Assortment planning is at the heart of retail. Businesses can thrive or dive depending on how accurate (or not) an assortment is for any given season. Technology plays a crucial role in assisting this process. For example: digital transformation for assortment planning can significantly improve the SKU rationalization process and SKU productivity analysis. Affinity logic improves the likelihood of adding more to a shopping cart. Merchants often rely on custom spreadsheets to manage their own unique process for creating assortments. Technology can even validate, and in some cases, predict the outcome of a given season’s assortment.

But even the most advanced machine learning approaches cannot entirely replace a merchant’s creative ability to design something entirely new, tell a compelling story to their customer, jump on a hot trend, respond to customer feedback, or collaborate with peers to build a coordinated theme.

Below, we’ll take a closer look at the roles of the merchant and technology in the assortment planning process, and how collaboration can bring out the best of both.

Assortment Planning

To understand how digital transformation fits in with assortment planning, let’s take a look at the process, which can be divided into three parts: Assortment Optimization, SKU Productivity, and SKU Rationalization.

 

1. Assortment Optimization

Since assortment optimization acts as the bridge between assortment planning and merchandise planning, I will address that in detail in a future article. I will focus instead here on SKU rationalization and SKU productivity, where you can see how collaboration should occur between a merchant and technology. Before I move on, though, I’ll give you a quick illustration of how assortment optimizations can lead to very different results.

When a computer attempts to rationalize a seasonal assortment, it might produce something like this:

A merchant, however, might produce a more thoughtfully crafted assortment, knowing that a well formed assortment isn’t evaluated successfully on one measure (in this case, gross margin).

2. SKU Rationalization

SKU rationalization refers to the process of building an assortment, which can be broken down into three distinct areas: strategy, profitability, and space planning. Technology solutions can provide the profitability and space planning analysis, but only merchants can provide the expertise required to address the questions of strategy.

 

3. SKU Productivity

SKU productivity measures the effectiveness of the assortment against strategic, financial, and customer expectation expectations. Since there’s no one right answer, I like to think of the optimal solutions along a frontier, a line that extends between natural ends of two sides. For example, should your inventory be shared in order to support omni-channel, or should it be distributed to support a destination strategy, or perhaps something in between?

This creates a natural and collaborative relationship between merchants and their technology solutions that help them make informed decisions. Based on the merchant’s strategy, the technology will highlight where on the frontier spectrum the assortment needs to be.

 

 

Here’s an example of how the process might work:

  • Step 1. Identify and create a customer’s purchase decision tree mostly driven by merchandising strategy and, if available, C-SAT surveys and RFID analysis if available.
  • Step 2. Perform a diminishing returns analysis to determine optimal counts.
  • Step 3. Thoroughly understand the substitution effect – the ability for a customer to choose one style, color, or size over another.  Each sales channel would likely behave differently. Discount stores, for instance, are probably more driven by ADT (high substitution) vs luxury stores, which are more driven by assortment (low substitution).
  • Step 4. Analyze the profitability by SKU, by SKU as part of an assortment, by assortment, and by overall strategy.
  • Step 5. Iterate on decisions by applying all relevant nuances

Typical measures used in SKU productivity analysis:

  • GMROI. Gross margin earned for each average inventory dollar invested
  • GMROA. Gross margin earned for each average advertising dollar invested
  • GMROS. Gross margin earned for each average sq. ft. utilized
  • Sell Thru, WOS
  • ADT, AUT, AIR, Conversion, AUR
  • Obvious Measures. Sales, Margin, Returns, Discounts, Average Inventory

Other factors to consider:

  • Store differences (e.g, traffic, sales volume, assortment availability, selling sqft)
  • Regional differences
  • Style, Color and Size Counts
  • Merchandising (space planning)
  • Substitution sales
  • Price elasticity
  • MD product penetration (AUR analysis)
  • Promotions
  • Halo effects

The key takeaway here: although technology has vast capabilities to improve the assortment planning process, it cannot replace a merchant’s insight, input and intuition. Recognizing that each function has a distinct and important role to play will help the retailer on a sound path toward assortment planning success.