TRIPLE CX Impact Just From Open Ends
CX.AI Codes Text Like a Human.
Then Uncovers CX Drivers Like a Genius.
Simple two-question survey
Two questions are all you need – the CX measurement question plus an open-ended feedback question asking “why.” Our natural language processing engine automatically categorizes customer feedback into content categories and our award-winning causal AI engine automatically reveals the key drivers of loyalty.
We put a dozen text analytics software systems to the test and found one that outperforms by far all in its accuracy. Its the technology of Caplena.com which we choose to integrate into the CX.AI platform. Caplena.com is a leading AI-aided platform for coding verbatim.
Deeper insights than with any other platform
Conventional CX measurement programs are descriptive, not predictive. They don’t provide insight into WHY your CX score changed or reveal the HIDDEN, non-obvious success drivers needed to improve CX outcomes.
Our powerful web-based dashboard shows it all: Trended CX scores, our proprietary AI-powered impact of key loyalty drivers, and an explanation of the change in your CX measure compared with the last wave.
CX.AI Dashboards …
… describes the status
The dashboard reports the key CX metrics along with promoter vs. detractor shares and visualizes its trend.
… visualizes key drivers
The key driver matrix has the frequency of mentioning a content theme on the vertical axis and the total impact (=importance) of such a theme as the horizontal axis. Topics with low frequency and high impact are typically those key success drivers that are usually overlooked when focusing on frequencies
… explains why CX measure changed
It happens after every wave of recurring surveys. CX measures improve or decrease, and everyone immediately starts thinking “Why the hell …?”. What you need to do to explain the change in your CX measure is to look at those drivers that do have most impact AND significantly changed. This is precisely what the Explain Variance chart is doing.
… visualizes the causal network
Our causal machine learning considers indirect effects, while conventional key driver analysis only measures direct effects by assuming drivers are independent of each other. A common indirect effect in CX.AI models is those that drive the sentiment variable. The sentiment variable captures the emotional impact of every topic. With CX.AI those causal relationships are transparent at you fingertip.
… simulates actions and predicts outcomes
What is the exact impact of “very important”? Is it realistic to achieve the industry benchmark with a certain initiative? What will be the combined impact of a realistic set of improvements? To answer these questions, we implemented a predictor into the CX.AI dashboard.
Dr. Rajul Jain (MICROSOFT) about
CX.AI at Esomar Client Summit 2019 in NYC:
Frank Buckler presenting
CX.AI at ESOMAR 2018 in Berlin:
CX.AI has all you need
Frequently Asked Questions:
“Can we use our existing survey program and just use CX.AI’s intelligence and dashboard?”
Sure. This is a flexible solution that can be customized to your needs.
Can we use all the context information that we know about our customers (like age, gender, customer type, value, history, etc.) in the modeling?
Yes. This is available as part of a customized program.
Which languages does CX.AI support?
We support all major languages and many of the less common languages spoken in the world.
Is there a minimum number of responses needed to feed the AI engine?
Not really. If you have just 100 responses, we will use a maximum of five content codes (driver categories). If we have more customer responses available, then we will develop a more elaborate codebook.
Can we as a client give you input on which content categories are most important to us?
Definitely. We will leverage any suggestions that you can offer from past research or industry knowledge.
We like to track multiple touchpoints, countries, and segments. Can CX.AI cope with this?
Yes. Any touchpoints, countries, segments or a combination of this get its own one-page dashboard. It depends on the sample size and the available budget if an own key driver model is set up for every split or a rather a higher level split is used.
Does it also work in a B2B context?
Yes. B2B audiences can be very different. Often they have even higher response rates and give more details in an open-end question. In case they are resistant to answer, we probe for more elaboration. Research shows that with NPS-style surveys respondents truly enjoy answering. There is no better way of surveying a broad audience of customers.
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