sentiment review

How does the sentiment analysis AI understand it? It breaks down this piece of text into smaller ones, such as:

  • “It’s lightweight, compact, and a brilliant all-round hoover.”
  • “I’d buy another in a heartbeat.”
  • “The tank is small.”

The AI then assigns a sentiment for each block of text. The first is very positive, and so is the second. The third is somewhat negative, though it can be considered neutral when taken into the larger context.

Decision makers can then understand what customers think about specific parts of the product or look at the overall – in this case, positive – picture.

Another industry where understanding customer sentiment is vital is the beauty industry.

This eyeliner review paints (no pun intended) a somewhat negative picture.

sentiment review

The review starts with “The pencil itself is great,” which the AI can mark as a positive sentiment. But then come blocks of text saying how it “breaks and is impossible to sharpen,” which are very negative. The review ends with a scathing “will not be buying another.”

Sentiment analysis will help the brand understand that the customers are disappointed with their product and why. In this case, they’ll know work is needed on the durability of the pencil rather than its quality.

As you can see, it’s something a human can do. But the key differentiator for sentiment analysis is the speed and accuracy it can analyze these reviews, something even a team of experienced analysts can’t achieve.

How Revuze performs sentiment analysis

The general themes of NPL, ML, and the various algorithms play a crucial part at Revuze. But to give our customers a competitive edge, we take a step further, using a personalized model for our automated sentiment analysis, helping to maximize accuracy and success rate.

We do it through “local models,” which allow us to adapt our technology to the specifics of each case study or client. We can generate local dictionaries and models within just a few days with 90% accuracy.

Here’s how it works: Revuze’s AI algorithms extract many unique topics, ranging from high-level ones (like user satisfaction and price) to granular topics (such as “softness” for toilet paper or “moisturizing strip” for disposable razors). 

Instead of limiting ourselves to only 8-15 generic topics, we analyze 40-80 topics that are highly specific to each business or product we work with.

When you want to understand consumer sentiment around a certain product’s features, you cannot afford to use a sentiment analysis tool limited to generic topics. Personalization is key, and more on that later.

Revuze explorer example
Revuze explorer example

What are the challenges in sentiment analysis?

While algorithms can be very advanced, some text can be difficult for a machine to dissect and interpret.

Sarcasm

Users may write: “We had to wait 45 minutes to get a table. Great!” To a human being, it’s clear that the adjective “Great!” is used sarcastically. 

How do we know it? Because of context. 

We read the previous sentence, which talks about a long wait time, and we understand that the comment is not positive. 

A good sentiment analysis tool has to be able to detect sarcasm from the broader context. Otherwise, you’ll get inaccurate data about your brand at the end of the analysis.

Nuance

Another issue has to do with nuance. 

The comment “The movie was not bad” is saying that the movie was not bad, maybe even good. But it also implies that the expectations regarding this movie were so low that the movie is not as bad as one would have expected. This is called “negator.”

“Intensifiers” can also be challenging for sentiment analysis. A user who writes, “The company’s comment on this issue was pretty good,” creates a nuance that would not be there if we read the same sentence without the word “pretty.”

In conclusion, it’s important not to rely on basic sentiment analysis tools, which will not capture the complexity of human sentiments expressed through text.

Why is sentiment analysis important, and what can it do for you

Sentiment analysis gives you more information than simply whether an individual’s interaction was positive or negative. 

Using advanced AI techniques, the specific emotion behind a person’s communication can be extracted, leaving you with a much better idea of how they felt when they wrote those words.

sentiment emotion

Ultimately, ecommerce customer experience is about emotions, and good customer experiences aren’t just about the end product. 

A top-of-the-line service in which you were treated poorly will have a far more negative impression than a middling service in which you were treated well. 

The specific emotion behind the text being analyzed indicates how you should proceed when continuing the interaction. 

