AI/Watson

Four things you’re missing with customer feedback surveys

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For a long time, InMoment has specialized in capturing the voice of the customer. Back in the early 2000s, we started with voice comments in surveys. The stories were so powerful that we made mix tapes and gave them to our clients for the holidays. It’s impossible to forget the richness of those stories—the humor, passion and character, the personality, the tone, the voices themselves. They added a lot more than just color to the statistics and numbers we gave our clients the rest of the year.

The tough part—the really frustrating part for me—was knowing that so much vibrant customer feedback data never saw the light of day, let alone went toward better understanding customers or improving their experiences.

There was an ocean of incredibly actionable insights out there, and all we could really see was the surface. InMoment has always pushed our listening technology to get closer to the core of customer feedback; to better understand the emotion behind the numbers. But before cognitive capabilities, I felt like we couldn’t reach the real depths of motivation, where all the nuances can add up to a wave of realization that the numbers never fully capture.

How technology sees beneath the surface

If I tell clients that their Net Promoter Score increased this week, they’ll ask, “Why?” Clients need to know the real reasons behind customer trends, and the answers I’m able to give them now are deeper and more dynamic than ever before. Cognitive analysis dives into the snapshots that come out of customer feedback surveys in four ways:

1. Customer voice

When you see the results from customer surveys, you feel like you’ve heard what they said. But have you? Really? Internet-based survey tools deliver powerful insight, but when things get elevated many customers give up on websites and call. The honesty and freedom of a customer on the phone can just drop your jaw. This forum can give you clarity and perspective that you’d never get online. It used to be impractical to analyze all calls, but now I can use cognitive technology to listen and analyze in real-time. This ability opens the floodgates for so many more insights that used to be difficult, if not impossible, to find.

2. Dynamic adjustment

Rigid surveys can badger customers, asking a host of questions that don’t matter to them, and more importantly, never getting to what really impacts their experience—and spending. But cognitive systems can turn on a word or a phrase. If a customer says, “It was great,” the system can ask, “What was great?” If a customer mentions a jacket they bought, the system can ask them to talk more about it. Now, I can help design conversational feedback experiences that pivot off of what customers want to tell you to get even richer data. Because if it matters to them, it should matter to you.

3. Instant response

With live analysis of text and voice, you can trigger real-time alerts to any place in the business. If a customer says, “Call me right now,” an alert can be instantly routed to customer care or, if it mentions a legal-safety issue, to your risk management team.

4. Emotion boosts accuracy

We can also factor customer sentiment into insights and recommendations. After layering the sentiment from unstructured content on predictive models, we’re seeing accuracy rates of 20 percent higher than running them on structured data alone. With one client we were able to identify locations that were likely to miss quarterly sales targets, and why. We delivered this intelligence far enough ahead of the end of the quarter that the teams were able to put effective action plans in place, reverse the trends, and hit their targets.

Now, you need to dive in

If your surveys just give you a score, then you’re missing most of the value. Customers have a lot to say that can significantly improve your business, and now the technology exists to transform those stories from simple insights to always-on, customer-sourced intelligence for your entire enterprise.

 

Note: This solution uses the IBM Watson Speech to Text service, through the IBM Watson Developer Cloud, to transcribe customer feedback from videos and phone calls. Then, it uses the IBM Watson Natural Language Classifier service to analyze the key terms and phrases in the customer feedback from transcriptions, online surveys, social media posts and other structured and unstructured content. Finally, it characterizes the tone of customer comments with the IBM Watson Tone Analyzer service.

  

For more, watch the IBM interview with Spencer Morris below.

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InMoment SVP of Data Science

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