Last updated: Product assortment intelligence: The right products, prices, and time

Product assortment intelligence: The right products, prices, and time


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Consumers have become increasingly empowered with access to data that supports their purchasing decisions, and many retailers have joined the online price-matching race. The problem is that there are no winners in this race. When competing retailers are charging the same prices for the same items, everyone loses margins. Enter product assortment intelligence.

Retailers need to find new methods to drive sales and maintain margins, and one of the best solutions is to provide a unique product assortment that responds directly to customer desires. This helps them to differentiate and compete more effectively, without focusing primarily on price matching.

Retailers are constantly asking themselves what consumers want, with the goal of offering the right products at the right price, just in time to capitalize on peak trends. Leveraging data from past transactions, anecdotal data from suppliers and market research, store comparison shopping tends to dominate as inputs for assortment decisions, though that is beginning to change.

Product assortment intelligence: 5 questions retailers must answer

There are five questions retailers should consider in order to develop a consumer-oriented assortment:

  1. What do consumers want?
  2. Why do they want the things they want?
  3. Are we offering the products that consumers want?
  4. Are we providing these products at a competitive price?
  5. Are we providing the products within a competitive timeframe?

The rise of the digital consumer is forcing merchandisers to adopt more consumer-centric methods for assortment decisions. The key to determining what to carry is in the free, democratic data that consumers are constantly providing – from search data and product reviews to social signals including Facebook likes and Pinterest pins. By accurately analyzing demand signals and cookies, retailers can predict product popularity, which can then be used to inform assortment decisions.

How retailers can use product assortment intelligence to leverage in-the-moment sales

While retailers can infer trending products, they can also leverage this fast, rich data by matching it with competitors’ assortment to inform competitive pricing opportunities, and to bridge key assortment gaps.

Let’s look at an example, just in time for the World Series. There are about 6,500 unique baseball bats on the market, including all shapes, sizes and materials.

To create the optimal assortment, retailers need to:

  • Determine the most popular bats in time for the start of spring training and see if there are any they don’t carry
  • Determine which competitors are carrying these bats, and at what price
  • Identify what attributes are driving popularity (e.g., material, length, bat drop)
  • Determine which bats are unique to them and which are exclusive to competitors
  • Understand competitors assortment strategy by reviewing assortment composition by brand, price and attributes
  • Look for opportunities to compete on price and offer unique products with markup potential
  • Leverage some of this data to develop private label offerings

Sports retailers can investigate social signals to make timely assortment decisions on any number of product categories – from researching running shoes ahead of the Boston Marathon to researching soccer cleats ahead of the World Cup – but assortment intelligence goes well beyond just sporting goods.

Knowing which televisions to have in stock before the Super Bowl can provide a huge advantage for electronics retailers, for example, and there are an endless supply of examples across retail categories, for everything from consumer electronics to office supplies, home improvement, toys/hobbies, home and housewares.

In some categories, assortment decisions are primarily gut based decisions. However, it is beginning to evolve from a reactive, gut-based practice to a data driven science that provides retailers a competitive advantage that can significantly impact their business.

As for the other retailers that have been undertaking data driven assortment decisions and embracing retail automation, herein lies a new source of data – one that is more current, richer, more cost effective and being sourced from online sources – a super set of store plus online. At the very least, retailers need to supplement this as a feed into their existing decision making infrastructure in order to keep their edge.

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