Today, companies have the benefit of data analysis to measure customer perception. That data comes in many forms, from social media chatter to formal instruments such as surveys. But sentiment analysis, a discipline of machine learning, is what makes that data so useful.
Sentiment analysis uses natural language processing to apply an algorithm to data sets. By searching for keywords or other markers, it reveals insights that organizations can incorporate into future planning. In essence, it delivers a baseline analysis for whatever an organization wants to measure and then helps to guide future decision-making or the implementation of necessary course corrections.
Retailers and Service Organizations Depend on Customer Perception
Retailers are among the biggest users of sentiment analysis. It’s one of the most effective ways to measure customers’ responses to products or brand campaigns, for example. A company could put a new product into the marketplace and then, in a few months, seed social media channels with prompts to elicit feedback. Social media insights can be extremely valuable, of course, but companies should balance them with other data for the most complete and accurate picture.
Customer service environments, such as call centers, use sentiment analysis to analyze interactions between customers and service agents. Algorithms that analyze voice tones and word choice can help to evaluate the quality of customer interactions. That, in turn, can alert managers to agents who generate high numbers of complaints, so they can provide training or redirect those employees into more suitable positions.
In healthcare, sentiment analysis has become an important method of understanding the patient experience. Customer service is a central tenet of this industry, so organizations need a systematic way to create visibility into their service delivery. That could mean measuring wait times in emergency rooms or the usage of an outpatient center — data that can inform decisions about staffing models or capital expansions.
Precise Measurement Ensures That Variables Yield Desired Insights
Sentiment analysis is unquestionably an effective way to capture insights that otherwise can be elusive, but there is one caveat: Often, so much data is available that analysts can go down a rabbit hole quickly. Organizations must be extremely focused in their use of sentiment analysis, because it’s easy to be overwhelmed by data.
To identify their ultimate objectives and which variables they want to measure, companies often partner with marketing or consulting firms. The next step is to build an algorithm and a method capable of measuring these variables. Alternatively, organizations can use one of the many predefined algorithms available online and tweak them as needed for their circumstances. Finally, other organizations may deploy an analytics tool to measure the variables and then to report or visualize them in a meaningful way, because that’s where the real value lies.
Ultimately, sentiment analysis is about generating data-driven insights that can accurately and efficiently guide actionable outcomes. Achieving optimal results requires a skillful hand, but it’s worth the effort. This type of analysis empowers companies to understand and manage one of the most important assets they have: their reputation among customers.