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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.

Greedy TURF is fast but suboptimal. Advanced TURF methods attack the problem from multiple angles. Exhaustive search tests all possible combinations — perfect for small problem spaces but exponentially expensive. Swap optimization starts from the greedy solution and systematically tests whether swapping individual products improves the result. Reverse pruning starts with all products and removes the least contributing one at each step.

Reflect's hybrid optimization combines these methods. We use reverse pruning as an independent search path (it often finds different solutions than greedy), swap optimization as polishing of the best candidates, and exhaustive search as validation at feasible problem sizes. The result is that we consistently find better solutions than pure greedy.

In practical tests, hybrid optimization improves reach by 1-3 percentage points compared with greedy — and for volume optimization the difference can be even larger. It sounds small but for an assortment with hundreds of millions in revenue, every percentage point is significant.

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Traditional TURF vs advanced TURF comparison

Key takeaways

  • Greedy TURF is fast but suboptimal
  • Exhaustive search guarantees optimum but is computationally expensive
  • Swap optimization systematically polishes the greedy solution
  • Reverse pruning searches from the opposite direction and often finds new solutions
  • The hybrid method combines all three for the best result

Example

When optimizing an assortment with 30 candidates and 8 slots, greedy found a solution with 76.2% reach. Hybrid optimization found an alternative combination with 78.1% reach — a difference corresponding to approximately 15,000 more households reached.

Related articles

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?

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.

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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