Retention and churn benchmarks when there's no cancel button

Basic formula:

Worked example: A shelf-stable soup brand sells to 100,000 households in Q1. Of those, 38,000 buy the brand again in Q2. RPR = 38,000 / 100,000 = 38%.

Is 38% good? It depends entirely on the category's purchase cycle, the average time between purchases. Toilet paper has a short, predictable cycle (weeks). A specialty marinade might be bought twice a year. Comparing RPR across categories without adjusting for cycle length is a common analyst mistake.

Benchmarks (estimates, as of 2025 to 2026)

Numbers vary heavily by category, so treat these as illustrative ranges, not universal truths:

• Packaged food staples (US, per NielsenIQ and Kantar panel commentary, publicly summarized in trade press): repeat rates for a healthy household brand often sit in the 40 to 60% range quarter over quarter.
• Personal care (shampoo, skincare): typically lower, often 25 to 40%, because switching costs are low and novelty-seeking is high.
• Private label and store brands: often show higher repeat rates than national brands in value categories, since the switching trigger (price) is removed once a shopper adopts the store's own brand.

A widely cited public reference for category-level buying behavior and repeat patterns is the Ehrenberg-Bass Institute, whose "How Brands Grow" research is the closest thing FMCG marketing has to an empirical bible on penetration and loyalty.

Category-switching churn: the real leakage metric

RPR tells you if someone rebought the brand. It doesn't tell you where the lost 62% went. That's where category-switching churn comes in: tracking whether a lapsed buyer left the brand for a competitor, or left the *category* entirely (e.g., stopped buying yogurt altogether, not just your yogurt).

Churn decomposition:

This distinction matters commercially:

• Brand switchers are a marketing problem: pricing, promotion, shelf presence, formulation.
• Category dropouts are a bigger, structural problem: lifestyle change, category decline, substitution (e.g., fresh juice losing share to bottled water).

Panel providers like Kantar Worldpanel and Circana (formerly IRI) build this by tracking the same household across periods and coding what else landed in their basket. If your soup buyer's basket now contains a rival soup brand, that's a switch. If it contains nothing soup-shaped, that's a category dropout.

Penetration versus loyalty: the diagnostic test

This is the crux of the lesson: most brand growth in FMCG comes from penetration (more buyers), not loyalty (existing buyers buying more). This is the central, well-replicated finding of the Ehrenberg-Bass "Double Jeopardy Law": bigger brands have both more buyers *and* slightly higher repeat rates, but the buyer-count gap dwarfs the loyalty gap.

Practical test to distinguish real loyalty from habitual restocking:

1. Compute Share of Category Requirements (SCR): of all the purchases a household makes in this category, what % goes to your brand?

2. Compute Penetration: what % of category buyers buy your brand at all, even once?

3. Compare your brand's SCR and penetration to the category norm line (the expected relationship between the two, derived from Ehrenberg-Bass style benchmarking).

If your brand's repeat rate is high only because your penetration is low (a small, niche buyer base who are enthusiasts), that's a warning sign of a ceiling, not deep loyalty. If your repeat rate is roughly in line with what the norm predicts for your penetration level, your "loyalty" is mostly just the mathematical partner of your reachreachThe number of unique people exposed to your message in a given period. Unlike impressions, reach counts each person once, no matter how often they see it.View full definition →, not a special emotional bond.

Quick illustrative calculation:

• Brand A: 5% category penetration, 55% repeat rate.
• Brand B: 30% category penetration, 45% repeat rate.

Brand A looks "more loyal" on repeat rate alone. But per Double Jeopardy patterns, a brand at 5% penetration is *expected* to have a lower repeat rate than one at 30%, not a higher one. Brand A overperforming its expected repeat rate slightly is a mild positive signal, but its tiny base means far more revenue risk sits with Brand B; losing a few points of Brand B's penetration costs vastly more volume than anything happening in Brand A's loyal-but-small base.

Building it from data: a simple structure

If you're working with loyalty-card or panel-style transaction data, here's the shape of the calculation in pseudocode:

This is intentionally simple. Real panel analytics add weighting (panels are samples, not full populations, so results are projected to represent the total market) and multi-period smoothing to avoid one slow week distorting the picture.

Watch out for these traps

• Seasonality masquerading as churn. Sunscreen "lapses" every winter. That's not churn, it's category seasonality. Always compare year-over-year, not just sequential quarters.
• Pack-size effects. A household buying a larger multipack less often can look like churn in a naive period-over-period count, when volume is actually stable or growing.
• Promotion-driven false loyalty. High repeat rates during a deep discount period often collapse once price resets. Always check RPR at full price versus on-deal.

🎬 [VIDEO: "How Brands Grow: Byron Sharp explains the laws of marketing" - youtube.com - a condensed explainer of the Ehrenberg-Bass penetration and loyalty findings underpinning this lesson]

Key Takeaways

• Without a cancel button, FMCG retention is inferred through repeat purchase rate (RPR), the share of buyers who rebuy in the next period, always benchmarked against the category's natural purchase cycle.
• Decompose churn into brand switching (a marketing fix) versus category dropout (a structural issue), using panel data that tracks the same households over time.
• Real loyalty diagnosis requires comparing your brand's repeat rate and penetration jointly against category norm lines, not looking at repeat rate in isolation; per the Double Jeopardy pattern, small brands are expected to have lower repeat rates, and high repeat rates from a tiny buyer base are not necessarily a sign of strength.
• Illustrative benchmarks (estimates): food staples often show 40 to 60% quarterly repeat rates, personal care often 25 to 40%; always treat these as directional, category-specific, and check against current NielsenIQ, Kantar, or Circana panel reporting.
• Watch for seasonality, pack-size shifts, and promotion-inflated repeat rates distorting your churn read before drawing conclusions about brand health.