Skip to content

Space Optimization

In today's retail industry, the pace of change is relentless. As competition intensifies, it's more important than ever to adopt a customer-centric approach to building categories that are designed to win. This is where category managers come in. Category managers act as stewards of the category, ensuring consumer needs are met, product offerings are optimized, inventory is maintained, and profitability is secured.

One critical component of successful category management is space optimization. This strategy involves maximizing the use of available retail space to increase sales and profits. To build a successful space optimization strategy, you need to have a strong understanding of historical performance, competitive landscape, and consumer and industry trends. This foundation is essential to ensuring there is a strong basis for data-driven decisions and intelligent context setting.

Here are a few tips for building a successful category foundation upon which strong space strategies can be implemented:

  1. Understand the full taxonomy of your category through the eyes of your shopper. The assortment optimization process talks a lot about breadth and depth in ensuring the right product mix is achieved and the high level assortment aligns with the designated role of the category in-store. These decisions must be made from a strong position of shopper empathy, as it matters far more how the shopper segments the category in their mind and makes their purchasing decision than it does how we, as brand stewards and retail strategists might wish for the category to look. A category strategy should not be the output of hopes, dreams, and speculation. It should start with a clear and nuanced understanding of the shopper decision making process and category hierarchy.

  2. Incorporate shopper behavior analytics into the testing process for all resets. Suppliers and retailers should come to the table with an agreed upon set standards of required shoppability that must be achieved by any shelf reset. This should look deeper than simple measures of category sales, but should, instead, look at category sales velocity, which helps provide a full picture of impact, taking into account actual variations of in-store traffic and category exposure.

  3. Approach SKU rationalization with surgical precision. Get down to the SKU level, and look closely at conversion rates to build a strong, evidence-based rationale for all space-related recommendations. Isolate each individual lever of category performance to assess effectiveness - from promotions, to out-of-stocks, to secondary location behavior modifiers.

In today's fast-paced business world, category strategists cannot afford to make decisions based on intuition alone. Data-driven decisions are necessary to stay ahead of the game. That's why category strategists must now come equipped with a full arsenal of category analytics tools that revolutionize the way they make decisions. These tools provide insights into customer behavior, market trends, and competitor analysis that were once impossible to obtain.

One of the most powerful category analytics tools predictive analytics, which uses statistical algorithms and machine learning to forecast future trends and provide complete context to illuminate all of the factors in-store that can motivate shopper action. It provides category strategists with a more comprehensive vision of the category and the behavior of shoppers. By taking occurrences at the micro level and aggregating them into big picture insights, predictive analytics can bring context and clarity to the in-store environment.

The insights generated by predictive analytics are particularly powerful because they enable category strategists to identify trends and patterns that might not be visible to the naked eye. By analyzing data from a range of sources, including sales data, in-store environmental factors, and observational research into shopper behavior, machine learning can help category strategists understand what motivates shoppers to make a purchase, and quantify the power of those triggers.

With the help of AI, category strategists can develop more effective strategies for optimizing the in-store environment. This might include adjusting product placement, changing pricing strategies, or developing targeted SKU-level optimization plays. By using predictive analytics to gain a more complete understanding of the factors that drive shopper behavior, category strategists can make better-informed decisions that lead to increased sales and customer satisfaction.

VideoMining has spent over a decade pioneering the use of AI-powered analytics to bring a robust and detailed understanding of shopper behavior at retail. Our proprietary Behavior Labs™️ empower category strategists to experiment in real stores with real shoppers during real trips – building complex understanding of shelf-level decisions and tailored decision-making analytics to build strong category performance in-store.

What would you do differently if you could see the big picture of shopper behavior analytics? Join the leading category minds and find out.