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Using TURF analysis with your survey data

 So, how do you know how many choices are enough, and what’s the right combination of options? Let’s find out.

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Customers love having a choice, but not too many options. It can be tempting to attempt to satisfy the desires and preferences of every possible customer by offering a broad range of products. In reality, offering too many choices can actually reduce sales. In this article, we’ll show you how to use TURF analysis with your survey data to streamline your product portfolio—putting you in the best position to maximize sales. 

Total Unduplicated Reach and Frequency analysis (TURF) is an insightful statistical technique that can be used to optimize product placement, offerings, communication, and promotional strategies when resources are limited. Essentially, TURF analysis estimates how many customers might be reached through a specified product portfolio strategy. Through TURF analysis’ powerful insight, you can decide the best combination of products and services to offer and which product ranges to abandon, giving you the best opportunity to maximize sales. 

Let’s say, for example, that you run a fitness study. You can potentially offer a range of different classes, like aerobics, Zumba, Pilates and yoga, but you don’t know which days and times would maximize class sizes. This is important knowledge: paying instructors to hold classes that are half empty could seriously undermine your profits. A TURF analysis will help you determine the configuration of class types, days and times that would reach and satisfy the maximum number of clients without overlap. This is called maximizing unduplicated reach.

Ultimately, TURF analysis will help you to:

  • Reach the maximum number of unduplicated consumers who are likely to buy certain products from within a specific portfolio
  • Optimize your product range by narrowing focus to the products with the best selling prospects and market potential
  • Identify the specific contribution that each product makes to your revenue model

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TURF analysis is especially useful when you’re launching a new product range, or when you’re new to the market entirely. You might consider creating a survey to administer to your prospective customers in order to identify their preferences from a range of possible options.

For example, let’s say you’re planning to launch a new line of one-size-fits-all swimsuits. You could offer the swimsuit in five different colors, with five different necklines, and you might offer both high waist and standard waist versions. With just these three product features alone, you have 220 possible combinations of stock keeping units (SKUs)!  In reality, though, customers might only be interested in two or three colors, one neckline and one rise. In this case, you can identify the optimal product range by asking three very simple questions:

  • Which of the following swimsuit colors would you definitely buy? (please select all that apply)
    • Red
    • Green
    • Pink
    • Orange
    • Brown
  • Which of the following necklines would you definitely buy? (please select all that apply)
    • Plunge
    • Halter
    • Asymmetric
    • Scoop neck
    • Sweetheart
  • Which of the following rises would you definitely buy? (please select all that apply)
    • High waist
    • Standard waist

Using the answers to these questions, and the application of a TURF algorithm, we can identify, for example, the top two colors preferred by customers, the best and least liked necklines, and the top choice of the waist types. By assembling different combinations of these features, we can calculate two key metrics:

  • Unduplicated reach

This is the number of respondents for who at least one of the combinations is appealing

  • Frequency

The average number of combinations that each respondent finds appealing

Based on your market research, you’ll be able to optimize your product range to maximize the total number of customers who are willing to buy at least one SKU.

Let’s look at how to perform a TURF analysis step by step:

First, you need to find an audience that represents your target market. TURF helps you determine the combination of products that reach the greatest proportion of the market. So, if you capture data from the wrong audience, you might end up with a suboptimal product range. Unsure where to find a panel of respondents that looks like yours? Use SurveyMonkey Audience to quickly identify a target audience that has the same characteristics as your customers.

Next, prepare questions that ask respondents their preferences from all the possible options you’re evaluating. There are a few different ways to do this. For example, you might use a multiple choice question to ask respondents to indicate all of the items in a specified range that they would be interested in buying. Another approach is to ask respondents to rank their top choices. If you need help deciding which questions to ask, let our research consultants help.

After you’ve administered your survey, view the results. A bar chart, for instance, can help you quickly visualize the most popular items of all those surveyed, or the frequency with which individuals chose certain items.

But, a bar chart is not enough to tell you which products to include in your portfolio. An example will illustrate this best. Imagine you’ve surveyed an audience of 1000 to find out the yogurt flavors they would be interested in buying if you offered them. The frequency analysis reveals that the top three choices are vanilla, coconut, and raspberry. You might stop your analysis there—those are the clear winners. But, what if those three items were selected by the same respondents? That means there’s a whole subset of the market left unserved.

To find out the best possible combination of products that will satisfy the maximum number of customers, a TURF test will be necessary. While a frequency analysis will help you understand the frequency with which products were selected, the TURF analysis will show you unduplicated frequency. Furthermore, unduplicated frequency is shown alongside reach, allowing you to make the best possible decision about the product mix. Better still, you can filter your results according to characteristics of your customers (like gender), allowing for some very powerful comparisons between segments.

Let’s take a look at a few examples of TURF analysis in market research.

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The classic example of a TURF analysis is where an ice cream seller has a large number of potential flavors for sale, but there is only room in the stand for three flavors. How does she determine which three flavors to include? The ice cream seller could prepare a brief market research survey which asks customers to rate, on a 5-point scale where 1= strongly disagree and 5=strongly agree, whether they would buy each flavor if it was available for sale. She might then narrow her focus only to those individuals who strongly agreed that they would buy the flavor, since this shows the greatest preferences:

I would buy this flavor if it were available on the serving stand:

% who strongly agree6%70%35%15%25%75%50%

Based on the frequency analysis alone, the seller might decide that Vanilla, Chocolate and Cherry are the best three flavors to sell, since they are the most popular. Including these in the stand would reach 70% of all consumers. However, this observation doesn’t take into account the three flavors that have the greatest market potential overall. Assume, for example, that further analysis reveals that:

  • 70% of all respondents like at least one of Vanilla, Chocolate and Cherry. In other words, many of those expressing a strong preference for the top three flavors are actually the same respondents.
  • In contrast, 90% of those that strongly agreed they would buy Cinnamon did not strongly agree with any other flavor.
  • Similarly, 50% of those that strongly agreed they would buy Pistachio do not like any other flavors.

