Author: Boaz Grinvald

One of Henry Ford’s most known quotes about products was “Any customer can have a car painted any color that he wants, so long as it is black”. This was when Ford was focusing on delivering affordable cars of high quality and had to compromise on colors. The car in question here was the Ford Model T and there were 15 million cars sold. So, Ford Motors made the right decision in focusing on some abilities of the Model T over others (colors).

You’d expect that with time, modernization, etc. brands will have hit products like the Model T more often, but the reality is that over 80% of new consumer products fail, with 70% of these launches are by established brands and not newcomers.

According to McKinsey, the way to measure R&D and Product success is primarily in the ratio of new product contribution to revenue. Basically, for every $ spent on R&D, you get back X$ in new product sales that grow your overall revenue.

In short, a key measurement of R&D ROI for consumer products is the successful sales of the new products.

Voice of the consumer (VOC) challenges

According to Bain and Company, 80% of brands believe they deliver superior customer experiences, but only 8% of customers feel the same – hence the “VOC gap”.

Because the number of feedback channels today is growing and the volume of feedback is scaling as well (chat, calls, email, social media, online reviews, Q&A…). According to IDG, over 90% of the world’s data is soon to be made of such customer opinions. So, whatever VOC gap we have so far – it’s not going to get any better if we keep doing the same in an environment that is becoming more complex.

What is common to all existing solutions today that analyze customer opinions is that they heavily rely on human experts in the loop – data scientists, analysts, IT folks that train generic AI machines to search for specific patterns and expressions. Just like any other industry where manual labor is involved – things get slower, expensive, and biased.

In addition, this approach of relying on experts to predict what patterns to look for is reactive in nature. The experts can’t predict a new trend or competitor, so they can only add these to the list of patterns once there is enough evidence that this is mandatory.

This all leaves us with several key challenges:

• Long time to insights (long setup, reactive methodology, experts are shared resource)
• Expensive
• Limited results (Bias, human accuracy in our experience is around 60% at best)

Why voice of the consumer (VOC) impacts Product Development ROI the most

Due to the inherent dynamics of the VOC industry, insights are slow to arrive to the organization. VOC is reactive in nature, relying on experts to decipher and it typically takes many months for insights to find their way into the brand. 

In addition, these insights may be limited in nature or biased, which means there may be a need to do additional research or that the insights you are acting on are not the right ones.

When you add it all up what you are looking at is a massive amount of time spent. 

It could take a year until you learn that a product has a major issue. 

Now that you learned about it, R&D cycles to develop a fix, test it out, move it to production etc. are pretty long. Typically many months as well…which is why R&D and product development are the departments that suffer the most from lack of credible, timely VOC.

Other departments can rebound faster from bad news or new insights. R&D takes longer…

Why real-time voice of the consumer (VOC) benefits Product Development ROI the most

Consumer products can fail even if just one specific feature does not meet expectations. Hence the well-documented example of the Samsung Note 7. This is why brands need to make sure they are aware of ALL the aspects that are important to their consumers so that all bases are well covered. Now couple this with Especially with the research from Harvard Business School that 95 percent of new consumer products fail. 

This drives the following key conclusions –  

• You want to get full, unbiased feedback on your products
• You want to get this feedback ASAP

Now imagine a world where machines deliver the real-time voice of the consumer insights, on-demand, without the need for experts in the loop, without the wait, without bias…. Imagine the impact of this on brand R&D and product development. Suddenly you get:

• Unlimited insights, about ALL issues consumers report with your product
• Insights are reported as they are generated by the consumers, in real-time

This becomes the “radar” of R&D. Suddenly you know where issues/enemies are and how to maneuver and prioritize. 

Referring to a couple of our older blogs, imagine if as Product Development for a toothpaste company you’d know right from the start that Taste is the Most Important For Consumers, or that as part of Product Development for a coffee maker brand you’d know that Taste is Only 7th in Importance in Coffee Makers… 

Conclusion

Consumer product development is difficult. It takes a long time to innovate, and once a product is out there understanding how to make it better takes time and consumers don’t like to wait, which means until you get it right, you’re not gaining market share or revenue.

Having a traditional voice of the consumer stack of solutions also doesn’t help as these are:

• Slow
• Limited
• Expensive

Shortening the fix cycles for R&D is critical. As R&D cycles themselves by nature are long, and adding long wait times for insights really doesn’t help. This is why Real-Time VOC is the most important for R&D and Product Development organizations, as it gives them the head start they need.

Selecting the right VOC tool is critical. We’d recommend that you prioritize tools that are:

• Holistic: Providing the data, data cleansing, and analysis all in one spot
• SKU level: Provide sentiment analysis by product and feature, not just brand
• Self-service: Do not require experts in the loop but allow direct use by business users

In the age of digitalization, the best opportunity for businesses lies in how they can delight customers both online and offline.

Customer experience management seeks to provide a holistic brand experience across all customer channels. It goes beyond traditional measures of customer satisfaction.

As per the Grand View Research report, the global customer experience management market is worth $7.6 billion in 2020. 

Over the coming years, the market is set to continue growing and sustain an impressive growth rate. The same report also shows that the compound annual growth rate is expected at 17.7 percent from 2020 through to 2027.

Hence, customer experience is something that brands should not ignore because customer loyalty and retention is directly related to experience. 

This article explains customer experience management (CXM) and details the purpose, strategy, methodology, and importance of CXM. It also explains how to set-up CXM operations in depth. 

What Does Customer Experience Management Mean?

Gartner defines CXM as the practice of reacting to customer interactions to meet or exceed customer expectations, increasing customer satisfaction, loyalty, and advocacy.

Hence, CXM is the effective management of customer interactions at each stage of the buyer journey to retain customers and increase revenue. 

What is the Purpose of Customer Experience Management?

