Loyalty Ladder demo: Afriquia Retail Oil Study

Demonstration built on Latenta's Afriquia Retail Oil Study (Morocco, Q4 2023). Mixed-methods design: 8 focus groups (×6) followed by a CAWI behavioural survey — 12,224 surveyed, 2,350 completes, 2,030 final clean sample (effective ≈1,988, 98%); IR 16.6%, LOI 35 min; weighted to 70% men / 30% women; significance by False Discovery Rate (FDR, p=0.05). Loyalty, cross-selling and frequency modelled with XGBoost classifiers; share of wallet by regression; interpreted with SHAP. All figures are real study results.
The through-line: climbing the rungs, patronage, fuel & non-fuel spend, share of wallet, satisfaction, Kenz'up usage and openness to cross-sell all rise, while demand for improvements and orientation to short-term gain fall. The strategic message: a personalised, tiered loyalty programme that graduates each rung upward — tactical goal “+1 visit per month per customer.”
The Loyalty Ladder — the theory behind this view

What it is. The loyalty ladder is a relationship-marketing model popularised in the early 1990s (Christopher, Payne & Ballantyne’s Relationship Marketing, building on Raphel’s ladder). Its premise: a customer base is not one mass but a sequence of rungs of increasing attachment, and marketing’s job is to move people up one rung at a time.

The rungs. Prospect — aware but has not yet bought. Customer — has bought once or occasionally; no bond. Client — buys repeatedly but transactionally, often price-led and open to competitors. Supporter — likes the brand and buys habitually, yet stays passive. Advocate — actively recommends the brand and defends it; some versions add Partner above. Each transition has its own mechanism — trial, habit, trust, identity, voice — and asking a rung for behaviour two rungs up (e.g. asking a Customer to advocate) typically backfires.

Why climbing matters. Retention is cheaper than acquisition, spend and share of wallet rise with attachment, and Advocates recruit new customers at zero media cost — so the ladder shifts budget from perpetual acquisition to graduation mechanics: onboarding, habit loops, loyalty programmes, recognition and referral schemes.

How this study uses it. An XGBoost classifier assigns each of the 2,030 respondents a rung from surveyed behaviour (frequency, spend, Kenz’up engagement, attitudes). The theory’s prediction holds in the data: patronage, spend, share of wallet, satisfaction and cross-sell openness all rise with the rung, while improvement demands and short-termism fall. The engagement’s tactical translation is one number: +1 visit per month per customer. Prospects were excluded by design and are recommended as the next study.

Dashed grey outline = NET, the total sample (all 2,030 respondents) on the same axes — where the rung bulges past it, it over-indexes; inside it, it under-indexesAxes normalised to the strongest rung on each metric · hover an axis label for the source question
Share of wallet: out of each 100 dirhams spent on fuel, how many go to Afriquia (and the non-fuel equivalent). Below, the leakage picture: the share of each rung that also fuelled at a competitor in the last three months. The RFM logic is visible throughout — SOW rises with frequency and spend.
Which promotional hooks move which rung, and where non-fuel demand concentrates. Positive cross-sell drivers (SHAP): satisfaction, long-distance travel, non-fuel spend and loyalty itself.
Colour shows the relation to the total sample (NET): blue = above NET, orange = below NET; an arrow marks a difference of ≥ 8 points vs NET (FDR p=0.05 applied in the technical report). Hover a row label for the source question.
Reading the dots: the dashed hollow circle is the same attribute for the total sample (NET); the solid dot is the selected rung, and the thin line shows how far the rung deviates from NET. x-axis: improvement demand (Q25, “dramatic + considerable improvement needed”) · y-axis: satisfaction (Q42, top-2-box). Six analyst-matched attribute pairs; “fix first” = high demand, low satisfaction. Clients and Customers light up the fix-first quadrant. Base n = 2,030 · effective ≈1,988 (98%) · FDR p=0.05.
Compare: vs
An illustrative deterministic what-if on real inputs — measured rung sizes, visit frequencies, spend levels and shares of wallet — not a forecast. Revenue is shown per 1,000 Afriquia customers per month; fuel spend per visit uses analyst point estimates within each rung's measured band.
Baseline mix: Advocate 23.7% · Supporter 50.6% · Client 22.6% · Customer 3.1% (n = 2,030, weighted). Fuel spend/visit points: 250 / 150 / 100 / 150 DHS within measured bands; non-fuel ticket from Q40 means (392 / 359 / 298 / 397 DHS). Customer figures rest on n = 62 — treat with caution.
Models trained on the full clean sample — base n = 2,030 · effective ≈ 1,988 (98%) · significance by FDR p = 0.05. Scores shown honestly against naive baselines; Customer rung (n = 62) carries limited signal.
Answer five quick questions to see where you would sit on the ladder. A playful heuristic mirror of the real classifier's top SHAP drivers — not the production model.
Drive a customer up the ladder. Each station is a rung — to pull away you must name the mechanism that graduates it and back it with the evidence. Right answers refuel the tank; wrong ones cost fuel but show the real finding. Everything on this road is measured in this study.
The verbatim source questionnaire (Q1–Q77), grouped by chapter. Every trait shown elsewhere in this demo links back to its exact wording — dotted underlines throughout are hoverable.