Customizing products to appeal to target segments or even individuals is a common theme in marketing. Many companies dedicate significant resources to developing a portfolio of products that it hopes will appeal to unique segments. For line extensions, the challenge for any company is to design new product(s) that take share from its competitors without stealing an unacceptable amount of share from products within its existing line.
One common approach to designing an effective line extension is to use the conjoint data to segment the market into latent (not observed) market segments (sometimes referred to as clusters) that have similar preferences. These segments are termed "latent," because they are not simply defined based on explicit variables such as gender, income or company size. Rather, the underlying segments are revealed through a statistical segmentation technique such as cluster analysis or latent class modeling. Segments are formed with the goal of maximizing the differences in preference between groups while minimizing the differences in preference within groups. Once these latent segments have been identified, one can profile them in terms of other variables in the survey (i.e. demographics, usage or media habits), though usually none of those other variables in the survey will be a perfect predictor of segment membership.
For example, let's assume that a cluster analysis revealed three relatively different segments for the hypothetical example we've been using throughout this section. Let's also assume that you merged a variable containing the segment membership information into the data set as a "banner" variable called "SEGMENT." If you open the choice simulator and click the Utility Report icon on the Reporting ribbon group, you can click Segments and select SEGMENT as the segmentation variable and refresh the utility report.
By examining the part-worths and importances for each group, you can gain insight into the product features that might appeal to each. You also should bear in mind the size of each segment, as this represents its demand potential. Consider the following part-worth utility preferences:
Part-worth Utility Preferences
Segment1 Segment2 Segment3
n = 128 n = 283 n = 216
BrandA 39 -51 -44
BrandB 5 39 -29
BrandC -44 12 73
StyleA 61 -52 -34
StyleB -23 45 -9
StyleC -38 7 43
$100 56 55 50
$150 7 2 6
$200 -63 -57 -56
We can study the part-worths to learn about the differences among the segments. We can also use these preferences to simulate market choices for the following market scenario (by clicking the Segments icon from the My Scenario Settings tab and selecting the segmentation variable):
Product Specifications:
Product Name Brand Style Price
"BrandA" 1 1 1
"BrandB" 2 2 3
"BrandC" 3 3 2
Shares of Preference for Products, by Market Segment:
Segment1 Segment2 Segment3 Total
n = 128 n = 283 n = 216 627
BrandA 84.8 21.5 22.2 34.7
BrandB 7.4 40.0 14.2 24.5
BrandC 7.8 38.5 63.6 40.8
(Note that these shares do not match the shares reported for earlier examples in this section. Since these results are for illustration only, no significance should be attached to this difference.)
Let's assume your company produces BrandC with StyleC at $150. Your total share of preference is 40.8%. We see from the simulation by segment that yours is the most preferred product within Segment 3, and the second-most preferred product in Segment 2. BrandA clearly dominates Segment1 (the smallest segment).
Let's assume that your company was interested in offering an additional product. We could examine the table of part-worth utilities presented earlier as a first step in formulating hypotheses about what additional product might be successful.
Starting in order, you may first consider Segment 1, but this segment does not seem to offer many opportunities for your brand. BrandA offering StyleA at a low price has got this relatively small segment nearly wrapped up, and this segment doesn't seem very receptive to your brand: BrandC.
You next consider Segment 2, which seems to represent a better opportunity for your brand. It is a relatively large segment that prefers BrandB, but also seems receptive to BrandC. Note also that Segment 2 strongly prefers StyleB, but your company currently offers only StyleC. By offering a StyleB product, you might be able to convert some current BrandB customers from within Segment 2 to your product line.
You currently dominate Segment 3 and should probably not consider designing another product to appeal to this segment, since a good deal of the possible share to be gained from a new product would be taken from your existing product within that segment.
Let's simulate what happens if in addition to your current product (BrandC, StyleC, $150), you offer another product (BrandC, StyleB, $200).
Shares of Preference for Products, by Market Segment:
Segment1 Segment2 Segment3 Total
n = 128 n = 283 n = 216 627
BrandA 82.2 17.2 18.6 31.0
BrandB 7.2 32.0 11.9 20.0
BrandC (old) 6.8 27.7 47.8 30.4
BrandC (new) 3.8 23.1 21.7 18.7
The new product has somewhat cannibalized the existing product, reducing its share from 40.8 (see the previous simulation) to 30.4, but has resulted in a relative overall gain of 1 - [(30.4 + 18.7)/40.8] = 20% in preference.
For line extension simulations you conduct, the answer will likely not be so clear and the process so direct as we've shown here. You'd certainly want to investigate other product configurations to make sure you weren't overlooking even better opportunities to enhance share. You would also want to consider the cost implications of different options for line extensions. Also, you would probably want to conduct sensitivity analysis for the new product with respect to price, to determine a strategic price point (given your costs and market share goals).
Viewing the preferences and shares by segment is not required in designing an effective line extension. However, viewing the separate market segments can help you more quickly recognize patterns of preference, size the different segments of the market, and thus more easily arrive at a good solution.
This exercise of viewing segment-based preferences and designing products to fill heterogeneous needs is a useful approach. However, it would seem more efficient to let an automated search algorithm find an optimal product or set of products rather than to proceed manually.