Model Behavior: Harnessing the Power of Predictive for Deeper Insights
There is no sense arguing it anymore; we officially live in the future (at least in the world of predictive analytics marketing).
Every day, big data flows from numerous sources all across the web into the most innovative data management software available, providing B2B marketers with all the information they need to do what was once considered impossible, namely, anticipate customer buying decisions.
It is called predictive analytics marketing. Though you may already be using your data for some basic forecasting activities, few are fully harnessing the power of their predictive analytics to generate deeper insights.
That is what this article is geared to help you do. You know the basics, but now it it time to take a deeper dive into three specific areas: behavioral clustering, propensity modeling, and collaborative filtering. If those terms sound foreign to you now, read on. By the end of this article, you will ready to implement them into your own predictive analytics marketing strategy.
Generally, big data can be split into three major categories:
- Demographic or firmographic (who you are)
- Behavioral (what you do)
- Psychological (why you do it)
Customer segmentation often makes use of all three data sets, but at a basic level, segmenting your customers based on a combination of demographic and behavioral data often gives you more than enough to forecast future buying behaviors.
The key here is you are letting your data decide the customer segments, not a marketing team. Numbers do not lie and big data can shed light on customer trends that even the most savvy of marketers would not have been able to identify.
For example, predictive analytics can help segment based on:
- Content ingestion leading up to the sale
- Average purchase amount
- Average buying process length
- Engagement with sales or customer support
- Time between initial sale and next purchase
When that behavioral data gets combined with demographic data, marketers can create accurate profiles of future customers to target with new campaigns. That practice, called lookalike targeting, can be one of the most valuable components of your predictive analytics marketing strategy.
The next section explores how you will leverage those new profiles for huge conversion rates, but before you get there, consider a brief note on the importance of data unification, completion, and cleanliness.
Segmenting customers is just the first step in harnessing the power of predictive analytics marketing. That said, it is critical you get this one right. The root cause behind most failed predictive campaigns is bad data. Leveraging a great data management system can help you ensure your data is unified, clean, and complete.
For more on improving data quality, sign up for a free trial or get a demo of ReachForce SmartForms, a data enrichment tool guaranteed to set your predictive analytics marketing campaign on the right track.
Now, consider how you can use your customer profiles to predict the future.
Behavioral clustering is all about analyzing the past, or who are your current customers and what behaviors did they demonstrate in becoming customers?
Propensity modeling takes that historical data and uses it to make intelligent assumptions about future customer buying behaviors. In short, marketers are using the behavioral clusters to predict the future buying decisions of their target customers. Propensity modeling answers such questions as:
- When are you most likely to buy?
- What content can I share that will push you over the line from lead to customer?
- Where do I need to share that content with you to get the best exposure?
These are all questions B2B marketers implementing propensity models have to ask everyday. In the time before big data, marketers answered those questions often based on intuition alone; there was no data to guarantee where, when, and how customers made buying decisions.
That is no longer the case, of course. Now, it is just a matter of analyzing that historical data to determine an accurate forecast of where a lead is in the customer journey and what channel is most likely to get you visibility with the lead.
And that is just on the new business side of things. Propensity modeling is equally as valuable when it comes to predicting growth from existing customers as well as forecasting churn. Retaining existing business costs significantly less than new business development, so if you are able to track historical data of customers who have churned in the past and leverage lookalike targeting to look for red flags with existing customers, you can potentially win on both the offensive and defensive side of your business.
Though collaborative filtering sounds a bit intimidating, it is actually the bit of marketing most people experience on a daily basis. Whether it is supersizing your McDonald’s meal or being shown “viewers also liked…” on Amazon, you have likely been on the receiving end of collaborative filtering at some point in recent history.
In this area of predictive analytics marketing, focus is on the next sale for an existing customer. That could be a legacy client who has used the same solution for years, or a brand new customer who just recently committed to purchasing. In either scenario, collaborative filtering leverages the same historical data from your customer profiles to map ideal upsell or cross-sell recommendations.
Again, much like with churn mitigation, it is about using big data to maximize your existing customers’ lifetime value rather than solely thinking about the relationship between big data and lead generation.
These are just three of the big ideas surrounding how predictive analytics marketing can shape the future of your marketing strategy. Remember, your predictions are only as good as the quality of your data. That is where ReachForce SmartForms can help.