Predictive Analytics Gaining the Competitive Advantage

Demand for predictive analytics tools has risen dramatically in the past few years. Although the tools have been around for decades, more and more companies are grasping the reality that analytics are a competitive necessity.

Predictive modeling is use:

To support numerous business initiatives. However, the recent rise in demand can be attributed to the need to stay competitive in today’s economy by maximizing. The lifetime of a company’s most valuable customers. By applying algorithms and model scores to a database. Customer flight risks and cross-sell opportunities can easily be identified.

Customer retention is crucial, especially when considering that. It is much more expensive to acquire new customers than it is to retain existing ones. When model scores are applied to the client database, a more proactive retention strategy can be achieved. If a business knows beforehand that a customer is likely to turn to a new provider, intervention can be taken to retain that customer.

According to a recent article in CRM magazine:

The return on investment (ROI) can be significant for companies who use predictive analytics to leverage customer retention strategies. A large British telecommunications carrier, Orange U.K., retained an additional 4% of their most valuable customers each month by applying predictive modeling scores to determine customer flight risk. This equaled to a nearly $40 million per year gross operating profit. US-based company, 1-800-Flowers, boosted customer retention by 10% during the recession. This equated into an additional $40 million in revenue.

Cross-sell opportunities can also easily be identified through predictive modeling.

Companies have massive amounts of data, however this data must be mined and analyzed to discover cross-sell potential. When predictive analytics such as customer behavior metrics are applied to this data, an organization can discover a wealth of untapped customer potential. This directly leads to higher profitability per customer and strengthening of the customer relationship.

Common Applications of Predictive Analytics include:

Market analysis: Where are the risks and opportunities?
Increasing customer loyalty and value: How can loyalty and share of wallet be increased? How can cross-sell targeting be improved?


Acquiring the best customers in the market: Which competitors’ customers are more likely to defect? What products are they most likely to buy?
Improving marketing ROI: How can marketing resources be better aligned to current opportunities and risks? Which marketing programs can be refocused?

The applications and future possibilities of predictive analytics are endless. With financial scoring models, text-analysis, social media monitoring, and even speech analytics to determine a client’s mood, companies truly can own the power of prediction.