In October of 2018, Forrester released a report titled, "For B2C Marketers, Customer Data Platforms Overpromise And Underdeliver.” In this report, they made the statement – more scathing than the title – that, “CDPs lack crucial capabilities to solve for identity resolution, data hygiene, and cross-channel orchestration.”
This started a flurry of action by CDP vendors to employ AI techniques to solve the identity resolution problem. Some have done a decent job at this; Segment’s Personas offering is quite compelling. But the issue of data hygiene is still unsolved, and more importantly, the issue of tracking what the identified users are doing is left as a sort of unaddressed black hole in the value of a CDP.
With all the recent talk about third-party cookies, and Google and Apple’s stances on the matter, those in the CDP world are very worried about identity. And they should be! The problem just got infinitely harder to solve. But even if they solved it perfectly, knowing the identity (in the abstract sense), of your consumer is only one piece of the puzzle.
Knowing what they like, and what they have done is the key to creating targeted messaging and increasing conversion.
Let’s use a brick and mortar example to highlight this point
Sandy walks into her local hardware store.
Sandy is well-known in the community, so everyone knows who she is.
She browses around for a bit, and ultimately leaves without buying anything.
Later that day, the cashier tells the store manager that Sandy had stopped by.
“What’d she buy?” asks the Manager.
“Nothing.”
“Why not?” the manager asks curiously.
“I don’t know.”
“What did she look at?”
“I don’t know.”
In this example, the store manager knows exactly who the customer is, but has no idea what they want, or why they didn’t buy anything. This type of data is known as behavioral data. In the e-commerce world, behavioral data is collected through various degrees of complexity of click tracking.
Anything from clicking a link on a Google ad, to hovering over a product (but never actually clicking) is behavioral data. This is Sandy looking at a product and choosing not to buy it. The best CDP in the world can tell you who Sandy is, and they can tell you where she’s been, but they can’t tell you what she did when she was there. That’s kinda not their problem to solve — it’s yours.
So how are you supposed to solve this problem?
Your developers need to instrument your site with the correct behavior tracking capabilities to match the strategies you’re trying to drive through data.
Perhaps you want to know all of the people that considered the blue variant of a pan you sell, only to find that it was sold out. You want to remarket to those potential customers when blue is back in stock. Well to do that, your developer needs to know that’s a strategy you want to employ, and ensure that color selection – and even hovering – is tracked as the user navigates the page.
The magnitude of this challenge explodes when you think of all of the strategies you might employ someday. Multiply that by all of the UI components the user might interact with, and you have a pretty nice-sized problem.
So what are you supposed to do?
I met with someone who runs a data analytics consultancy, and very early in the meeting, he said, “I am SO sick of chasing developers around asking them to insert this bit of javascript into the site.” It seems like a completely intractable problem, and only promises to get worse with the adoption of Headless commerce.
Now, we’re seeing a marked increase in interest in Headless commerce. There are myriad benefits to developing on an API-first commerce platform, including the flexibility that comes with completely unbounded frontend development. This unbounded flexibility also introduces unboundedness in the way the frontend can and needs to be instrumented to properly track the behavior of your users.
To do this just right, your developers have to have a full understanding of the strategies you intend to employ in your marketing efforts, and a full understanding of the entire data pipeline and data model that is used to perform the downstream analytics.
This is where Chord comes in.
We have built a set of front end starter kits – complete with all of the components one would need to build the richest of e-commerce experiences, and all instrumented with the proper tracking required to understand your user behavior. All of this is built with full interoperability with Chord’s proprietary tracking plan, data model, and data transforms to ensure that our users get the very most out of their CDP — not just the WHO, but the very important WHAT.
Ready to learn more about your customers through comprehensive behavioral data? Book a demo now to learn how Chord can solve your analytics problems, once and for all.
