Driver Analysis — which two or three things should we improve first to move the metric?
Identifies which attributes most influence a key business outcome — NPS, CSAT, intent to repurchase, churn. Crowdmines fits up to a dozen models in parallel, compares them on cross-validated performance, and produces a defensible importance ranking — with optional SHAP explanations for tree models. For NPS, a two-stage model explains promoter and detractor behaviour separately. The deliverable is a full report — markdown, PDF, PowerPoint and JSON metadata — generated automatically.
The business question it answers
“Of everything we measure, which two or three things should we improve first to move our key metric?”
Say your NPS has been flat for three quarters. Feed in your tracker — the NPS column plus 18 attribute ratings (network quality, billing clarity, app usability, store experience, contact centre, etc.). The chat returns: network quality and billing clarity together explain ~60% of the variation in NPS; app usability and contact centre wait times each contribute ~10%; the other 14 attributes are noise. The quadrant chart positions network quality as high importance, low performance— the priority-one investment. Three slides of conclusion, ready for next week’s stakeholder meeting.
How the methodology works
The platform fits up to twelve model families in parallel — tree ensembles (random forest, gradient boosting, XGBoost, LightGBM, CatBoost), linear and logistic regression, partial least squares, MARS, Bayesian ridge, Bayesian networks and a small neural net — then compares them on cross-validated performance and reports which one fits best. For tree models, importance can be reported using SHAP values — a standard technique for quantifying how much each feature contributes to a model’s prediction — when the user opts in; otherwise model-native importance is used.
- Two-stage NPS model. Separate models for Detractor → Passive and Passive → Promoter, because the things that create promoters are rarely the inverse of the things that create detractors.
- Quadrant chart. Importance vs. current performance. Anything in the top-left (high importance, low score) is the priority-one investment.
- Importance tiers. Features are categorised Primary / Secondary / Low Priority using quantile thresholds (75th and 40th percentile of normalised importance).
- Optional calibration weighting. Inverse- frequency weights correct for over- or under-representation without requiring external population targets; effective sample size is reported alongside.
What you see in the chat
A model-comparison summary (which model performed best and why), an importance ranking — SHAP-based for tree models when enabled, model-native otherwise — the quadrant map for prioritisation and the two-stage NPS breakdown when applicable. The complete report is downloadable in markdown, PDF, PowerPoint and JSON metadata — the auto-generated PowerPoint is the deliverable that closes the gap between running the analysis and having something to present on Monday.
- What should we improve first to lift NPS?
- We measure 30 things in our tracker — which ones actually matter?
- Our experience team is trying to prioritise investments and needs evidence.
- Why is NPS dropping? / What’s behind the satisfaction lift in Q2?
- We need a defensible story for the board on what’s moving the metric.
The auto-generated PPTX
The deliverable that closes the gap between running the analysis and having something to present on Monday — model comparison, importance rankings, quadrant maps and an executive summary, all in a slide deck you can take straight into a stakeholder meeting.
From tracker upload to PowerPoint export.
Driver Analysis has a richer interactive flow than the simpler tools — research-question framing, variable mapping, DV recoding, and model selection are all guided by the chat.
How Crowdmines compares to SPSS, specialist tools and consulting agencies.
Driver analysis (also called key-driver, importance-performance, or relative importance analysis) is a staple of CX, brand and product research. Traditional approaches range from manual regression in SPSS / SAS / R, to specialised tools (Displayr, Q Research), to consulting engagements where an agency runs the analysis and delivers a deck two weeks later.
| Capability | Traditional (SPSS / R / SAS) | Specialised tools (Displayr, Q Research) | Agency / consulting | Crowdmines |
|---|---|---|---|---|
| Setup effort | Write regression syntax, manually select models, configure SHAP libraries | Point-and-click, but still requires variable selection and model configuration | Brief the agency, send data, wait | Ask a question in the chat |
| Number of models | Typically one model per run; analyst picks which | Usually one or two model types | Agency picks a model (often just regression) | Up to a dozen model families fit in parallel, auto-compared |
| SHAP explanations | Requires separate Python library, custom code | Some tools support it; many don't | Rarely included — agencies prefer simpler methods | Available for all five tree ensembles when enabled |
| Control variables | Analyst hand-codes the partialling | Rare — most tools don't partial out controls | Sometimes included | Built-in — controls partialled out, reported drivers renormalised |
| NPS two-stage | Must manually split data, run twice, combine | Rare — most tools treat NPS as a single outcome | Some agencies do this; many don't | Automatic when NPS scheme is selected |
| Quadrant mapping | Analyst builds manually in Excel or ggplot | Some tools include it | Delivered in the agency's deck | Auto-generated, interactive |
| Calibration impact | Run weighted + unweighted, diff the rankings by hand | Rare | Rare | Built-in weighted-vs-unweighted comparison with a rank-change table and AI narrative |
| Report generation | Analyst builds slides manually (hours to days) | In-platform view; export is limited | Agency delivers a deck (days to weeks) | Auto-generated markdown, PDF, PowerPoint and JSON metadata in minutes |
| Cross-validation | Analyst must code it | Some tools support it | Rarely transparent to the client | Up to 5-fold CV across every fitted model, reported automatically |
| Turnaround | Hours to days | Hours | Days to weeks | Minutes |
Three slides of conclusion — ready for next week’s stakeholder meeting.
Built for CX, insights and brand teams who need a defensible story for the next stakeholder meeting. Multi-model comparison, SHAP importance, two-stage NPS, quadrant map — and a presentation-ready PowerPoint.