Realizing potential from an old customer
The growth of a firm in an IT industry or for the matter in any industry, mainly revolves around the acquisition of new customers. This could involve finding customers who previously were not aware of your service, or they could also be the customers who were previously doing business, but went away because of poor service or feeling of dissatisfaction. Going behind previous customers could be an advantage as you have more data available about them or a disadvantage as they have already built an impression about your service.
In any case, using traditional methods such as data mining can often help segment these prospective customers or previous ones and increase the response rates that an acquisition marketing campaign can achieve.
The Traditional approach of Customer Acquisition
The traditional approach to customer acquisition involved a marketing manager developing a combination of mass marketing methods such as magazine advertisements, billboards, etc. and direct marketing methods such as telemarketing or e-mail campaigns based on their knowledge of the particular customer base that was being targeted.
In the case of a marketing campaign trying to influence prospective traders to purchase a trading software, the mass marketing advertisements might be focused on providing the benefits and savings of using the software. The ads could also be placed in more financial domains whose readership demographics (age, profession, field, etc.) were similar to those of the financial products.
In the case of traditional direct marketing, customer acquisition is relatively similar to mass marketing. A marketing manager selects the demographics that they are interested in (which could very well be the same characteristics used for mass market advertising), and then work with a data vendor. The data vendor may have a database of prospective customers or can build a list using data mining methods. Again the prospect selection depends on demographic criteria (age, gender, interest in particular subjects, etc.).
To prepare for an “insurance application suite” direct mail campaign, the marketing manager might request a list of prospects from the data vendor. This list could contain people, aged 18-58, who are associated with the insurance sector, like insurance companies, agents, advisors, etc. The data vendor will then provide the marketer with a computer file containing the names and addresses for these customers so that the company can contact these customers with their marketing message.
Readers must note that because of the number of possible customer characteristics, the concept of “similar demographics” has traditionally been an art rather than a science. There usually are not hard-and-fast rules about whether two groups of customers share the same characteristics. In the end, much of the segmentation that took place in traditional direct marketing involved hunches on the part of the marketing professional.
At the end, the success of a marketing campaign depends on how many relevant contacts you are able to reach. Data Vendor’s role is important here, hence they should be chosen with utmost care.