You’d be surprised how many marketing and sales departments operate entirely on assumptions. It’s very common and not necessarily a bad thing; in most cases, broad intuitions can be right on about who a customer is and what they care about. But sometimes those assumptions can be off, and knowing about your customers will always help to positively affect sales.
Data is quantifiable proof of activity, and when thinking through your customer journey it’s as good as gold. With the right data and ways to dice it up, you can easily understand how customers buy from you, where leads fall off in the funnel, and how to create the right messaging for the right people at the right time. So how do you do that?
Dividing up your customer journey from data is a complicated process that requires multiple systems, a treasure trove of data, and the time available to analyze it for insights. We at Element Three lean on data to help clients make the right marketing moves, so today I’m going to walk through an example of what to look for as it pertains to collecting and understanding a buyer’s journey through your funnel.
Surprise caveat:
Totally not a surprise! I’ve probably said this in my last 10 blog posts, but here goes. Your business is a beautiful snowflake, just like you! Your customers, product, and marketing lifecycle are just as unique, so what I’m about to tell you is the broad strokes. To get this done, you’ll need a far more detailed plan with experts who know your business and customers. Element Three specializes in this engagement, so reach out to us if you’re interested in hearing more.
Good data in, good data out
First things first: to analyze data, we need data. This data comes from primary systems like your CRM, web analytics, marketing automation, paid media platforms, and attribution platforms. You don’t need a massive or complicated data warehouse full of information (though it certainly wouldn’t hurt), but you need some kind of foundational information to go on.
Secondly, we need to make sure that the data being generated is accurate (or at least as close as we can get). Sources, dates, and information should be reliable, but if some systems are overwriting others, or coworkers on your team are changing information on the reg, then we might have a problem.
Mark the beginning and the end to purchase
So a customer’s journey occurs on several dimensions. In this case, I’ll talk about the journey to purchase—not necessarily what happens post-purchase.
First, the individual initiates activity with your brand—usually with a website, but it could be through other more traditional means, which can then be tracked. Basically this is the physical dimension, where leads are exploring purchases via your provided marketing material.
Next, leads are making decisions on their own (but also understood through tracking in the physical actions they take) based on what their interest with you is. This is the directional dimension, or which way they’re going as it pertains to the buying journey.
Last, the contact will, over the course of days/months/years, engage with your brand and assets and come to a decision. This dimension is time—the length of time it takes for them to complete a purchase.
Understanding all these components is critical, and at the very least, no matter what systems you have, you should be able to at least track time—from when you first engage with a lead to when they buy, or don’t.
A game of totals and averages
With your time-to-purchase range in place, the basic model for data collection and understanding would seek to discern total engagements (page views, social media interaction, email engagement, etc.) per lead within the purchase window. Then across all of those, you can distill them down to various stages of the funnel (how high is engagement among leads? MQLs?), what products or services they align to, and more.
Systems like HubSpot, Pardot, and Marketo can do this for you right out of the box. And if your purchases all happen online, you can even do it within a free tool like Google Analytics.
If your buying cycle starts and ends online, then you’re in good shape. Everything can be accomplished with GA and Tag Manager, with the right events, goals, conversions, and funnels set up. Things get tricky when the purchase cycle gets longer and sales reps get involved. Now we’re talking multiple systems, bouncing between physical locations and calls with sales reps. Not impossible, but getting hairy.
Lean into strengths, identify opportunities for growth
Once you have accurate data and can track customer activity from awareness to purchase, you can start uncovering trends in how your customers are purchasing. For example, if you know that leads close at a higher rate when they view a certain piece of content, you can make that piece of content more accessible and easier to find for leads.
The amount of times customers engage should also, hopefully, provide valuable information for your product or service. If you find that leads who engage with your brand 25 times or more throughout the buying process spend 50% more than those who engage less, you may consider creating an engagement strategy for customers moving through the buying process.
Every customer will take a different path to purchase. But if you can find themes in the data, you can improve the customer journey and increase revenue by adjusting accordingly.
It is what it is, but is it what it should be?
Something important to note here is that data can only be collected on what is known. So just because something works now, that doesn’t correlate to it being perfect. It might only take ten engagements for a lead to close, but if your content is thin, maybe they’re going somewhere else to figure out what they need.
Part of analyzing the data is really taking a step back and taking an honest look at what’s really happening. Reading between the lines to a certain degree can help, and putting that shelved intuition back to work can help ensure you’re coming to the right conclusions.
