How to use TURF to find something for everyone

Mark Travers is an insights specialist at Burke, Inc., a Cincinnati research firm. Readers interested in more information on the technical details of TURF-War are invited to contact him at mark.travers@burke.com. The author wishes to thank Joelle Gwinner, Megan Nicollerat and Sharon Bosche for helpful comments on an earlier draft of this article.

To talk about TURF is, in some sense, to date yourself. Indeed, anyone who has been in marketing research long enough has had at least some exposure to the analysis. Growing out of the days of old-school media-buying, TURF was introduced as a way to maximize the effectiveness of advertising campaigns. TURF helps figure out, for instance, how advertisers should spend their ad budgets to reach the maximum number of target consumers.

Nowadays, TURF is used for a lot more than just media buying. TURF is commonly applied to situations where, say, a juice manufacturer wants to optimize its flavor line to appeal to the widest number of consumers. Or where a credit card company wants to figure out what set of card options offers maximum consumer reach.

In short, any time the marketing objective is to determine how products, concepts and advertisements can reach the widest number of would-be consumers, TURF is a go-to analytical strategy.

And there’s good reason for TURF’s staying power. It is an intuitive technique that yields easy-to-digest and definite solutions. 

There is, however, one fatal flaw in many common applications of TURF and it has to do with the underlying psychology of consumer decision-making. While TURF figures out which product sets or advertising channels cover the widest number of consumers (but covers them only once), there are many occasions when revenue is maximized by reaching fewer consumers more than once with multiple products. In other words, there’s value in offering more than one product that appeals to the same consumer or running multiple advertisements seen over and over again by the same consumers. And this taps directly into the psychology of consumer choice. 

This article proposes an advancement to the TURF algorithm that quantifies the value of duplicating consumer reach. Comparing this modified TURF procedure, which I refer to as TURF-War, against a traditional TURF analysis, my goal is to show how your organization could be leaving revenue on the table by not reaching the same consumers more often and with more products.

The crux of TURF analysis is the idea of non-duplication (TURF, after all, stands for total unduplicated reach and frequency). Imagine you are a gum manufacturer and you produce five different flavors of gum. You have just opened a relationship with a retailer, who has requested three flavors of your gum to put on its shelves. How do you figure out which three flavors to send? TURF first identifies the flavor that appeals to the most people. Then, it looks only at the people who aren’t covered by this flavor and identifies the gum that appeals most widely to just those people. Then, the algorithm looks at the remaining people – not covered by either of the previous two flavors – and determines which flavor appeals most widely to them.

By conducting a TURF analysis, you can be guaranteed that you have, in a sense, “something for everyone.”

Maximize revenue

Typically, the core business objective of a TURF study is to uncover product sets that maximize revenue. By identifying sets that appeal to the widest number of target consumers, TURF analysis sets off in this direction. But TURF is a consumer coverage optimizer, not a revenue or product share optimizer. This is where TURF-War comes into play. 

To understand why it’s important to keep a focus on revenue when undertaking a TURF study, let’s continue with our gum example from above. Recall that a gum manufacturer has opened a new relationship with a retailer and the retailer has requested three flavors to put on its shelves. Your task, then, as a marketing manager is to figure out which three flavors to send to the retailer to maximize revenue.

Applied to this example, a traditional TURF analysis would identify the combination of three flavors that appeals to the widest number of gum consumers. This is a good starting point but it leaves out a critical facet of consumer decision-making: variety-seeking behavior. Sure, it’s good to have a flavor for everyone but, to maximize revenue, you also need to have flavors that appeal to repeat and variety-seeking buyers.

Enter TURF-War (total unduplicated reach and frequency, while adding revenue). TURF-War starts out from the standpoint of our traditional TURF, identifying the product with the maximum consumer reach. Then, instead of looking only at the consumers not covered by our top product, it looks at all consumers and balances variety-seeking behavior with new coverage and arrives at a solution accordingly. Thus, if it were the case that most gum consumers cycle back and forth between buying original mint flavor and spearmint flavor, the manufacturer would be better off putting both flavors on the shelf instead of just one of these flavors. TURF-War moves us to this optimal solution; traditional TURF, on the other hand, would only offer one of these flavors and call it a day. 

More than just one product 

Whenever there is variety-seeking behavior – and we know that variety-seeking behavior exists in almost every consumer packaged goods category – there is benefit to appealing to people with more than just one product.

For a second example, take the case of media-buying. Yes, it is often the goal to reach the widest number of target customers with advertisements. But we know from basic psychological research on attitude change (specifically, research from a subfield of social cognition called evaluative conditioning) that reaching someone one time with an ad is unlikely to invoke any significant behavior change. Behavioral change happens through the repeated pairing of stimulus and response. From this perspective, it is potentially more valuable to reach fewer people but reach them more often (something that some companies, such as GEICO, have figured out). And this is what the TURF-War algorithm does. It starts out in the same way as a traditional TURF then empirically calculates the value of consumer duplication and chooses ad channels based on the combination of these two factors.

In short, TURF-War is designed to do a better job of modeling the underlying psychology of consumer choice. And, in doing so, it can lead us to the revenue-maximizing answer.

To show how TURF-War outperforms traditional TURF when it comes to the goal of maximizing revenue, let us return to our gum example. To keep things simple, imagine we recruit a small sample of gum consumers and ask them a purchase-intent question for each of our five gum flavors. And let’s suppose our gum flavors are original mint, cinnamon, spearmint, citrus and grape. We also ask these gum buyers how frequently they purchase gum and whether they tend to buy the same gum over and over again or whether they tend to buy different flavors.

