If Only I Knew - how Data Mining techniques can be used

If Only I Knew. By Tim Graettinger If only I knew . - who will renew their service plan - who will defect to the competition - who will buy our new product - who will become a high-value customer Sound familiar? To make better decisions as business people, we all wish we could see into the future and deep into the hearts and minds of our customers. In response to the wishes above, this article asks the question, "Suppose I knew ., what would I do?" We will describe the process to translate the wish for customer knowledge into actionable strategies and tactics. We will look specifically at one common business problem, customer retention, but the same principles will apply to a broad range of business issues. The primary instruments involved will be a customer database/warehouse and a predictive model built via data mining. By the time you are finished reading, you will understand the wish-to-action translation process from end to end. And you will be able to apply the process to your own business problem(s) - before any money is spent or any project is begun. To keep the discussion concrete, we will consider the process in the context of a case 1study. Suppose that Donna is the VP of Marketing for a large trade organization . She is responsible for several trade shows and a large annual meeting. Over time, there has been a decrease in attendance at the annual meeting. She needs to increase retention. Donna asks, "Suppose I knew who will come to this year's meeting, what would I do?" We will continue Donna's exercise of imagination by: - mapping the territory, - segmenting the territory into groups or clusters, and - designing strategies and tactics for each group Mapping the Territory First, consider Donna's assumption, "Suppose I knew who will come to this year's meeting." Determining who will come is not as simple as querying the customer database. Why? The customer database can only reveal who came last year, but not who will come this year. More generally, a customer database or warehouse can report what happened, 2but not what will happen next. To look ahead rather than look back, data mining is an excellent option. Data mining is the discovery and modeling of hidden patterns in large volumes of data. It differs from database querying and reporting in that the data mining process produces a model. A data mining model can take the form of a set of "if-then" rules or a mathematical formula - either of which can be generated heuristically by Donna, by 3semi-automated statistical means, or by a combination of both . The model uses data about the past and present to predict future outcomes. In Donna's situation, relevant historical data about a prior attendee might include: how many times they attended previously, their age, their industry, and the size of their company. The model outcome is a prediction about whether or not they will come to this year's meeting. Such models have technical labels like predictive models, likelihood models, or scoring models. The names are actually quite descriptive for the current discussion. For Donna, a typical likelihood model will produce a score, say from 0 to 100, indicating how likely an individual is to attend this year's meeting. The larger the score is, the more likely the outcome is. One individual might score high because they have attended for many years and they are in the "Baby Boomer" age group. Another individual might have attended just once, resulting in a lower score. Conceptually, Donna's prior attendees are shown ranked from low to high in Figure 1. Each dot or mark represents a prior attendee, and we can see that some are highly likely to attend this year, while others are much less so. At this point, it is tempting to think that this is a sufficient (conceptual) map of the customer landscape. Others have jumped into data mining efforts with even less reflection than this. Why not jump ahead? Simply put, not all attendees or customers are created equal. To realize the full benefit of data mining for increasing revenue, reducing cost, and/or improving ROI, we need some notion of customer value. Then the territory map will be truly actionable. Different attendees at the annual meeting certainly have different... [download for more]