The Effectiveness of Up-sell/Cross-sell Offers

It’s easy to understand why companies are increasingly using up-sell and cross-sell offers as a low-cost approach to building revenues. Current customers are nearly always the best prospects for additional purchases – they’re already “sold” on the company. Further, it’s far simpler to sell to an existing customer calling your company than to clear the hurdles of the federal “Do Not Call” list, or to brave consumer hostility toward telemarketing calls.

Traditionally, contact centers have drawn on one of two fundamental methods to sell to inbound callers: ad hoc offers or blanket offers.

The ad-hoc offer allows each agent to decide which product or service to recommend – or even whether or not to present a caller with an up-sell/cross-sell offer. This approach is relatively simple to implement, but far more difficult to implement successfully, as it relies completely on agents’ intuition and skills. They must not only recognize a potential selling opportunity, but also be highly intuitive in deciding which products or services are likely to appeal to the customer. Some agents have that talent, some don’t—so the ad hoc approach depends largely on the individual agent, leading to spotty results.

With blanket offers, all customers receive the same pre-selected products or services offer. The product or service offered is generally determined by the manager of the contact center, or from within the enterprise’s marketing organization. This approach is essentially based on a one-size-fits all concept, with no regard to the customer’s actual needs. Thus, it is likely to miss the mark with many, and simply add to call-handling times. Worse, such offers may annoy customers who feel they are being pestered with irrelevant and time-consuming sales pitches.

While both of these methods will sell products, a new method promises greater revenue capture and increased customer satisfaction. This method, known as “predictive modeling,” relies on the real-time evaluation of customer data and demographic information to automatically select products that have a high likelihood of acceptance by that specific customer.

Creating the infrastructure for predictive modeling is not simple, however. It requires sophisticated technology, and a clear idea of how to apply that technology to mesh agents and information. But predictive modeling can pay off, because it embeds the matching of customers and offers into the contact center’s processes. And that, in turn, can significantly strengthen the company’s ability to consistently and accurately provide the right offer to the right customer at the right time.

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