Take, for example, the BookBinders Company, which sells books directly to consumers, this is, operates through direct marketing without any intermediaries. This approach allows them to have a clear view of their customer base, evidenced by their detailed database containing historical purchase volumes and margins per euro. BookBinders is considering a promotional campaign offering new titles to a limited number of customers on its database. The question the company faces now is which customers to target for this promotional campaign. To take this decision BookBinders would like to know what the expected profitability is of reaching out to each customer in their database with this offering.
BookBinders has a database about thousands of customers with information like Table 2:
1.
CustomerID: A unique identifier assigned to each customer, which is typically used to track and manage customer information within a company's database.
2.
Purchase Volume: The historical amount of money (in dollars) that the customer has spent on purchases over a certain period.
3.
Margin per $: The profit margin per dollar spent, representing how much profit the company makes for each dollar sold to that customer. Why may this margin vary by customer? This reflects the cost efficiency of sales to that customer or the premium pricing of products they tend to buy. For instance, customer 7 tend to buy only low margin books, and this is reflected in low profitability.
With no additional information, BookBinders may estimate the potential profitability of its customers multiplying the historical purchase volume times the margin per dollar fir each customer (Table 3).
But some consumers will be more likely to make purchases than others. This occurs due to variations in individual preferences, needs, and circumstances among consumers. Factors such as product relevance, pricing, promotional offers, and past purchasing behavior can influence their likelihood of making a purchase. Suppose the company creates a model to predict the probability of each customer making a purchase following a direct marketing campaign. This model takes into account various factors like demographics, past interactions with the company, and response to previous marketing efforts. Now, to determine the expected profitability of these customers after incorporating this new information (the purchase probability), the company would multiply each customer's estimated probability of purchasing by the potential revenue from their purchase. Then, they would subtract the estimated costs associated with reaching and serving that customer. For the shake of simplicity, suppose the cost of reaching each of these customers is $3,50, and the same for all. This calculation allows the company to prioritize resources and tailor marketing efforts to target those customers with the highest expected profitability based on their purchase probability. By focusing on customers more likely to purchase, the company can optimize its marketing strategies and improve overall profitability. In summary:
(1) Profitability without considering purchase probability: €442,88 (€407,88 when marketing costs are factored in).
(2) Profitability considering purchase probability: €2,21.
(3) Profitability considering purchase probability and targeting only profitable customers: €9,29.
The intriguing aspect here is that scenario (1) is improbable since it overlooks crucial information. By reaching out to all customers, the actual expected profitability reduces to just €2,21. However, by focusing solely on the most profitable customers (those with positive expected profitability here), the anticipated profit rises to €9,29. 翻译一下这段