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Cross/up-selling strategies: how to make them work through mathematics

Cross/up-selling strategies provide an important opportunity to increase business sales and profits and also contribute to customer loyalty. However, in order to really work and produce concrete results, they need to be based on analytical software and calculation algorithms that are calibrated to the target and give reliable results.

The secret to creating successful cross/up-selling strategies lies in the ability to target the profile and needs of the potential customer and present a personalised offer at the right time and in the right way. This is why it becomes essential to rely on professionals in the sector, with the technological skills and mathematical knowledge to design a solution in line with the company’s commercial needs.

What are cross/up-selling strategies?

The terms “upselling” and “cross-selling” refer to two different and complementary marketing strategies that allow companies to increase the revenues generated by the sale of products and services by leveraging customers’ willingness to purchase while shopping.

Specifically, the upselling technique consists in offering the customer a product or service similar to one already selected, but superior in quality or quantity and, obviously, in price range. For example, the seller can suggest to the potential buyer a more advanced camera instead of the basic model chosen, or a tablet with more disk space than the one initially decided on (32 instead of 16 gigabytes).

Cross-selling, on the other hand, consists in bringing to the customer’s attention a series of accessory products or services in addition to the item they intend to buy: travel companies normally promote hotel rooms or car rental when the customer is about to purchase a flight.

Upselling and cross-selling strategies both take advantage of a specific moment in the relationship between supplier and consumer, when the customer is about to make the purchase (for example, when he or she is at the checkout or selecting the items to place in an online shopping cart). A person who has already decided to buy and is completing a purchase is psychologically more open to spending, and therefore may be enticed by complementary or higher-value products and services.

The secret to successful cross/up-selling strategies

To develop effective cross/up-selling strategies, a number of variables must be considered. First of all, the products and services that may appeal to the same buyer and be accepted by him/her as a valuable alternative must be correctly correlated. Formulating a valid cross/up-selling proposal requires not only an assessment of the features of the items, but also consideration of the purchasing behaviour and preferences of the individual consumer.

 Cross/up-selling strategies have always existed, but the Big Data now available to companies provides new possibilities. It is now possible to monitor the transaction data and purchasing behaviour of each individual consumer, allowing companies to switch from general to personalised cross/up-selling. Machine learning techniques, trained on large amounts of data, can generate automatic recommendations that reach the right customer at the right time. The algorithms feed on the feedback received and are able to increasingly refine their proposals to respond promptly to the consumer’s changing needs.

Recommendation platforms are a concrete example. They are e-commerce portals that can trace users’ profiles and actions and suggest the right commercial offers at the best time as an alternative to purchase decisions or to complete them.

Clearly, mathematics is the driving force of the entire technological system. The ability to develop correct analytical models and algorithms is therefore the essential requirement for developing successful cross/up-selling strategies that can generate value both for the consumer (who will gain greater satisfaction from the products purchased) and for the company (which will increase its sales total and customer satisfaction).

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