Effective Strategies for Assortment Planning in the Digital Age

  • May 17, 2023

If you’ve been relishing the opportunity to shop in physical stores again, you’re certainly not alone. In fact, physical stores today account for 87% of total retail sales in the U.S. But the experience of in-store shopping has changed, with many consumers demanding seamless interactions between a brand’s physical and online touchpoints. From building customized assortment plans and store-within-a-store partnerships to leveraging circular economies and trading relationships, retailers today can apply innovative Artificial Intelligence (AI) and Machine Learning (ML) technologies to capitalize on this shifting demand. In this article, we’ll explore the latest solutions shaping the future of customer engagement, loyalty and profitability.

The impact of assortment planning

Despite steady increases in year-over-year profit and growth, the retail industry is still rife with unmet consumer expectations. The fifth edition of Salesforce’s State of the Connected Customer finds that 73% of customers expect companies to understand their unique needs and expectations and that 47% of customers prefer to shop in-store rather than online.

Assortment planning helps retailers deliver in-store experiences that harmonize with their online interactions. By optimizing choices about which products to sell and how to allot those products between different locations, retailers can increase both customer satisfaction and profits. Carrying the right assortment of inventory is vital for retailers because incorrect estimates and errors can result in significant losses, such as:

  • $129.5 billion lost a year due to out of stocks
  • 123.4 billion lost per year to overstocks
  • 4.1% of revenue lost on understocks (per average retailer)
  • 3.2% of revenue lost on overstocks (per average retailer)

Optimized assortment planning strategies

So how do retailers today determine the right inventory mix for their stores? It’s a deceptively complex question. Retailers must strike a precarious balance between offering variety (number of categories available), depth (number of SKUs within a category) and service level (number of items of a particular SKU). Offer too much depth and customers may become overwhelmed, only choosing products they’re familiar with or have purchased before. Cut back on variety and customers could defect to a more responsive competitor.

What’s more, there's no effective ‘one size fits all’ assortment strategy, especially in today’s ever-evolving market. For instance, brands could implement a wide assortment strategy by offering a product mix of several product lines with limited product variations. Or they might follow a deep assortment strategy that emphasizes product variety over product lines. Big box retailers like Walmart, Target, and Home Depot have traditionally offered mass-market assortments that emphasize both wide and deep product mixes. Today, many are experimenting with consumer-centric assortment models, where a store’s product mix varies by its location, store size and layout and customer base.

In the past, retailers have relied on Excel spreadsheets, historical data and even intuition for their assortment planning process. However, advances in AI and ML now provide retailers with innovative tools to build better assortment planning strategies. These strategies center on the end consumer, update in real time and evolve as customer behaviors change. What’s more, AI and ML programs provide:

  • Hyper-Granularity: AI enables assortment planning to move beyond broad store classifications and clusters to provide customized 1:1 assortment strategies.
  • Unique Experiences: ML can analyze customer shopping patterns, provide product recommendations in-store and even anticipate future buying behaviors.
  • Insights: AI connects the dots between historical sales patterns, shifting customer behaviors and impulse-triggered sales.
  • Ecosystem: AI and ML look at both internal and external data sources to generate data-driven conclusions that help drive better decision-making.

In-store shopping in a digital world

Ecommerce offered customers added safety, stability and choice during the pandemic. But many consumers — especially Gen Z customers — now prefer the physical interactions and connections of in-store shopping. In fact, 81% of Gen Z consumers reported they prefer to shop in-store to discover new products. Over half stated they shop in-store because it allows them to disconnect from social media and the digital world for a while.

Capitalizing on this new demand will be top of mind for many retailers. Some 55% of brands are prioritizing foot traffic in 2023. AI and ML can help retailers gain a competitive advantage by building optimized assortment strategies for each store, even those within the same region or city. Key challenges facing in-store retailers today include:

  • Limited Shelf Space: the demand for physical shelf space has never been higher. NIQ, a leading consumer intelligence company, estimated that a 2% increase in shelf space could drive a 1% increase in revenue. Retailers will need to look at each store and determine the correct balance between variety, depth and service level to make sure their shelf space is being used effectively.
  • Trading Relationships and Product Cannibalization: trading relationships occur when manufacturers put marketing money into getting as many of their own products on shelves as possible. This not only creates shelf clutter and customer indecision, but it can also result in product cannibalization and loss of revenue.
  • Supply Chain Issues: with price increases at home and abroad hitting unprecedented levels, retailers must find new ways to deal with rapidly rising costs without driving away existing customers. Reliability is a key driver of customer loyalty, particularly with in-store shopping.
  • The Circular Economy: a trending topic among retailers and consumers, the Circular Economy emphasizes the sharing, leasing, reusing and recycling of existing products. Retailers will need to develop new methods that emphasize a “next use” product mentality. For instance, Best Buy offers a trade-in program for their customers that reduces environmental waste while enhancing the customer experience.

Other trends impacting assortment planning

Over the last 12 to 18 months, retailers have opened physical stores at a more rapid pace than previously expected. Yet, the days of all stores within a brand being created equal are long gone. Many of today’s top retailers are experimenting not only with customer-centric assortment but also with different store sizes to better fit the needs of their customers.

Bloomingdale’s is now opening Bloomie’s, a 22,000-square-foot format that is much smaller than a typical 200,000 square foot Bloomingdale’s. The Bloomie’s stores’ smaller footprint allows the retailer to focus on rotating trends, optimize shelf space, and provide interactive experiences for their customers. Dollar General, conversely, has opened DG Markets that are twice the size of their standard stores and offer additional products like fresh produce, meat, dairy and perishable foods.

Another trend impacting assortment planning is Store-within-a-Store partnerships. For brands, this concept gives them the power to manage their own inventory, determine the prices of their products and independently develop their own marketing campaigns. For retailers, this model can offer enhanced prestige, convenience and new clientele to existing chains.

For example, Target and Ulta have partnered to launch Ulta Shop-in-Shops. These 1,000 square-foot Ulta shops are designed to look exactly like stand-alone Ulta stores, with brand colors, vivid graphics and independently trained sales consultants. Other Store-within-a-Store partnerships include Kohl’s and Sephora, and Nordstrom and Glossier. As more Store-within-a-Store concepts materialize, retailers will need to modify their assortment planning strategies accordingly. For instance, an Ulta Shop-in-Shop might cannibalize existing sales of makeup products, but it might also boost new sales through the strategic positioning of complementary products.

The future of consumer centric assortment

More customers have returned to shopping in-store and are likely to stay. But to sustain this influx of new business, retailers will need to deliver more seamless experiences that cater to the consumer, not the brand. Managing inventory with Excel spreadsheets and historical data doesn't deliver the most valuable curated insights. Rather, retailers must create customized assortment plans to stay ahead of their competition. By leveraging AI and ML technology, retailers can make sure that each store in their portfolio is unique, consistently evolving and optimized to boost customer engagement and loyalty.

If you’d like to discuss assortment planning or other challenges you may be facing, please don’t hesitate to contact us.

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Andrea Riberi

Andrea has more than 30 years of experience in data, business intelligence and analytics in the CPG and Retail Industries. She has collaborated and delivered solutions to multiple clients in the areas of Assortment, Consumer Strategy, Revenue Management, Trade Promotion Optimization and Digital Experience.


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