Based in Bentonville, Arkansas, Jeff Ireland ex Coke, Newell Brands and IRI talks about the challenges and future of category management analytics
Meet Jeff Ireland:
With 18 years in category management, Jeff Ireland is an Analytic Consultant at HIVERY. Before HIVERY Jeff was the Senior Manager of the Category Strategy and Analytics Team at The Coca-Cola Company focusing on the creation of revolutionary AI products deployed at Walmart. This was HIVERY Curate. So, Jeff decided to leave Coke to join HIVERY to continue to bring this revolutionary solution to retailers and CPGs.
During Coke, Jeff was the Category Advisor to Walmart in the sports drinks category where he focused on helping provide effective store level space recommendations for Walmart. Jeff also worked at Retail Solutions Inc. as a Senior Product Manager and Senior Analytical Consultant as well as at Newell Brands as a Category Advisor.
In this podcast you will learn:
• 2:00 - 7:02: What current challenges are faced by the category management discipline. Key insight is how time consuming and extremely labour intensive the process is as well as variability from team to team and time to time.
• Why the industry has limited to clusters planograms generation and how this generates an average of averages approach.
• 7:03 - 10:15: How artificial intelligence/machine learning techniques have been solving for these category management challenges: speed in delivery, speed in category strategy simulation, running unlimited tests and what-if scenarios, and optimising for them.
• 10:16 - 14:54: The shifts in category management: from clusters based on averages to clusters based on store-level analysis and insights.
• What "space-assortment aware" means and how it is applied at HIVERY.
• 16:12 - 22:25: What the most common constraints or business rules are used by retailers or CPGs before building planograms. For instance, shopper safety and global deletes or additions, forcing in new products or minimising assortment churn.
• 22:25 - 24:35: How a machine learning model is used to challenge these constraints or business rules by running different and rapid scenarios. For example, if you removed a particular SKU or brand, it would present the outcome of those scenarios.
• 24:36 - 29:57: What the benefits of store-level analysis are for any cluster dimension or count you use.