An Introduction to Sentiment Analysis, the Next Great Customer Experience Tool
People are irrational creatures when it comes to shopping. If you as a retailer can trigger the right emotions, you have a much higher chance of increasing your revenue. The key is to scan customers’ reactions to your offers and adjust them accordingly.
When you send well-versed sales agents to close the deal, they know what to do based on their extensive experience. The question is, how can you replicate this in online sales? How can you improve customer service just by automatically scanning emails and reviews, or judging by the time spent on page and mouse movements?
Enter Sentiment Analysis
Sentiment analysis is an umbrella term for a set of algorithms that strive to make sense of a users' brand perceptions based on the words they use, punctuation, and other parameters, such as emojis.
At its most basic form, it classifies a reaction as positive, neutral, or negative. Current advancements in the technology enable it to characterize reactions in a more specific way, identifying emotions like anger, curiosity, depression, happiness, stress, and so on.
This is possible through mining millions of social media posts and product reviews and creating a dictionary based on individual words and context. It is essential to use data sets that are very similar to customers’ expected input in terms of both the medium and the language used.
It’s More Than Just Sentiments
Although it is straightforward why sentiment analysis is an excellent tool for marketers, it is not enough on its own. To be efficient, it needs to be linked to natural language processing to find recurrent themes in customers’ feedback. For example, if you run a hotel and you get negative reviews, it’s important to know what exactly causes them—messy rooms, poor Wi-Fi, lack of parking availability.
Since people use a wide variety of language to express the same ideas, the real challenge is to classify all variations correctly. The use of sarcasm, for instance, can pose a linguistic problem for machine-based analysis. Even positive words can be misleading when one or more has negative connotations; take the typical British “bloody excellent” as an example.
Polite expressions can also be challenging because they can hide a negative impression under neutral words. A phrase such as “not bad” should be treated in context, as it is somewhat positive despite both words conveying negativity.
Sentiment Analysis: Use Cases
Sentiment analysis is about reading customers’ minds and pinpointing those actions that can change their mood, including promotions, product launches, price variations, social projects, and more.
Here are a few practical ways to put this tool to work for your business.
The simplest way to fail in business is to be oblivious to market segmentation. You shouldn’t address everyone if you want to succeed; you should focus your marketing efforts on those groups who can naturally embrace and advocate your products. Sentiment analysis can help you determine who these people are and what makes them happy about your business.
Once you get the results, filter them by demographics and define user categories. You could even borrow a concept from user experience and define user personas. Correlate the words and attitudes of these customers with their spending habits and set up your marketing efforts accordingly.
Product Launch or Redesign
When preparing for a new product launch or a redesign, you can use sentiment analysis on your competitors’ social media pages to see what draws people to their products. And when you go through a redesign, you can use this tool to check how your audience perceives the new identity and adjust your communication to meet expectations.
Finding Brand Ambassadors and Brand Detractors
The rise of social media brought to attention new categories, even new jobs: influencers and brand ambassadors. While many brands choose highly prominent stars for these roles, you shouldn’t neglect the micro-influencers who have small but loyal communities around them. And you can find them using sentiment analysis.
This technique is also useful for another category, the perpetual detractors who are always talking negatively about your brand. Once you identify them, you can either block them, contact them directly to offer explanations, or take other measures, even legal ones, if necessary, to protect your brand.
As your brand evolves, you can use sentiment analysis to get both real-time feedback from your customers as well as long-term evaluation.
This can be a helpful method to assess the success of your brand awareness campaigns, to measure the effectiveness of your crisis management, or to track subtle shifts in your market positioning.
Until now, machines have been mostly classifying reactions based on written text. The advancements of AI will bring in more ways to do this based on voice recordings and images. When these are put into the evaluation mix, the result will be far more accurate, since non-verbal and meta-verbal language can eliminate some of the uncertainty regarding context, sarcasm, and double meanings.
Marta Robertson is an independent data analyst involved with business requirements analysis, application design, development, testing, documentation, and reporting.