One of the toughest marketing challenges is determining which products and services to offer customers. Cross-selling to existing customers is an efficient way to boost revenue and profits, but it requires one to be able to identify which product is most relevant at any given time. This challenging problem is compounded when we need to deliver relevant cross-selling messages across many channels.
While there are a variety of approaches to predict what an existing customer will purchase next, one very effective approach is to calculate the product adoption propensities within a single model, based on the idea that a customer is choosing between various products. For example, the cash a customer brings to a bank might be placed into a savings account, CD, or investment account, but a particular customer is likely to open only one such account at any time. Similarly, in retail, consumers make choices between product categories in any particular shopping trip, although the “market basket” may contain products from more than one category.
The output of such a model is an array of customer-level scores where the columns represent product categories (for example, electronics, books, appliances, furnishings, and apparel). If we ordered customers in descending order by any product column, then we could use this model as a traditional targeting model. If we look by row, then we can determine the best product(s) to offer any given customer.
No matter what method we use, the value of such a model cannot be realized until it is used to deliver relevant offers and messages. Unless you are one of the rare marketers with the luxury of engaging customers through a single channel, you will need to deploy this customer intelligence across channels.
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