Gone are the days when the net promoter score (NPS) was considered the holy grail of measuring customer satisfaction and, subsequently, company growth. We now know that customer satisfaction alone isn’t enough to achieve customer loyalty. Nor NPS — which acts as a sweeping generalization and cannot identify driving factors behind customers’ responses to questions, provide actionable insights, and capitalize on opportunities — can work by itself.

Fortunately, artificial intelligence makes it easy for businesses to analyze NPS and unravel meaningful insights from customer feedback data. In this comprehensive article, we discuss how AI helps with NPS analysis.

The NPS Quandry: When Quantitative isn’t Enough

NPS can help with understanding why customers are leaving, yet it has some pitfalls. While it may work for small companies with simple offerings, it fails to work for companies with numerous offerings and multiple points of customer interaction and does not provide enough information to take meaningful action. Below is a list of reasons that highlight the limitations of NPS:

Lack of actionability

NPS is a quantitative metric that provides a numerical score yet lacks the qualitative aspects of customer satisfaction i.e., understanding the “why” behind the scores and driving actionable changes.

For instance, a detector might be unhappy with the product quality, but NPS doesn’t tell you the aspects of the product that are problematic and what specific actions to take to resolve them.

Self-limiting

The limited context drawn from the scores, along with limited demographic representation, can make it hard for marketers to include complex customer journeys. For instance, a retail company fixating on NPS could ignore other aspects, such as revenue growth and operational efficiency, along with age, gender, or racial background.

Wild fluctuations

NPS is known to be volatile, especially if the sample size is small not to forget response bias, where people who are extremely satisfied or dissatisfied with your offerings are more likely to respond to surveys, thereby skewing the scores. For instance, a dissatisfied customer responded negatively to a survey in December owing to seasonal maintenance work but may have a positive response a couple of months later.

A combination of qualitative data & quantitative data to determine NPS?

So far, we know that a quantitative approach isn’t enough. Over two-thirds of Fortune 1000 companies use NPS to measure customer satisfaction. We need a qualitative approach to pinpoint the anomalies of businesses. Here are the reasons why:

Provides context & insights

With open-ended survey questions, you get qualitative feedback from respondents that offer free-form data. They allow you to truly focus on your customers, follow the whole customer journey by letting them open up about their experience with your offerings, and explain their pain points in their own words.

Now, sentiment analysis and text analytics can help you uncover the reasons behind a respondent’s answer, identify patterns and trends, and provide contextual insights that add to the quantitative information. A perfect melange of quantitative and qualitative!

For instance, a telecommunications company asks this question to its customers and gets the following responses:

Q: “Can you describe a recent experience when your internet service went down during work hours? How did it make you feel?”

Response 1: “It was a bit of a hassle, but I’ve to give it to you guys for the prompt support.”

Response 2: “It sucks! I had an important business presentation. You guys are very unreliable.”

Response 3: “It was inconvenient when my internet went down in the middle of my all-hands meeting, but I understand that technical issues like these occur.”

Now, using text analytics, you can process and structure the responses to identify keywords, phrases, and themes and extract valuable information. In this case, the relevant themes could be “service outage,” “response time,” and “customer emotions.”

Next, you can use sentiment analysis to understand the emotional tone of the emotions and categorize them as positive, negative, and neutral. Themes like “appreciation” and “prompt service” are positive, while “furious” and “disappointment” are negative, and “understanding” and “technical issues” are neutral.

The insights derived from such responses can help you identify common themes, understand customer sentiments (in this case, annoyance and disappointment), pinpoint issues (in this case, service outage and inconvenience), and tailor strategies for improvement (in the above example, service reliability and prompt issue resolution). Neutral responses are good pointers for adding more context to the underlying issues and highlighting opportunities to improve, even in situations where emotions are not highly negative. This is a microscopic view of what it looks like, but when you zoom out, where there are a million rows, you can extrapolate this and make sense.

Focuses on external factors

A combination of quantitative data and qualitative data provides NPS scores that can be tracked over time and lets patterns and trends emerge. Qualitative data gives context to these scores and captures the sentiment and emotions behind them.

For instance, An electronics company has been monitoring its NPS scores over the past few quarters, and they’ve noticed a significant drop in its NPS score from 60 to 45. While quantitative data alone indicates a decline in overall customer satisfaction, a combination of quantitative and qualitative data helped the company to understand that the external factor of supply chain disruptions and the resulting customer frustrations were key drivers of the NPS decline.

Adapt strategies

By providing context and specific reasons behind customer feedback and finding external factors, you can identify recurring themes or issues that are consistently mentioned by customers in surveys and thereby find out which aspects of your business need improvement. This can help you tailor your strategies and solutions to address the root causes identified in the qualitative data.

For instance, a consumer goods company uses its NPS score and realised the drop (from 75 to 60) to determine the overview of the shift in consumer sentiment. Using open-ended survey responses, it finds out that consumers have frequently mentioned packaging issues, such as damaged products upon delivery and concerns about sustainability.

Using sentiment analysis and root cause analysis, the company finds a mix of negative and neutral sentiments around packaging and sustainability that can be deduced as significant drivers of the NPS score decline. The company decides to invest in sustainable and eco-friendly packaging solutions to address both customer concerns and environmental responsibility, while continuing to collect NPS data and qualitative feedback.

At ConvertML we do exactly that. ConvertML’s AI-powered solution takes both quantitative and qualitative data to understand the “why” factor in your customer’s behaviour. Our tool also:

• Makes it easier to source data from multiple sources, be it structured or unstructured, and merge it to provide you with a unified view.

• Enables you to get richer customer insights through interactive dashboards, live reports, and advanced analytics, without having to write a single line of code.

• Protects your data integrity and enables you to stay compliant with GDPR and CCPA.

• Provides in-depth insights into customer behaviour so you can maximize your efforts toward improving customer engagement.

• Predicts customer churn and guides you and your team in developing the right retention strategies.

Choose ConvertML. Schedule a demo with us to learn how we help businesses realize the true potential of customer data. And a guaranteed 10X ROI!