6 ways to Upgrade Consumer Goods Category Strategy Using Data Analytics
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The consumer packaged goods (CPG) industry produces and distributes consumer goods through third-party retailers, generally in the grocery and superstore sectors and is one of the largest industries in North America. The industry encompasses a wide range of categories, such as cleaning items, cosmetics, and packaged foods. CPG executives must craft effective and scalable strategies to manage their massive operations.
While brick and mortar stores are still the primary channel for most CPG brands, and e-commerce platforms – though significant – encompass only a small chunk of the market. This means that CPG companies that want real visibility into their market need to dive into offline performance. While retailer-provided sales data can provide helpful information, many companies find that getting a comprehensive view of the offline retail landscape requires a more thorough approach. Foot traffic data can show how shoppers are behaving on a store, chain, or regional level in close to real-time. Using location analytics allows both retailers and CPG companies to rely on a single source of truth to optimize their merchandising and marketing practices and adapt to shifting consumer trends more effectively and efficiently.
With offline data increasingly accessible, CPG companies have the opportunity to drill down to each industry, chain, and store to identify the perfect partners to carry their products and adjust their merchandising strategy accordingly.
Location data can be aggregated to present industry-level insights, providing a comprehensive view of how different industries and product categories are performing offline. The ability to examine industries’ performance relative to past metrics or other industries allows CPG executives to better identify opportunities or implications for their brands.
For example, CPG managers noticing an uptick in grocery visits and a downturn in superstore visits might consider shifting some inventory to grocery stores. Similarly, a downturn in restaurant visits could correlate with increased demand for packaged foods, meal kits, and frozen dinners.
Category managers need to understand how different retailers are performing in light of market changes and identify specific consumer trends per chain or store. By tracking shifts in foot traffic patterns, CPG executives can evaluate how economic developments are impacting brick-and-mortar performance, how retailers are performing in new or legacy markets, and how new retail concepts are being received by customers.
For example, grocery stores embracing prepared meals format are likely to experience some uplift in visits. Snacks category managers can monitor the performance of such initiatives and think of how their products can complement those formats with unique offerings or special promotions.
Category managers mostly rely on retailers sharing sales data to understand competitive market shares. But sales data can only provide limited granularity to support region or category-specific analyses and does not indicate consumer demand in a wider context. Location data can supplement retailer data, help CPG professionals easily navigate crowded markets and assess national or regional competitive dynamics in real-time.
Advanced foot traffic tools such as Placer XTRA custom reports can easily answer questions like – what are the most popular chains in a given area? What places do shoppers of a specific chain go to before/ after their visit? What are the local market shares by visits? And in which chain can I better reach my target audience? These insights allow CPG managers to optimize their product distribution and achieve greater efficiency and increased sales.
Favorite grocery chains that visitors to a Kroger location in Cincinnati OH frequently visit. The table indicates that the top 3 favorite grocery/ superstore chains are Target, Walmart & Meijer.
Foot traffic data helps CPG companies build a hyper-local consumer model, better than any periodic consumer study or sample store observations. Location analytics reveal distinctions between consumers at different stores of the same chain and highlight differences in regional shopping habits. Understanding what characterizes shoppers and what distinguishes them in a given location can contribute to a wider view of the marketing funnel, helping CPG professionals to optimize the product mix offered in each store.
Traditionally, consumer goods suppliers strategized on a chain-wide basis. This approach assumes that shopper needs and missions are the same across the entire chain or channel, a belief that is now considered outdated.
Location data and intelligence are ideal for gaining a granular understanding of your category strategy and dramatically improving performance bottom-up across all functions.
Sales data can tell CPG executives what are the best-selling products in a store or chain, but it can’t give insights as to why. Combining foot traffic data and socio-demographic datasets builds a more comprehensive picture of store-level customer preferences, which allows store managers to modify their assortment and planograms to best suit the needs of the customer. Category managers armed with location analytics insights can work with store managers to optimize distribution, layout, and sell-in ratios.