The primary purpose of CXM is to improve the overall experience of a customer with a brand. 

More than 75% of customer management executives consider customer experience highly important to the business. 

The Consumer Intelligence report by PwC shows that 73% of consumers consider customer experience as an important factor in their purchasing decision. 

Therefore, to achieve excellent customer experience, potential objectives include a focus on the following: 

• To retain your existing customers 
• To attract new customers
• To receive a higher greater share of the wallet from existing customers 

What is a Customer Experience Management Strategy?

A CXM strategy guides you to take actions that deliver a remarkable customer experience across all customer touchpoints.

CXM requires brands to have a 360-degree view of customers, with integrated, up-to-date data on customer accounts.

A successful CXM strategy considers several factors, including competitive insight, consumer research, marketplace data, and mission. 

According to Salesforce, 52% of marketers adapt their marketing strategy and tactics based on customer interactions and feedback.

These are the key steps involved in an effective customer experience management strategy:

1- Understand Your Customer

The first step is to understand your customers’ needs, motivations, and behaviors. 

Two ways to understand your customers better are:

• Profile the types of customers to know how to market to them. Create a document that lists the interests, buying patterns, demographic characteristics, and pain points of your customers. Here is an example of a describes the real customer:
• Create buyer personas. It helps you implement customer segmentation based on factors such as age, interests, and spending habits. Buyer personas help you in product development to suit the needs of your target customers. Thus, it helps attract high-value visitors, leads, and customers to your business. Here is an example:

2- Create a Customer Journey Map

The customer journey mapping research report shows that nearly 67% of the participants use a customer journey map. As per the same study, around 90% of the surveyed customer journey mapping practitioners said that their program is delivering a positive impact.

Create a customer journey map. It helps you to identify customer touchpoints and anticipate how customers will interact with your product or service. 

It outlines customer needs, pain points, opportunities and gives you a view of the end-to-end customer experience. Below is an example of a customer journey map.

3- Develop an Emotional Connection

As per a report by Deloitte, Trustworthiness (83%), integrity (79%), and honesty (77%) are the emotional factors that consumers feel most align with their favorite brands.

Create a brand personality that evokes emotions and connections for a customer. It helps establish a relationship between the customer and the company.

Use consumer insight tools like Revuze to understand customer emotions. Here are the top features offered by Revuze:

• Automates the process of sentiment analysis to maximize its accuracy and success rate. 
• Offers sentiment analysis not just by brand but also by-products and features.
• Automates product review analysis by understanding the voice of the customer using qualitative eCommerce opinion insights.

4- Capture Customer Feedback

Research by Bain & Company shows that 80% of companies believe they are providing great customer service, but only 8% of customers agree with them. 

Measure customer satisfaction in real-time by using post-interaction and real-time feedback surveys. Tools such as Revuze are highly useful. It collects and turns unstructured data into market insights so that anyone can make decisions based on consumer opinion data. It allows you to scan millions of opinions in real-time and get the deep insights you need to respond to issues as they arise.

Customer feedback helps you to track customer perceptions. It enables quality monitoring and measures the success of the customer experience strategy.

How to Improve Customer Experience Management?

As per the McKinsey report, maximizing satisfaction with customer journeys has the potential to increase customer satisfaction by 20% while lowering the cost of serving customers by 20%.

Here are the best ways to improve customer experience management:

1- Leverage The Power of Data

Measure customer experience and collect data on their satisfaction levels across key points of interaction with the company. 

Customer satisfaction metrics are the numerical scores that indicate the health of your brand’s offerings and relationships with the customer.

The top three surveys and their scoring are:

• Customer Satisfaction Score (CSAT): It measures the satisfaction level of a customer with the product and service of a company. The higher the CSAT score means the better CXM. A CSAT score of 80% or more is a good indicator of success
• Net Promoter Score (NPS): It measures the loyalty of customers towards a brand. A high NPS means your CXM strategy is perfect. NPS scores between 0-6 are considered as detractors or potential detractors, 7-8 are passive, and 9-10 are promoters.
• Customer Effort Score (CES): It measures the ease with which customers execute a task using your product/service. Customers taking the survey choose between multiple answers ranging from “Very Difficult” to “Very Easy.”

2- Make Personalization A Priority

Personalizing means designing your services and products to meet customer’s individual requirements.

Nearly 56% of customer experience professionals aim to improve and personalize the customer experience.

A customer personalization strategy allows you to identify customers with specific needs, helping you to create a unique and targeted experience for them.

Here are the key ways to personalize your content:

• Collect Data: Analyze all your customer-related data to acquire valuable feedback to enable you to design a working strategy. 
• Segment Your Audience: 39% of businesses see an increase in their open rates who use email-segmentation. You can segment your audience based on location, age, gender, or online shopping habits. Use behavioral tracking to target your email customers to help boost your email’s relevance. 
• Execute Your Strategy Across Multi-Channel: Businesses need to evolve towards cross-channel strategies. 75% of consumers expect brands to offer continuous experience when moving between channels, so they do not have to start over each time.

3- Make the Best Use of Your CRM System

Investing in both CRM and CXM helps your business to deliver a positive and meaningful experience.

CRM and CXM have their own operational strengths that enable you to capture and manage valuable data.

A CRM system captures a wide range of information about your customers and how they interact with your business. It informs you about how best to implement future campaigns and business strategies.

74% of businesses using CRM claimed that their customer relationships are stronger through good customer experiences. 

Whereas CXM helps you identify how your customers think and feel when engaging with your brand. 

