Craig Hodnett Shares How to Build a Best Practice CDT
In a 30+ year career working in category management and sales with Fortune 500 manufacturers, I’ve had the opportunity to build and use dozens of Consumer Decision Trees (CDTs). From strategically guiding retailers in assortment, aisle and shelf arrangement, to leading internal brand teams in exploring white space opportunities and exploring product innovation, those who know me know I’m always on the hunt for “best practices.” Best practices generally save time, money and lead to better results. For CDTs though, I’ve learned to evaluate “best practices” through a much narrower lens. In my experience, best practices in CDTs are those that best simulate the shopper’s real world experience.
My experience in partnering with shopper research suppliers in building CDTs has also taught me that the best research partners are those that place significant emphasis on how three critical market factors disproportionately influence the efficacy of a CDT. They are:
- The rapidly changing shopper
- Segmentation with various occasions and need states
- Innovation in a retailer smaller footprint
The Rapidly Changing Shopper
Most CDTs are based on three years of historical data. In other words, traditional CDTs look backward not forward. While historical perspective has merit, today shoppers’ needs, wants, desires and even beliefs can change quite rapidly. Models that rely on historical guidance do not predict change well. Going back beyond a single year may provide significantly flawed direction.
The beer category is a great example. Craft Beers drive a disproportionate amount of growth in the total beer category. Much like today’s music industry, the beer category has evolved from a stable of steady national brands to “breaking” local brands that catch a tailwind of regional popularity. For certain shoppers in certain categories, “newness” is intrinsic to their shopping decision. Building trees from historical data is a poor methodology for a category such as this.
Understanding how the changing shopper sees and groups products in a category is essential. And, understanding how shoppers substitute products along with the why gives manufacturers and retailers essential intelligence and guidance as they build their category assortments and product arrangements. A shopper-built CDT from Decision Insight provides excellent direction for both groups by viewing and measuring actual expressed buying, and importantly: the reason why.
Segmentation with Occasions and Need States
Historically, CDTs and Segmentations have been built in isolation. But if a category is driven by a strong set of occasions or need states, another best practice is using a phased process to address situational purchasing. Again, using the Beer Category, there are buying occasions based on need states that drive beer purchases (for example a Super Bowl game watch party with old friends versus a family lake trip, or having the boss over for dinner). This may be a “Phase One” view for a manufacturer to better understand purchase drivers through the shoppers’ eyes.
For “Phase Two” that same manufacturer could incorporate the purchase occasions into their CDTs. A key question might be whether an occasion based on need states is strong enough to warrant how both assortment and shelf arrangement should be built.
Channel matters too. Consider Grocery and Convenience Stores. In Convenience, a shopper might want immediate consumption; in Grocery, they may want beverages for later consumption at home. Building a CDT with just one channel for both may end up helping neither.
Innovation in a Retailer Smaller Footprint
Innovation is at a record pace and shows no sign of slowing. Yet retail square footage is shrinking as growing numbers of consumers are returning to city urban centers with smaller available retail space. Retailers build smaller stores for time-starved shoppers that want convenience in their return to Urban. The question that looms for Innovation is if they deserve shelf space in smaller store formats.
Being able to quickly and easily place new product concepts into the CDT process matters. Doing so, even before bar codes are established, might be the difference between success and failure. The opportunity cost of replacing an existing product with Innovation is high. Choosing a partner who can readily and knowledgably incorporate innovation into the CDT process is certainly a Best Practice.
Best Practices = Best Real World Approach to CDTs
DI’s real world approach is built on best practice solutions:
- Uniquely shopper-centric methodology: Grounded in shopper behavior and attitudes within the context of the retail environment, DI provides forward-looking recommendations rather than a simply historical analysis of prior purchasing.
- Both quantitative and qualitative learning: Generating rich category structures with supporting interviews, quotes, and text analytics resonates with both internal teams and retail partners.
- 100% commitment to client activation of results: This is not merely research reports: The DI team is actively engaged in research interpretation and hypothesis generation that leads to research-supported strategic solutions embraced by retail.
Decision Insight’s approach to Consumer Decision Trees is designed to provide a deeper understanding of rapidly changing shopper behavior including their motivations. We’d enjoy a chance to share real world results with you.
Craig Hodnett is SVP, Client Solutions. He can be reached via email firstname.lastname@example.org or call (706) 416-0039.
Contact Leslie Downie at (816) 437-9852 to learn how we can put DI CDTs to work for you.