Customer Focus
Insightfully picturing your customer base,
to optimize decision making enabling
maximum revenue at minimum risk
CRM ANALYTICS
Segmentation is a process of dividing the customer base in distinct and homogeneous groups, supporting the development of differentiated marketing strategies according to their characteristics. Unlike customer segments created using business rules, which present inherent disadvantages, advanced analytics create data driven segments, using clustering algorithms which analyze all available data. The algorithm, identifies the natural groupings of customers and suggest groups founded on observed data patterns.
Acquisition campaigns aim at drawing new and potentially valuable customers from the competition. Advanced Analytics and more specifically classification models can support the development of successful acquisition marketing campaigns by analyzing customer characteristics and recognize the profile of the valuable customer.
Customer growth is achieved through, cross selling, up selling and deep selling campaigns. Advanced analytics and more specifically propensity models can support the development of targeted marketing campaigns by analyzing customer characteristics and recognizing the optimum profile of customers to target. Propensity models can identify the right customers to contact and lead to campaign lists which outperform random selections as well as predictions based on business rules and personal intuition.
Retention campaigns aim at preventing valuable customers form terminating their relationship with the organization. Advanced Analytics and more specifically propensity models can support the development of targeted retention campaigns, by analyzing customer characteristics and recognizing the profile of the customer likely to churn. This insight leads to the optimization of marketing retention efforts by addressing with priority presently or potentially valuable customers at risk.
The Advanced Analytics models should be put together and used in the everyday business operations of an organization to achieve more effective customer management. The knowledge extracted by advanced analytics can contribute to the design of a next best activity (NBA) strategy. The customer insight gained, can enable the setting of personalized marketing objectives. The organization can decide on a more informed base, the next best marketing activity for each customer and select an individualized approach, for preventing attrition, or for promoting the right add-on product to customers with growth potential, or for imposing usage limitations and restrictions on customers with bad payment records and bad credit risk scores.
Customer Lifetime Value (CLV) is the current value of the future cash flows or the specific value of business attributed to a particular client during their lifetime relationship with a company. This is remarkably important metric, as it allows business leaders to make intelligent decisions regarding sales, product development, marketing and customer support. Historical CLV fails to shed a bright light on the actual value of new subscribers, while Predictive CLV represents a whole other level in the world of Customer Lifetime Value Analysis, allowing marketing professionals to optimize customer service as well as acquisition and retentions campaigns.
RISK MANAGEMENT ANALYTICS
Credit Risk is the risk of a borrower not repaying a loan, a credit card or any other type of loans. Credit Risk Modelling refers to the process of using data models to find out on hand the probability of the borrower defaulting on the loan and on the other hand the impact on the lender’s financials if this default occurs. The introduction of Machine Learning, Big Data and Analytics enable Credit Risk Modelling to become more scientific, in order to predict whether someone will default on any given loan in a much more unbiased and accurate way.
When a borrower defaults on a loan some of the debt will be recovered during the subsequent collections process. It is very important to have an effective collections strategy – one that can help realize measurable improvements while focusing resources where they are needed most. The purpose is to maximize debt collections efforts and make more profitable decisions. By scoring, segmenting and prioritizing accounts, someone can develop better strategies to collect more debt. Collection Advantage provides features to help increasing efficiency at all levels of recovery efforts.
Fraud Detection means the identification of actual or expected fraud to take place within an organization. It is applicable to many industries including banking and financial sectors, insurance, government agencies, law enforcement, and more. The ultimate goal of an effective fraud management is to identify suspicious transactions early enough so that they can be addressed before any financial losses materialize. Fraud analytics is the combination of analytic technology and fraud analytics techniques with human interaction, which will help to detect the possible improper transactions either before or after the transaction is done.
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