Here is how you can integrate and use CXM and CRM together:

• Use CRM to collect customer data, such as their spendings, gender, or age. Take this data and align it with the experiential data such as NPS or CSAT. This gives you an opportunity to follow trends on experience, rather than just data about customer actions. 
• Bring CRM data into your CXM to predict how customer experience changes on transactional KPIs.
• Build customer relationships by incorporating CXM data into your CRM. Pull out customer history using CRM. Then, add CXM data to see whether the customer is facing any issues. It helps you to identify customers at risk of churning. 

4- Take Control of Social Media

As per Statista, 48% of consumers expect a response to social media questions and complaints within 24 hours.

Also, 71% of consumers with a good social media service experience with a brand are more likely to recommend it to others.

There are several avenues within social media platforms. From visual platforms, such as Instagram and YouTube to network-based platforms like LinkedIn and Facebook. 

Here are the best ways to boost your customer experience management on social media:

• Consistently post new content to your social feeds to build your brand awareness.
• Have conversations with your followers.
• Use relevant and trending hashtags.
• Post unique content.
• Respond to comments, questions, and brand mentions.

You can use tools such as Sprout’s Group Report, Sparkcentral, or Hootsuite to supplement your social media insights.

5- Leverage Artificial Intelligence 

As per Gartner, the use of AI grew by 270% over the past four years.

As per Forbes, 95% of all customer interactions will be supported by artificial intelligence by 2025. 

Here are how AI customer support can be used to better serve your customers and enhance growth:

• Chatbots: AI-based chatbots are helpful to personalize customer interactions and to accelerate resolution. 
• Face Recognition: With the help of AI, you can enable customers to even place orders or access devices with fingerprints or face recognition.
• Predictive Analysis: AI predicts future events based on historical data to enable marketers to take proactive steps and make intelligent decisions to improve customer experience.

Why Should Brands Use A Customer Experience Management System?

Research by Gartner shows that 89% of companies compete primarily based on their customer’s experience. 

Here are the advantages of using a customer experience management system:

1- Analyze Customer Feedback and Leverage Insights

CXM allows you to get feedback from customers. Analyzing and using this feedback, you can improve or fill up crucial gaps in customer experience processes. 

A CXM system leverages insights to enable you to act swiftly on customer issues and concerns, thereby further reinforcing customer satisfaction and loyalty.

2- Improved Customer Experience

A report by McKinsey & Company shows that a great customer experience increases customer satisfaction by 20%. It increases revenue by 15% and even lowers customer care spending by 20%.

CXM helps businesses to optimize operations and utilize new business digital models to fuel growth through a sustainable differentiation.

3- Positive Sentiment 

Approximately 60% of consumers visit a brand’s website after reading positive reviews, and over 70% trust a brand more after seeing such reviews. 

Customers who are fully connected with a brand are 52% more valuable than customers who are just satisfied. 

CXM provides smooth connectivity between companies and customers for achieving organization goals and customer expectations.

4- Increased Sales and Customer loyalty

According to Forrester Research, companies that work on customer experience management increase their advantage by 14%, in terms of the customers’ initiative to buy more.

92% of consumers are more likely to stay loyal to the brand if a positive emotional connection is created with a brand.

CXM combines customer satisfaction, loyalty, retention, user experience, relationship management, experiential marketing, and customer-centricity to retain their market share in the competitive world.

Customer Experience Management Book We Recommend

Below is the list of the best books on Customer Experience Management (CXM):

• The Effortless Experience by Nick Toman, Matthew Dixon, and Rick DeLisi.
• Hug Your Haters by Jay Baer.
• Winning the Talent Shift by Berta Aldrich.
• Customer Understanding by Annette Franz.

Final Thoughts

Keeping your customers satisfied by understanding their needs and behavior is crucial for your business growth. Therefore, investing in customer experience management is a good decision to create strategies that add value to your business. 

Customer experience management software such as Revuze combines behavioral analytics with automated engagement actions to drive customer retention and product success. The right CXM strategy, along with an optimal customer experience platform, reduces costs, increases brand value, and allows you to gain the edge in the market to stand out from your competition.

What Is Sentiment Analysis?

Sentiment analysis is the automated process to analyze a text and interpret the sentiments behind it. Through machine learning and text analytics, algorithms can classify statements as positive, negative, and neutral.

This process, also known as “opinion mining,” is often used by companies and brands as a strategy for social media monitoring to manage large amounts of data and gain consumer insights to learn more about customers’ sentiment and competitors.

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What Is Sentiment Analysis Used For?

Sentiment analysis is used to analyze social media posts, tweets, and online product reviews, as a way to track opinions, reactions, and ultimately improve customer service and experience. It’s great for market research, brand and product reputation monitoring, and customer experience analysis.

These tools are not only used for analysis purposes, but also for predictions. Previous research suggests, for example, that positive sentiments may have an upward effect on stock prices.

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) is looking 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 your product. The job doesn’t end here. Not all of the reviews are created equal. You must collect all the relevant reviews for a specific product, and then you must arrange them into the relevant hierarchies and compare them against the industry & against your competitors. A good

example Revuze can share would be the sentiment analysis using product review data we did on Lysol VS Clorox.
In the report, you can find out exactly how Revuze deciphered the relevant product features by tapping into the consumer sentiment and understand what’s working and what’s not.

The Definition of Sentiment Analysis

Millions of people around the globe today express their feelings on products and brands through the internet, whether it’s in a Yelp review, in a Twitter thread, or in a Facebook post. Companies have a strong interest in intercepting these online “conversations,” so that they can learn more about their customers and users, as well as the customers of their competitors in the market.

Given the magnitude of available data, it’s impossible for companies to manually search for reviews and comments, analyze them, and classify them as positive or negative. Thanks to sentiment analysis, this process can be automated, so that these insights can be gathered and evaluated through algorithms.

Of course, training machines to interpret sentiments written in textual form can be challenging, and that’s why not all companies that offer sentiment analysis solutions actually succeed at performing this task.

