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What is TURF analysis?

TURF (Total Unduplicated Reach and Frequency) is a method for selecting the combination of products or variants that reaches the most unique consumers. It answers the question: which X products should we carry to maximize the share of potential buyers?

TURF analysis originated in media planning but has become a standard tool in assortment optimization. The basic idea is simple: given that consumers have different preferences, which combination of products reaches the most unique people? It is a reach question — not a volume question.

In its basic form, TURF works with binary data: each consumer either accepts or does not accept each product. The algorithm then searches for the combination that maximizes the share of consumers who accept at least one product in the assortment. The standard method is a greedy algorithm that selects the most popular product first, then the one that adds the most new reach, and so on.

TURF is a good starting tool but has clear limitations. It treats all consumers as equally valuable (someone who accepts but rarely buys counts the same as a high-frequency buyer). It ignores cannibalization. And the greedy algorithm does not guarantee the globally optimal answer. Reflect uses TURF as a starting point, not as the final answer.

Key takeaways

  • TURF maximizes unduplicated reach, the share that accepts at least one product
  • Originated in media planning, now standard in assortment optimization
  • Binary data: accepts/does not accept
  • Greedy algorithm, fast but not always optimal
  • Good starting tool with clear limitations

Example

An ice cream producer used TURF to choose 6 flavors from 20 candidates. The TURF assortment reached 82% of consumers. But a manual adjustment — swapping a niche flavor for one with lower reach but higher frequency — increased estimated volume by 12% despite marginally lower reach.

Related articles

Limits of traditional TURF

Traditional TURF has three fundamental limitations: it maximizes reach instead of volume, the greedy algorithm can miss better combinations, and it ignores cannibalization between products.

From reach to volume

Reach tells you how many you reach. Volume tells you how much you sell. Assortment optimization should aim for volume, and that requires factoring in purchase frequency, conversion and cannibalization.

Advanced TURF: hybrid optimization

Advanced TURF combines exhaustive search, swap optimization and reverse pruning to find better solutions than the greedy algorithm. It is computationally intensive but delivers provably better assortments.

Volume-based TURF

Volume-based TURF weights not just who is reached but how much each person is expected to buy. It gives assortment recommendations that maximize actual sales instead of number of consumers reached.

Competitive landscape and assortment optimization

Assortments do not exist in a vacuum. Competitors' assortments determine where the opportunities are. Optimal assortment optimization factors in what competitors offer and where unoccupied positions exist.

How simulation improves assortment decisions

Simulation lets you test assortment changes before implementing them. By modeling how consumers redistribute their choices when changes occur, you can predict the effect of adding, removing or replacing products.

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