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Turning CRM data into distinct actions for basket size, frequency and digital activation

A segmentation has no value until it changes a decision. This project linked in-store purchases, online orders, CRM and digital behaviors to associate each population with a commercial goal — and to measure the actual effect of the action against a control group.

  1. 21% / 57%57% of turnover
  2. 14identified behavioural subgroups
  3. +26%average basket on the test dedicated to the value per purchase

This feedback comes from a project prior to CAIAC. It illustrates the operational mechanisms that contributed to the platform's design. The results are specific to the project context and do not constitute a performance guarantee.

The challenge

Why the business result was out of reach

Companies often have more customer data than they can activate. CRM, transactions, e-commerce, stores, analytics and campaigns each describe a part of the relationship.

These data produce dashboards, but rarely an operational answer to three simple questions: who should be activated, with what commercial purpose, and how do you know if the action has actually created value?

The risk is to build analytically interesting segments and then send everyone a variation of the same message. A good segment is not only descriptive: it corresponds to a different decision, action and measure.

The starting point

Data, processes and constraints

  • A fashion sign with a strong store presence.
  • A goal of development of e-commerce, still weak in the face of physical sales, without losing the knowledge accumulated in the network.
  • Data distributed between in-store purchases, online orders, CRM, media and analytics.
  • Several commercial situations to recognize: customers to reactivate, frequency to support, basket size to grow and in-store customers to help move into digital channels.
The approach

What was implemented

  1. 01
    Building a common analytical basisBringing together sources to connect customers, transactions, channels and behaviours. Advanced segmentation never compensates for poorly reconciled identities or divergent revenue definitions.
  2. 02
    Combining RFM with behavioursRecence, frequency and amount give commercial dynamics; categories, channels and purchase sequences complement it.
  3. 03
    Segmenting and validating the business senseThe clustering identifies groups; it remains to verify the standardization, redundancy and commercial readability of the groups obtained.
  4. 04
    Linking each population to one goalIncrease the value per purchase, support frequency, activate on digital customers mainly physical.
  5. 05
    Measure against a control groupThe right question is not "how much did the segment buy?" but "how much would it have bought without the marketing action?"
Results

What has been measured, and what it means

  1. Observed result

    The analysis identified a population representing 21% of clients and 57% of turnover, then adapted into 14 behavioural subgroups.

    Despite an activation conducted during a traditionally low period after the holidays, the tests reported +26% average basket on the value per purchase test, and the revenue measured on activation to the digital channel of customers until then mainly physical.

    Absolute amounts are not published in order to limit the risk of re-identification.

  2. Interpretation

    These results do not directly demonstrate a decline in churn or a complete increase in LTV. They establish that segmentation can be activated and measured on specific business behaviours — which is already a difference of nature with purely descriptive segmentation.

Why does it matter

How this translates into business impact

  • Prioritizing Value Populations

    Not all audiences deserve the same effort, offer or frequency of contact.

  • Changing channel behaviour

    Helping an in-store customer move into digital channels improves availability and personalization without replacing the store.

  • Differentiating growth objectives

    Basket, frequency and retention are distinct problems: treating them separately makes creation and measurement more accurate.

  • Measuring Incrementality

    A successful segment might have purchased without a campaign; the control group reduces the risk of giving marketing an revenue that would have occurred naturally.

The question of measurement that changes everything

The measure must not only ask "how much has the segment purchased?", but "how much would it have bought without the marketing action?". It is this question that separates an observed performance from a demonstrated impact.

The principle

What this project inspired in CAIAC

This project has taught us that a population, a business objective and a measure of Incrementality form a whole: without one of the three, the data does not change any decision.

CAIAC today industrializes this principle: integration of sources, identification of populations, association with an objective, definition of intervention, activation and measurement under control group. No data, model or segment of the historical project is included.

About this feedbackShow less

This feedback comes from a project prior to CAIAC. It illustrates the operational mechanisms that contributed to the platform's design. The results are specific to the project context and do not constitute a performance guarantee.

Items to be documented:

  • Accurate composition of control groups and groups.
  • Allocation windows selected.
  • Average basket and incremental revenue calculations.
  • Period of observation, marked by unfavourable seasonality.
  • Absolute amounts remain in the internal register.

Anonymized project · pre-CAIAC · unsecured results. Deployment is adapted to the data, systems and levels of autonomy available in the company environment.

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