Your CLV number was calculated from a spreadsheet two years ago by an analyst who has since left. Bidding caps, payback windows, and unit economics all reference it — and nobody is sure it is right.

Static CLV calculations miss cohort variation, behavioural change, and the survivor bias of long-tenure customers. The number drifts every quarter and the finance team quietly stops believing it.

We build probabilistic and ML-based CLV models that update continuously, reflect real cohort dynamics, and feed bid management, finance, and growth strategy with a number defensible to the CFO.

Retention teams only learn a customer is at risk after they cancel — or after a CS rep notices something on a call. By then the save is expensive and usually too late.

Without a predictive churn model, retention is firefighting. You save the ones you can see leaving and lose the ones you cannot, and the post-mortem always looks the same.

Churn prediction surfaces at-risk customers weeks before they cancel, with feature attribution so retention teams know which lever to pull — not just which customer to call. Scores sync directly to CRM and lifecycle marketing.

Headline retention looks stable, but underneath it the cohorts are diverging. New acquisition sources retain worse. A pricing change quietly killed month-six retention. Nobody can see it in the totals.

Without cohort analysis cut by acquisition source, plan, behaviour, and geography, the averages hide the segments that matter — and product and marketing keep investing against a number that is no longer true.

We build behavioural cohort views that surface where retention is improving, where it is decaying, and which segments deserve product or marketing investment. The decisions move from gut to evidence.

Everyone gets the same email cadence, the same win-back, the same discount. High-value customers feel spammed and low-value customers ignore you. The CRM list grows; the response rates do not.

Without segmentation by recency, frequency, and spend, lifecycle marketing fires the same gun at every target — and the cost of that uniformity shows up in unsubscribe rates and margin.

We refresh RFM segments continuously and sync them to your marketing platforms so cadence, offer, and creative actually match the segment. The classic that still works for lifecycle — done properly, not as a one-off export.

Your acquisition channels optimise to clicks, leads, or first conversion. The platforms have no idea which of those prospects are likely to become valuable customers, so bids are set on volume not lift.

Without propensity modelling synced to platforms and lifecycle, every dollar is allocated as if every prospect were equal. They are not, and the LTV-to-CAC ratio quietly drifts the wrong way.

We build propensity-to-purchase, upgrade, and expand models, then sync the scores to acquisition and lifecycle channels so spend concentrates on the customers where the lift actually exists.