While its digital roots give MTA the data it needs to dive deep into the customer journey, they can also lead to under-attribution of offline activities and a failure to take baseline conversions (those that would happen without any marketing efforts) into account. But its emphasis on digital is both a blessing and a curse. MTA’s granularity supports a deeper understanding of the synergies between factors as well, so you can make adjustments to placements and spend based on a holistic view into the performance of each step of the campaign. Multi-touch attribution has speed on its side, enabling marketers to understand and react to what’s happening on a daily basis. Aggregate data also struggles to show the nuances of the customer journey, making it difficult to drill down and target specific audiences and strategies. That longer time frame is also a drawback, however, impeding your ability to respond to circumstances in a timely way and optimize accordingly. As such, it’s valuable for metrics like the financial value of brand ads, for which a longer time frame and greater context are useful. MMM delivers the breadth of a 30,000-foot view into variables both inside and outside of the marketer’s control. This allows it to measure the impact of each individual marketing tactic.Ī largely digital practice, multi-touch attribution uses data science to turn account-specific, near-real-time data into insights about how marketing initiatives are performing at each level, from vendors to channels to creative.īoth of these approaches offer pros and cons. MTA looks at individual users’ digital journeys and analyzes the path to conversion across, as its name suggests, multiple touch points. Traditional multi-touch attribution (MTA) tackles marketing measurement from the other direction, taking a bottom-up, granular, user-centric approach. This analysis includes both internal and external factors, like marketing and pricing data, market elasticity, seasonality, and weather and news events (something like an election or hurricane, for instance, would play into an MMM evaluation.) It is usually performed once or twice a year. MMM analyzes aggregated historical data, customarily from offline sources like TV, radio and print, and delivers organization-level metrics around planning, spending and performance. What’s the difference between marketing mix modeling, traditional multi-touch attribution and today’s cross-channel attribution? Marketing mix modelingĪt a high level, marketing mix modeling (referred to interchangeably with media mix modeling, both shortened as MMM) works with top-down, macro-level information. Untangling all of those threads would make for a very long article, so let’s start small. It also gets jumbled up with other sophisticated measurement techniques like marketing/media mix modeling (MMM) and further complicated with data science lingo, like AI and machine learning. “Attribution” means different things to different people, especially across our dynamic ecosystem of vendors, agencies and brands, not to mention the ad tech side of things. But where there’s buzz, buzzwords will follow, and the marketing analytics space is no exception.Įven in my small corner of the world, attribution, the language used to describe methodologies and technologies is often murky. For those of us in the industry, it’s exciting to see so much interest and innovation around how we measure the effects of our efforts. Marketing analytics is one of the most buzzed-about categories in the current red-hot martech landscape.
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