What’s up everyone, today I have the pleasure of sitting down with Ron Jacobson, Co-founder and CEO of Rockerbox Summary: Multi-touch attribution doesn’t tell you what really caused a conversion or revenue, it’s a credit distribution system. It’s still a useful guidepost in understanding where your efforts are making an impact. Incrementality testing, on the other hand, digs deeper—helping you pinpoint what’s really driving results by answering, "What would’ve happened without this campaign?" But to get there, it’s not about finding the perfect model, it’s about asking the right questions. Don’t get stuck in the basics like Google Analytics. True measurement demands first-party data and statistical modeling, especially as third-party cookies fade. For startups, the goal is momentum—nail one channel before diving into complex measurement. Build success first, then refine with tools like MTA or MMM to truly understand what drives growth. About Ron Ron started his career as a software engineer before transitioning to product management at AppNexus where he ran the platform analytics team and later the real time platform product teamHe then took the entrepreneurial plunge Co-founding Rockerbox, first as a programmatic advertising platform then a multi touch attribution platform And today they’ve added a suite of marketing measurement tools that also leverage marketing mix modeling. Rethinking the Role of Multi-Touch Attribution Multi-touch attribution (MTA) often sparks debate around its effectiveness in driving marketing decisions. While many recognize it as a flawed tool, few fully grasp the extent to which it misses a crucial element: causality. When asked whether MTA should be seen as a credit distribution mechanism rather than a way to measure causality, Ron agrees wholeheartedly, explaining that this is exactly how his team has framed the discussion for years. Ron emphasizes that MTA’s purpose isn’t to assign cause-and-effect between marketing touchpoints and revenue generation. Instead, it's a retrospective tool designed to distribute credit across various touchpoints in a customer’s journey. He argues that marketing teams need to shift their focus from chasing causality to understanding how customers interact with marketing efforts. This approach helps marketers assess what channels or strategies might be working, even if the exact causal impact remains elusive. A specific example Ron highlights is when clients test new channels like OTT, CTV, or linear TV. Frequently, these clients aren’t sure if the new channel is even making an impact. The issue, he notes, isn’t necessarily that the marketing is ineffective—it’s that the data simply doesn’t reflect customer engagement due to gaps in tools like Google Analytics. While causality is still out of reach, MTA can at least show that the new channel is on the customer’s path to purchase, providing some reassurance that the efforts are not entirely in vain. Ron points out that this shift in perspective helps marketing teams function more effectively. Rather than getting bogged down by the impossibility of determining exact causality, teams can use MTA to answer more immediate, practical questions: What are the touchpoints that seem to drive the most engagement? Where should we focus next? It’s not about perfectly predicting outcomes, but about gathering insights that improve day-to-day operations. Key takeaway: MTA isn’t designed to establish causality, but rather to help distribute credit among touchpoints. When marketers focus on how customers engage with their efforts rather than trying to measure cause-and-effect, MTA becomes a valuable tool in refining strategy. Understanding the Value of Path to Conversion When diving into the value of the path to conversion, we often struggle with the fact that it doesn’t fully address causality. Just because a customer clicks on a Google link and converts doesn’t necessarily mean tha