Request Info

Direct Marketing Analytics

Understanding Customer Behavior

Marketing professionals are integrally involved in setting strategy as well as providing tools and techniques for tactical implementation to keep a company moving and growing. In addition to understanding strategic issues such as market dynamics, economic impact, and consumer spending patterns, marketers also need to understand specific customer behavior patterns. Understanding these behaviors includes obtaining the answers to six fundamental questions.

  • Who will respond?
  • What will they respond to?
  • When will they respond?
  • Where do they reside or do business?
  • Which response vehicle will work?
  • Why will they respond?

Data Mining and Scoring

While you might have an internal list or access to a rented or purchased list, identifying the best prospects and customers in today’s market requires good data mining skills. Data mining involves sorting through large amounts of data and picking out relevant information. The first task, identifying market segments, requires significant data about prospective customers and their buying behaviors. Data mining application software solutions automate the process of searching through mountains of data to find patterns that are good predictors of purchasing behaviors.

Data mining information is designed to feed actual marketing campaigns. Marketers need to recognize that the success of a campaign starts with a good data modeling. The central element is identifying the variables that can be measured for an individual or other entity to predict future behaviors. For example, a car manufacturer may consider factors like age, gender, past purchase history, or number of children when marketing automobiles to prospective purchasers. The complexity of the model creation typically depends on many factors, including database size, the number of variables known about each customer, the data mining algorithms used, and the modeler’s experience.

Next, the data needs to be scored. There are software tools available, given the model, to dynamically score prospects or customers. The score assigned to each individual in a database indicates that person’s likelihood of exhibiting a particular customer behavior. For example, if a model predicts customer attrition, a high score indicates that a customer is likely to leave, while a low score indicates the opposite. After scoring a set of customers, these numerical values should be used to select the most appropriate prospects for a targeted marketing campaign.

Then It’s Time for Campaign Execution

Consider customers of a bank who only use the institution for a checking account. Suppose an analysis reveals that after depositing large annual income bonuses, some customers wait for their funds to clear before moving the money quickly into their stock-brokerage or mutual fund accounts outside the bank. This represents a loss of business for the bank. With this data, the bank can transform lost business into new opportunities. Based on the size of the deposit, the bank can and should initiate a campaign that provides information about “personal banking services” to build wealth. Banks can trigger a marketing campaign based on the actual deposit amount.

Furthermore, banks that know the age of a depositor’s children can trigger college savings initiatives. As depositors age, they may also become more concerned about saving for retirement. By gathering and effectively mining data, marketers have the ability to substantially improve business results. With today’s technology, they can also track responses in real time and determine if any behaviors have changed. The bottom line is the profitability and ROI of all ongoing campaigns.