TURF analysis – Alida Help Center

What is TURF analysis?

TURF stands for Total Unduplicated Reach and Frequency. TURF analysis helps you determine the optimal combination of products, services, or offerings that will appeal to the largest audience possible.

TURF analysis looks at two different measures:

Reach: The number of consumers who like at least one of the offerings in a given combination.
Frequency: How often people like more than one offering in a given combination.

TURF analysis is useful in prioritizing options within a large set and maximizing the efficiency of resources. If no limits existed, you could offer everything and reach 100% of your customer base. But in real life, you have to deal with budgets, timelines, limited space, supplier constraints, and so on. Some options may appeal to large swathes of your customer base, while some options might only appeal to a small incremental percentage.

Is it worthwhile to invest in an expensive option that will only appeal to 1-2% more? How do you reach different groups and maximize reach by adding more options? What is the ideal combination of offerings to appeal to the largest possible audience? These are the types of cost-benefit questions TURF analysis can help you answer.

Benefits of TURF analysis

With TURF analysis, you can:

Better understand your customers and what they want.
Maximize the number of customers you connect to.
Reduce customer frustration by providing them the optimal mix of options, instead of inundating them and making them feel overwhelmed and frustrated.
Reap efficiencies for your business.

Limitations of TURF analysis

While TURF analysis is great for maximizing reach and frequency, it does have its limitations:

It tells you nothing about the relative value of a customer—how much they spend, how frequently they shop, how loyal they are. These are questions that are best addressed by progressive and in-depth profiling.
It's missing contextual information about other factors that influence purchasing behavior such as price, availability, and customer perception of the brand's reputation or quality.

TURF, Choice-Based Conjoint, and MaxDiff

Turf analysis, conjoint analysis, and MaxDiff are all market research techniques used to understand customer preferences and make informed business decisions. While they share similarities, they differ in their primary objectives and methodologies.

Note: MaxDiff TURF analysis is available in modern reports after MaxDiff analysis completes. Use the comparison table below for methodology context; procedural detail for reporting appears in Analyze and report on MaxDiff questions in modern reports.

TURF analysis

Choice-Based Conjoint

MaxDiff

Objective

The central questions being addressed are:
- How do I reach the largest number of customers?
- How do I maximize market coverage while minimizing overlap?

The central questions being addressed are:
- What combination of elements is optimal in maximizing interest and revenue?

The central question being addressed is:
- To what magnitude do people value one attribute (a feature, function, or benefit) in a product or service compared to other attributes?

Methodology

Multiple Choice question
Participants are asked to select all the options they prefer.

Choice-Based Conjoint question
Participants are asked to evaluate several product profiles in a choice set and choose the one they are most likely to purchase.
Comparative and inferential data is collected based on the chosen product profiles.

MaxDiff question
Participants are presented with a set of attributes. For each set, they must identify which is the most important and which is the least important to them.

Output and reporting

Table and graph views that show:
- The optimal combination at every size that maximizes customer appeal.
- How much your market share increases or decreases with each option you add or subtract.
- Where you start to see diminishing returns with added options.

A Choice-Based Conjoint Report that displays a bar graph with part-worth, importance, and relative importance for attributes and levels.
A conjoint market simulator that allows you to simulate product profiles, and assess the optimal combination of elements and how it performs relative to others.

In standard reports, a stacked bar chart and table view as described for the pre-modern workflow.
In modern reports, MaxDiff reporting adds Summary views plus optional MaxDiff Analysis with Zero-Centered Difference (ZCD) table and chart, convergence plot, and banner slicing on ZCD.

After MaxDiff analysis completes, you can run TURF analysis on MaxDiff attributes from the tile.

When should you use TURF analysis?

TURF analysis is useful for solving business problems concerning optimization:

Brand portfolio and product line planning
- Which combination of brands or products will appeal to the greatest number of customers within your target audience?
- If you introduce a new SKU, will this cannibalize any variants within your existing product portfolio or will it attract new customers not previously reached?
Marketing and advertising
- Which mix of channels will enable you to reach the greatest number of people?
- Which are the most impactful creative routes and copy options?
Service planning
- Which combination of service features will achieve the greatest reach?

