Today's customers are literally spoilt for choice. Their attention spans have decreased dramatically, they perceive fewer real product differences and sustaining loyalty is becoming increasingly challenging. Competitors try to attract best customers through offers that meet their unstated needs. Moreover, with the advent of the Internet, customers have gained substantial bargaining power, since they can readily compare competitor prices and product attributes.

Customer Lifecycle provides a concise framework for understanding customer behavior and critical events during the course of a customer’s relationship with a business. LatentView's customer analytics solutions, driven by predictive analytics can help Financial Services, Insurance, Retail, Telecom companies and Utilities to acquire the right customers and maximize the value of their customer relationships by predicting and proactively responding to important lifecycle events.

Acquire Ideal Customers Efficiently 
Companies only profit when the earnings from retained customers exceed the costs to acquire and service them over time. Ideally, companies should target only high value, low attrition-risk prospects, since the cost to acquire new customers is much greater than the cost to retain them. Predictive analytics can help these companies to precisely target and attract prospects that are most likely to respond to their campaigns.

Maximize Customer Value
Research has shown that often the cost of acquiring a new customer is much higher than the cost of retaining an existing customer. Customer Segmentation helps companies maximize the value of their relationships by deriving deep insights into their customer needs, attitudes, and drivers of loyalty and value.

Companies can use behavioral scoring to derive insights from ongoing customer behavior and find opportunities for cross-sell and up-sell

Retain Profitable Customers  
Not all customers are equal; some are more profitable and valuable than others. Even though, on average, it is less expensive to keep an existing customer than acquire a new one, companies need to decide those they want to keep. They can then use predictive analytics to determine which of these customers are likely to leave and decide on appropriate marketing interventions to convince them to stay.

Manage Risks  
Financial Services, Telecom companies and Utilities need to understand risks inherent in each customer account to balance these against the available opportunities. Predictive analytics can help these companies achieve the optimal balance by enabling them to forecast credit risk associated with each account.

Companies can also use risk to better manage ongoing relationships with its customers. They can build behavioral scorecards, based on customer behavior and credit history, to grant credit limit increases or collect more receivables with less resources.