Optimizing Category Management using Cognitive Computing

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Optimizing Category Management using Cognitive Computing

By Stephen DeAngelis, Enterra Solutions, LLC - 10/14/2014
The Category Management Association (CMA) notes that category management “is a collaborative continuous process between manufacturers and retailers to manage a shopper need state which we refer to as a ‘category’.”1 The best way to think about a category is to imagine all of the products that could be used to meet a consumer’s need. The “need” becomes the category. For example, there has been a lot written this year about the fact that Americans are snacking more. Snacking has almost become a way of life; especially for millennials. As a result, the category “snacks,” is becoming more important even as the products within that category are changing. Here’s the rub: Categories are not established by consumers but by retailers as a result of how they lay out their stores. For example, most consumers perceive refrigerated juices and their frozen counterparts as being in the same category; but, most retailers break those refrigerated and frozen products into two categories that are managed differently. That begs the question: How can you reconcile the differences between consumer-centric and store-related categories? The CMA provides a hint. “Category management,” the CMA states, “is data intensive and analytical in character. Category management is about understanding data. By contrast, shopper marketing is more about understanding emotions or motivations.”

Complicating the situation even more is the fact that individual stores, even those within the same regional area, can cater to customers with significantly different tastes. With so many variables at play and so many individuals involved, optimizing category management is simply too complex a subject to be managed manually. That’s where Big Data analysis and cognitive computing can play a significant role. Accenture’s latest technology vision, entitled “From Digitally Disrupted to Digital Disrupter,” asserts that to truly unlock the value of data “companies must start treating data more as a supply chain, enabling it to flow easily and usefully through the entire organization — and eventually throughout each company’s ecosystem of partners too.” The key capability for unlocking the value of Big Data, the study asserts, is “machine learning — which is a major building block of the ultimate long-term solution: cognitive computing.” As President and CEO of a cognitive computing company, Enterra Solutions, I certainly agree with that assessment.

If you can get category management right, the CMA asserts, manufacturers can “optimize shopper satisfaction and fulfill the role chosen by the retailer for that category within the overall portfolio of categories in the retail format. The end state of the category management process is that combination of assortment, price, shelf presentation and promotion which optimizes the category role over time.” Below are a few ways that cognitive computing systems, like Enterra’s Cognitive Reasoning Platform, can help CPG manufacturers improve category management.
  1. Run more successful promotions.
  2. Help establish more effective pricing (improved price/value proposition).
  3. Aggressively target other companies’ promotions.
  4. Use dynamic promotions to take advantage of short-term trends.
  5. Activate brand strategy through better execution in the category management process.
  6. Use advanced analytics and insights to more strategically engage retail partners to obtain better in-store merchandising opportunities.
  7. Use superior category insights to be appointed a category captain.
Because cognitive computing systems learn as they go and are constantly ingesting and learning from new data, they can respond much more quickly to emerging opportunities than human-managed processes. Such systems are also much more adept at dealing with all of the variables involved, including demographic differences at the local level. If the purpose of advanced category management is to optimize shopper satisfaction, as the CMA asserts, only the “down in the weeds” kind of local analysis that can be performed by cognitive computer systems can maximize outcomes. Companies can’t hire enough category analysts and technology contractors to meet the need. As Accenture concludes, cognitive computing is the “ultimate long-term solution.”

Stephen F. DeAngelis
Founder, President & CEO, Enterra Solutions, LLC

[email protected]
     Stephen F. DeAngelis is a technology and supply chain sector entrepreneur and patent holder with more than 25 years of experience in building, financing and operating technology and manufacturing companies. He is President and CEO of Enterra Solutions, LLC., a Cognitive Computing firm focused on next-generation big data-enabled predictive Analytics and Insights for companies and governmental.
     DeAngelis was a Visiting Scientist at the Software Engineering Institute (SEI) at Carnegie Mellon University, where he worked closely with SEI to create new systems diagnostic methodology (for the security, compliance and performance management areas) entitled Enterprise Resilience Management Methodology (ERMM). He was a Visiting Scientist at the Mathematical and Computational Sciences Directorate at the Oak Ridge National Laboratory as well as Executive Director of the Institute for Advanced Study in Global Resiliency at the Oak Ridge Center for Advanced Technology.
     A member of IEEE, DeAngelis earned a B.A. in International Affairs from the School of International Service at the American University in Washington, DC. He is the Founder and Chairman of The Project for STEM Competitiveness, a non-profit organization whose mission is to bring outcomes-measured STEM education to underrepresented populations through exciting and inspiring project-based learning.
[1] “Category Management,” Category Management Association, http://www.cpgcatnet.org/page/62774/.