Here are other examples of how TURF analysis can be used:

A mall wants to know which restaurant franchises to add to its food court. The number of open spots is limited and the chosen restaurant options should appeal to as many people as possible.
A hardware store chain wants to figure out which models of Christmas trees and string lights will be most popular with their customers during the holiday season. They have limited floor space so they can't stock every option. They need to prioritize to maximize interest and efficiency.
A beauty brand is launching a new skincare line. To determine the products in the initial lineup, they ask consumers which types of products (cleanser, toner, moisturizer, SPF, serums) they use most often. They do not want to go to the trouble and expense of developing a product that doesn't attract any incremental customers.
A beverage company wants to know which sparkling water flavors will appeal to the widest possible range of consumers. Right now they have classic flavors like lemon, lime, and raspberry. If they add more exotic flavors like passionfruit and mango, how much will that expand their customer base?

To better understand the types of insights you can get from TURF analysis, let's look at an in-depth example in the following section.

Example: TURF analysis in action

A yogurt company is trying to figure out which flavors to offer in order to maximize their appeal to their potential customer base. They ask customers to select their preferred yogurt flavors in a Multiple Choice question.

Which yogurt flavors do you prefer? Select all that apply.
Flavors	Results
Blueberry	30	35.71%
Cherry	21	25%
Chocolate	26	30.95%
Mango	20	23.81%
Passionfruit	17	20.24%
Peach	18	21.43%
Plain (Unflavored)	22	26.19%
Raspberry	22	26.19%
Strawberry	24	28.57%
Vanilla	22	26.19%
Other-specify	19	22.62%

The yogurt company runs a TURF analysis on their results. In the analysis, they always include vanilla because it's a permanent flavor offering. They exclude Other-specify and Did not answer from the analysis to focus on the flavors they want to evaluate and avoid skewing the results. They also choose to allow dropping options (which allows the application to drop less optimal options and swap in better ones) and show alternative combinations.

Now let's look at the results. Each row represents the optimal combination of a certain size. The first row represents the one-option combination that has the greatest reach, the second row represents the two-option combination that has the greatest reach, the third row represents the three-option combination that has the greatest reach, and so on.

In the first row, we see that vanilla is indicated as the flavor when only one is offered, reflecting the prerequisite that it is always included. If the yogurt company only offers vanilla, they reach 26.19% of customers. If they also offer blueberry, the reach jumps to 52.38%. Offering plain (unflavored) yogurt on top of those two options increases reach to 67.68%.

With each added flavor, the yogurt company can reach more customers. However, some flavors only drive a minimal amount of incremental interest. At a certain point, the cost of offering a larger number of flavors outweighs any potential benefit in terms of increased customers.

In the graph below, we can see that the reach and frequency lines increase dramatically when the # of Products is between 1 and 6, but the increases are smaller when the # of Products goes from 6 to 10. The combination of six flavors (Blueberry, Mango, Strawberry, Vanilla, Peach, Plain, Chocolate) seems to be the most efficient in terms of maximizing reach and frequency, and minimizing cost.

Typically, the reach and frequency lines run parallel to each other in the graph. However, in the screenshot above, the lines intersect going from nine products to ten products. When you hover over the plot points representing ten products (as shown by the screenshot below), you can see that the reach is 96% even though the frequency is 100%. This occurs because:

Some customers did not select any of the ten available flavors and only chose other-specify. Even if the yogurt company offered all ten standard flavors, the ones who only chose their other-specify option cannot be reached. Therefore, reach cannot be 100%.
When other-specify options were excluded from TURF analysis, they were not included in frequency calculations. The frequency percentage is the sum of all the times one of the yogurt flavors in the combination was chosen, divided by the sum of all the times any yogurt flavor was chosen. Excluding other-specify removes these responses from the sum of all the times any yogurt flavor was chosen and makes the denominator smaller. Therefore, a frequency of 100% is achieved.