  • Is the customer angry at a perceived slight? Apologetics and problem-solving are the tones you want to set. 
  • Is the issue that the customer dislikes a certain aspect of your product or service? You can point toward similar products that solve these issues.
  • Is someone excited about a new release and is sharing it all over the internet? Appreciation and thanks go a long way towards building a relationship.

As you can see, sentiment analysis isn’t just about correcting problems or gaining information on cropped-up issues. 

You’re trying to build a brand – build a personality, as it were – which requires you to interact with those consumers who have positive words to say about you too.

Now that we have plenty of information let’s explore how you can actively use this data to improve everything surrounding your brand.

6 ways to boost your brand with sentiment analysis

In brand building, it’s important to focus on what information sentiment analysis can give you about your current positioning within the market – your reputation, product strengths, weaknesses, etc. 

To that end, we compiled a list that will first help you understand your status, complemented with actionable strategies to improve it.

The various facets of customer experience

Real-time reactions

The key to dealing with customers is to factor in their emotional state and respond accordingly. 

This is easy to do face to face but isn’t quite as simple when you’re performing these actions over a text-based medium such as email, social media, or other messaging services.

Sentiment analysis brings a vital aspect to customer service with its ability to flag negative comments or communications for quick responses, allowing you to respond promptly and hopefully end the problem before it spreads. 

One disgruntled customer complaining can hugely damage your reputation as the story of their experiences spreads, especially when the reason for their bad experiences is one that other consumers will resonate with strongly.

In this case, sentiment analysis is paired with social media monitoring and other forms of software which will feed into it in real-time, letting you know as soon as a crisis of PR crops up and identifying the emotions behind it. 

The approach to solving these crises will depend entirely on the emotion behind the negative PR, whether that’s an outrage, sadness, disappointment, etc. 

Improving your product and service

The other way sentiment analysis can assist with CX is linked to product improvements and SWOT. 

Identifying problems in your service or deficits with your products and improving them is a definite PR win. More importantly, it comes from listening to your customers and acting accordingly. 

Consumers often rank wanting to feel heard and have their experiences taken into account as among the most important factors when choosing a brand or company to provide a service.

If you’re in the service field, paying close attention to what sentiment analysis can tell you about what your customers desire is crucial.

By monitoring the sentiment around your brand before, during, and after changes to your products or services, you can easily judge whether or not those changes were a success.

Because this is happening in real-time, it can all be measured to provide you with information on how you’re doing in the CX world and how to improve future relations with your customers.

Market research opportunities

Sentiment analysis isn’t just for customer experience. It can be used when you’re doing research too. 

When performing market research, sentiment analysis helps you dive deeply into your audience’s attitudes in ways that a human being could simply not do.

Most traditional forms of market research use controlled surveys, star ratings, and other similarly structured forms of data. 

While it’s certainly useful to use traditional forms of market research like controlled surveys, they are prone to human biases such as leaving feedback only after a particularly good or bad experience. These biases can skew information, affecting your ability to make data-driven decisions. 

True, sentiment analysis uses reviews to provide you with information. But to give a more rounded picture, it can search the internet and take information from areas that talk about your market specifically, such as forums, social media groups, and blogs. 

Information about what customers desire and what they’re willing to pay can be extracted from these areas, giving you deep insights into your target audience and how you want your business strategy to appease them.

Customer segmentation

Not only can you analyze customer sentiment with sentiment analysis, but with the right tools, you can break it down into segments that show a very different pattern than the whole. After all, not all groups of people are the same.

For example, customers who interact with you via a mobile app or website will have a different experience. Slicing and dicing your data by demographic factors such as age or gender may yield interesting results. 

Each group will likely have a different sentiment towards different aspects of your product, and this information will help you cater to them.

Idea generation

Using sentiment analysis, you can analyze people’s behavior when certain topics are brought to light and examine what potential leads you might be able to follow up on. 

For instance, a tin of paint sold in a certain size that a significant portion of your customers has been vocal about being too small for their daily uses. 

