Know your customers as cohorts, not as personas.
Cohort analysis, LTV modelling, and customer profiling that replaces brand-exercise personas with audience intelligence the marketing and sales teams can actually execute against.
360° profiling that converts.
Data-driven understanding of who actually buys.
Most businesses know their customer in two dimensions: a brand exercise that produced personas two years ago, and a customer service inbox that captures anomalies. Neither gives marketing or sales the profile they need to target, acquire, and retain — let alone predict how retention patterns will behave as the business scales.
We replace that with cohort-level understanding. Who actually bought in the last 12 months, segmented by acquisition channel, product line, and cohort age. LTV modelling that accounts for the real retention curves — not an assumed lifetime value multiplied across all customers uniformly. Behavioural segmentation based on what customers do, not what they tell a focus group they believe. Predictive profiling that identifies which new customers will become repeat buyers before they do.
The outputs feed everything downstream. The paid media team gets audience signals that move bidding toward customers who compound. The CRM team gets retention cohorts that merit dedicated treatment. The product team gets behavioural insight into which features drive retention and which drive churn. The leadership team gets LTV that holds up under finance team scrutiny.
What makes the difference.
Cohort-Level Analysis
Customers analysed by acquisition cohort, channel, and product entry point. Retention curves specific to each cohort, not averaged across the whole base. The reality masked by blended metrics.
LTV Modelling
LTV models that account for real retention dynamics, margin variation across product lines, and the seasonality that affects customer value. Not a single LTV number — an LTV function.
Behavioural Segmentation
Segments defined by what customers do — purchase patterns, product affinity, engagement cadence — rather than declared personas. Actionable at the targeting and creative level.
Predictive Profiling
Models that predict which new customers will become repeat buyers based on early-behaviour signals. Feeds bidding, CRM flows, and retention investment decisions.
First-Party Data Architecture
The data infrastructure that makes all of the above possible. Customer identity resolution, event pipeline engineering, and first-party data hygiene — built through our Business Intelligence practice.
Integration With Execution
The profiles are not documents — they are audience segments, bid modifiers, CRM cohorts, and product prioritisation inputs. Execution-ready output, not analyst deliverables.
Building the intelligence.
Data Foundation
Customer data audit, identity resolution, event pipeline health, and the cleanup work that makes cohort analysis actually meaningful. Without this foundation, everything downstream is noise.
Cohort & LTV Analysis
Historical cohort analysis, retention curve modelling, and LTV function construction. The underlying math that the subsequent segmentation and prediction work is built on.
Profiling & Segmentation
Behavioural segments defined, predictive models trained, and the profiles themselves documented in a form that marketing, sales, and product can each use.
Activation
Segments activated in ad platforms, CRM flows, and product prioritisation decisions. The intelligence becomes executable output across the organisation.
Common questions.
Ready to know the cohorts?
Let's talk about cohort analysis, LTV modelling, and customer profiling that produces executable output — not documents that sit unread.
Start a conversation