Getting inside the heads of your customers can make all the difference in your business. And over the last few weeks, we’ve discussed several ways you can collect consumer insights—surveys, focus groups, and working with a consultant.
But you don’t always have to look outward to gain consumer insights. So much of the data you have on hand—in product reviews, social media conversations, call center transcripts, and more—can be mined for key insights. You just have to know how to find them.
One way to gain key consumer insights from your internal data is through sentiment analysis. But if you’re more accustomed to traditional forms of market research, sentiment analysis may seem a bit foreign.
To take your consumer insights to the next level, you need to know what sentiment analysis is, why it matters, and how to do it well.
What Is Sentiment Analysis and How Does It Work?
Sentiment analysis is an automated way to process textual data and mine it for both positive and negative contextual insights.
Your customers (and prospective customers) generate millions of data points every day by sharing their thoughts on social media, giving star ratings and comments in reviews, and more. So the relevant information available for you is far more than could be understood simply by reading through it and taking notes. By automating analysis, you can sort through all those data points to find consumer insights that will improve product development, customer support, marketing, and more.
In some cases, sentiment analysis is simple. When someone says “X product is awful” on social media, there’s no questioning where they stand. And if every case were so simple, you probably wouldn’t need sentiment analysis.
However, most situations present subtleties and you can’t just rely on a computer to spot words that are clearly positive or clearly negative. You need to take the human capability to understand contextual information and scale it with an automated, AI-powered sentiment analysis.
But how exactly does that work?
While every vendor’s tool will have its differences, there are two main ways that sentiment analysis can work:
- Rules-Based Approach: With this approach, you would first manually define lists of positive words (good, great, happy, etc.) and negative words (bad, terrible, awful, etc.). Then, the tool uses natural language processing techniques to parse textual data to identify those words and sort positive and negative sentiments accordingly.
- Automated Approach: With a combination of big data, machine learning, and natural language processing, automated sentiment analysis tools leverage algorithms to apply human-level understanding to textual data at scale. Rather than being limited to a defined set of terms, algorithms can be trained and learn over time to continuously deliver stronger consumer insights.
While this is just a high-level, simplified explanation of how sentiment analysis works, the point is that you don’t need to be a data scientist to leverage automated and hybrid tools.
Instead, business users can pair sentiment analysis with something like social media monitoring to quickly understand why buzz is growing around their brand. For example, back in 2009, the social media attention for United Airlines exploded because of one customer’s viral video.
However, the passenger found that the airline staff broke his guitar and wouldn’t reimburse him, which resulted in negative attention from thousands of consumers. The faster you can identify negative sentiment like in the United situation, the easier it will be to get out ahead of potential crises that will hurt your business. Failing to capture subtle hints about negative (or positive) sentiment can result in missed opportunities, lost revenue, and damaged brand reputation.
Advantages and Disadvantages of Sentiment Analysis
Let’s be clear—not all sentiment analysis tools are created equal. It’s important to look at the advantages and disadvantages of each before investing in a solution.
With the right tool, you’ll be able to process massive amounts of unstructured textual data. By processing survey responses, product reviews, call center transcripts, and social media posts in one dataset, you’ll generate the most comprehensive consumer insights possible.
Some sentiment analysis tools are even open source, giving you an opportunity to implement them with the help of your IT team. Even before you invest in a formal tool, you can try out sentiment analysis yourself with Google’s free trial of Cloud Natural Language.
The problem is that most sentiment analysis algorithms will only achieve about 60% accuracy when divorced from the context of a specific message. Certain words can throw off the accuracy of analytics. For example, is the word “long” positive or negative? Without context, you wouldn’t know—and neither do many sentiment analysis algorithms. If you type subtle phrases into the Google’s Cloud Natural Language tool, you’ll find that the more complicated analyses will return with “neutral” sentiment, meaning Google couldn’t tell if the meaning was positive or negative.
To combat inaccuracies, you’d need to feed these types of examples into a machine learning tool so the algorithm learns to understand such subtle text. Supervised learning takes a lot of human resources and even then, you’ll still only be able to identify 8-15 topics to pull consumer insights with limited accuracy.
If you don’t take the time to find the right sentiment analysis tool, you could end up wasting money to gain few actionable consumer insights. But with the right solution, you can take sentiment analysis to the next level.
How Revuze Takes Sentiment Analysis to the Next Level
At Revuze, we take automated sentiment analysis and add another layer. We call it “local models.”
Within just a few days, we can generate local dictionaries and models with 90% accuracy. Compared to other tools that take months to develop local dictionaries and result in limited accuracy, we make sure you can benefit from valuable consumer insights faster.
Our AI algorithms extract many unique topics ranging from high-level ones like user satisfaction and price to granular ones such as “softness” for toilet paper or “moisturizing strip” for disposable razors. Instead of 8-15 generic topics, you can analyze 40-80 topics that are highly-specific to your business or product.
When you’re trying to understand consumer sentiment around a certain product feature, you can’t afford to have a sentiment analysis tool that’s limited to generic topics.
With Revuze, you can gain visibility into the hidden insights of modern consumer usage. If you want to learn how Dolby leveraged Revuze to do just that with smartphones and tablets in multiple countries, check out the case study here.