Deep Listening: More and better feedback from open-ends

By 2020-01-17T21:12:59+01:000000005931202001 Allgemein

About a chatbot as a part of an existing survey that motivates for more mutual feedback and as a side product provides real-time coding of verbatims.

Let’s face it. Respondents often don’t write much in open ends. Even many skip answering completely. What you get is like “xxx”, “nope”, “all good”.

If they write it’s too often hard to understand what they mean, like “Good accessibility”. Do they mean that the store is near or the hotline is 24/7 available? Also feedback can be very vague, such as “great service”.

On the other hand, if we could fix this weaknesses, a whole new world of insights will lay in front of us.

It’s a proven fact that respondents enjoy very much the opportunity to express what they mean in their own words. We measured in text-focused surveys a share of around 90% enjoyed the survey while conventional 10+ minute survey will often not exceed 50%.

With new text analytics systems it is now possible to quantify the given feedback on scale. If we are able to lead respondents to give more meaning feedback it would be a powerful path towards shorter, information-rich, enjoyable and explorative surveys.

How We Double Customer Feedback

Let me introduce you to Probe.AI. it’s a javascript survey plugin communicating with a prebuild cloud-based text categorization engine.

Probe.AI senses every topic a respondent raises in real-time. It then uses probing responses that are optimized to best spark elaboration.

Probe.AI success secret is …. its honesty. It asks the respondent for assurance. Reading how the bot rephrases respondents points, urges them to elaborate further. Still, Probe.AI does even more. It also verbalizes when it is unsure what has been said and shows interest in learning more.

Get Automatic Coding of Open-Ends Better Than Manual Coding

Respondents give feedback about how well we understood their issues. This trains the AI and makes it better – better than any manual coding can ever be. Who knows better, what he wants to say, than the customer itself.

The tool delivers a ready to use categorization of unstructured customer feedback, in a so-far unseen quality. All this is enabled by our deep learning text categorization platform Caplena.com – to us – world’s leading text analytics platform.

Implement with Ease into Every Survey

The best is that Probe.AI can be implemented in existing CX survey or Brand Tracker tomorrow. It is a JavaScript plugin that can run on all relevant survey platforms like CONFIRMIT, DECIPHER, DIMENSIONS, Qualtrics, Medallia, and many more.

To Triple Volume is Not Enough

It seems obvious. More feedback is better. Right? Not at all, if customers are just rephrasing the same basic argument with lengthy descriptions.

There is a way to validate whether the added feedback is truly a piece of new information. Only if this information can be used to explain and predict outcomes (Satisfaction, Loyalty, etc.), it’s unique and fresh information. This is what the we did to validate the additional feedback.

Gain +55% More Unique Insights

In contrast to many active-listening tools, Probe.AI has proven to deliver unique and new information. We measured, on average, 55% more explanation power – ranging per domain from +25% to +75%.

Uncovers Deeper Causal Structures of Success

The chat sequence delivers the data to dive deep into why your customers buy, stay, or leave. The sequence of topics raised gives us more in-depth information on the interconnections of issues. This information will be leverage with Success Drivers service “CX.AI” ( www.cx-ai.com ) by utilizing our proprietary causal machine learning platform.

Nutshell: More feedback, better insights, coded verbatims included.

You should experience the secret power of the active listening approach. It is a sincerely honest approach powered by pretty smart augmented artificial intelligence.

If you want to understand how Probe.AI works, visit www.probe-ai.com  and book an dem..