Category Management Plan Example: Assessment "What" Phase
Assessment WHAT Perspectives
Once the Category Role has been defined and the retailer and category management partners are aligned the next phase is the assessment phase. Generally, the assessment phase is broken in to the assessment "what" and assessment "why" phases. This stage is where the CatMan 2.0 process differs significantly from CatMan 1.0. In the latter, the assessment phase was largely limited to "shopper facts", a behavioral assessment of who, what, when, where, and how is the category bought? In CatMan 2.0, the assessment phase leans much more towards "shopper insights" and includes attitudinal & perceptional assessment - effectively answering the question "why" the category is bought.
Many differing perspectives need to be considered in the assessment phase. Most notably, the consumers' perspective needs to be understood, that being the overarching view of consumers in the category globally. The high-level geographic market needs to then also be considered to understand and account for regional differences. The target retailer perspective as well as the suppliers' perspectives are important to be considered in understanding the category. Finally, and possibly most importantly, individual shoppers' perspectives need to be considered to better understand What and Why they are buying the category.
Category management is generally considered a fact-based initiative. The assessment step provides the facts to the category management plan and is therefore a critical, analysis-heavy phase. It is important to ensure that all trading partners are aligned around the truths that are discovered in this process, and so it is critical that category management partners commit to an intensive evaluation of all relevant data.
- Performance should be reviewed by
- segment, brand, and SKU attributes
- shopper type (H/M/L user, demos)
- store geo-demo clusters
- in-market competition
- assortment variation
- pricing variation versus competitors
- in-store merchandising response
- new brand and item trends
Assessment "what" analytics and tools
Due to the analytical nature of this phase, it is critical to have access to a large variety of tools and data. In the case of Jones Grocery, there is also a corporate strategy that mandates the use of the latest data and analytical tools, so following is a list of the tools and data leveraged in putting together this ice cream category management plan example.
- Label Insight attribute data used by the USDA and FDA
- Experian household purchase data, 80 million loyalty cards
- In-store tracking data
- Artificial intelligence and cross-enterprise analytics
- Virtual reality pricing and planogramming data
- Label Insight supplier onboarding platform
- Label Insight Explore platform
- Nielsen Product Insider powered by Label Insight
- Heavy user study (using Jones Grocery loyalty card and Experian data)
- Assortment variety study (activity-based costing)
- Total store space optimization study
- Price elasticity study
- End-aisle placement study
- Need-state merchandising study
- Attribute merchandising study
- Cross-segment ice cream promotional study
8 critical assessment "what" findings with high-order attributes
These critical findings will be used to drive the scorecard, category strategies, and tactics. In the spirit of exploring the impact of taking an attribute-driven approach to category management, we have highlighted those findings that are directly or indirectly influenced by high-order attributes (underlined below).
- Jones’ category performance trails its competitors
- Individual attributes are driving disproportionate growth in the category
- Replacing slower-moving items with these power attributes will grow the category
- Certain attributes/brands are price inelastic, thereby offering margin enhancement
- Greater ice cream category space will increase total category and frozen spend
- Adding permanent ice cream end-aisle display freezers will build volume
- Special occasion “need-state” promotion will drive volume in ice cream
- Simultaneous promotion of distinct category segments drives volume
In the following sections, we will dive deeper into each finding to understand the data that supports it, and we will explore how high-order attributes may have played a role in the finding.
1. Jones Grocery category performance trails its competitors
Jones Grocery is not doing well in the Ice cream category versus its principal retail competitors. The Jones Grocery market share in ice cream (23.0) trails its overall grocery share of market (25.0). Furthermore, the Jones Grocery share of specific health attribute-intensive segments such as “hormone free” and “lactose free” lags competition badly.
Jones Grocery has more items in its assortment than any competitors, yet trails in share suggesting its item/space usage is poor vs. competitors'. Despite having more ice cream items than competition, Jones Grocery gives less overall space to the category, suggesting Jones Grocery is vulnerable to shelf out-of-stocks(O-O-S), especially on promotion.
Jones Grocery volume sold on promotion is 36%, far below its major competition, who average 41% on promotion. The annual number of brand promotions/year is less at Jones. Surprisingly, the number of private label promotions at Jones is higher per year than competitors.
