Marketing predictive models are proven to improve targeting for direct marketing initiatives. These models uncover the best prospects, those most likely to act. With this type of analytics, we will typically score an entire prospect population and then select the best contacts that meet the client’s marketing criteria. The modeling is simply identifying prospects that look most like our client's new customers. Often these models are produced without the benefit of data that identifies a prospect's interest in the product.
Following development of the predictive models, we compare the model results to those that actually acquired the product. Consistently we find that over the long term, the predictive models do a good job defining those most likely to act. However, we have seen from the results of individual campaigns that prospect scoring alone is not a strong predictor of campaign response. We believe this is because we have not identified who is currently interested in the product. And, we have no way of knowing what the prospect company’s most pressing needs are and how our client's product addresses the prospect's current needs.
We believe it’s critical to develop approaches that uncover those who are interested in a client’s products. We are looking to identify the lead development “sweet spot.” The lead development “sweet spot” defines those that are both highly scored to act and those that have demonstrated an interest in the product.
To identify those with a demonstrated interest, we look for individuals who have searched a client’s website and we also examine email campaign responses and look for individuals who are engaged - those who have opened the email and demonstrated some interest.
As you know, a web visitor’s IP address can identify which domain they are coming from, and the domain can often identify the prospect company. We look at the number of people from the same IP addresses that are visiting our client's website and how involved they are in their products. When we notice above-average interest, we include them in an appropriate direct marketing campaign.
We also look to the client’s prospect email campaigns to uncover who is interested. First off, we often include multiple people from the same company in a campaign. We believe the more arrows into a company, the more likely you will achieve a lead and have a sales entry point. We then look to scale interest based on the responder’s activity with the email campaign. Shown at the left is a typical interest scale by activity.
Depending on the campaign requirements, we normally recommend that those in the “sweet spot” should be touched with more effort. That typically involves some level of tele-contact activity.
Learn more about SIGMA's predictive analytics practice.