Taking this information into account, the ice cream seller has a much better option for a product mix: Place chocolate, cinnamon, and pistachio on the stand, since this would reach 96% of all consumers (i.e. the 75% that would definitely buy Chocolate, the 15% that would exclusively buy Pistachio and the 6% that would buy Cinnamon). However, is this the best option? It might not be. 

While this option does maximize reach, the total quantity of ice cream sold might be lower because two of the options —pistachio and cinnamon —appeal only to a narrow subset of the market. Furthermore, chocolate, which is loved by 75% of all customers, might sell out quickly, leaving only two niche products which could reduce overall sales further. In order to find the best combination that maximizes reach and frequency, TURF analysis systematically and exhaustively tests all possible combinations and allows decision makers to look at the reach and frequency of each product mix.

MixReachIce Cream FlavorFrequency

A TURF report might look something like this. Reach can be expressed as the overall number of survey respondents who preferred at least one of the product mix combinations, or as a percentage of the overall customer base. Frequency shows the number of times that each product combination is preferred.

TURF analysis demonstrates the importance of considering both reach and frequency in determining product portfolio. Although some portfolios might have high reach, they might have low frequency. It will often be necessary to make a trade-off between these two metrics in order to find the sweet spot for sales.

A researcher called Sheena Iyengar from Columbia University tested the principles of TURF analysis in a real life setting. She set up a table outside a grocery store and offered passersby a sample of jams. One on Saturday, customers were offered samples of 6 flavors of jam. The following Saturday, they were offered 24 flavors. The researcher and her colleagues observed the number of people that stopped to sample the jams, and how many they purchased.

The results were surprising. On the Saturday when 6 flavors were available, 40% stopped to sample the jam. When 24 flavors were offered, 60% of people stopped, suggesting that offering more flavors is better. However, of the customers that stopped on the first Saturday (when 6 jams were on offer), 30% made a purchase. Of the customers who sampled 24 flavors, only 3% actually made a sale. So, this analysis showed that (as with many things), less is more.

Done properly, TURF analysis can yield many payoffs, for both your customers and your business.

Through TURF research, you can better understand customers’ preferences, how they make trade-offs between different product features and what it is they’re really looking for.  For example, for years, restaurants across the country believed that menus comprised of multiple pages and dozens of meal options we

Ever tried to buy a health insurance plan? Having to choose between bronze, silver, gold and platinum tiers, a range of different deductibles, copayments and coinsurance levels can leave you with a huge headache. Providing customers with too many options can lead to what marketing professionals call choice overload, and it has a range of negative emotional, psychological and behavioral effects. In addition to making customers feel anxious and overwhelmed, according to Professor Ulf Bockenholt at Kellogg School of Management, choice overload is one of the biggest causes of behavioral paralysis, “where people are faced with so many choices that they can’t decide among them and make no choice at all.” So, not only does optimizing choice help your customers to feel more comfortable in making purchasing decisions, but by streamlining choices and reducing decision-related pressures on customers, it can actually drive sales. 

It may sound counterintuitive, but when you offer customers too many product options, you increase the risk that they’ll be back to return the item, possibly leaving without coming back. That’s because customers faced with choice-rich product ranges tend to wonder whether they should have bought one of the many alternatives on offer, leading them down the path of buyer’s remorse. Relatedly, some customers who perceive too much choice often make snap decisions to avoid the stress of navigating so many options which increases the risk that they’ll buy something that doesn’t satisfy their needs. Either way, customers will end up dissatisfied, driving them to your competitors. One study, for instance, found that toothpaste customers faced with too many product variants (like tooth whitening, tartar control and cavity prevention features) tend to switch to brands that only offer one option. That’s because customers prefer brands that don’t force them to make confusing tradeoffs.

All businesses have budgetary constraints, and for many firms, storing, managing and wasting inventory is one of many firm’s biggest costs. TURF tests can help reduce these costs in several ways. First, you can assign a predetermined maximum budget to the system, and the algorithm will find the best combination of options to keep inventory at that cost level. The algorithm can also be used to assess the impact of a range of different “what if” scenarios based on changes in the market. By helping you to refine your product line to the options that customers really want, returned purchases and unwanted inventory will be minimized

So, TURF analysis can give you much more insight into your ideal mix of products than simple frequency and descriptive analysis. At the same time, there are some limitations to this technique, and alternatives to overcome those weaknesses. Let’s take a look.

While TURF is excellent at optimizing reach, it says nothing about the value of each individual customer. In other words, purchase frequency, or spend per purchase are not assessed using this approach alone. Returning to the classic hypothetical example above, it is possible that those that like one of the ice cream flavors excluded from the stand, like Mint, would be more likely to return frequently, or purchase two scoops instead of one. That’s why TURF is best used with a survey approach that helps you better understand your audience’s buying behavior.

 TURF analysis assumes that once a customer is satisfied with a product choice, they will overlook the other available products. This is often not the case. For example, in the ice cream example, a customer might like two scoops of different flavors. Given these limitations, there are some alternatives to the approach.

Now that you understand what TURF analysis is and how it can be helpful, get started by putting together your survey panel, then get the responses you need in minutes—not months. Find out more about how to launch surveys quickly and easily with SurveyMonkey Audience.

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