2. Individual Attributes are Driving Category Growth
The new analytics enabled by Label Insight provide unique insights into shopper motivations and behavior. The ice cream category as a whole is growing modestly, but new analytics reveal that items with specific attributes are driving total category growth. For example, items with no hormones have grown 8.0 share points vs. year ago. That is over 4 times the growth rate of the total category.source: Nielsen Product Insider powered by Label Insight
3. Replacing slower-moving items with these power attributes will grow the category
We have modeled the category assortment using advanced analytics and virtual testing. This approach indicates that current space can be made more productive by replacing certain types of items with items having the growth attributes revealed by Label Insight data. Jones Grocery currently has over 400 approved SKU’s in the valuable limited space of the freezer case. We modeled replacing 60 SKU’s with 4 options involving 4 sets of power attributes and four different replacement options. All combinations increased estimated category spend.source: Nielsen Product Insider powered by Label Insight
4. Certain items and attributes are price-inelastic
Intensive analysis of pricing in Jones’ market area reveals some valuable insights about segments and items in the category. These findings tend to corroborate attitude research findings wherein shoppers claim they would pay more for certain attributes, especially health and wellness attributes. Here are the key findings:
Hormone-free and lactose-free items are price inelastic within a 10 cent/half gallon spread versus competition.
No artificial colors, flavors, or sweeteners are inelastic within a 5 cent spread versus competition.
Jones' value-priced private label is price inelastic within a 20 cent per half gallon spread versus branded economy brands.
Jones' premium private label brand is inelastic within $15 per half gallon versus both leading premium brands.
When modeling a combination of the various pricing flexibility options revealed above, Jones has the realistic possibility of increasing its category gross margin from its current 31% to something approximating 33%.
5. More ice cream category space will equal more category and frozen spend
Jones Grocery currently gives less space to the ice cream category than competitors. At the same time, Jones Grocery has more items than its competitors but a lower category share versus its overall grocery market share. Modeling driven by artificial intelligence helps explain what's happening: We modeled adding one more door to ice cream while changing the assortment in various ways. We left the assortment unchanged in larger space, reduced the current assortment in larger space, then reduced and changed assortment in greater space. The modeling suggests that adding space while reducing and changing the SKU count increases Jones’ volume by 10% and has a small positive effect on the balance of the frozen section in the ice cream aisle.
6. Adding permanent end-aisle display freezers will increase category volume
Using shopper tracking technology combined with loyalty card data, we learned that only 40% of Jones' ice cream category buyers go down the ice cream aisle on the average visit. But when display freezers are put on the end caps, shoppers are driven down the aisle in greater numbers. The extra traffic affects not just the items in the end cap, but the category as a whole. Apparently, the end cap reminds shoppers of their “need“ for ice cream. The table below shows how volume in the test stores significantly outpaced the stores without ice cream end-cap freezers.
7. Special occasion "need-state" promotion drives volume
In one distinct Jones marketing area, we tested a birthday party “need-state” promotion focus. This need state was chosen for two reasons:
- The average family has 3 birthday celebrations annually, meaning that at any given two-week promo period, nearly 12% of households are celebrating a birthday.
- Secondly, ice cream is a part of nearly three-quarters of birthday celebrations.
We created a birthday party need state landing page on the Jones web site, sent special e-mails to households with children and those for which we had birth dates on loyalty cards. We also analyzed past basket purchase data to identify what makes a “birthday party” need state. The table below shows that ice cream increased nicely vs. base and control, but the other items in the need state (paper plates, napkins, various dessert cakes) increased dramatically.
8. Simultaneous promotion of specific segments drives volume
Using artificial intelligence modeling and virtual testing driven by Jones loyalty card data confirms the basic category management theory of segmentation. Namely that shoppers tend to be relatively loyal to brands/price tiers and certain attributes and will not deviate even under promotion pressure. We modeled 13 weeks of promotion using Jones’ typical pattern of promoting one brand at a time and compared that to test stores with alternate scenarios in which various combinations of non-competitive brands or segments were run simultaneously.
The multiple-brand approach consistently performed better because the two brands/segments did not cannibalize one another. This approach has one subtle advantage: it allows Jones to tap the promotion budgets of more brands throughout the year.
Assessment "what" in an attribute-driven market
It is clear in the exercise above that there are plenty of low-hanging fruit opportunities where high-order attribute data can enrich the assessment "what" phase of analysis with more nuanced insight about the category and those who shop the category. For the purpose of clarity, we kept the example to a fairly basic level, but there is really no limit to how complex and sophisticated category managers can get when it comes to mining high-order attribute data to understand a category. In the assessment "why" phase we will dive further into attribute-driven category management.
What's next? "Why" assessment
In the next post in this series, we will look at the "why" step in the process. This stage seeks to provide the "why" behind the buy. We will aim to leverage the data and findings above to piece together a holistic picture of "why" shoppers buy the category. This will then lead us to wrapping up the process with concluding strategies and tactics.