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Predictive Analytics in Action - CRM Magazine

Predictive Analytics in Action - CRM Magazine | Higher Education | Scoop.it

Key excerpt...

 

To stay competitive, brands must take an analytical and predictive approach to better anticipate consumer behavior on an individual basis. Predictive analytics helps marketers to be dynamic and relevant by giving them the knowledge to present the right offer at the right time via the right channel, all based on what will best motivate the consumer to act. This allows companies to capture the attention of the audience quicker and reduce the total cost per acquisition. The predictive approach can be broken down into three parts that can work together to manage a fully engaged experience:

 

Demand creation stimulates a lead to take action by leveraging predictive algorithms that customize key marketing elements, such as Web content, resulting in dramatic conversion lifts and an optimized cost per sale.Demand activation appends CRM data and publicly available data to predict a lead's likelihood to purchase, down to the preferred price and product type. Leads are scored and prioritized to convert high-value prospects quicker.Demand conversion removes the guesswork of contacting leads by determining the best dialing strategy. Conversion-based routing and predictive analytics predict the right message, product, incentive, and channel to effectively convert a lead through the call center.
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iNeoMarketing's curator insight, June 10, 2013 12:37 PM

How long before MA vendors incorporate predictive into their platforms, if not integrate with third parties?


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Predictive Analytics: The Futurists' Formula - CRM Magazine

Predictive Analytics: The Futurists' Formula - CRM Magazine | Higher Education | Scoop.it

Key excerpt...

 

For companies that have yet to deploy predictive analytics software, it's important to remember that predictive is built on probability. "Almost all models have a margin of error, but significant impact can be driven even with models that are eighty to ninety percent accurate," Kulshreshtha explains. The key, he says, is to create a test-and-learn environment to continuously measure the impact of a model and then to introduce changes to improve the model until a very low margin of error is attained.

 

Forrester's Gualtieri concurs. Because a predictive model is designed to determine the probability of an outcome, it's up to a business to decide which of those probabilities—such as altering customer messaging or tweaking a sales model a certain way—it will invest in. Because the models rely heavily on historical data, a company must "continually refine and rerun" test models to account for external forces like the economy, markets, and competitive movement. "The good thing about [predictive] software tools is they tell you how good the model is—in that snapshot of time," Gualtieri points out.

 

Experts offer a word of caution before running predictive models—know your business before you try to automate it, Trident Marketing's Brown says. Another important factor is good, old-fashioned instinct, Herschel says.

 


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iNeoMarketing's curator insight, May 3, 2013 9:12 AM

This is a backgrounder article on predictive analytics, not a how to or a directional piece. If you need a backgrounder, please click through. The major point: predictive is a tool, not the be-all and end-all.


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