|
|
Response Modeling
Response modeling is often used to improve the performance of direct marketing programs. Many times, our clients are seeking improvements over traditional methods. Response models identify and score customers for their propensity to respond based on customer behavior data. The result is better decisions on marketing efforts and budgets. Marketing dollars can be properly allocated to customers most likely to respond.
Attrition
Reducing customer attrition (or increasing retention) is often a priority after it’s too late. That is, after the customer has already closed an account or terminated the relationship altogether. In banking, attrition takes many forms such as run off, run down, or churn. Attrition modeling identifies which customers are most likely to attrite and it provides insight about the causes. Knowing who is most likely to leave before they take final action gives you that critical window of opportunity to implement retention iniititatives with speed and precision.
Segmentation
Intelligently segmenting your customers can be a challenging task but it can also yield tremendous returns in marketing, sales, and risk management. If your company treats all customers or prospects the same way, or the current segmentation scheme is intuition based, then analytical segmentation may provide the insight needed to maximize returns. ModelMAX® provides a simple way to perform complex segmentation based on customer data. You’ll be able to determine how many different segments really exist, who belongs to each segment, and the characteristics associated with each segment.
Next Most Likely Product
Maximizing 'share of wallet', raising average order size, and motivating employees to cross-sell means knowing what the best offer is for each customer based on what products they have already purchased. This type of modeling called 'Next Most Likely Product' or NMLP is a powerful way to increase offer acceptance rates by tailoring offers based on the highest likelihood of acceptance.
Lifetime and Profitability Modeling
Value modeling, similar to response modeling, determines the likelihood of an outcome, in this case it is customer value levels. Many organizations model lifetime value and profitability and use these as key inputs for other marketing decisions. For example, a customer with a low likelihood of responding may be eliminated from a direct mail program. However, if that same customer modeled high for lifetime value, they may be included after all because their long term potential value is high. The combination of response and value modeling provides a very selective and powerful way to maximize customer economics.
Risk Modeling
Balancing revenue opportunities, risk, and processing costs can be a challenge. Risk modeling encompasses a wide range of predicting likelihoods that offer greater insight when making risk based decisions. Examples of risk modeling include bankruptcy, payment default, and fraud detection. Each customer can be scored for their specific risk propensity in any number of areas. Institutions that factor in customer risk as part of their everyday decision process benefit greatly from the extra screening.
Collections Optimization
Improving the performance of debt recovery operations as well as pricing debt portfolios for profit requires unique intelligence. ModelMAX® predicts the likely collections value of each account and helps direct the optimum collections treatment strategy to maximize operational efficiency. Knowing how your portfolio is likely to perform ahead of time gives your debt collections organization a true competitive advantage.
|
|
|
|
|
|
|
|
 |
 |
