Store-specific planogramming has been implemented in retail settings for some 20 years now—and had been floating about as an idea for even longer than that. But even today, no retailer can achieve completely store-specific planogramming without the aid of a planning solution that automates the enormous amounts of calculations this level of localization calls for.
In recent years, the conversation has taken a new direction as retailers are asking how space planning should be integrated into their broader category management processes. In particular, many are exploring how they should be leveraging their planograms to drive efficiency throughout the supply chain—all the way from suppliers to shelves.
Why Have Effective Planograms Eluded Even Retail’s Biggest Players?
Anyone working in retail today is fully aware of how discerning customers have grown, and traditional retailers face pressure to be as responsive to consumer needs in stores as ecommerce options are on their mobile devices. But if it takes about 1 million calculations to produce just 1 meter of store-specific planogramming, it’s easy to see why it’s so challenging to respond quickly to consumer signals.
The complexities can grow and grow as the promised benefits of localization recede into the distance, until stakeholders lose heart. Indeed, thousands of retailers have undertaken store-specific planogram initiatives, but too often they must significantly rein in the scope of their projects or abandon them entirely.
At the root of the trouble with store-specific planogram solutions lies a paradox: you must either 1) simplify your planograms so they can be produced by a generic, plug-and-play solution, or 2) hire a team of highly skilled specialists capable of producing complex code. Both options are far from ideal.
A planogram is quite a sophisticated thing—the final tool through which retailers can engage with their customers at the shelf edge. The most effective way to meet customer needs at each specific store is through assortment, layout, and use of space. However, the planners who can creatively visualize and plan space to drive customer engagement often lack the technical knowledge to automate planograms. On the other hand, those who can code the complex scripts needed to automate planograms often lack that subtle understanding of consumer drivers or the creative vision to build beautiful shelves.
Fortunately, today’s retailers can solve this familiar problem by taking advantage of modern planning tools that make use of the many technology advancements we’ve seen in recent years.
A Unified Approach to Category Management
Traditionally, retailers have improved outcomes with store-specific planogramming by adjusting their facings based on historic sales. This conventional approach can result in some improvements to availability and sales.
In a more forward-looking unified approach to category management, though, planograms are informed by granular, localized demand forecast data. When this unified approach is combined with advanced machine learning algorithms, retailers can automate the otherwise time-consuming, complex task of optimizing store-specific planograms to meet shopper needs at every store in their network.
With this automated, unified approach in place, we’ve seen retailers improve not just availability and sales, but also the efficiency with which they can replenish, stock, and maintain their shelves. A unified planning approach targets a one-touch replenishment model, minimizing excess stock in-store as well as the reducing delivery frequency of items. But the outcomes today’s retailers can achieve would not have been possible without the technological advances of recent years.
Cutting-Edge Tools Automate and Optimize Planograms
Modern planogramming tools have come a long way in the years since many retailers first installed their legacy solutions. The space planning team’s user experience itself has been much improved by advancements in 3D rendering technology and far more intuitive user interfaces.
More important for business outcomes, though, are modern advancements in AI. Machine learning algorithms make it possible for retailers to fully customize and automate the optimization for their demand-based, store-specific planograms. This saves retail planning teams enormous amounts of time that can be redirected to more valuable tasks, while also improving the accuracy and quality of the final product.
Furthermore, today’s cloud-native technology makes it possible to leverage these automated, optimized planograms directly into more efficient store and supply chain operations, helping to manage replenishment with both front- and backstage space awareness. Solutions that allow staff to feed valuable store-level information to central planning teams from their mobile devices enable retailers to achieve true end-to-end category management.
How Do You Get There?
Retailers implementing optimized store-specific planograms must first import existing planograms into their new solution—whether from a legacy system or their own retail data. The planning solution should be capable of optimizing those plans against specific store needs, adjusting inventory and space to meet the company’s specific objectives. The solution should draw from store-level demand data to reduce facings for over-spaced products and increase facings for under-spaced ones. Product placements are adjusted based on the merchandising requirements set by the retailer, but are still fully configurable by users on the planning team.
Within the space of a morning, a quality solution should be able to create store-specific planograms that maintain the principles defined in the company-level merchandising plan while still being individually tailored to each store’s needs. What’s more, minor planogram revisions can be completed in-flight while minimizing changes to the optimized planogram, so there’s no additional pressure on retail operations teams to constantly change their layouts with each planogram update.
So how big is the impact, at the end of the day? In our experience, about 30% of our customers’ planogrammed product positions change as they move from plans built on average data to store-specific data. We’ve seen customers reduce delivery rows (product-store deliveries) by up to 35% and cut excess stock by up to 25% while maintaining the same improvements to availability and sales seen with conventional store-specific planogramming. To us, the learnings are clear: a unified, machine-learning driven approach to store-specific planogramming is the best way to improve outcomes throughout the supply chain.