The Heart Of The Matter: Why Retailers Need Sentiment Analysis To Connect With Customers

By Dheepan Ramanan, Clarabridge

Viktor Frankl, the Holocaust survivor and writer of Man’s Search For Meaning once remarked, “Those who have a ‘why’ to live, can bear with almost any ‘how.’”

“Why” is the fundamental reason for action, the root of what creates meaning for people in their lives. People innately seek meaning in the choices they make. This is true in business and in life. Consumers today want their choices to align with a “why” — something more meaningful than the best value or the most features.

Whole Foods
, the biggest winner in the organic foods revolution, is one of the best examples of a company driven by a “why.”  The growth of Whole Foods plots neatly with the rise in organic grocery sales. Organic grocery sales have increased 573% since 2000, with Whole Foods growing store count 183% in the same period. However, this market has only gotten tougher. 

Conventional grocery stores like Walmart and Kroger have added organic produce, and regional upstarts challenge Whole Foods dominance with upscale consumers. Even with this fierce competition, Whole Foods has maintained enviable margins and revenue growth. What creates such fanatical loyalty among Whole Foods customers? Simple: It deals with a “why.”


When I buy food from Whole Foods, I connect with the mission of the company: To live healthier, to be environmentally responsible and to create a quality standard of living for the people who work there. Whenever I visit my local Whole Foods, I notice that the employees look happy to be there and the products are well organized and stocked. Every time I begrudgingly venture into the Whole Body section with my girlfriend, I see John Mackey’s book (CEO of Whole Foods) Conscious Capitalism.

As I check out, I see the big blackboard denoting the thousands of dollars donated during the last community giving day. I feel that my choice to shop at Whole Foods has positively impacted the world, and this feeling overrides any concerns I may have had purely based on price.

Executing The “Why”

The “why” is relatively easy to execute when you only have one location. Individual retail stores are initially at the one-to-one level of communication and feedback, and the “why” is readily apparent to all employees and customers. 

Any issue is easily resolved and the feedback loop is simple. The retail experience begins with a “hello,” and ends with a smile and a “thank you.”

However, businesses face a fundamental problem as they grow and scale. As companies add stores and add feedback channels, the complexity of unifying customer experience leaps in difficulty. Communication transforms from a one-to-one, to a one-to-many. 

What was once an intimate emotional connection between brand and customer dilutes. Customers engage the brand through online review sites, Facebook, Twitter, Instagram and Pinterest, and each of these areas is an opportunity to reinforce or erode brand sentiment. This information overload is why 80% of CEOs believe their companies have satisfied customers, but only 8% of customers say likewise.

Big Data, A Return To The “Why”

Luckily for business leaders, Big Data analytics represents an opportunity to bridge the information gap, and unify the “why across all aspects of business. One component of Big Data is particularly important in this new era of high volume and variable customer feedback: Sentiment analysis.

Sentiment analysis parses through the masses of untamed, unstructured data created by social channels and allows us to categorize customer feedback into nuanced levels of positive and negative. Business leaders who use this emerging technology to analyze all of this feedback are able to mine the hearts of their customers across all channels and data sources. Sentiment analysis allows for a return to the “why” by giving businesses the ability to predict, reduce and transform negative feedback at a one-to-one level.

Examples Of Sentiment Analysis

Reduce. In the wake of several hacking attacks in the past year, discussion around passwords doubled.  This spike in volume was paired with highly negative sentiment. Customers of one of the hacked companies complained that password strength requirements were weak and did not allow special characters like many other web sites. Comparing this spike in chat data to survey feedback, similar findings emerged, and the company noticed many of their highest revenue and net worth clients were the ones voicing concern about the lack of password strength. Once additional measures for security for passwords were added, sentiment around the issue dramatically increased. 

Predict. A major American big-box store had a big call to make: A celebrity with popular product lines was overheard making racist comments. The company needed to decide whether or not to drop the celebrity’s products in response. After examining customer sentiment, comments showed that very few customers were supportive of the celebrity and the overall sentiment was negative. With this evidence, the retailer dropped the celebrity’s products from their shelves. Afterward, sentiment increased around the brand as many voiced approval about the decisive action. 

Transform. A large consumer products company needed a solution to prioritize and streamline social care. By using sentiment analysis, the brand was able to take the overwhelming amount of unstructured social data and prioritize actions at a highly nuanced level. Furthermore, by incorporating Klout Scores, the brand was able to find the most potentially viral and brand damaging tweets. What was once a randomized process transformed into a system very similar to that used by call centers to prioritize their activities. By using sentiment analysis, the brand was able to respond to 300% more tweets, improve issue resolution and improve overall customer satisfaction.
Sentiment analysis transforms your ability to shape customer experience, allowing your focus to shift from the “how” of running your business to the “why.” This return to the why is critical to providing deeper, richer meaning for your customers and achieving longstanding success.   

Dheepan Ramanan is a data scientist at Clarabridge. As a data scientist, Dheepan Ramanan utilizes big data analytics and derives business insights about companies. Previously, he worked for Clarabridge as a Business Consultant and produced text analytics for Global 1000 companies. As social media’s presence becomes increasingly influential and critical to business performance, Ramanan works to guide companies to meet the challenges of this new frontier.    

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