Halt Customer Defection
• Nonprofits. As with catalogs, there commonly is a looser relationship between nonprofit organizations and donors. Again, any donor who has a history of donating a minimum of $25 to a nonprofit institution for the past five years, but does not donate in the sixth year, can be defined as a “defection.” While many organizations focus on creative and premiums to attract and keep donors, modeling will help determine which donors are likely to remain with the organizations and will allow marketers to focus more closely on the donors that are more likely to give.
• Local and long distance providers and ISPs. These companies have a “contract” with the customer that is defined as a signed agreement to pay for service one month at a time, with generally little or no penalty for switching providers. The competitive forces in telecommunications create a sense of urgency to proactively identify at-risk, valuable customers and provide appropriately customized offers and services to them.
Once you’ve applied the appropriate data and determined your differentiating factors, you then can rank your customers into 10 groups of equal sizes. The first group is made up of customers most likely to defect within the next six to 12 months, while the tenth group will be your most loyal customers. Within each group, rank the value of every customer as low, medium or high.
So, now you have a matrix that has 30 different groups that you can combine or rearrange to design cost-effective marketing programs that maximize profitability.
Model Life Expectancy
While the shelf life of your models can vary, it is best to do a model evaluation every six months. If the models still are robust and still are predictive—that is, if there has been very little slippage in effectiveness—leave the model alone, and re-evaluate in six months. A model is still robust if it’s accurately detecting defection rates consistent with the original results.