How predictive analytics empowers marketers

In a previous article we saw how ‘predictive marketing’ works: It unearths critical customer insights and helps marketers develop tactics to address the needs of individuals, rather than taking broad-based approaches that today’s audiences usually ignore. 



It does this by using analytics – advanced models and algorithms that analyze data to generate an almost intimate understanding of customer characteristics and demands. It infuses that understanding into the marketing process so that each engagement is optimized for the individual customer or prospect. The data used provides marketers with a roadmap that allows them to invest marketing budgets in ways that appeal to specific customer segments and channels. That, in turn, drives marketing’s alignment with the brand objectives to acquire, grow and retain customers.

Of course, that roadmap’s value lies in the actions it generates. By themselves, analytics are like a screw without a screwdriver, capable of connecting components but unable to accomplish anything by itself.
Today’s customers are demanding, impatient, specific, and not particularly loyal. We, as marketers, need to gain insights quickly, understand the customer’s level of attachment to our brand, and then take appropriate action.

Simplifying the Process
Yet in many companies, a number of hurdles stand between marketers and actionable insights. Sometimes it’s a lack of reliable, comprehensive and up to date data in the first place, especially if analyzing data silo by silo with no or irregular integration, or if looking in the rear view mirror only with no generation of foresight. Or it may be a process that relies on the availability of data scientists and puts your needs in competition with those of other departments. In addition, let’s just say it: Marketers today have too much to do and too little time to do it in. If we can’t get our hands on the insights we need quickly and easily, it’s nearly impossible for us to be proactive and keep our customers engaged.

In other words, marketers need hands-on access to intelligence about their company’s customer base, down to the level of individual customers. Looking not only at historical facts, but anticipating the future through an amalgamation of analyses. They need a system that analyzes their internal data, combines it with third-party information when necessary, helps them uncover new trends and empowers them to develop campaigns that target specific sets of customers based on their unique situation. And they need the ability to do this in as few steps as possible, without having to compete for resources.

A tool addressing those challenges would give marketers the insights they need to get in front of markets that are almost always in flux, a customer base that is demanding and impatient, and even identify which individuals are most likely to be engaged and satisfied, and which are likely to churn. The tool would combine speed, ease of use and continually refreshed data so marketers could get at customer intelligence quickly and directly, all so they can accomplish their goals more effectively, as soon as the need becomes apparent. And with that information, the marketer can take steps, if necessary, to reduce their customer churn rate.

To succeed, such a tool would be:
Designed from the marketer’s perspective: easy to use and understand, able to provide a snapshot of customers and their behaviors at a glance, while also allowing for further exploration with no technical skills required.
Time-saving and timely: It would eliminate many, if not most, of the steps marketers now face to access and present the most important nuggets of what their data can tell them even as the customer base changes on a regular basis. It would help them identify who to take action on, then feed that into their campaign system to execute right away.

Economical: If you’re with a smaller business, or a company that’s taking its first steps toward using analytics, you’ll need reasonable and predictable costs that reflect your business requirements. As your organizational readiness evolves, you want a solution that will grow with your needs, driving your customer engagements with ever increasing sophistication. Or, even if you do have the data scientist resources in-house, they will be freed up to do exploratory analyses rather than supporting your standard yet priority needs.

Better Results in Less Time
Of course, it sounds great on paper...

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