TURF analysis is Total Unduplicated Reach and Frequency, a statistical technique that determines the optimal combination of products, flavours, or messages to maximise consumer reach while minimising overlap. This method ensures your product portfolio or communication strategy achieves the broadest possible coverage.
We begin every TURF analysis by collecting consumer preference or purchase data across all items in your consideration set. Using survey data where respondents indicate which products they would purchase or which flavours they would consume, our algorithms test every possible combination of items to identify the subset that reaches the highest number of unique consumers.
The analysis accounts for overlap between items if two products appeal to the same consumers, adding the second to your portfolio provides limited reach benefit. TURF identifies the combination where each additional item adds the most new, unduplicated reach. This ensures your product line is lean and effective rather than bloated with redundant options.
TURF analysis requires consumer-level data showing which items each individual would buy or use. This is typically collected through survey questions where respondents select all items that appeal to them from a complete list. The data must be at the individual respondent level, not aggregated, because TURF algorithms need to calculate overlap patterns across consumers.
Sample sizes of 200-500 respondents generally provide stable TURF results, though larger samples allow more precise estimation of reach for small segments. We also collect respondent characteristics to enable subgroup TURF analysis, revealing whether the optimal portfolio varies by demographic or behavioural segments.
TURF optimisation uses combinatorial algorithms that evaluate reach for all possible item combinations. For portfolios with many items, exhaustive evaluation of every combination is computationally infeasible, so we use heuristic algorithms such as greedy search, simulated annealing, or genetic algorithms to find near-optimal solutions efficiently.
Our algorithms are customised to handle realistic business constraints including production minimums, brand coverage requirements, and shelf-space limitations. We can optimise for maximum reach at different portfolio sizes, showing the reach curve as items are added and identifying the point of diminishing returns where adding another item provides minimal incremental reach.
The incremental reach curve is the central output of TURF analysis. It shows how total unduplicated reach increases as each new item is added to the portfolio. Typically, the first few items capture the majority of reach, while each subsequent item contributes less. The curve identifies the optimal portfolio size where adding items no longer justifies the incremental production and complexity costs.
We analyse the overlap matrix between all item pairs, identifying which items share the most consumers and which attract unique audiences. This overlap analysis reveals opportunities for portfolio rationalisation where redundant items can be removed with minimal reach loss, and identifies items with unique appeal that might be worth retaining despite lower individual popularity.
Real portfolio decisions involve multiple constraints beyond reach maximisation. Our TURF modelling incorporates business rules such as minimum and maximum items per category, brand representation requirements, production volume minimums, and cost constraints. We run scenario analyses showing how different constraint sets affect the optimal portfolio composition and overall reach.
We also model competitive dynamics by incorporating competitor product data. This allows you to see how your portfolio reach would change in response to competitor product additions or removals. Scenario tools enable your team to explore what-if questions interactively, testing different strategic assumptions and their impact on market coverage.
TURF analysis solves the portfolio optimisation problem that simple popularity rankings miss. A product that is individually popular may appeal to the same consumers as other products, contributing little incremental reach. Conversely, a niche product that appeals to a unique segment may be highly valuable despite lower individual appeal. TURF identifies these patterns and optimises the portfolio for maximum aggregate coverage.
Our TURF analysis delivers practical, implementable results. We move beyond theoretical optimal portfolios to recommendations that respect your business constraints and strategic priorities. The combination of algorithmic optimisation and business reality ensures that your final portfolio achieves the broadest possible reach while remaining operationally feasible and commercially viable.
TURF stands for Total Unduplicated Reach and Frequency. Total Reach means the total number of unique consumers reached. Unduplicated means counting each consumer only once even if they like multiple items. Frequency refers to how many items each consumer would buy or use. The method maximises reach while managing frequency.
TURF can analyse any number of items, but practical limits depend on computational requirements. For portfolios of 20-50 items, exhaustive algorithms can evaluate all combinations. For larger portfolios of 50-200 items, we use heuristic algorithms that find near-optimal solutions efficiently. Most commercial applications involve 10-40 items.
You need individual-level data showing which items each consumer would buy or use. This is typically collected through a survey where respondents select all applicable items from a list. The data must be at the respondent level, not aggregated, because TURF requires understanding of overlap patterns at the individual consumer level.
Market share analysis shows which individual products sell most, while TURF shows which combination of products reaches the most unique consumers. A product with high market share might appeal to the same consumers as other products, adding little to overall portfolio reach. TURF optimises the portfolio as a system rather than individual items.
Yes, we can incorporate cost data to optimise for reach per dollar or profit per item. Cost-inclusive TURF identifies the portfolio that maximises reach within a budget constraint or maximises profit given item-level margins. This is particularly valuable for portfolio rationalisation where cost savings from item deletion must be weighed against reach loss.
Sample sizes of 200-500 respondents are typically sufficient for stable TURF results with 10-30 items. Larger samples of 500-1,000 are recommended when analysing many items or requiring precise estimates for small segments. The required sample size also depends on how differentiated the items are in their appeal.
For seasonal categories, we collect data covering the full year or conduct separate TURF analyses for different seasons. The optimal portfolio may vary by season, and we can recommend core year-round items plus seasonal rotations. We also track how consumer preferences and overlap patterns change over time through repeated TURF studies.
For messaging, TURF identifies which combination of benefit statements or messages resonates with the largest unduplicated audience. Each consumer selects which messages would appeal to them, and TURF identifies the message set that reaches the most unique consumers. This ensures your campaign uses messages that collectively attract the broadest audience.
Contact our TURF analysis team to discover the optimal product mix that reaches your broadest possible audience.
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