Here’s what the results look like: 60 percent of our sampled gum consumers indicate they are likely to purchase original mint, 30 percent are likely to purchase cinnamon, 30 percent are likely to purchase spearmint, 10 percent are likely to purchase citrus and 10 percent are likely to purchase grape. Moreover, gum consumers indicate that they buy, on average, one pack per month and 50 percent of consumers indicate that they are likely to seek variety versus always choosing the same flavor when they purchase gum.

According to a traditional TURF analysis, we would select our most popular gum, the original mint, as the first product to include in the three-item lineup to send to the retailer. Next, we would look at the remaining products to see which offers the highest incremental consumer coverage. Let’s imagine that, of the 30 percent of consumers who like cinnamon, none are covered by the original mint flavor. Of the 30 percent who like spearmint, all 30 percent are already covered by the original mint. Of the 10 percent who like citrus, all 10 percent are already covered by the original mint. And, of the 10 percent of consumers who like grape, none are covered by original mint. By the logic of TURF, we would next select cinnamon to include in our product lineup, because it adds the most incremental coverage (30 percent).

Finally, to figure out the third gum to include in our product lineup, we would look at the remaining products to see which offers the most incremental consumer coverage. Well, we know that all 30 percent of the people who like spearmint also like original mint, so spearmint doesn’t offer any incremental coverage. Of the 10 percent of people who like citrus, all 10 percent were covered by original mint, so that doesn’t offer any incremental coverage either. Finally, of the 10 percent of people who like grape, let’s suppose that none of these people care for the original mint or cinnamon flavored gums. Thus, we would choose grape as the third flavor to include in our product lineup.

In effect, we would have something for everyone, or 100 percent coverage (60 percent of consumers are covered by original mint, 30 percent of the remaining consumers are covered by cinnamon and 10 percent of the remaining consumers are covered by grape; 60 percent + 30 percent + 10 percent = 100 percent coverage). 

But here’s the catch. Given that about half of gum consumers tend to seek variety when they purchase gum, might we be better off going with a gum that had wider appeal than, say, our grape, but for which the appeal was duplicated across other flavors? This is where TURF-War takes us. Balancing the value of additional consumer coverage AND consumer duplication, TURF-War favors a gum lineup of original mint, cinnamon and spearmint (not grape). Yes, this solution only gives us 90 percent unduplicated consumer coverage (not 100 percent coverage like traditional TURF) but it gives us more duplicated coverage (30 percent of our covered consumers like both original mint and spearmint), which can improve revenue in a variety-seeking category such as gum buying.

And, a bit of arithmetic can quickly show the value of TURF-War in maximizing revenue.

Let us first model the sales according to our traditional TURF solution. Again, to keep things simple, let’s assume that gum costs $1 per pack and, as already stated, gum consumers buy, on average, one pack per month and about half of gum consumers are variety-seeking in their selections. Let’s also assume the size of the market is 1 million consumers.

Recall that the traditional TURF solution (original mint, cinnamon and grape) gives us 100 percent unduplicated consumer coverage. Because we know approximately 50 percent of consumers are variety-seeking and that our traditional TURF gives us no duplicated coverage, our monthly sales projection would equal $500,000. In other words, we would capture half of the 1 million consumers’ gum purchases (50 percent of purchases × 1 million monthly consumers × $1 per pack = $500,000).

That’s pretty good coverage, but can we do better with the TURF-War solution? Well, recall that the TURF-War solution (original mint, cinnamon and spearmint) gives us 90 percent consumer coverage. However, within that coverage, it also gives us 30 percent duplicated coverage among consumers who like both original mint and spearmint. This duplicated coverage is valuable, as it gives variety-seeking consumers another flavor to choose from, resulting in less defection to competing brands.

Modeled out, TURF-War’s 60 percent unduplicated coverage yields monthly sales of $300,000 (50 percent of purchases × 600,000 monthly consumers × $1 per pack = $300,000). Additionally, the 30 percent duplicated coverage yields monthly sales of $300,000 (100 percent of purchases × 300,000 monthly consumers × $1 per pack = $300,000). Notice the first term in the equation is now 100 percent instead of 50 percent because we are effectively offering the variety that consumers are seeking. And, adding together the two amounts we get $600,000 in monthly sales.

Thus, by sacrificing a bit of overall coverage for duplicated coverage, we make more money ($600,000 using TURF-War vs. $500,000 from traditional TURF). Although a $100,000 sales difference may not seem like all that much, scaling this up to yearly revenue (multiplying by 12) and taking into account an accurate market size (possibly 10 million monthly gum buyers), we are very quickly talking about a $12 million annual revenue difference.

This $12 million opportunity is realized by TURF-War but left on the table by a traditional TURF analysis. And, in today’s hyper-competitive business environment, you can’t afford to leave even pennies on the table.

An advancement

TURF-War is an advancement on traditional TURF analysis that takes into account the potential value of product, concept or channel duplication. It empirically calculates the value of duplication and includes this in its solution.

The beauty of TURF-War is that, in cases where consumer duplication is not valuable (for instance, in consumer categories where there is little variety-seeking behavior or repeat buying), TURF-War will yield the same solution as a traditional TURF. Only in cases where there is significant value in consumer duplication will the TURF-War solution differ from traditional TURF. 

In other words, TURF-War can only do better than traditional TURF but can never do worse.