Feed Predictive Analytics a United Data Diet Posted on : May 29 - 2016

Predictive analytics is currently one small piece of the MarTech ecosystem.

Brands juggle more than a dozen MarTech solutions and app SDKs to try and capture some return on investment (ROI). This limits the use of predictive analytics as a result, due to lack of data and follow-through.

A unified, mobile-first approach allows marketers to move forward with predictive analytics and overall marketing innovation. Here’s why:

Consolidation Provides Deeper, Richer Data for Predictive Analytics

Most marketers today only use predictive analytics for two specific measures: churn and lifetime value. That’s the bulk of what’s available right now, and it’s often built on basic data of limited value, like user profiles. Instead of using just surface-level conversion and customer record data, marketers need to feed predictive algorithms as much information as possible from the customer journey.

The problem is, even though marketers have data on all sorts of customer interactions they could harness, most of that data is unorganized or siloed into different marketing solutions, which stunts predictive analytics.

All marketing campaign data should be in one place, to provide a more holistic view of the customer. This would enable algorithms for smarter, more nuanced and granular predictions across all forms of engagement, tailored to both marketer and user needs.

Mobile Provides the Richest Customer Data Out There

If the goal is to get the deepest customer data possible to feed into predictive insights, that has to start with mobile. Nowhere is there more powerful data to build this holistic view of the customer than in the mobile user journey. It’s the primary device for most consumers and has a hand in every step of the customer journey, with a multitude of channels and touchpoints in one device.

For instance, predictive algorithms could integrate what users do with email, SMS, mobile web, in-app communications, push notifications and more. Marketers can even tell if a user consumes content in the app but doesn’t actively participate. And it’s this kind of granular data that can be very telling.

All of this mobile data together would allow marketers to build insightful measures for predictions, like engagement time, optimal channels for engagement, content users will find interesting, brick and mortar visits, and more.

Uniting Data, Action and Analysis Under One Roof Makes a Difference

The journey from predictive insights to real action and proof of ROI is often rocky, due to the narrowness and/or poor integration of tools. Therefore, not only do marketers need deep, nuanced data for better predictive analytics, they also need messaging and analytics working together in synergy, feeding into one another for effective engagement.

Once marketers gain access to sophisticated predictive insights, they need to be able to engage with customers accordingly. The more they can predict about unique users, the more they need fine-tuned and adaptable approaches.

Predictive insights tell marketers which users will likely respond well to a personalized push message, a tweak to the app interface, an email campaign, to name a few. Following those actions, marketers need multivariate testing and other analytics to ensure they’re acting on predictive analytics correctly for real results. And an underlying foundation of automation unifying the whole makes this possible.

Predictive analytics should be a mind-blowing capability, a game-changer. But for now, it’s just another tool that marketers run in conjunction with a score of others that may or may not inform and improve one another. Uniting data with analytics and actions in one powerful platform will allow marketers to unleash the power of predictive analytics. Source