Behavioural analytics · Customer Knowledge
The people behind the numbers.
Customer lifetime value, segmentation, churn, propensity, and forecasting — in one model that knows which customers your media is actually reaching. Reconciled to MMM elasticities so customer-level forecasts and channel-level decompositions tell a coherent story.
BTYD CLV·HDBSCAN clustering·Calibrated churn·Fast Forwards forecaster
What's in the box
One graph, four modelling layers.
01
Customer lifetime value
BTYD-family models — Pareto/NBD, BG/NBD with Gamma-Gamma — implemented via PyMC-Marketing. Bayesian uncertainty, hierarchical pooling across segments, the canonical method since 1987.
02
Behavioural segmentation
HDBSCAN as default — density-based, doesn't require choosing k, robust to non-spherical clusters. RFM as the interpretable floor. Longitudinal segmentation tracks customers across cohorts.
03
Calibrated churn
LightGBM workhorse classifiers with isotonic / Platt calibration. Reliability diagrams shown by default — the calibration matters more than the AUC. Survival analysis when time-to-churn matters.
04
Per-customer explanation
Why is this customer at risk? Surface the specific behaviours and product relationships that drove the score. Not a black-box prediction — an auditable inference.
05
Fast Forwards forecaster
Forward-looking scenario simulation reconciled to MMM elasticities via MinT (Hyndman et al. 2011). When you change the media plan, the customer-level forecast updates coherently.
06
Segment exports
Send a segment to MMM as a covariate, to Experiments as an audience definition, or out via the public API for activation. Same segment object, every direction.
Methodology
Canonical methods, current implementations.
The methods are mostly four decades old. The implementations are not.
Pareto/NBD CLV
Schmittlein, Morrison & Colombo · 1987
Original BTYD model. Hardest to fit, most flexible.
BG/NBD
Fader, Hardie & Lee · 2005
Easier to fit, near-identical accuracy. The practical default.
Gamma-Gamma monetary
Fader & Hardie · 2013
Pairs with BG/NBD for revenue forecasts.
HDBSCAN
Campello, Moulavi & Sander · 2013
Density-based clustering. Robust to non-spherical clusters and noise.
Calibration
Niculescu-Mizil & Caruana · 2005
Reliability diagrams as the trust signal — not just an AUC number.
Hierarchical reconciliation
Hyndman et al. · 2011
MinT — Fast Forwards reconciles customer- and channel-level forecasts.
FAQ
Customer Knowledge, in detail.
No. Customer Data Platforms (Segment, mParticle, Hightouch) ingest, resolve, and activate customer data. Kleyr Customer Knowledge consumes resolved customer data and gives you the analytics layer on top — CLV, segmentation, churn, forecasting. We accept resolved IDs from CDPs as input.
✦ Get started
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