Optimizing the Appeal of Your Product Portfolio

Optimizing the Appeal of Your Product Portfolio

Editor’s note: As a research company, we could easily tell you we’re the best and we know what we’re doing, but you would just be taking our word for it. Instead, we’d rather show you, through examples, that we know how to structure a research strategy that delivers actionable information to your business.

Do you have a product or product line but don’t know which offer or bundle of products optimizes the appeal (reach) of the brand? What exactly is moving the needle and how do you get the most out of your portfolio? One of our client’s had a similar set of questions. Let’s take a look at how we answered these questions. As a bonus, I’ve included an illustration that involves cake frosting. Who doesn’t like frosting, right?

Objectives

Our client wanted to understand how alternative offers appeal to current and prospective customers and which variety of offers make up the most appealing/compelling portfolio. To put it into question form, the two questions they wanted answered were:

  • What is the initial (baseline) appeal of my product or product line (prior to exposure to offers)?
  • What is the offer or portfolio of offers that maximizes the appeal (reach) of my product or product line?

Methodology

Knowing that we wanted to estimate the relative appeal of a range of possible items (i.e. how much is one item preferred over others), we used a Maximum Difference (MaxDiff) Scaling approach.

In a MaxDiff approach, respondents complete a number of choice exercises and are asked to select the best (most appealing) and worst (least appealing) of the subset of possible items.

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Participants in the study included current customers and prospective customers. The prospective customers were screened by a number of criteria including age, gender, income, geography, and general interest in the product category.

We then conducted a TURF analysis from the MaxDiff results. TURF (Total Unduplicated Reach and Frequency) is a method, originally developed for analyzing media, which identifies a portfolio or sub-set of items that reach the widest audience possible. Used within the context of MaxDiff analysis, the methodology determines the probability of a person choosing at least one item in a portfolio as best.

For this particular project, TURF analysis was used to identify the right portfolio of offers that would provide the largest number of people with their best option.

Illustration

Let’s say you’re Joe’s Frosting Company and you’re trying to figure out which frosting flavors to put on your shelf space in the store. Shelf space is limited so you’re constrained to 4 flavors. By conducting a MaxDiff analysis on your 12 frosting flavors you find that the 4 most appealing flavors are:

  • Milk chocolate: 35%
  • Dark chocolate: 25%
  • Double dark chocolate: 20%
  • Vanilla: 15%

The “35%” next to Milk chocolate is saying that if Milk chocolate was the ONLY frosting flavor on their self-space, it would appeal to 35% of people.

Chocolate is obviously most appealing, as it holds the 3 most appealing flavors. However, it may not maximize their overall reach.

After running your MaxDiff findings through a TURF analysis you find that the combination of Milk chocolate and Dark chocolate has a reach of 40%. You might say, “Wait a minute, shouldn’t the combined appeal of Milk chocolate (35%) and Dark chocolate (25%) equal 60%?”

TURF analysis is so useful because it accounts for any potential overlap in interest – meaning that the people who are interested in Dark chocolate are also interested in Milk chocolate. The incremental gain of adding Dark chocolate is small because it appeals to a similar audience.

However, the TURF analysis also shows that adding complimentary flavors like vanilla and lemon produce the frosting portfolio with the greatest overall reach.

The ultimate goal for Joe’s Frosting Company is to fill their shelf space with the portfolio of frosting flavors that maximizes the probability of shoppers choosing at least one of their flavors as best.

Is anyone else craving cake now or is it just me?

Findings

When doing a study like this it’s important to understand there is a certain “break point” – where adding an item does not generate as much increase in reach as prior items. In statistical terms, this is called an “elbow point.”

We found that the top 4 offers were consistent across both customers and prospects, but the order was different (meaning that the #1 offer for customers was the #3 offer for prospects). This is useful information because it allows you to tailor offers around the group you’re targeting (customers vs prospects).

In the graphic below you can see how the overall reach for each segment (customers vs prospects) changes as each additional offer is included into the portfolio. Remember – both segments had the same top 4 offers, but the order is different. The top 4 offers for customers equate to an overall reach of 89.8%. The same 4 offers for prospects equate to a reach of 80.5%.

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Do these 4 offers provide the highest possible reach? You can see in the diagram below that each additional offer included in the portfolio, beyond the top 4 offers, has a very minimal increase in overall reach. Part of this game is understanding what a realistic number of offers is for your customers and prospects to comprehend. Yes, 10 offers may give you the largest reach, but it’s not realistic.

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Takeaways

The combination of MaxDiff and TURF analysis is a great way to optimize your product portfolio. As media buyers well know, you have to adjust for any potential overlap in reach. In the Joe’s Frosting Company illustration, a form of chocolate occupied the 3 most appealing flavors. However, filling their shelf space with nothing but chocolate does not optimize their overall reach.

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