Key driver analysis is a powerful way of measuring the relative impact of topics on a KPI. Below is a detailed explanation of how Caplena combines open-ended text categorization with ratings and performance measures to deliver these insights.
This feature helps you understand how much a topic influences performance metrics, such as:
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Likelihood to recommend (e.g., NPS)
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Overall satisfaction
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Star ratings
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And other custom KPIs
What’s Included in the Driver Analysis?
The driver analysis includes four key elements:
| Chart Section | Details |
|---|---|
| Impact per row | Measures impact on the performance metric on an individual basis. Based on multiple regression analysis. |
| Net impact | Shows overall impact by combining strength and frequency of mentions. |
| Suggestions for improvement | AI-powered recommendations on how to improve key topics. |
| Driver Impact vs. Mentions | Scatter plot showing driver strength vs. frequency for easy prioritization. |
Impact per Row
Topic sentiment plays a key role in the driver analysis, allowing us to determine the impact of a topic based on positive and negative experiences.
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The color coding of the bars reflects sentiment-based impact.
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The numbers next to the bars show regression coefficients.
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The higher (or lower) the number, the stronger the impact on the likelihood to recommend.
The impact or driver strength can vary significantly by topic.
Examples:
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NETWORK QUALITY / Reliability
The red and green bars are nearly equal in length, meaning both positive and negative experiences significantly influence the likelihood to recommend.
→ Someone mentioning unreliable network quality would likely not recommend the provider.
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DEALS & PRICING / Price
A negative price perception has a very strong negative impact.
A positive perception does not significantly boost recommendation.
→ This indicates a “hygiene driver” (Kano model): customers expect good pricing and react strongly when it’s perceived as poor.
Net Impact
While impact per row looks at individual case strength, net impact considers how frequently a topic is mentioned.
Formula: Driver Strength × Frequency of Mention
The impact shown for each row reflects the case-specific interpretation. While a particular topic might be a strong driver of a customer’s likelihood to recommend, indicating high individual impact, it’s important to also consider how frequently that topic is mentioned across the entire sample to understand its overall influence on a KPI.
This calculation shows a topic’s overall contribution to the KPI.
Example:
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BRAND PERCEPTION / Overall perception
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High net impact due to many positive mentions
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Even though negative mentions have a strong effect, they are less frequent
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Result: +9.1 points to the NPS
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DEALS & PRICING / Price
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Strong negative impact
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Result: -3.8 points from the NPS
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The chart currently supports NPS and 5-Star Ratings as dependent variables. More metrics will be added soon.
Suggestions for Improvement
For each topic, Caplena provides AI-generated suggestions based on:
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Open text analysis
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Driver calculation results
In the example above, the suggestions touch on NETWORK QUALITY / Connectivity & coverage, but also intelligently bring in related areas such as pricing when appropriate.
Driver Impact vs. Mentions
Clicking the icon in the top-left of the suggestion box toggles a scatter plot that visualizes:
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Horizontal axis → Impact
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Right = Positive impact
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Left = Negative impact
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Vertical axis → Mention frequency
This allows you to identify priority areas quickly.
🎥 Need a Walkthrough?
Check out the video guide below for a step-by-step explanation of how to use Driver Analysis in Caplena.