For example, beverage category managers can learn in which retail locations they are most likely to find bubbly drinkers or beer and wine lovers, based on a psychographic analysis of stores' trade areas.
When planning a new product launch or sampling project, category managers can minimize their risks by using location data to see whether the new product will match local tastes. Harnessing location-based demographic and psychographic data can also help build a pitch to retail partners as to why store managers and retail buyers should stock the new product.
For example, a cooking sauce category manager who is launching a new Asian-style sauce can use foot traffic data, layered with data on consumer social media activity from Spatial.ai's FollowGraph dataset, to find retail chains or venues with trade areas that have large shares of Asian food enthusiasts.
Foot traffic data can also inform merchandising strategies. Typically, sales are scheduled and directed from the top down, on a chain or channel-wide basis. This approach often fails to consider local product demand and consumer spending patterns and doesn’t capitalize on local events that offer plenty of growth opportunities.
CPG managers can track local visitation trends, examine local spending patterns and identify daily or weekly visit peaks in each store, city, or region, to plan product promotions that are uniquely suited to each chain and location.
Foot traffic data can be particularly helpful for CPG companies looking to distribute their products through “mom-and-pop” local stores and independent chains. While owners of these smaller retailers usually understand the needs of their communities, their sales data is less available to share with suppliers. Category managers can use foot traffic data to gain in-store performance visibility and adjust their offerings for local needs.
In addition to increasing sales in existing channels, foot traffic data can help CPG companies expand their product distribution and reach in new channels and regions. Migration trends can point at regions with growing population and demand and support a decision to expand to new points of sale. Also, a list of Top Chains ranked by shopper visits can help CPG executives find the best partners to carry their products.
CPG companies can also search for partners in new segments by looking at the visitors’ socio-demographic composition. Beauty service providers like salons, barber shops or spas, for example, attract visitors with an interest in beauty and personal care. Category managers in the beauty space can demonstrate the business opportunity using location data, and partner with such providers to sell CPG products in their locations.
Foot-traffic data can be leveraged to plan more precise marketing initiatives, both in the physical world and online.
Branded pop-up stores in highly trafficked areas or malls can help CPG companies increase their reach and awareness. Location data is the ideal tool to help marketing teams choose the best locations to attract their target audience, translating to higher sales and customer engagement.
Read more about selecting sites for new retail locations in the Site Selection Guide.
When planning a media campaign, marketing teams can use location data to increase the impact of their offline campaigns. Vehicle traffic volume for surrounding areas of interest can help determine the best spot for billboards. Knowing where CPG brands’ visitors usually cross-shop also introduces new possibilities for brand partnerships along the customer’s shopping journey.
For example, if many grocery store visitors go to a nearby gym before their weekly shopping, CPG companies can leverage that insight to display ads within the fitness venue and increase customer engagement.
Top post/prior locations visited for a Kroger location in Cincinnati OH. About 3% of visitors came to the grocery location after spending some time at one of the nearby fitness centers.
Visitation data also serves to enhance online campaign precision. Campaign managers can target zip codes with the highest visitations, improve segmenting based on demographics and psychographic traits, or decide the best days and hours to deploy ads based on visitation trends to the store. Whether it be social platforms, display or even retailer media, location data can inform campaign planning and boost effectiveness.
Demand forecasts are oftentimes a focal point for the consumer goods industry in its ongoing effort to improve operational efficiency. Since consumer visitation data is available per location or aggregated on a chain, region, or industry level, it provides insights into micro trends and consumption and shopping patterns. This helps predict demand and improve operational efficiency. With a clear understanding of what is driving consumer demand, managers can stock up on popular products or brands and keep a lean inventory of less popular products.
Location analytics can also help CPG professionals analyze migration patterns so they can quickly adjust their regional inventory and distribution to the current population status, resulting in significant operational cost savings.
Integrating foot traffic data analysis into a marketing and merchandising plan can provide an extra layer of insights that sales data alone simply cannot capture. By understanding who is coming into stores, where they go before or after, and when they enjoy shopping, CPG managers can provide stores with the best value propositions for their customers.
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