We live in a world where companies seek to be continually driven by data. While there are heaps of data growing at alarming rates, there are limited resources to process these largely unstructured stores of data. It might be relatively easy to use tools such as customer surveys, focus groups, or Net Promoter Scores, but it has become increasingly difficult to derive information from the data collected from these tools and use it productively.
Most companies, especially those with SaaS offerings, focus considerable attention on running programs to measure the health of their customers. Little is done to organize the data and mold it into a structured and purposeful record of customer sentiment. Signs of dissatisfaction and opportunities for business expansion may be missed entirely. Sometimes key findings will surface, but most companies remain in the dark as to how their customers really feel about their product or service.
“People will forget what you said, people will forget what you did, but people will never forget how you made them feel.” —Maya Angelou
What Is Customer Sentiment Data?
Customer sentiment data, as the name suggests, revolves around a broad spectrum of customer emotions and feelings about certain products, services, or experiences. Sentiment analysis, on the other hand, refers to using machine learning to decipher the tone and meaning behind words used to express customer emotions.
When it comes to customer sentiment data, it is not always about whether the customer is feeling good about a product/service (positive experience) or whether they have ill feelings toward it (negative experience). Many companies make the mistake of classifying customer sentiment data in one of these two categories. What they fail to understand is that customer experience is not so black and white—customer sentiment data is complex and multifaceted, so there are shades of gray. To understand how a particular customer feels about a specific experience, one needs to understand the underlying factors shaping that experience.
Why Is It Important?
With volumes and volumes of unsorted data coming from multiple avenues, there arises a need to properly structure and organize it. Doing this manually is tedious and time-consuming. Customer sentiment analysis comes in handy here and helps to automatically sort through layers and layers of raw data.
The inherent importance of customer sentiment data lies in the fact that we live in a customer-centered world and that requires us to design our products/services as per customer expectations. What better way to understand customer expectations than through collecting and analyzing customer sentiment data?
How Can Departments Use Customer Sentiment Data?
Any business can deploy its resources and collect valuable customer responses about its products and services—but only a handful actually take those insights, turn them into meaningful and actionable data, and use it to improve their current value offering. Let’s take a look at how different departments can productively use customer sentiment data.
Product development. Product managers have access to huge repositories of data, which can be used for various product updates, enhancements, and potentially even new product offerings. Developing a great and innovative product and rolling it out isn’t enough; product managers need to take into account customer buy-in, impressions, and experiences, and use all of that information to enhance the product/service. Furthermore, customer data from surveys, online reviews, and Net Promoter Scores need to be incorporated in strategic matters related to product development and design.
Customer support. Customer sentiment data from tickets is a great source of learning for the future; it can detect what the ticket is about and provide a record of tickets over a course of a few days, months, and years to give a realistic understanding of which problems seem to persist.
Customer sentiment data can also be predictive in nature—it can interpret what a ticket will be in regards to and predict an appropriate response. This particular aspect of customer sentiment enables customer support to pick, choose, and modify replies, which saves time, effort, and energy spent on analyzing and drafting responses.
Communications. Customer sentiment analysis has drastically altered the way we communicate with our customers. Thanks to tools like search engine optimization (SEO) and search engine marketing (SEM), companies now have a greater edge with stats and information. Customer sentiment analysis uses the power of engagement on social media to find out what customers are saying about a company’s product, which keywords they are using, and the respective connotations of each of these keywords. This data alone, if used in the right manner, has the power to transform the way companies talk to their customers by using words and phrases that resonate the most with them.
The Bigger Picture
That SAP recently acquired Qualtrics, a leading research and experience management company, projects the future from a customer sentiment standpoint. This acquisition signifies that every organization will focus on adding customer sentiment data to all other data sources, including sales, marketing, and operations, in order to generate business outcomes for all stakeholders.
Customer sentiment data is the future. The faster companies jump on board, the better equipped they will be to thrive in this cutthroat, dynamic, and highly customer-centric SaaS space.
Shreesha Ramdas is the CEO and cofounder of Strikedeck. Ramdas was previously general manager of the Marketing Cloud at CallidusCloud; cofounder of LeadFormix (acquired by CallidusCloud) and OuterJoin; and general manager at Yodlee. Earlier in this career Ramdas led teams in sales and marketing at Catalytic Software, MW2 Consulting, and Tata. He advises several start-ups on marketing and growth hacking. You can find him on Twitter @Shreesha.
