You have six flavors in your portfolio, but the budget allows you to keep four. The question is: which four should you retain to lose as few customers as possible? Or conversely, you are planning to introduce new variants and want to know which combination will reach the broadest audience rather than duplicate preferences you already meet. These are precisely the questions answered by TURF analysis – a research method that measures the total reach of an offering and the degree of overlap in preferences, rather than just the popularity of individual products.
What is TURF analysis and when do you need it?
TURF stands for Total Unduplicated Reach and Frequency. The name comes from media research, where it was used to measure how many unique audiences a combination of advertising channels would reach and how many exposures it would generate. Today, TURF analysis in market research answers one specific question: which combination of offering variants reaches the largest number of customers overall, so that each of them can find something for themselves.
The key word is “unduplicated.” A common mistake in portfolio management is to assess each variant separately. Suppose you are studying preferences for yogurt flavors. Strawberry and raspberry rank highest in terms of popularity. Intuition suggests keeping both. The problem is that the same people who like strawberry often also like raspberry. By offering both variants, you are largely reaching the same group. A third variant, such as an exotic flavor, may rank lower in popularity but appeal to entirely different people – and it is this variant that actually expands the reach of the offering.
TURF can be used wherever several elements of an offering compete for a place in a limited portfolio. Typical contexts include:
- Selecting flavor variants in FMCG categories – beverages, snacks, dairy products, and confectionery.
- Optimizing a product portfolio while reducing the number of SKUs (assortment rationalization).
- Selecting features or packages in services – for example, subscription options or software modules.
- Putting together a promotional offer or a range of colors, sizes, or packaging options.
- Selecting marketing message content that collectively reaches the broadest audience.
The common denominator is this: the question is not “what is best?” but “which combination will cover the most people?” This distinction determines whether the method is the right tool.
How is TURF analysis conducted step by step?
The starting point is a well-designed quantitative research study. TURF analysis is not a standalone data collection technique – it is a way of processing data. The data most often come from a survey in which respondents assess a set of variants. The entire process can be divided into several stages:
- Defining the set of variants. A complete list of products, flavors, or features that are under consideration is established. This may include several or a dozen or more items. The larger the set, the more possible combinations need to be analyzed.
- Measuring respondents’ reactions. Each respondent indicates which variants they would buy, try, or consider acceptable. Defining the “reach” threshold is key – more on this shortly.
- Defining the reach criterion. This establishes when an offering is considered to “reach” a given person. Usually, this means that the respondent has accepted at least one variant from the set being studied.
- Calculating reach for combinations. The algorithm calculates what percentage of respondents is covered by each possible combination of variants of a given size – two-item, three-item, and so on.
- Identifying optimal sets. The result is a ranking of combinations by total unduplicated reach, supplemented by a curve showing how reach grows with each additional variant and information on preference overlap.
The most important element of interpretation is the curve itself. The first, strongest variant covers a large part of the market. The second adds less because some of its audience overlaps with the first. The third adds even less. At some point, the curve flattens – the next variant barely expands reach and simply duplicates people who are already covered by the offering.
As Hume’s Institute experts point out, having more variants in an offering does not automatically mean having more customers – TURF helps identify the point at which another product mainly duplicates what is already in the portfolio rather than opening access to a new audience. This insight is at the core of the method: it makes it possible to distinguish variants that genuinely expand offering reach from those that merely increase production and logistics costs without a meaningful increase in reach.
In Hume’s Institute projects, the acceptance threshold has been observed to have a significant impact on the result. If any declared interest is considered “reach,” reach will be high but the criterion will be less demanding. If a high threshold is adopted – for example, only a declaration of “I would definitely buy” – the picture will be sharper and more conservative, although it will still be based on stated data. Therefore, the threshold definition is established before the analysis, consciously and in relation to the research objective, rather than selected afterward to fit an expected result.
TURF is often combined with other techniques. Input data may come from a simple purchase intent question, but increasingly, the results of conjoint analysis are used, where preferences are modeled based on choices in simulated purchase situations. This combination usually provides more structured input data than direct declarations.