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How Does Sentiment Analysis Work, Exactly?

Let’s say you own a restaurant and you scout for online reviews. Sentiment analysis can analyze them and quickly classify them as “positive,” “negative,” or “neutral.” For example, “The food was delicious!” can be easily classified as strongly positive, while “The service sucks” will be identified as a strongly negative comment. Thanks to a “sentiment library,” a sentiment analysis tool can easily identify nouns, verbs, adjectives, and adverbs in these texts and recognize that “delicious” is an indicator of a positive reaction, while “sucks” is an indicator of a negative one.

If all reviews were so straightforward, it would be quite easy to train a machine to do the job. However, most reviews are more subtle and nuanced.

For instance, one reviewer may say, “The food was good, but the music was too loud.” Another might call the restaurant “Not bad.”

Sentiment analysis usually assesses the “score” of a text, placing it on a spectrum of attitudes that goes between +1 (totally positive) and -1 (totally negative). This way, machines are able to distinguish between an enthusiastic comment and a milder, still positive one.

For instance, let’s say your brand has recently put out a new commercial that has been played on television. You can use social media listening to see if people on Twitter have been commenting on your new ad. A sentiment analysis tool will be able to distinguish between different scores of positivity in the two following comments: (1) “I’m obsessed with this new commercial!” and (2) “That’s a cute commercial.” While both of them are positive, the first one will receive a higher score, as it’s clearly more enthusiastic.

Some Of The Challenges In Sentiment Analysis

As we mentioned earlier, a text can be quite hard for a machine to dissect and interpret.

A user 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 in a sarcastic way. 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 at all. A good sentiment analysis tool has to be able to detect sarcasm from the broader context, otherwise you’ll end up getting inaccurate data about your brand at the end of the analysis.

Another issue has to do with nuance. The comment “The movie was not bad” is literally saying that the movie was not bad, maybe even good; but it’s also implying that the expectations regarding this movie were so low that the movie is not as bad as one would have expected it to be. This is called “negator.”

Also “intensifiers” can 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 very basic and simple sentiment analysis tools, which are definitely not going to capture the complexity of human sentiments expressed through text.

Steps in Sentiment Analysis

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

In this pipeline sample, we’ll consider sentiment analysis for a given company or brand.

Step1: 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 period of 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 the analysis by removing stopwords (a, and, or, but, how, what…), punctuation (commas, periods…), and checking for stemming. These tools will allow us to “clean” or “strip” the texts from anything that might be irrelevant to the analysis.

Step 3:  Sentiment analysis (or opinion mining) 

At this point, we can use our sentiment analysis algorithms to analyze the data that 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, and so on. 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) in 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 we therefore might use sentiment analysis to understand the users’ reactions.

For example, an airline might notice a surge in mentions on Twitter due to some viral content regarding the airline. Given the magnitude of data on the social media network, the company might use data gathering to collect all those mentions; it will then perform sentiment analysis to study the reaction of the public to the viral content. Here’s why sentiment analysis is so important: Understanding whether the reaction is positive or negative can be useful for the company to decide to pursue one of the following actions:

1. If the reaction is positive, the airline might want to capitalize on the moment to push a new commercial campaign or pitch the content to the news media.
2. If the reaction is negative, the airline might want to prevent a brand crisis by taking action or publishing a statement as soon as possible.

2021 Updates For Sentiment Analysis

Sentiment analysis is a hugely popular and efficient consumer feedback analysis tool. And honestly, it is quite simple and straightforward. However, going into 2020 we have been seeing some new applications and innovations when it comes to using sentiment analysis for consumer feedback processing.

With eCommerce steadily growing over the past few years and enjoying a recent boost in popularity during the COVID-19 pandemic, online consumer feedback has also grown in volume. As a result, brands are now using sentiment analysis for more than just categorizing customer feedback.

First, more and more brands are more interested in identifying customer pain points. This is by no means a new application for sentiment analysis, but it has become extremely important during early 2020. Efficiently mapping and explaining customers’ experience or issues with a brand, product, or service became vital during the coronavirus crisis. And many brands turned to sentiment analysis tools.

In addition, early 2020 has proven the importance of quality NLP and Text analysis. With the rise of online shopping came the increased use of chatbots. Not being able to access stores, consumers were left with automated customer service. However, many people became frustrated with chatbots and automated call distribution (ACD) systems that did not understand how upset they were, providing or repeating the same answer pattern. 

Hoping to improve customer support and overall consumer experience, brands are turning to sentiment analysis tools in hopes to obtain better data and insights. Quality text analysis allows businesses to provide optimized service and fine-tune automated consumer engagement.

How Revuze Performs Sentiment Analysis

At Revuze, a premier big data consumer analytics firm, we personalize automated sentiment analysis to maximize its accuracy and success rate.

We do it through “local models,” which allow us to adapt our technology to the peculiarities of each case study or client. Within just a few days, we can generate local dictionaries and models with a 90% accuracy. Compared to other tools, which take months to develop local dictionaries, we ensure that our clients can benefit from valuable consumer insights significantly faster.

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.

The truth is that, when you try to understand consumer sentiment around a certain product feature, you cannot afford to use a sentiment analysis tool that is limited to generic topics. Personalization is key.

With Revuze, you can get a first look at the insights of modern consumer usage, which are typically hidden.

 

Hacking The Product Rating Race With Sentiment Analysis In 2021

According to recent research, product rating impacts conversion rate online by as much as 12% when increasing rating from 3 to 5 stars. Imagine growing your D2C conversion rate by 12%…12% more revenue…this could be a major leap forward for the business…

When looking online for ways to increase product ratings and improve online reviews, most sources point out customer service and manual efforts without providing a quantifiable way to grow your ratings and reviews score. Common advice is:

• Have the right customer service personnel
• Speak to buyers post-purchase for direct feedback
• Respond to negative reviews 

Some articles point to Customer Analytics as a way to grow customer value across exchanges, but this is just limited to existing customers and not all consumers who are considering your offerings.

