Redefining Digital Marketing Success: A Blended Measurement Approach

Navigating the labyrinth of digital marketing attribution can be a complex task, with the ever-mutating landscape of platforms and strategies presenting a significant challenge for marketers. Here, we delve into the insights and advice from a panel of industry experts, offering a fresh perspective on this enduring issue.

Dept UK’s growth director, Molly Deaville, observes that the problem of accurately measuring the impact of marketing investments is not new. She argues that the industry has become overly dependent on attribution as a definitive source of truth, a trend that is now being challenged by the rise of data privacy concerns. Instead of seeking a one-size-fits-all solution, Deaville advises adopting a blended measurement approach. This strategy should combine attribution with regular incrementality tests across both paid and owned channels, complemented by marketing mix modeling (MMM) and brand studies. She also emphasizes the importance of not solely relying on media platforms for data analysis.

Matt Triggs, head of analysis & modeling at Jaywing, addresses the criticism that attribution modeling has faced in recent times. He acknowledges that some models overestimate the influence of lower-funnel channels like PPC and underestimate upper-funnel or brand-led advertising. According to Triggs, Jaywing’s approach employs machine learning and AI to identify key drivers, providing a more accurate baseline and true mathematical incrementality.

Impression’s technology director, Aaron Dicks, highlights the ongoing debate surrounding digital attribution, further complicated by recent changes in third-party cookie policies. He identifies three key areas for attributing results in the modern multi-platform, multi-moment, and multi-device customer journey: media mix modeling, conversion modeling, and third-party all-in-one attribution platforms.

Kevin Joyner, director of data strategy at Croud, discusses the industry’s newfound respect for privacy choices and the resulting shift in digital attribution’s role. He stresses the importance of focusing on business impact rather than getting caught up in the latest industry trends. For Joyner, machine learning-enabled automated budget optimization could be game-changing for businesses ready to utilize it effectively.

Rawnet’s technical marketing strategist, Harry Daniel, turns attention to GA4 and data-driven attribution modeling as standard in Google Analytics. He suggests that visibility of randomized control trials and conversion probabilities for different paths could improve agency reporting and provide clients with better insight into conversion-driving user journeys.

Nick Elsom, director at FourForty, raises the question of what exactly is being attributed – direct sales or something less tangible like propensity or awareness? He believes that understanding customer expectations should be at the heart of any effective customer experience (CX) strategy.

Finally, Ryan Green, senior vice-president of marketing and innovation at Coegi, warns that GA4 is not the only factor that will disrupt attribution models. The latest iOS17 update will reportedly strip link trackers from being passed through message, mail, and private browsing, further eroding the effectiveness of last-click attribution and website analytics.

In conclusion, while challenges in digital marketing attribution are complex and evolving, they are not insurmountable. By adopting a multifaceted approach that incorporates new technologies and respects privacy concerns, marketers can navigate these challenges effectively. Whether it’s gym marketing looking to attract more gym members or advertising for gyms seeking to improve their gym lead generation through Facebook ads, these insights can empower businesses to make informed decisions and maximize their marketing ROI.