Marketing Measurement for Beginners | Part 2 – Data-Driven MTA (Multi-Touch Attribution)

Hi I’m Jan s, VP of measurement at Funnel And welcome to part two of four of this mini series on marketing measurement. Data numbers can be really confusing, but so awesome once you understand it.

So, on that note, let’s talk about data driven MTA Data driven multi touch.

Attribution is a method used to evaluate the effectiveness of various marketing touchpoints that a customer interacts with before making a purchase, Unlike traditional, single or multi touch models such as last click? Linear or position based data driven multi touch attribution, provides a more nuanced understanding of the customer journey by considering all interactions, including non converting ones.

These are the key concepts to it.

First, let’s start with touchpoints, Basically any trackable interaction.

A customer has, with your brand, such as email and clicks on social media search, ads or website visits.

Second, the attribution model, The method used to assign credit to different touchpoints for a conversion.

So how does MTA work? It starts with data collection.

You gather user level data data across all marketing channels.

This includes every trackable interaction. A user has, with your brand independent of whether it led to a conversion or not.

This is mostly based on tracking data, ideally through server side tracking, to increase tracking quality Second model building.

You then use statistical and machine learning models to analyze the user journey, click track data and assign credit to each touchpoint in a conversion journey based on its influence on the conversion And then third optimization.

Finally, you adjust your marketing strategies based on insights from the attribution model to optimize ROI across all channels.

What are the problems with? Last click attribution Well, first of all, it’s an oversimplification.

Last click attribution gives 100 credit to the final touchpoint before converting ignoring the influence of previous interactions.

This oversimplifies the customer journey and provides a skewed view of marketing effectiveness.

Second, inefficiency: By focusing solely on the last interaction, valuable insights from earlier touchpoints, are lost, leading to inefficient budget allocation.

What are the limitations of static? Multi touch attribution models.

These models overlook the individual and dynamic nature of customer journeys, and they fail to account for the time between touchpoints Static models, also ignore non converting touchpoints, leading to biased results To understand better. It’s similar to only evaluating a midfielder in football based on the successful passes that he played, ignoring the missed passes and the overall pass rate.

So why use data driven attribution? Well, it allows for more comprehensive analysis, Data driven models, analyze all trackable, touchpoints, converting and non converting ones and their actions providing a holistic view of the user journey.

Second, they allow for dynamic allocation.

These models use algorithms to dynamically, assign credit based on the actual influence of each touchpoints, leading to more accurate insights, Improved ROI By understanding the true impact of each touchpoint marketers can allocate budgets more efficiently, enhancing overall marketing performance.

What are the benefits of data driven? Multi touch attribution: Well let’s start with accurate insights.

It provides a more accurate understanding of how different channels and touchpoints contribute to conversions, while also accounting for the sequence and time interval between touchpoints.

This then helps for better budget allocation.

It helps to optimize marketing spend by identifying the most effective, touchpoints and channels It allows for an enhanced understanding of the customer.

It offers deeper insights into customer behavior and the entire purchase journey.

So what are the limitations and practical considerations for data driven multi touch attribution. Well, first, it’s limited to trackable signals.

Data driven, multi touch attribution can only include touchpoints that are trackable, which is usually limited to ad clicks and doesn t include views Second, Therefore, it’s not suitable for branding and upper funnel activities.

Since data driven MTA is most limited to click based touchpoints, it cannot properly account for mid to upper funnel and branding campaigns and activities.

Third data quality and tracking set up User journey tracking should be implemented through server side tracking, As it provides much better tracking coverage compared to client side tracking.

Fourth, continuous updates are required to account for changes in marketing tactics, campaigns and consumer behavior And fifth integration.

You can combine data driven MTA with other measurement methodologies such as MMM to gain a full picture of marketing effectiveness.

Multi touch attribution is essential for understanding the trackable customer journey and optimizing marketing efforts.

Traditional models, like last click and static, multi, touch attribution fall short due to their limitations and biases Data driven attribution offers a more accurate and dynamic approach.

Providing deeper insights and better ROI Marketers use should leverage MTA to enhance their strategies and drive more effective marketing outcomes.

If you like this video subscribe, so you don’t miss part three and four, where I ll you about incrementality, testing and triangulation And, in the meantime, check out our other videos, where you learn more about digital marketing, marketing data and analytics and data visualization tips And tricks All the great content, you need to shortcut your way to become a better marketer. .

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