Sentiment analysis, when leveraged across retailers and brands, should be your answer on hacking the product rating for your offerings. Let’s see why.

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Sentiment Analysis Used On Ratings and Reviews – Across An Industry

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

Why is UGC (Ratings and reviews in our context) important?

• It’s important to millennials, as 86% of millennials say that UGC is a good indicator of the quality of a brand 
• Research from the Spiegel Research Center shows that reviews by verified purchasers (vs anonymous) can bump up purchase likelihood by 15%. 

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

Now imagine being able to mine 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, a shopping experience, etc… (“I like product A, the price was great and it was available on stock but delivery was not quick”).

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

The reason this is so valuable and important is not just the breadth of the information, but also the depth. This is the high-quality raw material (ratings and reviews) and 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)

 

Hacking Sentiment Analysis – Cross Reviews With Product Ratings

These millions of verified purchasers’ feedback on your competitors’ products and yours can each be cross-referenced against its product rating. So for example you can learn which topics are positive drivers for 5-star reviews and which topics are drivers of negative 1 or 2-star reviews.

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

See this example analysis of the Converse Chuck Taylor All Star Core Ox athletic shoes –

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 (28% less 1-2 stars reviews)
• Not suffering from fakes sales (12% fewer 1-2 stars reviews)
• More durable (9% less 1-2 stars reviews)

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

• Sentiment analysis 
• Done at the product level 
• Cross checked with low product ratings reviews vs high product rating reviews

 

Conclusion

While product ratings today are the gold standard that drives online sales and higher conversion rates, finding a quantifiable, measurable way to impact them is very rare.

The growth of eCommerce Ratings and reviews offers a way to align with the top of mind of customers in your industry, what they like and dislike. This is done by leveraging sentiment analysis across:

• Retailers
• Brands
• Products

Because sentiment analysis technology is not limited to one retailer or a number of opinions, this is a way to get industry-wide intelligence, covering your brand as well as competitors on any topics buyers talk about – brand, product, purchase, and service.

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

With this you are able to analyze, change and impact any product rating:

• Optimize what consumers are happy about on a Product Description Page (PDP)
• Fix product issues that consumers care about and drive low product ratings
• Address product rating differences between retailers
• Understand shopping experience and customer service impact on the product rating

Selecting the right sentiment analysis tool is important. We’d recommend that you prioritize solutions that are:

• Holistic: Providing the data, data cleansing, and analysis all in one spot
• SKU level: Provide sentiment analysis by product and feature, not just brand
• Self-service: 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)

In short, want to grow your revenue by 12%? Now you have the hack to do it…go go go!

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Product Launch In D2C Era

In current times online sales (eCommerce and Direct to Consumer – D2C) are booming. Many brands embrace D2C and other online sales methods to boost revenues while building a more direct relationship with consumers. To highlight a few recent examples –

1. P&G recently reported unprecedented numbers amid the pandemic (Due to 35% eCommerce growth)
2. Nike’s digital sales soar 75%
3. L’Oréal Q1 2020: e-commerce saves the day (53% growth)

Traditionally, when brands launched a new product, the focus was not put on the online launch and online sales as the majority of sales and growth came from traditional retail (In 2019 online sales were 16% of the US retail sales). Now when eCommerce and D2C become front and center (predicted to be over 30% of US retail sales) you need to plan your New Product Launch with eCommerce in mind.

Start with online reviews and ratings in mind

The biggest driver for online sales is credibility. Credibility is gained via ratings and reviews.
According to research, 91% of people read them and 84% trust them as much as they would a personal recommendation. Another study shows that the average customer is willing to spend 31% more with a retailer with great reviews.

Online reviews matter to consumers as they are typically coming from verified customers and are pretty detailed by nature:

1. Store-specific (Commenting on the specific experience within one online store)
2. Low ratio of noise-to-insights (Low “chatter”, unlike social media)
3. High level of granularity (Detailed feedback)

But how do you build out a product review base for a new product that is just launching?
You need to make sure that online reviews will be pouring in. For example:

1. Offer free samples to employees, partners, retailers and ask for an online review in return
2. Provide incentives to consumers to post a review – coupons, discounts, free products, t-shirts…

Why samples matter to reviews

According to recent research, we did using Revuze Explorer, analyzing over 300,000 reviews, and comparing incentivized reviews (A review written by a consumer who got the product for free or got a coupon for the review) to regular buyers – incentivized reviews have a higher sentiment towards the product by 8.33%!

This could be the difference between a 3.7-star reviews and 4.1 stars review! Which is the eCommerce world that could be significant to your revenue.

Shortening the feedback loop via reviews

There are many other benefits on the path to reviews to drive online sales, all derived from shortening the feedback loop. Unlike traditional retail, online reviews can be given immediately and collecting and analyzing them is much easier and faster compared to any traditional feedback method (surveys, focus groups, etc.).

Especially with a new product launch, getting feedback promptly is critical, as according to Harvard Business School, about 95 percent of new consumer products fail. The typical reason is the tendency to fall in love with the initial feedback from friendly consumers –

1. The new product launch hype causes consumer euphoria for the first few weeks.
2. Consumer brands fall in love with the euphoria and lower its guard.
3. Product issues start to surface 3-6 weeks post-launch.
4. Brands can take up to 5-7 months to get across the euphoria phase and become aware of the issues.

 

While we hear from brands every now and then that they “read reviews online to monitor them” it is obviously not a way to do it in scale. Especially as eCommerce becomes a key channel and consumers can buy on multiple online stores.

