Every retailer knows that having the right products in the right place can make or break a store. Yet most companies still rely on historic sales patterns for product placement. They default to echoing previous behaviour without adequately accounting for evolving variations in local demand. It is here that demand data really excels.
Demand data in the context of a store is the indicator that tells us what is being bought by customers, when, and how often. It includes buying behaviour, transaction volume, seasonality, and even minor localised trends. Retailers all too frequently think of demand as something uniform around the world, but it changes from block to block. By tapping into local demand data, retailers are able to optimise product placement strategy and offer wiser outputs for shoppers and their bottom line.
Why local demand data is important
Demand is never the same. A product that sells in one location might remain unsold in another. This variation is due to reasons too many to list, differences in demographics, local culture, climate, seasonal behaviour, and even availability of proximate competition. Consider, for example, the product offering in a densely populated city convenience store, which will have a ready-to-eat meal and single-serve foods prominently displayed. At the same time, a suburban supermarket will have family packs and bulk foods highlighted.
The need to realise these differences means there has to be a methodical approach to demand data analysis. By gathering and analysing demand signals at local or area levels, retailers can identify trends the centralised approach might overlook. These trends enable businesses to create product location plans that are attuned to the tastes of the individuals who are actually in each store.
Ikano Insight achieves this with offerings such as Area Prioritisation Engine and specifically Product Targeting, which enhance retailers’ knowledge of who their consumers are, what they buy and how their needs vary across regions. It allows retailers to move away from decisions based on one size fits all to adopt finer, fact-based merchandising.
Product placement strategy in context
Product placement strategy includes the way products are positioned on a shelf, through the way categories are packaged in the store. In physical retailing, there is limited shelf space, and it comes at a cost, so these decisions matter. In the digital space, where there is no limited space available, but attention is scarce, placement is recommendations, order of listing, and advertising placement.
A good retail product placement strategy considers three things
- Product priorities – Which products should be most visible or accessible?
- Space allocation – How much shelf space does each product require, in proportion to demand?
- Positioning – Where in the store, or in the internet store, should each product appear?
Demand information can guide each of these decisions. Hot-demand products may call for premium in-store placement or front-page billing on the web. Seasonal or regional products may demand more room in some markets but significantly less in others. Sun lotion in a beach resort, for example, may fill an entire shelf during summer but use only a fraction in a northern town.
Ikano Insight’s product recommendation engines take this idea to e-commerce and digital media, meaning web shoppers get to enjoy smarter placement that takes into account their wishes as well.
Demand data and supply chain optimisation
Beyond the shop floor, demand data has an important role to play in supply chain optimisation. Retailers continually balance the trade-off between overstocking cost and stockout risk. Both are tricky, empty shelves infuriate shoppers and lose sales, while excess stock ties up money and threatens waste.
By placing product placement in conjunction with local demand signals, stores can more accurately forecast demand and take action to adjust the amount of stock based on that. As an example, a drugstore chain may find that cough syrup sells more rapidly in some markets and in some months, and vitamins sell better in other markets. By feeding those inputs into the supply chain, inventory can be redirected to the most critical markets.
This not only supports availability but also reduces waste and supports sustainability goals. Ikano Insight’s value modelling allows retailers to quantify these impacts, illustrating how smarter shelf-level decisions cascade into the entire supply chain.
Role of analytics and decision support tools
To know demand and to act upon it are two very different things. That’s where analytics tools come in. Geospatial analysis and geodemographic profiling bring richness of insight that makes demand data usable. They do more than tell you what people are buying, they tell you why by linking behaviours to place, demographics, and social context.
For instance, demand for meat alternatives may be concentrated in young, high-traffic urban centres for one retailer, whereas other markets are dominated by more traditional grocery staples. Execution of that knowledge entails balancing national product strategies with local execution.
Ikano Insight’s Area Prioritisation Engine assists retailers in making such decisions with confidence. By combining demand insights and area analysis, it points to where shops will make the greatest difference for individual product ranges. It is not a case of telling the retail team the solutions but giving them the evidence they need to make strategic, well-informed decisions.
Similarly, location planning ensures the product placement strategy is considered in the broader scheme of warehouse and store locations. By integrating these efforts, demand information becomes more robust, connecting customer behaviour with logistics and operating choices.
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From data to data-driven merchandising
Retailers tend to say they are “data-driven,” but the true value is in the application of that data. Demand data analytics can transition product placement from a game of guessing to fact-based planning. It enables teams to optimise assortments, optimise layouts, and match supply with actual local demand.
It does not mean erasing human experience, by any means. Store managers, merchandisers, and supply planners bring world experience and creativity. Demand data merely enables them to make more knowledgeable decisions with more solid evidence and more accurate tools. It helps ensure strategic decisions are made based on customer realities, not assumptions.
The result is data-driven merchandising that not only powers sales but also powers the customer experience. Shoppers can get what they want, when they want it, in a format that suits their lives. Retailers, in the meantime, benefit from better performance, less inefficiency, and a supply chain that is better aligned with real demand.
Conclusion
Product placement in stores has long been an art and science combination. The type of data that drives those choices is evolving, however. Demand data provides a more accurate picture of the behavior of customers, allowing retailers to tailor strategies to suit the unique conditions of each location. When combined with the likes of geospatial analytics, geodemographic segmentation, and Ikano Insight’s Area Prioritisation Engine, it makes supply chain optimisation and more effective, customer-driven merchandising possible.
The news is out, traders who embrace demand data analytics will be in a position to put the right products in the right place at the right time. And that is what attracts repeat customers.
Written by Matt Craddock
Global Head of Data & Analytics
Matt is a data science leader with expertise in heading up global teams that deliver game-changing solutions. He’s passionate about solving real-world problems with data-driven decisions, and combines hands-on technical skill with commercial insight to help businesses translate complex data into impactful outcomes.
