Suppose you lead the White Space & Innovations department at a smoothie company. You’ve been tasked to select the optimal flavors that the company will launch in the summer. You and your team come up with four potential new flavors, but for practical reasons your company can only launch three. You decided to survey 100 of your current customers and received the following results:

If you are looking at the results with only frequency in mind, it would make the most sense to choose the flavors that were selected most often, removing Raspberry Peach from contention.

However, this type of analysis ignores one important factor: the same consumer might like multiple flavors. For instance, both Banana Pancake and French Toast similarly fill the “weekend-breakfast” flavor for many of the same consumers. On the other hand, those who preferred Raspberry Peach may not like any of the other flavor options- which means that those within that consumer segment will go unreached. Thus, if you choose to include both French Toast and Banana Pancake options, the two flavors will end up competing against each other, making the Raspberry Peach a missed opportunity.

While the market segment that prefers Raspberry Peach might be smaller, including two redundant flavors in your menu, like Banana Pancake and French Toast, will not help you expand your customer base whatsoever.

Via TURF Analysis you will be able to analyze consumer data and choose the optimal flavor sets to capture the widest possible audience.