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By David Meer, Edward C. Landry and Samrat Sharma, partners at Strategy&
Consumer packaged goods (CPG) companies have a big problem: They have almost no idea which of their new products will end up being popular with consumers. Despite big data, heavy investment in innovation and efficient R&D, failure rates for new products have hovered at 60 percent for years. Two-thirds of new product concepts don’t even launch.
What’s more, although CPG companies are extremely good at the early stages of innovation—creating new product ideas in promising growth areas—and at the later stages of testing and commercializing concepts, there’s a conspicuous hole in the middle of the process. They don’t have a clear grasp of which combinations of features, packaging, price, and even labeling will persuade consumers to make a purchase.
There’s a way to fill that hole, but it won’t be easy. Based on our experience, we think it will require progress in three key (and intertwined) areas. None of the three will work without the other two, and all will compel CPG executives to rethink aspects of their traditional business model.
First, companies need to adopt dynamic modeling to gauge various combinations of features. When companies test a product concept today, they’re limited by the relative primitiveness of the tools available to them. Testing a preset combination of options (for example, cinnamon-flavored cookies, in 6-ounce individual packages, at 79 cents per pack) produces a basic thumbs-up or thumbs-down assessment as to whether the product will be financially viable. However, the results apply only to that combination. If you change one element, the test results become much less useful. Worse, the testing is expensive and time-consuming, with turnaround times that are measured in months, which makes testing every single combination impossible.
Companies should be able to test various combinations more dynamically, adjusting the flavor profile, pack size, price, labeling, distribution channel, and any other aspect of the value proposition—even the brand name. Developing a simulation model that can evaluate a wide range of scenarios by altering the various elements and seeing how each factor affects the outcome while the product is still in the development stage is an effective way of doing so.
How much more would consumers pay for low-calorie cinnamon cookies? Would they prefer 8-ounce packs? And should the cookies be sold at a convenience store, a big-box retailer, a warehouse store, or online (or all of the above)? The right model would break such product propositions into their component parts, reassemble them in novel ways, and estimate demand for the new combinations. This in turn would require detailed data on which features consumers value, how much they’re willing to pay for those features, and where they’re willing to make trade-offs.