Marketers are obsessed with data. The mar-tech industry has been so successful in pushing a narrative of attribution models while touting the certainty of data driven marketing decisions, no one dares make a decision without a dozen charts and tables to back it up.
To be clear, I love, love, love mar-tech. A godsend for stretched marketing teams, it boosts productivity. Improves campaign accuracy. Speeds time to market. Empowers teams to pivot and iterate creative on fly. Simplifies A/B tests. Makes conversion optimization possible. And a million other benefits I could go on about for paragraphs… but I won’t. You’re here, so you get all that.
It’s not the data that’s the problem. It’s your faith in it.
In fact, there’s a good chance your data is lying to you. You might even sense it. The numbers say one thing but something feels ‘off’. You just can’t quite put your finger on what it is and going against the data is risky.
Except, what if the truth opened up a whole new way of looking at your campaigns. What if your data is obscuring which efforts are actually planting the seeds of your future growth.
The Mar-Tech Data Fairy Tale
The typical mar-tech sales pitch weaves a pretty narrative about tracking customer behaviour across the relationship. It promises automatic insight so that you can stop wasting money on the 50% of your advertising that isn’t working. Sounds good but isn’t quite true.
Mar-tech data is incredibly helpful for making small, incremental improvements that optimize results over time. It’s much less useful when you need to extract the kind of insight that can reinvigorate a pipeline or dramatically impact conversion rates.
Data without context is just… data.
Ever opened vendor’s report and thought “What the *bleep* does this all mean for my business…?!”
Invited to help a client evaluate a costly marketing campaign, I was handed a massive slide deck. Pages upon pages crammed with complex tables and vanity metrics showed that the program was actually quite successful by industry standards. Digging a little deeper, CAC was so high it appeared that my client was losing money money on every sale the campaign generated.
We had mountains of data about how their efforts were performing compared with other, similar companies but no useful information about the program’s impact on the bottom line. We needed to reconstruct missing strands of information from offline behaviour to see the full picture and make an informed decision about how to move forward.
What Went Wrong
Your tech stack can only report the data it has access to. Too often, that means vanity metrics and last touch attribution models that ignore much of the customer’s path to purchase.
Most small and medium businesses aren’t in a position to integrate data collection across the full buyer journey. And even when they do, there are so many assumptions built into the model that it’s impossible not to skew the outcome. And while we may spend a lot more of our time at the keyboard, because human beings still live and interact in the physical world, it’s unlikely your mar-tech has eyes into your customer’s entire path to purchase in a way that puts that data into usable context.
Relying solely on mar-tech data to drive your decisions is a bit like driving a car with your eyes closed and counting on your GPS to know when to turn.
Routinely Question Your Mar-Tech Data
Make qualitative data collection an integral part of your process is an easy and relatively inexpensive way to fact check the conclusions your mar-tech stack is suggestion. Ask your customers probing questions. Pay attention to what they say and how they say it. Then compare their answers to what your quantitative data is telling you.
You might be surprised at the differences. In our experience, the gold lives somewhere between what the customer tells you and what the data shows.
You don’t even have to take our word on it. Our colleagues over at Refine Labs have been quite vocal about the need for marketers to rethink attribution models on the DemandGen Live podcast.