At this stage of the cycle the emphasis is still on investment, but the focus shifts to the identification of opportunities for increased business, and the development of systems that support the relationship. If the new customer represents a significant proportion of the supplier’s business, new systems and structures must emerge to meet their requirements.

For suppliers operating in consumer markets, the changes to the organization will be less significant – the new customer’s requirements will programmes – strategy, structure and systems usually be dealt with through existing systems. Nevertheless, the costs will still be relatively high in relation to the returns. For a customer to open a new bank account, for example, requires the processing of the application, credit checks, recording contact details, and the dispatch of new cards and cheque books. When viewed against the revenue generated by an average customer, the costs of these activities is significant. The supplier should ensure that the customer also makes a commitment to the relationship at this stage. There are a number of ways to encourage this, which are discussed below.

Increasing the scale or scope of the business relationship

Donaldson and O’Toole’s (2000) research indicates that a relationship grows stronger as economic content increases. Colgate and Stewart (1998) also found that the more frequent the contact between the customer and the supplier, the more positive the view of the former towards the latter. The first and most obvious way of increasing the relationship is, therefore, to increase the volume and/or variety of products sold to the customer. In business-to-business markets, opportunities to do this will be revealed through dialogue with the individual customer. In mass consumer markets, due to the number of customers, the marketer may have to identify micro segments with which to target new product offers (Grossman, 1998). Johnson (1999) describes how banks use a predictive modelling technique called the Next Logical Product. The process segments household types according to buyer behaviour, product ownership and, where possible, response to product offers. The data is then used to predict the probability that a given household would respond positively to an offer of a given product. Those with the highest scores are then targeted. Although success rates are not high, they are nevertheless better than random mail-shots, and less likely to annoy existing customers with offers of unwanted products