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 article, we will focus on 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 (or products with certain claims).

Conjointly’s user-friendly TURF Analysis Simulator is an automated tool that helps automatically find top claim combinations. It can be used for product claim selection, range optimisation, and other applications (both using data from Conjointly experiments and from other sources.

A simple example of TURF analysis

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

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 flavours by consumer preference. The same study will also give you individual-level preferences, such as:

Each cell shows the partworth utility of a certain flavour for a particular respondent. We can then assume that if a particular flavour 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 flavour will be the most or the second most liked by each respondent:

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

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

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:

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 flavours from your brand. This is the way to go.

Deciding 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 a 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 call 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 produce and promote an SKU
• Companies generally favour lower complexity (i.e. a minimum number of SKUs in the range)

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