Couple this with tracking competing offerings and you can actually sit down and read all day…this is where review analytics solutions come into play. By automating the monitoring and analysis of reviews right from the start you have a shot at discovering and addressing issues with the new product launch data immediately and at scale (BTW this is also true for ongoing product support and product evolution as consumer tastes shifts, there could be new trends, new competitors and many other factors that could impact a mature product, too).

Conclusion

This is all good news. The move to eCommerce/D2C makes online commerce a key communication and feedback channel with consumers, which in turn helps the feedback arrive at brands much faster (if they listen).

Launching a new consumer product is challenging to the point that it’s almost guaranteed to fail; however, if you cover your basics and listen to the feedback you can guarantee the success of your launch, as well as the ongoing success of your offering:

1. Focus on driving online reviews for your product right from the start
2. Leverage samples, coupons, etc. to drive reviews volume and sentiment up
3. Use reviews analytics solutions to stay on top of feedback for the launch and ongoing.

Online commerce, becoming front and center, is a huge opportunity. For brands, it offers great potential to launch products faster and with a higher success rate. In parallel, it makes it easier to peak into your competitor’s backyard and see what is going on there – what is working or not.

The only challenge here is how to make sure that you stay on top of your eCommerce analytics and are constantly analyzing and benchmarking what is going on in your store and in your competitor’s stores. eCommerce moves so quickly that one miss could become very, very expensive for a brand.

Announcing Unlimited Market Research

While market research becomes more and more important as a way for brands to understand their audiences, especially now, it has 2 traditional, key pain points:

• Cost
• Time

According to McKinsey, as consumers changed their behaviors and preferences already due to the pandemic, for brands to keep sync with their audiences will require more than monitoring product sales numbers. Brands will have to do extensive consumer-insights research work, with a focus on identifying changed behaviors, beliefs, and motivators to fully understand the new consumer decision journey.

McKinsey further elaborates that qualitative techniques will be key in providing research directions that can later be further explored via large-scale surveys, testing etc.

While this underlines well the importance of intelligence as a way to survive and win the war on the changing consumer landscape, the wide range of research methods and tools underline the fact that this is going to be costly, and that launching these tools one after the other is a time-consuming exercise.

Why research is expensive & slow

I’ll jump right to the end and save you the suspense – research today typically requires experts that specialize in research channels and tools (typically different experts for different tools/methods) and so to get to a research answer requires staged use of these experts and tools one after the other which eventually takes months and is costly.

If you take a typical research flow per tool/method:

• Identify the expert resources to rely on
• Develop a research hypothesis
• Build it to a plan
• Execute the plan
• If there are issues, go back to hypothesis or planning

If you do this for several research methods, you can understand why many months and lots of money is spend. Take a typical cycle of:

• Use qualitative feedback to identify a new trend
• Run several surveys to validate and crystalize the trend
• Develop a proposed new offering
• Validate the offering via surveys
• Deeper validation via focus groups 

And there you go….

What is Unlimited Market Research 

Unlimited Research is the answer to all of the above issues. 

Imagine being able to perform an unlimited amount of research for a fixed cost? That will already take care of one big hurdle.

Now also imagine if you had a way of pooling in what an entire market is thinking about – all customers (yours and your competitors), about all the brands, about all the products, about every single purchase/usage/experience consideration, and the associated sentiment…. wouldn’t this huge pool of feedback be a one-stop-shop for most of your research needs?

For example, if you already know what every potential customer is thinking about in toothpaste, why do you now need to come up with questions for a survey and to run it and analyze it? Similarly, if you can know in detail what consumers love or hate about a specific competing product, why now run a benchmark with a focus group that will take time to design, run and analyze?

Putting it all together unlimited research means:

• Fixed cost of access
• To everything that is discussed within an entire industry 

With that, you can speed up research and cut down on research costs, becoming nimble and competitive.

How does it work?

Data is all around us. User-Generated Content (UGC) – mostly online reviews and opinions, is growing like crazy (Per IDG is growing at a rate of 62% per year). That’s a lot of feedback data. 

A typical survey will get you dozens of responses…and will generate responses to specific questions…

UGC on the other hand is authentic. The majority of customers share authentic moments and experiences from their lives with the hope to make a difference in their friend’s and family’s lives, sharing lessons learned and spreading the word on what works or not.

For example:

• 89% of customers would post about a positive travel destination experience
• 85% of customers would post about a positive dining experience
• 65% of customers would post about a positive health or beauty product experience

So the data for deep, actionable market research is there…now how do we harvest it?

UGC is a form of unstructured data. Unstructured means there is no specific pattern to the data. Unlike structured data such as an address or credit card numbers that have a well-known pattern and thus are easily recognizable by technology, UGC can be of different length, structure, format, content etc. 

New technologies are now able to analyze this free form feedback data, categories it to topics and provide a sentiment read per sentence/topic.

Let’s look at an example on how feedback data can turn into a product benchmark: 

1. • “Love the screen of this Apple iPhone model X Pro” = Apple/X Pro/Screen/Good (+1)
   • “Hate the sound of music on the Apple iPhone model X Pro speaker” = Apple/X Pro/Speaker/Bad (+1)

Basically, market sentiment of Apple iPhone X Pro/Screen = Apple/X Pro/Screen/Good / (Apple/X Pro/Screen/Good + Apple/X Pro/Screen/bad).

In the same way, eventually, all these features discussed are rolled up to provide the sentiment for the entire phone model. This way if 79% of customers say good things about Apple iPhone X Pro features, it ranks 79%. If the equivalent Samsung has a 74% rank – we’ve got a benchmark!

Conclusion

With so much UGC data floating around you’d expect brands to be able to shorten feedback loops and tighten customer relationships. However, per a recent Nielsen study, 80% of small and mid-size CPG companies report that information gaps limit their ability to grow.