It would be worth investigating whether you can produce the product in a larger tin or multipacks so that these customers might be satisfied.

You can also take positive sentiment and turn it into ideas for future usage. 

Did you know that bubble wrap was originally sold as textured wallpaper? As time went by, the creators took note of the positive sentiment surrounding its ability to protect fragile items in transit (and how fun it is to pop!), adjusting their marketing approach until it had radically changed from their original intentions.

Competitive Analysis

Sentiment analysis doesn’t just give you information on your standing within the market. It can give you insight into how your competitors are doing too. 

Online reviews and social media buzz are open and visible to anyone. Using them as a source of competitive intelligence is perfectly acceptable in the business world.

Sentiment analysis can give you information on how the consumer base feels about your competitors, whether as a brand or on an individual product-by-product basis. 

Revuze has taken the step to combine consumer sentiment with other forms of data in order to give powerful pieces of information and insights into the minds of your competitors. A few examples of such are:

  • Sentiment vs. star rating: The perceived expectation of quality that a brand or specific product has in the eyes of consumers.
  • Sentiment vs. total sales: The ability of a brand or specific product to maintain customer satisfaction across a broad spectrum of consumers.
  • Sentiment vs. total product variations: How easily a brand can maintain overall customer satisfaction while expanding into a diverse range of products.

Our AI insight engine, Sentimate, can help you perform these analyses in great detail, from examining a brand as a whole to an individual product out of thousands. 

Using data extracted from online reviews and chatter, you can gain an incredible amount of useful information as long as you have the tools to analyze it.

Ratings and reviews across an industry

Ratings and reviews are part of the User Generated Content (UGC) realm. It is exploding and is expected to be over 90% of the world’s data soon. 

UGC (ratings and reviews in our context) is important to millennials, with 86% saying it’s a good indicator of a brand’s quality. 

Further research from Spiegel shows that reviews by verified purchasers vs. anonymous ones can bump purchase likelihood by 15%. 

This is why brands encourage customers to leave reviews and provide feedback. 

Now imagine being able to gather all these consumer opinions from online retailers and analyzing them for sentiment and topics. 

What consumers like or not – why they buy, what they like or hate about a product, a service, or a shopping experience. 

This is possible across an entire industry – all brands, all products, all reviews, and ratings, analyzed via sentiment.

The reason it’s so valuable and important is because of the breadth of the information and the depth. This is the high-quality raw material (ratings and reviews) and is highly focused on this medium of commerce, meaning:

  • Low ratio of noise-to-insights (Low “chatter”).
  • High level of granularity.
  • Store-specific feedback (Walmart has it in stock, Amazon doesn’t).

Getting started with sentiment analysis: the four main steps

As we dig further into understanding this powerful marketing and branding tool, let’s look at the pipeline of steps usually applied in sentiment analysis.

We’ll consider sentiment analysis for a company or brand in this pipeline sample.

Step 1: data gathering

First of all, we need the data that we will later analyze. 

We can gather data from social media, namely Twitter, using scraping tools, APIs, customers’ data feed, and so on. We can also gather data from user reviews on services like Google and Yelp.

We’ll be looking for all mentions of the company or brand over a specific time. 

This practice is very common in all forms of social media listening.

Step 2: text cleaning

Text cleaning tools will allow us to process the data and prepare it for analysis by:

  • Removing stopwords (a, and, or, but, how, what…).
  • Taking out punctuation (commas, periods…).
  • Reducing words to their stem. 

These tools will allow us to “clean” or “strip” the texts from anything that might be irrelevant to the analysis.

Step 3: analyzing the data

At this point, we can use our sentiment analysis algorithms to analyze the data we have gathered. 

As we saw earlier, the most common classification is the spectrum between “positive” and “negative.” However, more refined tools may also identify more complex sentiments such as anger, sadness, etc. 

The algorithms will use a sentiment library to identify opinions and classify them.

