Alcohol Retail CLTV Intelligence

Problem Retail teams were spreading retention spend too broadly because customer value was not clear.

Focus I built the CLTV framework, defined usable segments, and linked them to weekly campaign planning.

Outcome The analysis helped focus effort on higher-value customers and reduce low-impact targeting.

// analyst.signals

Segmentation logic Retention planning Campaign prioritisation

Overview

Retail teams had weekly campaign pressure but no reliable way to rank customers by long-term value.

Business Context

Retail teams were under pressure to drive repeat purchases, but campaign decisions were still driven by short-term sales thinking. Without a clear customer value view, retention spend and promotional effort were spread too broadly.

Problem Statement

The business needed a more practical way to distinguish high-value customers from low-value or one-time buyers so retention effort could be prioritised more effectively.

Analyst Objective

Build a customer value framework that could support retention planning, improve targeting decisions, and connect campaign effort to likely commercial value.

Stakeholders

CRM and marketing teams needed segments they could use in campaign planning.
Commercial teams needed a better view of where repeat value was coming from.
Management needed clearer justification for retention spend allocation.

Key Questions

Which customers were likely to generate the most future value?
Where was retention budget being wasted on low-value audiences?
Which customer signals were most useful for practical segmentation?
How could model outputs be translated into campaign-ready actions?

Workflow Thinking

Customer transactions and loyalty behavior feed into value scoring and segmentation.
Segments then inform retention actions, promotional choices, and weekly campaign planning.
The analysis only becomes useful when model scores are translated into simple operating decisions.

KPI Framework

Business metrics: margin contribution, repeat purchase value, campaign ROI.
Customer metrics: recency, frequency, retention probability, segment movement.
Operational metrics: targeting efficiency, low-impact campaign reduction.

These metrics mattered because the goal was not just to score customers, but to guide better retention decisions.

Approach

Prepared 24 months of basket-level transactions and loyalty history.
Engineered recency, frequency, margin, and product affinity features.
Trained a baseline probabilistic CLTV model and calibrated segment thresholds.
Published a Power BI dashboard with segment drill-down and campaign recommendations.

Insights

A relatively small share of customers was driving a disproportionate share of margin.
Uniform targeting was diluting retention spend across low-value segments.
Segment-level visibility made it easier to plan targeted actions instead of generic promotions.

Deliverables

Customer value problem framing
CLTV segmentation logic and threshold definitions
Retention-oriented dashboard views
Campaign recommendation layer by segment
Decision support for weekly CRM planning

Results

Identified the top value segment representing 18% of customers and 54% of margin.
Reduced low-impact campaign targeting by 27%.
Enabled weekly retention planning with measurable segment-level uplift.

Next Steps

Track campaign results by segment to see which actions are actually working.
Review whether product affinity should play a bigger role in retention offers.
Revisit the segment thresholds over time as customer behavior changes.