What are the limitations and most common mistakes in TURF analysis?
TURF is a precise method within its scope, but it can easily be misused or misinterpreted. Below are the main pitfalls to consider when choosing this approach:
- Confusing reach with sales value. TURF maximizes the number of people covered, not revenue or margin. A variant with broad reach may be inexpensive and low-margin. The analysis tells you how many customers you will cover – not how much you will earn from them. Business interpretation requires overlaying profitability data, which lies beyond the method itself.
- Ignoring cannibalization within the portfolio. TURF shows unduplicated reach, but it does not automatically model how variants take sales from one another. High overlap between two variants signals a risk of cannibalization that must be interpreted consciously.
- An excessively large set of input variants. The number of possible combinations grows rapidly with the number of items. With a very broad set or numerous additional constraints, the analysis becomes more computationally challenging, while respondents assessing a dozen or more variants quickly become fatigued, which reduces data quality.
- Declarations instead of behavior. If the data are based solely on declared interest, the result may be overstated. People declare willingness to try more variants than they actually buy. For this reason, it is worth combining TURF with behavioral data or choice modeling.
- Treating the result as the sole basis for a decision. TURF is one tool, not an oracle. Optimal reach should be considered together with costs, production constraints, and brand positioning.
It is also worth understanding how TURF differs from related approaches. Traditional market segmentation divides customers into groups with shared characteristics, but it does not directly indicate which set of products will cover the most segments at the same time. Conjoint analysis models how individual product attributes influence choice, but it does not itself optimize portfolio reach. TURF serves as a decision-making layer above these data – it takes modeled or stated preferences and translates them into a question about the combined reach of a set.
Another limitation can be the static nature of the analysis. TURF describes the situation at the time of the study. If preferences change rapidly – for example, in seasonal categories or categories highly dependent on trends – the result has a shorter useful life and needs to be repeated.
When should you use TURF, and when should you choose another approach? Selection criteria
Before commissioning TURF analysis, it is worth checking whether the issue actually concerns portfolio reach. The following list helps assess whether the method fits the research situation:
- You have a set of variants from which you are selecting a subset – TURF makes sense.
- You want to cover the broadest possible group of customers rather than maximize sales of a single product – TURF is a good fit.
- You suspect that variants in the portfolio overlap in terms of preferences and that some of them are duplicative – TURF will verify this.
- You want to understand how individual product attributes influence choice – you are more likely to need conjoint analysis, possibly as input for TURF.
- You want to divide the market into audience groups and describe their profiles – this is a task for segmentation, not TURF.
TURF-based product portfolio optimization works best when the business question can be reduced to: “how many different people will we cover with this set?” The closer the question is to this formulation, the more appropriate the method selection.
Frequently asked questions
What is TURF analysis?
TURF analysis (Total Unduplicated Reach and Frequency) is a method for processing quantitative research data that measures the total unduplicated reach of a set of offering variants and the degree of preference overlap. It answers the question of which combination of products, flavors, or features reaches the largest number of unique customers. It does not assess individual products in isolation, but rather their combined reach in the market.
How does TURF help select a product line?
TURF calculates reach for every possible combination of variants and ranks them by the number of customers covered. This makes it clear which variants expand reach and which merely duplicate audiences already served by other items. It makes it possible to build a portfolio from elements that complement one another instead of competing for the same group.
When does adding another variant stop increasing reach?
This phenomenon can be seen in the reach curve, which grows more slowly with each additional variant and eventually flattens. The point at which it flattens means that the new variant mainly attracts people who are already covered by the offering, adding only a minimal increase in reach. This signals that further portfolio expansion may increase costs without genuinely expanding the customer base.
Want to find out which set of variants will reach the broadest group of your customers without unnecessary cannibalization? Ask about selecting your offering portfolio using the TURF method – Hume’s Institute will help design the study and interpret the results.