How ISAAC offers an industry leading approach to text analytics
Delivering on customer expectations supposes you know what they are. A continuous feedback process is a great start, but you need a top-notch AI to help you figure out exactly what your customers want and need. We were already delivering on that, but with our new, industry-leading approach to text analytics, we’re taking it to a whole new level.
The problem with most customer feedback platforms
Most text analytics tools can roughly tell what customer feedback is about. A popular approach is breaking down feedback in categories and subcategories.
Here’s an easy example:
personnel < information = negative
While it is not incorrect, it’s still up to the CX manager to read through the open feedback to figure out what’s wrong with the information delivery. This is time-consuming and by leaving so much up to human interpretation, improvements and investments could still be debatable or a plain hit-and-miss. After many talks with our customers and using our own feedback technology ;-) , driven by our passion for CX, we understood companies need more clarity and granularity to make smarter decisions. That’s why we stepped it up and today we’re proud to present our new version of our text analysis AI.
Next Generation Text Analysis
In this new version, we improved all AI background processes so we can work with a multi-level breakdown of feedback.
Imagine a customer commenting that it was hard to get an appointment at their local branch. The old prediction would be
Personnel < agent interaction
And now it becomes:
personnel < agent interaction < availability
Taking it one step further, we can even link predictions now. Imagine a customer who isn't fully satisfied with the information he/she received about a loan. Any other feedback tool will analyse this as:
personnel < information
In ISAAC it will now show up as:
personnel < information about product < loans
It can’t get any more accurate than that. This is what the analysis of a piece of feedback looks like on the platform.
On top of that, we expanded our sentiment scale from 3 sentiments to 5. It now ranges from very positive to very negative, so you can easily select those at risk of churning. Or the other way around, identify your brand ambassadors with a click.
“Data is power. We're now able to capture, analyze, and quantify almost everything that's mentioned in the open feedback. These insights put a CX manager in the best position possible to improve internal processes and help them launch initiatives that support the employees. When you can engage your employees and help them to get the job done, you ultimately create value for the customer as well.” -Leslie Cottenje, CEO.
An international approach and great usability
The improved predictions work in all 25 European languages and make no mistake: it works in real-time. All predictions show up only seconds after the feedback comes in. The background processes might be complex, but we designed ISAAC in a way that it's easy to use for anyone in the organization. The page also contains more than just the data flow graph. For those short on time, we revamped our top 10 positive/top 10 negative categories chart with the new predictions.
Another great innovation linking feedback to the actual business processes, is the business area filter, which we co-created with our customers. Per industry, users can access relevant filters to highlight areas like digital experiences, customer services, and branch experience. This gives you already a very high-level view on how the different business areas are performing.
There is so much to say about this new version of ISAAC that we could go on forever. Want to know more? There are more details and product views here but the best way to fall in love with the magic of ISAAC, is to see how it works for yourself.
Net Promoter Score, NPS, and the NPS-related emoticons are registered U.S. Trademarks, and Net Promoter Score and Net Promoter System are service marks, of Bain & Company, Inc., Satmetrix Systems, Inc. and Fred Reichheld.
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