Value segments can provide useful information for the development of effective Acquisition models. Acquisition campaigns aim at the increase of the market share through expansion of the customer base with customers new to the market or drawn from competitors. In mature markets there is a fierce competition for acquiring new customers. Each organization incorporates aggressive strategies, massive advertisements and discounts to attract prospects.
Value segments can provide useful information for the development of effective Acquisition models. Acquisition campaigns aim at the increase of the market share through expansion of the customer base with customers new to the market or drawn from competitors. In mature markets there is a fierce competition for acquiring new customers. Each organization incorporates aggressive strategies, massive advertisements and discounts to attract prospects.
Analytics can be used to guide the customer acquisition efforts. However a typical difficulty with acquisition models is the availability of input data. The amount of information available for people who do not yet have a relationship with the organization is generally limited compared to information about existing customers. Without data you can not build predictive models. Thus data on prospects must be collected. Most often buying data on prospects at an individual or postal code level can resolve this issue.
A usual approach in such cases is to run a test campaign on a random sample of prospects, record their responses and analyze them with predictive models (classification models like decision trees for example) in order to identify the profiles associated with increased probability of offer acceptance.
The derived models can then be used to score all prospects in terms of acquisition probability. The tricky part in this method is that it requires the roll out of a test campaign to record prospect responses in order to be able to train the respective models. However, an organization should not try to get any customer but it should focus on new customers with value prospects . Therefore, an alternative approach, which of course can be combined with the one described above, is to search for potentially valuable customers.
According to this approach the model is trained (again a classification model) on existing customers, it identifies the profile of the high value customers and then extrapolates it into the list of prospects to discern the ones with similar characteristics. The key to this process is to build a model on existing customers using only fields that are also available for prospects. For example, if only demographics are available for prospects, the respective model should be trained only with such data.
Acquisition marketing activities could target new customers with the ‘valuable’ profile and new products related to these profiles could be developed, aiming to acquire new customers with profit possibilities.
