TURF, or Total Unduplicated Reach and Frequency, is a statistical technique originally used in media planning to estimate the number of consumers reached by a particular communication source and how often that happens. The analysis was then applied to market research, which usually focuses on the unduplicated reach of a combination of items to evaluate their market potential.

The Conjointly TURF Analysis Simulator is an automated tool that helps you analyse your data from Conjointly experiments or custom data to discover top item combinations that appeal to the most people. It can be used for product claim selection, feature selection, and range optimisation.

What is unduplicated reach?

The unduplicated reach of a combination of items is the percentage of individuals who are activated by (or are reached by, prefer, like, hear, etc.) at least one of the items in that combination.

For example, if you consider offering products A and B (but not product C), and know that:

• Sally likes A.
• Billy likes B.
• Zosja likes C.
• Peter likes A.

The unduplicated reach for A+B is 75% (because only Zosja doesn’t like either A or B).

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:

Calculation of unduplicated reach and frequency for two-factor combinations

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!

Calculation of unduplicated reach and frequency for three-factor combinations

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 only a handful of SKUs to avoid destocking by your retail channels.

Say, your total market is 2 million units a year, and you can sell 1 unit per consumer a year. That means that any new SKU 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)

You can also use TURF analysis to address these research questions.

What is the difference between TURF and conjoint analysis?

Conjoint analysis is both a way of asking questions and an analytical framework, while TURF analysis is a type of calculation that runs on previously collected data.

Conjoint data can be used in TURF analysis, but only to analyse combinations of levels within a specific attribute. For example, if you had an attribute of flavours with levels like sweet, sour, and tangy, TURF can help you find two flavours that will cater to most of the market. Although you can do similar analysis through preference share simulations, it will take into account all attributes, not just one attribute.

Conjoint analysis, especially when combined with simulations, is more versatile than TURF and can be used to analyse mixing features in a single product (i.e. combinations of levels that go well together, not combinations that will appeal to different people).

Learn more about the differences between conjoint preference shares and TURF analysis.

FAQs

Want to identify the most appealing product, features, or claims combinations? Conjointly’s team of research experts are here to help.