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In a world in which Amazon can predict which books you’ll read and online dating sites presume to predict racial dating preferences, it seems completely absurd that we still receive marketing emails and targeted ads that are completely irrelevant to our lives or interests. The technologies and applications we use track enormous stores of data about our activities, connections, and preferences, but marketers still struggle to connect the dots between messages, products, and customers.

Predictive tools have emerged as the marketing department’s solution to the overwhelming volume and complexity Big Data has introduced into our revenue cycles. Yet a 2013 KPMG survey found that while more than half of CFOs and CIOs around the world recognize that data changes everything, less than a quarter are actually putting any data-related insights into practice.

Predictive marketing to date: a focus on internal data

Organizations are amassing huge data sets, but they aren’t doing anything with them. Predictive marketing strategies changed that. They offer CMOs an opportunity to realize ROI from these data investments.

Predictive lead scoring, for instance, helps sales reps prioritize prospective buyers so they focus on the leads likely to spend the most money on products and services.

Predictive analytics can predict which marketing channels might deliver the highest responses and warrant the greatest investments.

Both of these solutions address internal data: the data marketers own within their CRMs.

The convoluted world of external data

shutterstock_58039090But what about the frightening and convoluted world of external data: the prospect and customer data that floats around the web, or sits in databases of questionable repute?

Marketers invest in external data to fuel their pipelines, and until that data is in their marketing automation or CRM system, their ability to predict which data will perform well is very limited. Some marketers run test campaigns to determine the accuracy of an external data list, or to identify fit. Others invest in external data to enrich existing data and determine which data signals matter most. Both of these options require extraneous time and budget.

Inefficiencies + defunct processes add up to higher costs

Because external data requires so much effort and money before marketers can actually use it to run campaigns, they’re limited to a defunct process.

A lot of the bad marketing we see stems from this process. Marketers don’t have an efficient way to close the feedback loop between existing campaign success and net new prospects. They can use predictive lead scoring to determine which of the leads they’ve ingested into their funnels are the best leads.

However, what they really need is the ability to identify net new leads that resemble their top performing customers. Or better yet, a software that can communicate with their marketing automation and CRM systems to predict which market segments will perform best.

Internal + external data together identify the best leads 

Predictive market segmentation applies data science to billions of data points — both internal and shutterstock_187197341 external — to predict clusters of leads with the highest  likelihood to buy. Predictive market segmentation can also  help you identify the size of potential markets so you can  distribute territories fairly as well as the revenue opportunity  available in each segment so you can decide how much to  invest in a specific segment.

As marketing technology grows more advanced, predictive  marketing segmentation can critically change the game for  marketers. Where a faulty process once clogged a top- heavy pipeline with complicated processes just to qualify  leads, predictive marketing can free up the sales funnel so leads don’t get trapped in the bottleneck of scoring; if you know which segments perform best and which leads you don’t already have in your funnel, the only leads that enter your database come prequalified. Once the guesswork is eliminated from the demand generation process, marketers can focus on generating revenue instead of wrangling with unreliable data resources.

In a recent interview for AdAge, technology magnate Larry Ellison proclaimed, “The CMO’s role is going to be more important next year than it is this year, and more important the year after that.”

Next year’s CMOs aren’t going to invest in a cornucopia of tools that incrementally improve each separate marketing function; they’re going to invest in platforms that will reinvent the way marketers connect with their customers — platforms like marketing automation and marketing intelligence; platforms that will offer predictive segmentation, lead scoring, and analytics. Until these platforms exist, it’s important for marketers to understand how they can improve their marketing processes to incorporate data-driven technology.

To learn more about the future of marketing technology, download the Radius Marketing Intelligence Guide.

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