Assortment Optimization

Assortment Optimization is an extension of or a mature form of assortment planning. The task of a retailer to plan the assortment mix in a given store is complicated by various factors such as the differences in the format of the stores, size of the stores, demographics of the population around the stores, the product categories’ performance in the given region, the life cycle of the product/product category etc. Retailers start from a strategic plan with respect to the assortments they want to carry and then refine the product offerings into different categories, sub categories and SKUs. The assortment plan will be made category by category, channel by channel, cluster by cluster. (Cluster would mean the store clusters formed based on certain characteristics – Please refer Store Clustering).

For instance, the retailer may want to classify the products broadly under food and non-food. Food products can be classified as shown below:

The “Types” shown above can branch out to different SKUs. The task in front of the retailer is to come up with the demand forecast for the different SKUs and stock the right mix of SKUs or product categories in the right stores/channels in right quantities. Demand forecasting is being done by various retailers using various tools. These tools employ a variety of scientific techniques that range from a simple “moving average” method to more complex algorithms like “Exponential Smoothing with trend and seasonality”, “Auto Regressive Integrated Moving Average” (ARIMA) etc. Keeping aside the science & techniques involved in demand forecasting, let us try to understand certain other factors that influence the merchandise mix.

Factors influencing Assortment decisions

The decision to select on the appropriate merchandise mix will be primarily based on few attributes of the SKUs and product categories. Some of those attributes are given below:

a)      As we all know, 80% of the sales in a store will be driven by 20% of the SKUs. So the SKUs or product categories can be ranked in the descending order of sales and the retailer would try to retain those products that drive high sales volume and try to discard those SKUs which do not contribute to sales.

b)      There may be some products which will be sold in very less in number of units but their profit margin may be significantly high. Obviously, retailers would try to retain those products that have a high profit margin and discard that do not.

c)       There may be some products which may not really drive huge sales in a store but then it may be of significant importance to some of the loyal customers to that store. Discarding those stores may result in dissatisfaction of some of the loyal customers. Hence retailers would try to retain those products or SKUs.

d)      There may be certain SKUs which have a tendency to drive the sales of other products. I.e. Those SKUs will influence the sales of other accompanied products. (Market Basket Analysis can help in figuring such associations between products)

e)      Certain products may be key differentiators for the retailer and may provide a competitive edge over its competitors. A retailer would wish to retain such products in the offering.

f)       Few other key performance metrics can be collected for product categories and used to determine, whether or not to retain those products. For instance, percentage growth of the product category or the SKU can indicate the performance. Whether or not the product category/SKU has achieved the sales target can also be a performance index. Such performance metrics should be measured store by store or cluster by cluster, format by format to decide upon the assortment mix.

The effect of substitution within product sub categories or across different SKUs should be taken into account as well. For instance, if a pack of cream wafers in orange flavour is not available in a store shelf, the consumer may choose to buy cream wafers in strawberry flavour. So, while deciding on the quantity of strawberry flavoured wafers to stock in a store, the retailer should be aware of the distinction between the original/uninfluenced sales of strawberry wafers and the sales influenced by the absence of orange flavoured wafers. Another factor that should be considered is the lifecycle of the product category itself. I.e. A product category like an MP3 player is in “Decline” phase. A product category like a “Laptop” is in “Mature” phase. A product category like an “iPad” is in “Growth” phase. However, the product life stage may not be the same in all market segments. For instance, laptops may in a mature phase in a tier-I city in a country like India but may be in a growth stage in a tier-II city.

Thus, based on the demand forecasting (that was determined from historic sales) for different product categories and based on the different attributes listed above, the assortment mix for different formats and different store clusters can be arrived at and optimized.  Assortment optimization also covers the channel of distribution as well. In the world of e-commerce, the retailers need to have mechanisms to optimize the assortment mix in both the physical stores and in online catalogues. Thus the retailer is supposed to monitor the performance of products in different channels as well and decide upon the assortment mix accordingly.

Significance of Assortment Optimization

Once the assortment mix is determined by retailers, for different clusters and for different channels, that information will become a vital input for store space planning or planogram design. For instance, the product categories which the retailer wants to sell more should have more shelf space and the facings per shelf should be more as well. One can also appreciate the fact that the assortment mix will in turn drive other key decisions of a retailer including the supply chain and marketing promotions.  If certain product categories have a huge potential to sell than the retailer can offer in a certain store, it may pave way for opening new stores as well. At the same time, if certain SKUs are not selling well, the retailer may consider devising markdown strategies for those products, to clear off the stock. These factors underline the importance of the assortment optimization.

There are sophisticated tools available in the market to do the assortment optimization but to deploy it in a retailer’s IT landscape, understanding the issues mentioned above is of utmost significance. The tools can even provide provisions to understand the impact of increasing or reducing or deleting certain product categories from the merchandise mix. With such advanced technological tools, the retailer can do analysis on product revenues and other performance metrics for different SKUs or product categories and use the information for future product launches. Such information will be very handy and practical to use, when new products are launched by the retailer and also to tune the assortment mix to adapt to changes in various environmental factors.

I believe this post gives the first hand information about assortment optimization and the factors that one should keep in mind, while trying to optimize the assortment mix.