Demand Transference

Assortment is a key element of a retailer’s marketing mix. It differentiates a retailer from its competitors and has a very strong influence on retail sales. Retailers face the problem of selecting the assortment that maximizes category profitability, without sacrificing customer satisfaction.

Although some headway has been made in the context of assortment optimization, practitioners and academics agree that more research is needed to provide feasible solutions to realistic assortment problems. Specifically, the challenge of assortment optimization is compounded by the fact that the demand for an item cannot be assumed to be fixed; it is instead affected by the presence of other items as a result of product substitution.

One of the important challenges is to account for similarity effects: an item is a stronger substitute for similar items than it is for dissimilar items. Demand is also driven by own- and cross-marketing mix instruments such as price, promotion and by heterogeneous preference across stores. Capturing these aspects in a response model is further complicated by the fact that assortments and prices observed in empirical data are unlikely to be exogenous. Finally, retailers have to decide about not only the assortment, but also about the pricing, and these decisions need to be customized at a store level.

In the process of optimizing the store assortment, it is important to understand the process of demand transference. Demand transference is defined as the process of transfer of demand among the items in a store, once a change in assortment is realized. In a store, for a given category, there may be two realizations of an assortment change:

• When one or more items are dropped from the assortment, customers who intended to buy any of the dropped items might either choose to opt for another ‘substitutable’ item or walk away from the store, without a purchase.
• When one or more items are introduced into the assortment, customers who purchased any of the new items might either buy the new item out of impulse or replace purchasing an existing item with the new item.

A better understanding of this underlying process would help in identifying the optimum assortment for the particular category in the particular store.

Demand Transference (DT) helps you to compare products based on their similarities in order to determine what, if any, products customers might buy if the product they want to buy is for some reason unavailable. In this way, planning and ordering can be optimized. DT calculates similarities by comparing the attributes of the two products. If you are using CDT in conjunction with DT, you also have available the similarities calculated by CDT, which are based on customer-supplied transaction data.

Demand Transfer and Convenience Goods

Demand transfer is also very common with convenience goods, where customers will select an alternative brand if their choice brand is not available.

Convenience goods are products in our lives which we simply cannot do without. This is not in regards to products we love like our phones or our favourite pair of shoes, but more in regards to products which are staples and essential to daily living. These products are classified as convenience goods, items which are widely available and regularly bought with very little effort.

Convenience goods are usually inexpensive and have a low opportunity cost for customers, but this also means a higher sensitivity to price. In this case, the main objective for retailers is to balance out price and demand, ensuring that incremental price increases don’t have a negative effect on quantities of goods sold.

As a result, to make maximum profit retailers need to ensure they sell large volumes of convenience goods at a fast pace. Examples of convenience goods include food, newspapers, cleaning products, and personal hygiene products.

Demand Transference Modelling for item assortment management

A demand prediction component analyzes item attribute data using a demand transference model to calculate a magnitude of demand transfer between items in a set of substitute items associated with a proposed item assortment. The proposed item assortment includes at least one assortment change. The assortment change includes a set of one or more items to be added to a current item assortment and/or a set of one or more items to be removed from the current item assortment. The demand prediction component generates a demand transference result including the calculated magnitude of demand transfer for each item in the set of substitute items and/or a predicted walk-off rate associated with lost demand. An assortment recommendation component generates an accept recommendation and/or a reject recommendation based on the demand transference result, the predicted walk-off rate, and/or a demand transference score.