The Key to Closing Long Purchase Cycles? Correlation.

A common problem I encounter with clients is a lack of understanding of how to sell products and services over a longer period of time (say, 100+ days). For smaller purchases and ones that occur online, there is more existing software and understanding out of the gate, because activity can happen all in one session and it all happens digitally, where activity can be watched like a hawk. But what happens when leads have multiple touch points? Or go weeks without thinking about the purchase? Or where prices are custom and negotiated by sales? Or where the purchase even occurs offline?

Maybe we’re stubborn over here in the martech department. Or maybe as a ginger, my higher pain tolerance prevents me from shying away from threatening situations. But for whatever reason, Element Three jumps into those kinds of problems head-first; we enjoy a good challenge over here. And across multiple clients and industries, we’ve engineered ways to track and engage with leads for purchase cycles as long as 720 days, scoring and automating engagement throughout the whole process.

Each client and system deployed and tracked is completely customized to the business model, the products or services being sold, and the customer, so unfortunately there isn’t a magic bullet when it comes to marketing systems and tactics. But through multiple iterations we have learned quite a bit, and found some correlating similarities across the systems and tactics that have seen success.

Begin with the hypothesis: who, what, and how long

First things first: you gotta start with what you “know.” (Those quotes are definitely intentional.) And at its most basic, we like to understand:

• Who is the purchaser?
• What is the product/service?
• How long do they typically take to buy?

These data points are the most basic foundation for our marketing initiatives. Understanding who we think the purchaser is allows us to create specific content for them over the course of our estimated purchase cycle, across email, web pages, downloadables, ads, videos, anything. Their job role, current pains, and where they hang out online all factor into a dedicated full-scale marketing strategy that then delivers messaging on an automated and user-behavior-driven basis.

What do I mean by user-driven? Well, with a super long purchase cycle, you have to move at the speed that the lead moves, which is damn near impossible to do manually. The utilization of marketing automation and marketing technology systems allows us to deliver marketing information to the lead based on their engagement with other online assets. Which leads me to my next point…

Correlate your digital activity to data

With our hypothesis in hand and our martech systems in place, a strategy can be generated to understand a lead’s activity. For a full-scale strategy to work, a complete account of all online assets must be categorized and watched via tracking tools, matching activity to an individual lead record.

The trick here is to think through the online assets and create an interconnected web of trails, leading a contact to get more and more information about the prospective sale throughout the stages of the purchase funnel. Engagement with each activity and digital asset would then automate the setting of information about that lead, which we call inferred data. Viewing page #1 means the lead is ready for email #1, and clicking on the call-to-action in email #1 sends them to page #2.

For example, let’s say we’re trying to sell yachts. A category of yachts and their information might be housed within a dedicated subfolder of the website. If a lead begins to explore this subfolder, we have our marketing automation or CRM set a field within their record marked as “product exploration.” The presence of this field then includes them in an email workflow over the course of three months that feeds them information about the quality of our yachts.

Increase the close rate, decrease the rate to close

This inferred data allows us to predict (to a certain degree of relativity) that that individual is possibly interested in exploring products. Various other tracking can keep them included in messaging, exclude them entirely, or narrow their focus to a specific product. The important thing to note here is that we aren’t waiting to collect known data about the lead. No one has spoken with them about whether they want to buy or not, they haven’t outright said they want to buy a yacht at this point—but based on what we can track them doing online, we can assume that they have a high degree of interest in one area.

Utilizing this inferred data from correlating activity allows our marketing to then be agile. Known data is very accurate, but it’s hard to get and asking for it can scare users off. Instead, we can estimate that they deserve certain types of messaging, and treat automation for the individual as a test case. If they engage, they keep getting the messaging. If not, it stops.

Working off this methodology allows marketing to move to increase performance. And with such a long purchase cycle, two key performance indicators are improving the lead-to-close rate and shortening the time for a lead to purchase. With automation, we can set the length of messaging to try and speed up the process, and the influx of marketing material can better inform leads about why they should buy—and weed out the noise.

Numbers don’t lie

A system is only as good as the insights that can be ascertained from it. What good is a strategy if you can’t track the sales, or hang your hat on a key performance metric? As such, getting the right systems in place to execute the strategy and then analyze the performance is just as important as running the damn thing.