Jul-Sep 2012

Using Business Analytics to Improve Product Assortment

Retailers with multiple  stores of commodity products face the problem of product assortment that incorporates the varying geographic and demographic conditions of the locations they serve. How can such retailers use the sheer volume of available transactional data to their best advantage? In this article, Professors Xue Bai, Sudip Bhattacharjee, Fidan Boylu and Ram Gopal of University of Connecticut, present a data mining and optimisation based decision model for product assortment across multiple stores of a company.

Global and Local Patterns of Sales Merchandising managers and retail buying executives frequently decide on the assortment of products to be carried in a retail store. Product  variety is important for decision making, and so  are the  consumer demographics ser ved  by  the retail store as  well  as meeting  the inventor y  constraints   at the location. While consumer  products and brand name items can be marketed using pricing, promotions, bundling and other techniques, this is usually not the case for commercially used commodity products  such as oil, steel and plastics, where markets are competitive and pricing  and product differentiation  are not salient sales tools. For retailers selling such products,  product assortment and availability are important determinants of sales success, while price  is usually closely tied to the cost of the product. Retailers with multiple stores in different geographical locations have the problem of deciding on the most profitable product assortment for each store, and frequently do not have a common metric to compare the profitability of different stores, given the differences in product assortment and the diverse demographics ser ved by the stores (see Figure

1). Measuring such a commodity  retail store’s sales effectiveness   is  usually   achieved   through metrics such as total revenue, average turnover and operating


margin. While useful, these efficiency measures do not usually provide growth goals for managerial decision making and planning.

To achieve  the best product assortment  and performance for a given store, a first step involves identifying   global  patterns of sales  of associated products through data mining of transaction information  of the different  stores of a  firm  (see Table 1). However,  without a  method to identify demographics  around a given retail store and estimate differentiated  sales opportunities  for existing stores, a centralised  retail  director  may set similar growth goals  for all  the stores, which  we have frequently obser ved to be the case in industr y engagements.  It is not uncommon, therefore, to find some stores that easily exceed expectations,  while others seem to lag significantly behind  the goals. It is possible  that the stores that do not meet goals are already performing at peak efficiency. This creates subsequent  planning problems   as  well  as  personnel   related equity and performance disparities.


In  this  article, we  present a   data  mining and optimisation  based product assortment and performance assessment methodology for each store of a firm. Our methodology allows a merchandising manager to glean global knowledge from sales patterns and identify frequently purchased itemsets. We use a dataset from an industr y leading plastics manufacturer and retailer in the United States to demonstrate the utility of our model.

Frequently Purchased and Revenue Generating Itemsets

The  complete set  of  transactions   captures the purchase behaviour of client companies. To extract product dependencies,  a commonly  used approach in data mining is  frequent itemset analysis  over transactions.  However,  a  concern that arises  with frequent itemset analysis  is  the large number of itemsets that are generated. Moreover, the frequent

Our methodology allows a merchandising manager to glean global knowledge from sales patterns and identify frequently purchased itemsets.

itemsets can enhance the sales  of a  product while others can dampen it. Translating  this knowledge into a viable decision-making  model  to further firm objectives  has remained an unaddressed  challenge. Key among  these objectives  is the development of efficiency measures for each of the stores and related product assortment selections.

We developed a robust mechanism to prune the large number of resulting itemsets and also developed a metric to identify revenue generating items that can subsequently be used to choose beneficial itemsets (see Table 2). Using data from one of the industr y’s largest plastics manufacturers and distributors in the United States, we show that when the itemsets, after pruning, are examined for a given industr y segment, the initial set of product association rules (in the magnitude of tens of millions) can be significantly  decreased. After pruning,  the resulting number  of itemsets  is reduced to a ver y manageable  size (in the magnitude of hundreds). Our computational  results  also show



that frequently purchased  itemsets  that are bought by  one industr y  segment   significantly   differ from those bought by another, with only a small number of overlapping products.  This suggests that product offerings in a given store should be carefully calibrated depending on the industr y segment potential around the store location.

Efficiency metric for Performance
Evaluation and Growth Projection

We developed a metric to measure and compare store performance  using an average, or quartile, or other ranking measure, which helps to provide differentiated growth goals for each store. This metric utilises global knowledge to optimise a store’s product assortment by taking  into account the local constraints around a  given  store. In addition to the value created  for existing stores, the methodology can also be extended to determine locations to open new stores based on location demographics.

Our  analysis   compares current and optimal revenues  across  10 stores based on the “average” revenue capture ratio (see Figure 2). For most stores, the optimal  revenues are higher than the current revenues,  signifying that these stores are currently performing  below  average and can be targeted for
growth to meet the average revenue capture ratio for all the stores.

When the same analysis is  performed   with a “90th percentile” revenue capture ratio, some stores remain in the 90th percentile while others drop out – suggesting that these stores could improve under the “90th percentile” metric criterion (see Figure 3).

In industry, it is the usual practice to fix growth goals for all stores at the same rate, only to subsequently find that some stores easily achieve the goals, while others do not. That is the reason we see stores that are already performing at the highest level and cannot be expected to improve growth on par with other stores that have a better potential to grow. We can thus label these high performing stores as “turnips,” because as managers well know, “you cannot squeeze blood out of a turnip.” Our method can identify lower performing stores, and as managerial incentives are frequently tied to growth performance, the firm can set data-driven differential growth projections for stores, as opposed to a “one size fits all” target.

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