It is common to think about using conjoint / discrete choice to configure products and test pricing, but it also extremely useful for finding opportunities to shrink product lines, testing whether additional products cannibalize or add to preference, and uncovering segments and aligning products with their preferences.
Imagine this scenario: A customer is looking at a huge array of service choices and wondering, “Why are there so many?” And, perhaps, she just walks away thinking the decision is too complicated, so she’ll make it another day. Lost sale…
Or, this scenario: A customer is looking at two product choices: a spiffy model that is missing the one feature he most wanted and a boring model that has that one feature. Perhaps he walks away thinking they might make the right combination someday. Another lost sale…
And, in our workplaces, we may face other situations related to products:
Sometimes there is a sales manager who has a “critical customer” who needs a different set of features. The next thing you know, the service line has to be expanded just to meet one customer’s needs. This can lead to a product portfolio that is so large, it can’t be printed out without using a font-size of 2.
Other times, there is a single product that was designed based on market research, but it’s not hitting sales targets.
These scenarios all deal with product/service lines… mixes that get too big or, at the other end of the spectrum, single offerings that are under-performing.
Market simulation models, based on conjoint or discrete choice analysis, can help diagnose and solve these problems. In this post, we’ll provide a couple examples—one from each end of the spectrum.
Too Many Products?
We often conduct conjoint analysis in complex markets with many products. Particularly in service businesses, when you don’t have to gear up manufacturing to make another version of your product, proliferation seems to be a common problem. While we are rarely asked to help pare down product lines, we often find we can use the conjoint-based simulation model to help manage the size of the product line.
We start by modeling the current market offerings, putting in one product description for each offering of our client and their key competitors. It’s common to use the model to run “what if” scenarios that change products. But, for product line management, we can run scenarios in which we delete products and see what happens to market preference. If we delete a product, does share stay within the client’s product line by moving to other products the client offers? Or, does it move to competitive offerings?
Many years ago, we worked with a bank that offered about a dozen consumer mortgage products. In an unexpected twist, we started deleting products from their line in our simulation model and found that a product line half the size maintained the entire predicted share for our client’s offerings. The client implemented a strategy with the reduced product line and found their share actually increased significantly. Not only did they keep the demand with the reduced product line, their marketing and advertising became more clear, crisp, and compelling. Demand increased!
Think back to the customer I described at the start of this entry… leaving because he or she is overwhelmed. Now, the customer stays and purchases.
Not Enough Products?
What if you aren’t selling as much of your product as you expected? You did research on what people preferred, and you are offering them what they said they wanted. The problem may come from the law of averages, that is, that you based the product specs on the average preferences of all of the market instead of the preferences of a specific market segment. Here’s a simple example that illustrates this issue:
You studied bottled water and you got results that said:
• 70% want a flip-top
• 30% want twist-off
• 60% want no flavoring
• 40% want lemon flavoring
So the optimal product is a flip-top with no flavoring, right?
What if the market really looks like this?
Segment 1 prefers a flip-top with lemon flavor and Segment 2 prefers a twist-off with no flavoring. Your flip-top with no flavoring doesn’t look like the best offering anymore. If you can only offer one product, you might go with the flip-top with lemon flavoring. Of course, to make the decision, you would want information about size of each segment, price sensitivity, profitability, etc.
Determining market preferences in this simple example, with just a couple of dimensions, could be researched in many ways. But real products and services have many dimensions, and conjoint analysis / discrete choice is often necessary to tease out the winning combinations. By determining the preferences of individuals and performing a segmentation analysis on those preferences, we can avoid the trap of the “average” product that appeals to no one. Instead, we can find the groups of people that prefer a common set of features. Then, whether you develop a single product with strong appeal to one particular segment, or a product line with each offering targeted to a particular segment, you have created products that are strongly aligned with segment preferences.
The simulation model can also be used to see what happens when we add products to an existing product line. Starting with the existing products of clients and key competitors in the model, we can add a product to the client’s line and see what happens. We can see if the client’s line garners additional market preference or if the new product simply cannibalizes existing products. We can test whether particular configurations, features, or pricing help the new product gain preference at the expense of competitors (instead of cannibalizing). Preference results from the simulation model help inform important and costly decisions when considering gearing up for a product line expansion.
The Bottom Line
It is common to think about using conjoint / discrete choice to configure products and test pricing, but it also extremely useful for:
- Finding opportunities to shrink product lines
- Testing whether additional products cannibalize or add to preference
- Uncovering segments and aligning products with their preferences
Using this type of research to inform product line management decisions can solve the problems of the customers we described at the start of this post and turn those “lost sales” into increased revenue.
Sawtooth Technologies Consulting
- Northbrook, Illinois
- We are experts in applying conjoint analysis to product development and pricing issues, leading to better informed, customer driven decisions.