TURF stands for Total Unduplicated Reach and Frequency. It is a technique that came into prominence during the 1950s in the space of media planning. In this post, we are covering only the “reach” component of this technique.

 Reach is the percentage of respondents for whom at least one of the claims in a particular combination is their most preferred claim. That is, it is a measure of how many respondents can be “activated” by a combination of claims. Importantly, claims are tested one-by-one (not in combinations). Therefore combinations analysis through TURF is not the optimal way to assess preferences for combinations of claims.

Spot On AI-empowered analysis is an automated tool that helps you to automatically find top claim combinations.

An example is worth a 1,000 words

Imagine you are launching a new brand of 🍹 vegetable juices. As you are preparing for the launch, you want to have a range of flavors that will appeal to (“reach”) the largest number of potential customers. But what flavors should you offer if your budget allows only two flavors?

Let’s start by listing all possibilities:

• 🥑 avocado
• 🥔 potato
• 🥕 carrot
• 🌽 corn
• 🌶 pepper
• 🥒 cucumber
• 🥦 broccoli
• 🍄 mushroom
• 🌰 chestnut

What do you do? One way to solve this problem is to run a Product Variant Selector study, which will help you rank flavors by consumer preference. The same study will also give you individual-level preferences, such as:

Respondent ID	🥑	🥔	🥕	🌽	🌶	🥒	🥦	🍄	🌰
1	4.9	1.5	0.6	-0.8	1.2	1.8	3.9	-8.5	4.0
2	4.5	0.8	-1.3	0.1	-0.2	1.7	3.8	-8.4	2.0
3	5.5	-1.0	0.3	-0.3	1.4	0.7	3.6	-7.3	2.9
4	5.7	-1.2	-1.0	6.0	0.0	0.1	5.2	-8.9	3.1
5	4.3	-1.5	-0.3	-1.5	-0.8	0.8	5.9	-7.8	3.7
6	3.2	0.8	-1.0	-0.2	0.2	0.2	3.7	-8.5	3.5
7	3.1	0.2	1.2	-0.2	-0.9	0.5	3.1	-7.5	1.5
8	5.1	0.5	-0.1	-1.4	-1.4	0.3	3.1	-6.5	3.8
9	4.6	0.9	-0.9	-1.4	1.0	0.1	3.4	-8.0	1.2
10	3.2	0.9	0.5	-1.0	0.4	0.3	3.6	-7.8	1.7
11	3.4	-0.5	-1.4	5.0	0.2	1.5	5.8	-7.9	2.2
12	4.8	-0.9	0.5	0.3	-1.0	2.9	3.0	-7.2	3.7

Each cell shows the part-worth utility of a certain flavor for a particular respondent. We can then assume that if a particular flavor is among the top two most liked by a person, then we call it appealing to them ✔. Therefore, these scores can be used to identify which flavor will be the most or the second most liked by each respondent:

Respondent ID	🥑	🥔	🥕	🌽	🌶	🥒	🥦	🍄	🌰
1	✔								✔
2	✔						✔
3	✔						✔
4	✔			✔
5	✔						✔
6							✔		✔
7	✔						✔
8	✔								✔
9	✔						✔
10	✔						✔
11				✔			✔
12	✔								✔
…	…	…	…	…	…	…	…	…	…

Next, we assemble several possible combinations of flavors and calculate a couple of metrics:

• (Unduplicated) Reach, the percentage of people for whom at least one of the flavors is appealing.
• Frequency, the average number of appealing flavors per respondent.

Respondent ID	🥑 + 🌽	🥑 + 🥦	🥑 + 🌰	🌽 + 🥦	🌽 + 🌰	🥦 + 🌰
1	✔	✔	✔✔		✔	✔
2	✔	✔✔	✔	✔		✔
3	✔	✔✔	✔	✔		✔
4	✔✔	✔	✔	✔	✔
5	✔	✔✔	✔	✔		✔
6		✔	✔	✔	✔	✔✔
7	✔	✔✔	✔	✔		✔
8	✔	✔	✔✔		✔	✔
9	✔	✔✔	✔	✔		✔
10	✔	✔✔	✔	✔		✔
11	✔	✔		✔✔	✔	✔
12	✔	✔	✔✔		✔	✔
…	…	…	…	…	…	…
Reach	11	12	11	9	6	11
Reach %	92%	100%	92%	75%	50%	92%
Frequency	1.1	1.5	1.3	1.1	1.0	1.1

As you see, everyone likes at least one of 🥑 avocado + 🥦 broccoli (Reach = 100%). This combination is a winner!

If you have more budget to launch three combinations, you can do the same analysis with three-way combinations:

Respondent ID	🥑 + 🌽 + 🥦	🥑 + 🌽 + 🌰	🥑 + 🥦 + 🌰	🌽 + 🥦 + 🌰
1	✔	✔✔	✔✔	✔
2	✔✔	✔	✔✔	✔
3	✔✔	✔	✔✔	✔
4	✔✔	✔✔	✔	✔
5	✔✔	✔	✔✔	✔
6	✔	✔	✔✔	✔✔
7	✔✔	✔	✔✔	✔
8	✔	✔✔	✔✔	✔
9	✔✔	✔	✔✔	✔
10	✔✔	✔	✔✔	✔
11	✔✔	✔	✔	✔✔
12	✔	✔✔	✔✔	✔
…	…	…	…
Reach	12	12	12	12
Reach %	100%	100%	100%	100%
Frequency	1.7	1.3	1.8	1.2

This time, all combinations have equally good reach. Now you need to look at frequency. If you offer the combination of 🥑 avocado + 🥦 broccoli + 🌰 chestnut, for an average consumer, there will 1.8 liked flavors from your brand. This is the way to go.

NB: This is not an analysis of mixing flavors in one juice product. If you want to look into combinations of features in one product, you are better off with a Claims Combination Test or another type of conjoint analysis.

Deciding the number of SKUs to launch through TURF

TURF analysis can be used to determine an efficient number of SKUs to launch for your range. For example, based on your internal finance calculations, you can determine that you must sell at least 100,000 items for any single SKU to recoup advertising investment and that you generally want to have only a handful of SKUs to avoid destocking by your retail channels.

Say, your total market is 2M units a year and you can sell 1 unit per consumer a year. That means, that any new SKU launched must reach 5% of the market.

Now, we need to model a few scenarios:

• What is the reach if you only launch 1 SKU with the best potential?
• What is the reach of the additional SKU if you only launch 2 SKU with the best potential?
• What is the reach of the third SKU if you launch 3 SKU with the best potential?
• What is the reach of the fourth SKU if you launch 4 SKU with the best potential?
• And so on.

This type of analysis is called the TURF ladder:

In this example, it is efficient to launch six SKUs because the additional share of the seventh SKU is below 5%. Adding it means you will not recoup advertising investment.

This analysis is most helpful for most FMCG/CPG situations because:

• Companies have fixed costs for products and promote an SKU
• Companies generally favor lower complexity (i.e. a minimum number of SKUs in the range)

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