Case Study: Boosting Your NPS with AI-Powered Data Analysis

positive feedback unearthed
solutions identified
dining experience

A company’s Net Promoter Score (NPS) is often the metric they rely on to understand customer sentiment. It’s determined by asking respondents to rank how likely they’d be to recommend a given brand to others. While most businesses zero-in on the score itself, there is a wealth of information within the open-ended comments below the numeric ranking that is rarely maximized to its fullest potential.

Companies that take the time to dig into this unstructured feedback properly typically hone in on the right issues faster and improve their NPS more efficiently because they understand why a customer gave them a specific ranking. Sadly though, this data is often underutilized because of the time and money required to run this level of analysis. 

Enter, Viable. This revolutionary GPT-4 powered AI distills unstructured data into an easy-to-digest natural language report, without expending valuable resources. Don’t believe us? Let’s dive into a real-world example of how one company used leveraged AI to work on their NPS. 

A popular senior housing operator with 18 properties across 10 states regularly polled residents for feedback using a traditional NPS survey but struggled to prioritize feedback. Our AI quickly ingested their qualitative feedback and generated a comprehensive report that unpacked their NPS score, detailing the compliments, complaints, and requests found within 814 survey responses. 

The good news: more than 70% of NPS tickets analyzed contained positive feedback on the company. By digging in, our AI was able to reveal specific aspects of the communities that residents loved and could be used as selling points for future customers: staff, facilities, community, and more. It also teased out feedback on specific properties within their portfolio. This combines to give a holistic picture that includes both a high-level overview and granular details of what residents enjoy about the communities, and where the company can double-down to maintain customer satisfaction.

While the positive feedback is reassuring, constructive criticism often provides more fertile ground for companies to understand how they can improve the customer experience and, ultimately, their bottom line. Within the “complaints” section of the report, our AI generated seven primary themes, each with a summary, urgency level, product details, supporting quotes, user profiles, and more.

The most common complaint centers on dissatisfaction with housekeeping and maintenance of the properties. By looking at the provided metadata, our AI identified that the majority giving this feedback were female residents and NPS detractors. It also calls out that a lot of this feedback is specific to a certain property, where a remodel was underway. The residents are asking for fairly straightforward and easy-to-fix solutions: new chairs in the common area, more consistent housekeeping, thoughtful solutions for mobility-challenged residents during the remodel, fastened-down boards in a bridge between buildings, etc. To convert these detractors to promoters, the company need only address these seemingly minor maintenance and housekeeping needs specifically outlined by residents and easily surfaced within our automated reporting.

The second most common complaint is about the quality of food. Once again, our AI analyzed the NPS data in such a way that it found this issue is primarily at one property. This detailed insight saves valuable time and resources, allowing the company to focus on only those communities with specific, recurring complaints. Beyond the food itself, our AI was able to determine that residents at this specific location were struggling to sit with their friends at lunch and that the service tended to be slow – both easily addressable problems that, should they be fixed, would quickly improve NPS.

Beyond summarizing the findings, our AI interprets the data and assigns an urgency level to each theme based on the churn risk represented by that feedback. Looking at the "Requests" section of the report, our AI assigned the second highest request — “Residents Council and Management Visibility” — a "mild" ranking, indicating this is not an urgent issue. What is even more interesting about this specific theme is that it calls out feedback from “passive” respondents (those who feel neutrally about the company). Typically, their feedback is lost in the sea of promoters and detractors, but our AI can tease it out and help your team understand how to convert them to promoters.

As you can see, there is a treasure trove of insights housed within your qualitative data that can help improve NPS with unparalleled speed, ease, and accuracy. And with our GPT-4 powered generative analysis, you can unlock those insights without ever touching a spreadsheet.

Try Viable and see what our AI's generative analysis can do with your NPS data. 

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