Step 4: understanding the results 

At the end of the process, we should be able to see the data grouped into major categories. We should be able to see if we have more positive, neutral, or negative reactions. 

Having each sentiment tagged with its original date is particularly important, as a timeline will show us if we had “peaks” (surges of positive sentiments) or “valleys” (surges of negative sentiments) at specific moments in time. 

We might therefore be able to find correlations between something that happened on a specific date and a surge of opinions regarding our brand.

While we might identify a peak or a valley while performing sentiment analysis, the opposite might happen—we might notice a surge in mentions on Twitter and use sentiment analysis to understand the reactions.

Peak Valley
Peak Valley

So far, we have talked extensively about ideas and strategies. While it’s all well and good talking hypotheticals, nothing beats seeing sentiment analysis in action to get a feel for how useful it is.

Sentiment analysis examples

We’ve handpicked some examples from our Revuze Explorer & Sentimate engines to give you an idea of what this sentiment analysis looks like and how it can be used.

Sentiment analysis using product review data

Sentiment analysis using product review data is perhaps one of the most important things every company (and consumer insights expert) looks after. After all, the best way to understand if your customers like your product or service are by understanding their sentiment towards it.

The easiest way to find out what your customers think about your product is by asking them to review it. The job doesn’t end here. Not all reviews are created equal!

You must collect all the relevant reviews for a specific product, arrange them into the relevant hierarchies, and compare them against the industry and your competitors. 

A good example we can share would be the sentiment analysis using product review data we did on Lysol VS Clorox.

In the report, you can find exactly how Revuze deciphered the relevant product features by tapping into the consumer sentiment and understanding what’s working and what’s not.

sentiment chart

Further, these millions of verified purchasers’ feedback on your competitors’ products and yours can each be cross-referenced against its product rating. 

You can learn which topics are positive drivers for 5-star reviews and which are drivers of negative reviews.

This correlation can be quantified with sentiment analysis to let you know the exact percentage of driving terms towards product ratings.

sentiment SWOT

In this example, it’s clear here that the top drivers for 5-star reviews are:

  • Fit.
  • Comfort.
  • Shipping.

What is also pretty clear here is that this product could have gotten MANY more 5 stars if it was:

  • True to size.
  • Not suffering from fake sales.
  • More durable.

This is a measurable, quantifiable way to boost your product rating for consumer products and services in an industry that includes ratings and reviews:

SWOT analysis

Sentiment analysis can also provide SWOT analysis, which stands for Strengths, Weaknesses, Opportunities, and Threats. 

SWOT analysis is used in product design and marketing to great effect, as it shows not only the strengths and weaknesses of your product or service but also those subjects which may become strengths or weaknesses in time.

Using sentiment analysis, you can measure customer satisfaction rates of a specific aspect alongside its importance. 

This example shows a SWOT analysis of a 24” laptop.

sentiment STAR

Looking at the chart above, we can see the following listed as strengths:

  • Display.
  • Color.
  • Compatibility.
  • Size.
  • Speed.

These are the areas of the product which customers are greatly satisfied with. More importantly, they’re areas where customers expect high quality. These can be assumed to be the main drivers of good reviews and high sales.

The weaknesses are as follows:

  • Battery & Charging.
  • Audio Devices.
  • Camera.

These areas are those in which the product is lacking and needs to be improved ASAP.

Product design teams should focus on improving these areas in the next model or making accessories that circumvent these weaknesses.

Further, you can see some opportunities in:

  • Performance.
  • Assembly.
  • Quality.
  • Mouse & Touchpad.
  • Upgrades.

These areas are where the product greatly satisfies customers but aren’t that important to overall satisfaction.

The laptop manufacturer has a couple of options. Emphasize these aspects to niche users, improve them further to give the product an edge over the competition, or simply leave them be.

Finally, the following threats were identified:

  • Keyboard.
  • Ports.

Threats are low-rated product features, but ones with a low importance rating to customers. 

