Predictive Analytics can be used to determine the best course of action at every decision point in your interactions
with current or potential customers.
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
span different stages of the customer lifecycle.
Driven by predictive analytics, our solutions 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.
In marketing, companies have adopted 'deep personalization' to ensure
customers get the right treatment during every interaction. Telecom companies have utilized
analytics to dramatically reduce customer turnover by predicting attrition of profitable customers,
and making the right offers to convince them to stay. Financial Services, Telecom companies
and Utilities have used predictive analytics to collect more money from their customers
while reducing direct employee costs.
Decision Management
Companies have used predictive analytics for improving the effectiveness of operational decision making in real time.
'Decision Management' is a term used to describe the application of predictive analytics for real time decision making.
'Predictive Models' are embedded in business processes and activated during 'live' customer transactions. These
models are 'trained' to detect patterns in data that point to the likelihood of the occurrence of a specific business event.
Business Rules are deployed to determine how best to react to these events as they occur.
For example, during the course of a customer interaction or a transaction, these models can be used to determine
whether to approve an increase in credit limit, or recommend the right product for cross-sell, or
block a potential fraudulent transaction.
Descriptive Modeling
Predictive Analytics can also be used to describe relationships between data elements, like customer purchases
and product placements. The objective is to gain increased understanding of the relationships
between data elements that can potentially deliver novel and useful business insights. For example,
they can lead to the discovery of a new segment of customers that has different needs, attitudes
or behavior compared with existing segments. Or, managers can use this to validate intuitive hypotheses with
implications for marketing or customer relationship management.
Such 'Descriptive Models' may not have direct implications for automating operational decisions, but
insights gained while building these models can be used to improve decision making or better predict customer behavior.
