You’re spending money on PPC ads, on improving SEO performance, and attending multiple events. How do you know which of these investments is paying off? That’s what Marketing Attribution is all about. In this episode of The Marketing Mix, we’re talking to Steffen Hedebrandt, CMO and co-founder of Dreamdata, about the importance of marketing attribution for small and mid-sized B2B companies.  We discuss the challenges of tracking complex buyer journeys, and the different attribution models that can be used – from First Touch to Last Touch and everything in between.  Steffen highlights the importance of data hygiene, and making sure you have your processes set up to collect the right data at the right time.  So that you can then make informed decisions about marketing investments.  And we talk about the limitations of Google Analytics for B2B attribution, where understanding account activity is as important as tracking individuals. Key Takeaways: Marketing attribution is about understanding the path or journey your customers take, to then focus on the most successful marketing tactics and scale them effectively. First-Touch Attribution: For B2B, particularly with long sales cycles, the first touch model can be very useful. It highlights the initial marketing activity that brings potential customers into the funnelData Collection: Start logging data as early as possible. Ensure your website is tracked appropriately, salespeople log their interactions in CRM systems, and other digital traces of customer interactions are captured effectively.Tracking Accounts. B2B attribution requires an understanding of Accounts as well as people, which Google Analytics can’t handle directly. A first party data solution is required.Connect with Steffen on LinkedIn And check out Dreamdata's B2B revenue attribution platform Timestamps 02:30 Defining attribution 05:50 Justifying the investment 09:50 Comparing attribution models 18:55 Importance of collecting data early on for analysis 21:35 Limitations of Google Analytics for attribution 27:20 Finding proxies for good outcomes and shorter feedback loops