December 14, 2018
Austin Chang of Initialized Capital recently wrote a nice, succinct post on the importance of mapping out your growth channels. Here’s his high-level description of the process they use:
His guidance is simple, but key for any business—from startups to established companies. If you step back and think about it, these steps outline the core challenge that most businesses constantly work on, which is why Chang recommends re-visiting the process each year.
Reading the article, though, reminded me of a story about a company that followed these steps exactly, but created such a mess of their data along the way that by the time they were able to figure out channel-level ROI, they’d wasted over a year of time and a massive amount of capital.
Recently I had the chance to talk with a former leader from a well-funded, venture-backed startup out of Silicon Valley. He told me about the most difficult phase that they faced as a company, when they really buckled down on figuring out how to scale.
They’d done the tedious work of figuring out initial product-market fit and had a really good idea of who their ideal customer was. Then they entered a phase of mapping existing pathways, planning for optimization and rapidly testing new channels to build on their success.
Things seemed to be on the right track, until they got to Chang’s third exercise: Rank & Analyze Pathways to Find Efficiencies.
When it came to analyzing acquisition channels and their customer journey, their data was such a mess—spread across a rocky landscape of spreadsheets, software and exports from old tech—that the process of figuring out ROI was detrimentally time-consuming and lacked helpful detail. As my friend described it, “we knew something was working, but we didn’t know exactly what it was or how much it cost.”
Like many companies in their situation, they resorted to using brute force. Because of the necessity of figuring out the equation for scale as quickly as possible, they couldn’t afford to go back and re-tool their entire martech stack, but they also couldn’t afford to keep acquisition spending at pace without optimizing it for better results.
So, they paid the price with months of late nights marrying data in spreadsheets, filling in gaps, and making best guesses with anecdotal data to complete the picture. My friend said that the real cost, though, was lost time in optimizing—or, more directly, unrealized revenue and scale. All in all his estimate was that they wasted a year of time.
My friend said they did get there, eventually, but even then they had a model in a spreadsheet—not the martech stack to run and optimize it. Optimization was possible, but far from efficient and scalable. So, really, the work had only begun.
Starting in January, we’ll begin a short series that walks through the fundamentals of mapping out a basic customer data plan.
This topic is deep and varied and can become extremely complex, but in our experience, getting the fundamentals right is most important—and the point at which most companies need to start. Like any discipline, operating at an advanced level is largely a factor of mastering the basics.
Here’s an overview of what we’ll cover in the coming weeks, using Chang’s channel mapping steps as a guide:
We’re excited to dig into some practical details with you in 2019. Until then, happy holidays!
Eric was formerly CMO of The Iron Yard, which at its peak was the largest coding school in the world. There he grew the business 10x in less than 2 years by building out a data-driven acquisition practice and full-funnel attribution models across a dozen software systems. He is also a consistent lecturer in MBA programs and sought-after speaker on growth topics.
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