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With increasing market competition, enterprises have come to realize that it is easier to maximize profit by cross‐selling services to existing customers than to attract new customers. It can often be observed that consumers sequentially purchase multiple products and services from the same provider. Accordingly, this commonly observed situation offers huge opportunities for companies carrying multiple products and services to “cross‐sell” other products and services to their existing customer group. The purpose of this paper is to find out a convenient way to identify the customers with cross‐selling potential.

In this paper, the authors investigate the customer demographic data, including age, income, gender and educational level, and study the relation between the variables and the customers' cross‐selling potential based on counter propagation network (CPN).

The authors set up a cross‐selling model successfully. After inputting age, gender, education level, and income into the input layer of the model, the model will show us which products the potential customers should buy in the output layer. This process can provide useful information for the enterprise to persuade the customers into buying the unpurchased products and provide the products to the right customer.

In this paper, the authors set up the cross‐selling model based on the CPN. The model can predict the customer cross‐selling potential successfully according to the customer demography data – age, income, gender, and educational level.

To present a new model for customer targeting when the information in customer databases is limited.

An original conceptual framework is proposed, the customer targeting funnel model, supplemented by a composite mathematical model for optimizing decisions related to customer targeting for marketing initiatives and a flowchart to guide its implementation by marketing planners, all in the business‐to‐context and under conditions of information shortage. This “toolkit” facilitates the targeting of customers most likely to enter into closer relationships with the company, even when a significant proportion of their key characteristics have to be estimated.

A case example from China, describing a computer‐based support system for the implementation of the model, shows that the user company strengthened its customer service strategy, won higher satisfaction and loyalty levels, and achieved sales growth 50 percent above the industry average.

The model is at an early stage of its development, and such additional features of customer behaviour as churn should be incorporated in future. The effectiveness of the model and system needs to be tested in a wider range of cases and situations..

Techniques of customer targeting based in consumer marketing need to be adapted appropriately for transfer to industrial marketing, and this paper offers one case in point. Shortage of customer intelligence is common in various sectors worldwide, and especially in developing countries such as China.

An innovative approach to customer targeting in the special conditions of industrial or business‐to‐business marketing.