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By Gina Woodall, Senior Vice President
Product developers often strive to offer enough value in a new product to attract new customers, but not too much, making the product expensive and turning off potential buyers. Ideally, this goal is met while accounting for redundancy in the feature mix. TURF1 analysis is a versatile research tool that can be used in a number of new product development situations to achieve this balance. The following are two examples of studies where Rockbridge used this tool.
Case Study #1: Developing a Member Benefits Package
An association representing small businesses wanted to re-design its member benefits package to increase retention of current members, as well as attract new members to the association. A series of potential member benefits was developed from qualitative research and the association's current offerings. Rockbridge conducted a concept screening survey to identify the most attractive benefits from the list that would encourage current members to stay with the association and new members to join.
Members and non-members were asked a series of diagnostics about each benefit, including the likelihood the potential benefit will influence their membership decision. As the graph below shows, Benefit G is most likely to encourage members to continue their membership, followed by Benefits D, B, C, and E. The association could offer just these top benefits, but it is not clear how far down the list to go to satisfy members. In addition, there may be particular niche benefits at the bottom of the list that a small group wants more than other benefits, and including them in the package would influence intentions among a broader group of members.
To address these issues, Rockbridge conducted a TURF analysis based on members' intention to stay with the association given the benefit, and non-members willingness to join the association if the benefit is offered. The graph below shows that Benefit G will influence 35% of members to continue their membership, but if Benefit D is added, an additional 8% will be influenced to continue their membership. If the association has the resources to offer three benefits, then it should add Benefit H to the mix to influence another 5% of members. Benefit H was 6th on the list in the chart above, so it may not have made the cut had a TURF analysis not been conducted. Benefit H appeals to a group of members not yet satisfied with the top two benefits, which broadens the reach of the benefits package.
A separate TURF analysis was conducted with non-members to see if their priorities are different. Not only are their priorities different, but the overall effect of the benefits package is more pronounced among non-members than members. The client learned that a benefits package is more effective for acquisition, than for retention. A different set of benefits would need to be developed and communicated to each group to achieve their acquisition and retention goals.
Case Study #2: Prioritizing a Product Feature Set
In the concept testing phase, product development teams are often faced with determining which features to include in the product to attract the largest number of buyers. Ideally, they would like to know: 1) the features that are critical to include for the initial launch of the product; 2) the features that are enhancements to the product and more appropriate for the 2.0 version of the product; and, 3) the features that are unnecessary to include because they add little incremental value. In addition, development teams want to know the combination of features that attracts the highest number of buyers, accounting for redundancy in the feature set.
Rockbridge conducted a study to answer these questions for a technology company introducing a revolutionary new online service. Using TURF analysis (shown on the graph below), we determined that the combination of features H, J, and B will attract 42% of the market, which has the broadest reach of any combination of three features. If feature A is added to the three core features, the product will entice another 15% of the market to buy the product, for a total of 57% of the market. With a combination of nine features, 83% of the market is likely to buy the product, and the remaining six features do not increase the likelihood much beyond this point.
Based on the analysis, the client was able to prioritize feature development for the product, determining the core features that needed to be present at roll-out, features that could wait until version 2.0 to pull in a greater share of the market, and features that could be dropped.
TURF analysis is a valuable and versatile tool that can be used in situations like paring down product lines, optimizing benefits packages, and planning development priorities. It can be used in many situations to help product development teams make tough decisions about the design of their potential new products and services.
1TURF stands for Total Unduplicated Reach and Frequency, and was originally used in advertising planning to avoid duplicative exposure to messages. The same methodology can be used to avoid redundancy in product lines or a product feature mix.
For more information about TURF analysis, contact Gina Woodall, SVP at 703-757-5213 ext. 11 or gwoodall@rockresearch.com, or Charles Colby, President, at 703.757.5213 ext. 12 or ccolby@rockresearch.com.
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