Threats aren’t currently problems that need solving immediately, but you need to keep an eye on them as times change, product uses shift, and what was once irrelevant becomes very important.

Let’s take a step back and look at the bigger picture, starting with the top two drivers of purchase are:

  • Color.
  • Display.

These two factors are rated the highest in customer expectations while also being highly rated. As color rates are higher in customer expectation than display, greater care should be taken to maintain quality in the next iteration.

However, this laptop could have gotten more sales and higher customer satisfaction for the least effort if the battery and Charging had been addressed.

Since battery & charging are rated the most important to consumers, they should be tackled first. Following that are two other weaknesses, slightly less important to consumers: camera and audio.

As the camera function is not only rated as more important but boasts a slightly lower customer satisfaction rate, it should be placed in priority before the function of the audio device. 

Using sentiment analysis, we’ve identified the main features that drive purchases of this big-screen laptop. When tackled, we also identified which weaknesses would give the greatest theoretical return on investment. 

Of course, this assumes that all weaknesses cost the same amount to overcome, which is incredibly unlikely. However, using SWOT analysis and cost estimates combined, you can judge which weaknesses will have the greatest benefit for the smallest cost.

Monitoring chatter to track overall sentiment

Customers’ importance on product features isn’t the only way to sort product features. 

There’s also the volume of sentiment around said features, which lets you judge which topics will please the most customers rather than indirectly.

Let’s look at this 12-cup coffee maker and the chatter surrounding it.

sentiment map

As identified in the graph above, the product’s functionality is the most commonly discussed topic. This has an overall negative sentiment, which means it should be high on the list of adjustments.

Looking at the most negative topics, we can identify the water reservoir capacity, durability, and the lighting on the coffee machine as topics that create very negative chatter. 

However, those topics all consist of a much smaller proportion of talk around the machine than that of functionality. 

This means while fixing them will create the most positive sentiment in those who were unsatisfied, the overall numbers might not lead to as much of an overall increase in customer satisfaction.

Market comparisons

Another factor that you may want to consider in product design is the overall state of products in the market. 

A quick look at the coffee maker mentioned above can make the following comparisons to the market averages.

sentiment sliders

sentiment slider

The vertical lines above represent the market average sentiment for each feature, with the red and green dots representing the sentiment around those particular features.

Looking at the chart, we can see that while the functionality of the coffee maker is below the market average, it is only by a hair. Thus, improving the functionality of the coffee maker is something that would make it stand out.

Similarly, the machine’s durability is quite close to the market average, meaning that while the chatter around this topic is negative, it’s a market-wide issue and not a specific weakness.

Switching to the water reservoir feature, we can see that the sentiment is far below the market average for a machine of this type. Not only is this a problem, but it’s likely one that causes a lot of negative reviews. Similarly, product defects seem quite severe, causing a lot of negative sentiment.

In conclusion, comparisons to the market averages tell us our coffee maker should prioritize its water reservoir in the design stage.

Additionally, the manufacturer should take a look at their production to limit the number of defective products that seem to be received by customers. This can be achieved in various ways like stricter quality control.

Wrapping up

Product ratings and chatter are the gold standards that drive online sales and higher conversion rates. Finding a quantifiable, measurable way to analyze and impact them is imperative.

Sentiment analysis is an incredibly useful tool for extracting information, but when you pair it with other forms of software, the true strengths start to shine through. 

With AI-powered engines capable of using machine learning to grow and expand when new factors are introduced, sentiment analysis software will continue to grow and adapt to the language, slang, and syntax changes.

This constant evolution will help sentiment analysis keep up with the growth of ecommerce ratings and reviews, offering a way to align with the top of mind of customers in your industry and what they like and dislike. 

This is done by leveraging sentiment analysis across retailers, brands, and products. With this, you can drive conclusions as to what drives product rating success (or failure):

  • For your product portfolio.
  • Learning from your competitor’s portfolio.
  • Comparing across retailers/audiences.

