Customer Research
What customers actually value, switch for, and pay for, measured, segmented, and sized.
Customers are polite in interviews and inconsistent in surveys; good research designs around both. We combine qualitative depth (jobs-to-be-done interviews) with quantitative structure (conjoint, MaxDiff, statistically clustered segmentation) so you learn what customers do and trade off, not just what they say.
Who this is for
Product and growth leaders choosing what to build, for whom, and how to position it.
How the work is done
Qualitative depth
Jobs-to-be-done interviews with laddering to surface the progress customers are hiring for, switching triggers, and anxieties. Sampling designed for disconfirmation, recent switchers and churned customers included, not just fans.
Quantitative structure
Survey instruments with bias controls (sampling frames, attention checks, social-desirability mitigation). MaxDiff for feature priorities; choice-based conjoint for willingness-to-pay and packaging trade-offs, analyzed with hierarchical Bayes where sample size allows.
Segment and size
Segmentation via k-means or latent-class clustering on need and behavior variables, not demographics for their own sake, validated for stability, then sized and mapped to acquisition channels.
Translate to decisions
Findings land as decisions, not posters: which segment to lead with, which features move choice, what price structure the trade-offs support, and the message hierarchy the evidence justifies.
Methods and models we draw on
- Jobs-to-be-done interviewing
- Laddering
- MaxDiff analysis
- Choice-based conjoint (hierarchical Bayes)
- K-means & latent-class segmentation
- Survey bias controls
- Switching analysis
Methods are chosen for the problem, not the brochure, expect a subset of these, applied properly, plus whatever the evidence demands.
The decision this enables
Build/position/price decisions grounded in measured preference, not the loudest customer’s opinion.