What, you might ask, is the difference between predictive and classic descriptive analytics? The Relevancy Requirement puts it this way, “Think of it as historical versus future.” Historically being descriptive analytics, future being predictive.

When we say descriptive we mean one is looking at data and analyzing past events for insight as to how to approach the future. When we say predictive we mean using data to determine “the probable future outcome of an event or a likelihood of a situation occurring.”

Predictive analytics applies a filter to users’ online interactions with the aim of delivering more value. By tracking factors like a user’s sites visited, their searches and location, we can “guess intent based on behavior” and display an ad that a user is more likely to click. A user gets an ad that more specifically targets their needs, and the advertiser increases customer satisfaction – win/win.

Predictive analytics have the power to significantly optimize customer relationship management systems.  They can help enable an organization to analyze all its customer data therefore exposing patterns that predict customer behavior.

“Because a solid predictive analytic solution can be integrated back into a CRM system for real-time use of in-session data, such a solution can enable customer-facing agents to act – in market real time – in ways that hold the greatest potential for increasing the value to the organization,” writes Marcel Holscheimer for AllBusiness.com.

Potential Pitfalls:

• Even with this oracle-esque application, data-driven marketers must always remember that predictive analytics must work in tandem with good strategy. Until an organization has defined their goals and objectives, developing predictive analytics will be of no use.

• An organization must invest in the right people to handle the predictive analytics business, either a team of experts working together or a specialized statistical analyst.

• The biggest hurdle however is creating the proper statistical algorithms and finding and accessing the needed data.

Therein lies the rub.  Until you have the proper algorithms in place, the data team ready to handle the information and a strategy fleshed out of how to want to use the predictive information, you’re not quite ready to employ these tactics. In the meantime building your understanding of predictive analytics and the potential they offer your brand is the best place to begin.

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