Then, you can analyze, change and impact any product rating by:

  • Optimizing what consumers are happy about on a Product Description Page (PDP).
  • Fixing product issues that consumers care about and drive low product ratings.
  • Addressing product rating differences between retailers.
  • Understanding shopping experience and customer service impact on the product rating.

All of this is possible when you select the right sentiment analysis tool. We recommend that you prioritize solutions that are:

  • Holistic: Providing the data, data cleansing, and analysis all in one spot.
  • Cross-level: Provide sentiment analysis by product and feature, not just brand.
  • Self-serve: Do not require experts in the loop but allow direct use by business users.
  • Ecommerce focused: Focus on eCommerce retailers as a data source (Verified buyer’s feedback)

If you want to give Revuze a go, we’d be happy to show you around the platform.

What Is the Difference Between Text Mining and Sentiment Analysis?

Posted on by Simone Somekh

A few years back, data was a vogue word; but things have dramatically changed. We are now in the era of big data; most businesses depend on data for their daily transactions and decision-making.

A Forbes article reports that the amount of data created, captured, and copied in 2020 reached 59 trillion gigabytes; an almost whopping 5,000% departure from the 1.2 gigabytes of 2010. While a large volume of data is created and downloaded daily, it’s important to note that the vast majority of the data we can find online is unstructured.

Data that can be used for business purposes and decision-making must be in a structured format, and this is where the problem lies, as most of the data out there is not structured. Technology is advancing at a very rapid speed and with tools such as text mining and sentiment analysis, the problem of structuring and analyzing large volumes of data is now automated.

Text mining, or text data mining, is the process of transforming unstructured text into a structured format; having 80% of data in the world residing in an unstructured format, text mining enables you to identify meaningful patterns and new insights.

On the other hand, sentiment analysis — or opinion mining  — leverages natural language processing (NLP) to classify data or reviews into positive, negative, or neutral sentiments.

While the two processes might appear to be similar, there is a world of difference between them. But first, it’s necessary to understand what data formatting is before exploring the differences between text mining and sentiment analysis.

  • Structured data: This is the format that can easily be used by organizations since the standardization into a tabular format with numerous rows and columns that can include names, addresses, and phone numbers allows you to store and analyze with machine learning algorithms.
  • Unstructured data: This format is not predefined. You can source unstructured data from social media, product reviews, video and audio files, as well as Q&A forums.
  • Semi-structured data: The name depicts that it’s a mix of structured and unstructured data formats. To an extent, it has a level of organization, but it lacks the requirements of a relational database; you still need to do some sorting to qualify it for analysis. XML, JSON, and HTML files come under this format.

The essence of exploiting text mining and sentiment analysis is to make better business decisions. Advanced analytical techniques, such as Naïve Bayes, Support Vector Machines (SVM), and other deep learning algorithms, are enabling organizations to discover hidden relationships and make better sense of their unstructured data.

Text mining vs. text analytics

It’s not uncommon to have people mix up the terms text mining and text analytics. While text mining is extensively used to derive qualitative insights from unstructured text, text analytics is used to provide quantitative results. You can use text mining to understand if a customer is happy with your product through the analysis of reviews and surveys. 

To have a deeper insight such as identifying a pattern like a negative spike in customers’ experiences or trends, you use text analytics.

The relationship between web analytics, text mining, and sentiment analysis

Virtually every company or organization has a website today. Customers visit websites to source products and services; they leave large volumes of data on their trail through the visits and actions they perform online. Web analytics enables you to collect, report, and analyze website data.

However, you need to integrate text mining and sentiment analysis to make useful sense out of the data that you gather from your website. Data from most visitors are usually unstructured; text mining will be used to structure the data, while you deploy sentiment analysis to understand the real significance and nuances in the data.

With this, you can determine the success or failure of those goals, have a data-driven strategy and improve the user’s experience.