With the latest AI and text analytics solutions, you can now turn UGC into a powerful benchmarking solution for your organization, helping every department make educated, quick decisions:

• Marketing
• Product
• Customer service
• Sales 
• eCommerce

These new tools have a flat cost (typically annual/monthly subscription) and offer you unlimited usage and access to UGC analysis. All you need to do is turn the insights generated by the tools into actionable decisions. With this, you can save on research costs and speed up your decision making and pace by a huge factor!

In Revuze’s recent Athletic Footwear Industry Report we pointed Sthat sneakers and athletic footwear are leading the charge on sustainability in the apparel market. While most apparel products are simple, sneakers are different as they are complex and require various parts, compounds, and materials. AThe more “sporty” the shoe is, it usually requires more petroleum-based synthetic materials, making it more difficult to manufacture with sustainability in mind. 

According to a recent McKinsey report, consumers, led by millennials and Gen Z, demand sustainability in apparel. For example, the report mentions that online searches for “sustainable fashion” nearly tripled between 2016 and 2019.

As sneaker brands consider these younger consumers as their target buyers, they place both their marketing and product efforts on sustainability. 

For example, in 2017, Nike had released shoes made out of “Flyleather”, a material made out of 50% recycled leather fibers and combined with synthetics for durability. Adidas, on the other hand, is incorporating recycled ocean plastics into their shoes. 

Recently, Revuze analysts performed a study into the athletic footwear category. Using our Artificial intelligence, they collected over 1,080,000 consumer opinions from different US eCommerce sites that include consumer reviews and ratings from 1/1/2017 to 4/30/2020, covering over 500 different brands (though the top 5 brands accounted for 50% of the discussion volume).

Here’s what we found

Sustainability discussion volume on the rise: Matching the insights gathered by McKinsey’s report research also revealed substantial growth in the discussion volume of discussion towards sustainability.

The most sustainable brands according to the discussion volume are: Adidas, Sketchers, New Balance, Nike and ASICS.

Adidas fans embrace the new line of shoes made of recycled ocean plastic. This entire product line has over 91% positive sentiment read and accolades:

Consumers are frustrated with brands which are not considered environment-friendly: 

When looking at the overall sentiment for consumer discussions around the environmental aspects of shoes, the average consumer sentiment is 68% compared to 77% across all consumer discussions for this category. 

Diving deeper into the data shows that consumers are very frustrated when they buy a product that doesn’t seem eco friendly or purchase a product from a brand that doesn’t seem to have a clear environmental policy. Here are few representing quotes as an example:

• “I also feel hesitant on buying a brand which is not quite up to date with its ethical and environmental responsibilities” 
• “the toxic odor from I’m assuming the dye they used is horrible trying to air them out may have to return them because of it”
• “Don’t recommend these for many reasons but what really guts me is the environmental neglect”

Conclusion

With lockdown increased the public’s awareness of global warming and growing awareness of the impact of humanity on the environment, consumers care about the ingredients used to make the shoes they buy and its overall environmental impact. They also consider whether or not the brand has a clear policy on its carbon footprint and efforts to reduce its environmental impact and recycling plans.

Further strengthening the sustainability case is the increased volume of discussions and the sentiment around footwear, which fails to meet these requirements, which is 12% lower compared to the industry benchmark.

Therefore it comes with no surprise that the overall Sustainable Footwear Market Size is expected to reach USD 11.8 Billion in 2027.

The more sneaker and athletic footwear brands become eco-friendly and product more eco-friendly products, the more interest, and sales they will gain from the new and conscious consumers.

What Is Digital Transformation Meets EBITDA

EBITDA is essentially net income (or earnings) with interest, taxes, depreciation, and amortization added back.  Every organization knew the needed to go digital before the pandemic. There is no argument that the pandemic accelerated the move. The big question is when the dust settles what will Digital Transformation (DX) looks like. We see this evolving across 3 time-ranges: short term, mid-term, and longer-term:

• Short term impact of the pandemic on digital transformation
• Mid-term needs of the new world
• Long term EBITDA ( will force things a certain way

Short term impact of the pandemic on digital transformation

According to a 2018 survey, 70% of respondents said that their companies either have a digital transformation strategy in place or are working on one – proving that DX is not new or formed by the pandemic. It is however accelerating and growing – a recent research report sizes the global Digital Transformation Market to be around US$ 911B by 2025 and to grow at a CAGR of more than 18.5% on route there.

However, before the pandemic, DX had more loosely defined goals – to use new digital technologies to solve enterprise problems. Obviously, each organization had its own problems to solve, hence DX meant different things to each business. Some wanted to go green and be paperless while others wanted to move to the Cloud and guarantee business continuity. 

The pandemic crystalized things for organizations, focusing them on these key technological areas that are a must for their business to function:

• Enabling 100% remote workforce: This represents a major functional undertaking. Organizations needed to make sure that their workers can function, have all their data and applications available remotely, and do that in a secure fashion without risking data leakage
• Sales and Marketing without travel: In a world without trade events, conferences, face to face meetings, how do you keep selling and marketing? The answer is straight forward – it’s all digital and remote. Phone sales, omnichannel, CRMs, marketing automation, etc.

This is the “bare bones” functionality needed for businesses to continue to operate – move to 100% remote workforce scenario with all functions still performing, and make sure the business side of things (sales, marketing, partnerships, etc) continue to perform remotely from the customers as well.

While this is a major achievement it is also short-lived. Once we achieved the “we’re not dead in the water” basics, organizations can continue to function, but productivity and efficiency are not necessarily there yet.

Mid-term needs of the new world

Just like the Maslow pyramid of needs, once the functional basics are working and your workforce can continue to work remotely and has the means to also sell and market, what is the next step?

In short – productivity!

