aCRM’s effectiveness extends beyond customer service and into campaign design where it offers a series of tools to support analysts. One example is in the construction of the target group. In this, analytical CRM helps select the target group most likely to choose the product being offered, and it is more the rule than the exception in these cases to achieve a proportion of positive responses of up to 40 percent.
Isolating a target group that will guarantee the success of a marketing campaign presents analysts with a stern test. The conventional approach involves imposing a set of restrictions on the characteristics of the people the advertising message is meant to reach. For example, 20 – 40 years old, married, credit-card holders, etc. These rules generally arise from assumptions about the customers we expect would welcome the product or service being offered and from the monitoring of sales data. There is no doubt that this is a very swift and inexpensive method.
It has, however, significant drawbacks. First, the analyses behind the decisions are often too straightforward: the trend may have deeper causes and failing to comprehend these can deliver results contrary to those we had expected. Second, only a few characteristics are selected, and these are usually the most obvious. Meanwhile, there are many more analytical categories available and we cannot rule out the possibility that these are the more fruitful ones. Third, the decisions customers actually make do not fit straightforwardly into categories such as ‘women aged 24 – 34 who are credit card holders’.
These over-simplifications lead to imprecise
descriptions of potential customers and so to very low levels of positive
responses to marketing initiatives which, at best, amount to between two and
Deploying the capacities of the aCRM (analytical Customer Relations Management) system means taking a stride towards avoiding the limitations we have already discussed. What is critical here is to provide the system with data from a previous marketing campaign that is similar to the one now proposed or supplying it with the sales figures up to the present time for the products that are to be promoted.
The system analyzes the features of the people who have taken part in the campaign in question or have already bought the product and uses these features to build profiles of the customers that should be targeted in a new marketing initiative. There is no limitation to the number of features to be analyzed as the system can handle scenarios where hundreds of features are taken into account.
A further asset of the aCRM solution is that it delivers the capacity to execute fully automated reviews of all the associations between the customer characteristics under analysis. This makes it likely that each trend can be directly linked to the reasons that brought it into being. Furthermore, the borders of the target groups are not as sharply defined as they are in the traditional customer profile building model. The system automatically reveals mathematical connections between the customer features under analysis and so can eliminate certain values from the target group model while it is being constructed.
Finally, in reference to the values determined for particular characteristics of people belonging to the target group, the system can assess how likely it is that a promotion will be well-received. Consequently, the analyst has an additional tool for on-the-fly regulation of the magnitude and effectiveness of the target group.
This method of preparing profiles of the customers involved in a particular promotion may appear complicated. But the extra work it involves is amply rewarded because it triggers a rise in the proportion of positive to negative replies of up to 40%. This means that making contact with potential customers costs less.
The opportunity to take advantage of the standard functionality of an aCRM system is also not without significance. For example, the system makes it possible to save target groups not only as lists of names, but also as the rules generating those names. The system remembers these rules so that producing further lists based on the rules involves only naming them and deciding how many people should appear on the list.
In this way the system makes sure that once people are selected for a target group they are not selected for that group again. Furthermore, aCRM defines rules for successive marketing initiatives. In this way it is possible to run a number of scenarios for one target group. The one that is put into effect depends on the channel the customer uses to respond to the offer.
Finally, the results of the marketing initiative can be used to improve the rules used to select the target group. Thanks to this, successive customer lists should contain more and more names of people who are potentially interested in the offer.