Predictive Analytics: What is it?

Earlier today, I posted in my LinkedIn feed, "Marketing is not about being everywhere; it's about being where your customers are." In response, ?? Oz Platero commented, "Danny's next marketing post: Marketing pre attribution / intent prediction is about being where your customers *will be*."

While I didn't have an article in the queue for #PredictiveAnalytics strategy per se, I did have one mostly complete answering the question of what it is.

So here it is, by request.

Predictive analytics is gaining popularity among B2B marketers, particularly those utilizing account-based marketing (ABM). This is because marketers selling to accounts often deal with a group of buyers displaying various behavioral signals. Predictive analytics, a programmatic method for analyzing big data (yes, I know that's a 2010 term) to make predictions about the future, can be a valuable tool to leverage in such situations.

In marketing, predictive analytics aims to understand the customer journey. By identifying patterns in customer behavior, marketers can determine the “next best action” for nurturing leads through the funnel. For example, analyzing 100 completed deals may reveal that a potential customer typically reads online reviews, views a blog post, and downloads a whitepaper before entering the buying cycle. By identifying this pattern, marketers can serve up relevant content to continue guiding the customer towards a purchase.

In an ABM framework, predictive analytics can be used to predict the behavior of individuals in a target account and determine the stage of the sales cycle. For example, a senior manager attending a conference on risk management compliance and requesting research on related software could indicate progress in the customer journey and prompt the marketer to take the “next best action”, such as offering a demo or attending an event.

However, it's important to note that predictive analytics alone isn't enough. Campaign attribution aka marketing attribution, and funnel metrics are crucial in determining which content drives pipeline and revenue.

Tools such as Dreamdata and campaign attribution models can help analyze the customer journey and engage in remarketing. Funnel metrics can be used to measure velocity and compare results through A/B testing.

While predictive analytics can be a useful tool in understanding customer behavior, it's important to remember that successful content and programs must drive pipeline and revenue. By exploring the customer journeys of closed deals and finding patterns, marketers can improve their approach for maximum results.

A shoutout to Carey Picklesimer of Condurrio Marketing + Design and Laura Erdem of Dreamdata , for the inspiration to dive into this further.

#digitalmarketing #marketing #advertisingandmarketing #strategy