The differences between text mining and sentiment analysis

Let’s take a look at the main differences between text analytics and sentiment analysis:

Text mining Sentiment analysis
What it does: Shows what has been written by customers about your product or service; what ideas are commonly linked in the text. It also shows which subjects and topics are most discussed by users and customers. What it does: Allows you to understand if your customers are reviewing your products or service positively, negatively, or neutrally. You can even go beyond non-text feedback, such as video, audio, and images. When a customer smiles, you can easily understand that the customer is satisfied compared to when a customer frowns.
How it can help you: Helps identify early warnings as an indication that your organization is heading into troubled waters or that there is an issue with your product or service. How it can help you: Negative scores indicate that your customers are on the verge of churning your product or service.
How it works: A patented NLP technology processes text-based data just like the human brain, but this is done with proprietary algorithms to identify parts of speech, words, or ideas that are linked, and comprehensively determine patterns and trends in your database. How it works: The focus is on determining whether words and phrases are positive, negative, or neutral. This is mostly done on a scale of -1 to +1, where -1 is extremely negative and +1 is absolutely positive.  

Popular text mining techniques

A lot of activities go into text mining; these activities are essential for the deduction of useful information from unstructured data. You, however, must begin with text processing for the cleaning and transformation of data into a usable format.

Tokenization, part-of-speech tagging, language identification, chunking, and syntax parsing are necessary steps for proper data formatting before you can embark on the actual analysis. After the completion of text processing, you then proceed with text mining algorithms for veritable insights from your data. 

Some common techniques you can use for text mining techniques include:

  • Information Extraction (IE)
  • Natural Language Processing (NLP)
  • Data Mining (DM)
  • Information Retrieval (IR)

Information retrieval (IR)

Information retrieval is the automated process that responds to a set of predefined queries or phrases to enable the return of relevant information or documents. IR systems can accomplish this task by using algorithms to track user behaviors and discover any data that is relevant. 

Library catalog systems and search engines such as Google make use of information retrieval. 

Some tasks you can use IR to execute include:

  • Tokenization: Enables you to break down a text that is long-formed into sentences and words called “tokens.” The tokens become the input for other processes such as parsing and text mining. 
  • Stemming: This is the process of removing the suffixes and prefixes attached to words. The essence is to have only the word stem. It’s very important in NLP. When you do stemming, it improves IR by reducing the size of indexing files.

Natural language processing (NLP)

Natural language processing (NLP) is that branch of artificial intelligence (AI) that gives computers the ability to understand the text and spoken words the way humans do. By combining computational linguistics with statistical, machine learning, and deep learning models, NLP enables computers to use these technologies to process text or voice data with a clear understanding.

Some sub-tasks you can use NLP to do include: summarization, PoS tagging, text categorization, and sentiment analysis.

Information extraction

Information extraction (IE) is an automated process of extracting structured data such as entities, entities relationships, and attributes describing entities from unstructured data, and storing the information in a database. Some sub-tasks of IE include feature selection, feature extraction, and named-entity recognition (NER).

Data mining

When you have big data sets, and you are trying to identify patterns and extract useful insights, you can use data mining. This technique helps you evaluate structured, unstructured data, and semi-structured data to obtain new information. 

Sales and marketing professionals can deploy data mining for the analysis of consumer behaviors. 

Conclusion

The processes involved in gathering customers’ data, and analyzing their sentiments can be overwhelming, but it is absolutely necessary for any brand that wants to remain competitive and relevant in the global market. Text mining and sentiment analysis must go together for you to improve customer experience, and embarking on this manually will ordinarily take you months. 

Revuze has integrated AI into sentiment analysis, which is what you need to actually classify your customers’ sentiments into positive, negative, and neutral. A platform like Revuze can automatically carry out the gathering, collation, identification, and extraction processes of trending discussion topics from any set of unstructured data.

Nowadays, understanding context with exceptionally high precision and delivering actionable business insights is of high essence, and that’s where Revuze comes in.