Things that used to be easy before when your team was in one place (or most of it was there) are now taking longer:

• Meetings need to be pre-organized, you can’t just pop into someone’s office
• Water-cooler information sharing is not happening
• Supporting resources are not at easy reach – IT, experts of sorts, even your peers that used to give you tips or advice
• Keeping motivation up is also challenging. Suddenly there is no office and Happy Hours or a supporting manager 2 doors next to you – you need to keep the team spirit remotely 

This all boils down to your bottom line. Whatever used to take 1 week to achieve could now take 2 weeks. Motivation, tools, meetings all need to somehow make up for the impact of remote working so it “feels” like it’s not remote. And this is not trivial. 

There are the easy things that should help like:

• Team building meetings and activities
• Better remote collaboration tools for meetings/sharing/building projects together

But there is this X-Factor that exists in a group of people working together in an office regularly that was lost, and to compensate for this requires new tools, such as:

• Personal business intelligence tools to empower decision making individually
• AI and automation solutions as a way to cut routine/lengthy tasks short
• Predictive tools that will replace the water-cooler ad-hoc brainstorms

Long term EBITDA will force things a certain way

Again with the Maslow pyramid of needs, once the functional basics are working and productivity is back to norm again, organizations will need to look at their long term profitability model (i.e. EBITDA).

As all of this transformation/DX is costing the organization, plus the business still keeps the office space, organizations will need to figure out ways to drive EBITDA up, meaning any type of combo around:

• Higher revenue
• Lower expenses

To drive higher revenue the organization will need other DX tools, for example:

• D2C: Brands can cut channel costs if they go Direct to Consumer
• AI/BI: Any leap around business intelligence-driven by technologies such as AI can help drive more business and faster. Whatever took experts X months or years to figure out, AI can figure out in hours/days
• Competitive intelligence: In tough times competition is tougher. In addition to DX, your competitors will execute faster, which means you need to stay on top of them on a regular basis

To drive expenses down the organization will need other DX tools, for example:

• Remote workforce: Leverage stage I of the pandemic driven DX to save some office costs
• IT Automation: With organizations getting used to remote IT support it will encourage them to invest more in automation 

Conclusion

The pandemic forced an accelerated and different DX than organizations envisions. While we’re far from done on this DX, we envision 3 waives of innovation and transformation. These will be exciting times where only the organizations that successfully complete all three phases of DX will be strong enough financially to survive

Mobile Game Reviews

Online ratings and mobile game reviews matter to eCommerce, as they directly impact conversions and sales. We’d like to now highlight the golden insights that hide in these opinions, specifically for gaming companies.

There’s plenty of research showing how more reviews and better star ratings drive more purchases. Here’s one recent research from the Spiegel Research Center showing that leveraging reviews can increase online conversion by 270% and that reviews by verified purchasers (vs anonymous) can bump up purchase likelihood by an additional 15%. 

This is why brands encourage customers to leave reviews and provide feedback. Not many brands are religious about mining valuable insights from these opinions, and here’s why they should.

Based on our experience in market research for mobile games. We see 4 key areas of feedback for online games when analyzing online opinions in app stores or other online resources:

• Brand feedback (like/hate)
• Product feedback (This works, that doesn’t)
• Commercial experience (Points, gifts, ads, prices)
• Customer service (Instructions, specs)

1. Brand feedback

While this is the most generic aspect of the feedback, brands should still know and care about what is said about them, about their competitors and how they are faring and trending.

Typical topics talked about by consumers under this domain are:

• Overall satisfaction 
• Would you recommend? 
• Meets Expectations
• Price/Value for Money
• Quality

Example sentences here would be “It’s got great games good quality and very addictive” or “That’s junk software”.

These types of feedback can help brands understand overall perception, analyze the trends of perception in different areas across time, and also benchmark themselves against competitors and the industry in general

2. Product feedback

Here is where things get interesting. You get to learn in details what consumers appreciate or not in the game experience. Again, here as well you can compare to the industry and competitors and also explore trends.

Typical topics talked about by consumers under this domain are:

• Ease of Use
• Colors
• Customer Wishlist
• Game Speed
• Gaming Experience
• Group Play
• Music

Example sentences here would be “Love all the colors and animals” or “I hope you could add the chat function in the next upgrade”.

These types of feedback can help brands improve the game experience, add desired capabilities, analyze the trends of game satisfaction in different areas across time, and also benchmark themselves against competing games

3. Commercial experience

Here you will get to learn in details what consumers appreciate or not in the commercial experience around your game – purchasing, upgrading, ads etc. Again, here as well you can compare to the industry and competitors and also explore trends.

Typical topics talked about by consumers under this domain are:

• Advertisements 
• Casino Chips 
• Frauds & Scams
• Game Money 
• Gifts 
• Payouts
• Points & Rewards
• Promotions

Example sentences here would be “Great game good payouts” or “Bombarded with advertising to buy more chips or this or that”.

These types of feedback can simply help brands grow revenue as well as improve customer loyalty

4. Customer service feedback

Customer Experience is the battlefield of modern consumers. Here you’ll get to learn what consumers appreciate or not in the service level that they are getting from your team etc. Here, too, you can compare to the industry and competitors and also explore trends.

Typical topics talked about by consumers under this domain are:

• Instructions 
• Learning Curve 
• Loyalty 
• Refund policy
• Updates & Upgrades
• Support

Example sentences here would be “Had a problem with some points I emailed the staff and they took care of it right away” or “I keep getting the update notice but when I hit the update button it says update pending and will not load”.

This type of feedback can help brands improve customer experience and loyalty

Conclusion

Game makers should take the time to stop and listen to the crowd, talking about their games and the competing games. There’s so much to learn around the brand, the game, the customer service and the pricing model and with this information, you can take your game to the next commercial level.