From First-Touch to Multi-Touch Attribution With RudderStack, Dbt, and SageMaker | RudderStack

neub9
By neub9
4 Min Read

An Overview of Architecture, Data and Modeling for Multi-Touch Attribution in Marketing

Do you know where your marketing budget would be best spent? Determining which campaigns are effective and which ones aren’t can be a real challenge, especially with the vast amount of data collected along the customer journey. Although reviewing metrics in advertising platforms provides some insight, it only captures a fraction of user interactions on the path to conversion.

The limitations of this model are clear, as it only analyzes ROI from paid ad campaigns and a narrow view of the steps that lead and influence a sale. The modern data teams and marketers are left wanting more advanced attribution solutions, as today’s marketing world demands a deeper understanding of customer behavior throughout the entire journey.

However, complexities arise when trying to move past basic single-touch attribution. It involves collecting and normalizing data from many sources, and the application of statistical models that fall outside the typical marketing skillset. These are problems that the marketing team alone cannot solve, as comprehensive data collection is a data engineering issue and statistical modeling falls under data science.

For most marketers, this results in the fall back on last-touchpoint models or the use of third-party attribution tools that lack a complete picture of the attribution data. Unfortunately, these methods fail to provide the crucial, in-depth insights needed for an overall, cross-channel understanding of marketing performance throughout the entire consumer journey.

RudderStack addresses these challenges by creating a data set that combines user touches and associated metadata for analysis. This approach supports a variety of machine learning models, addressing lead scoring, and likelihood to repeat a purchase. Our article will explore an e-commerce client’s use of various attribution models by starting with a high-level architecture review, explaining their data collection using RudderStack, and showing how they used it to prepare the data for modeling with dbt and RudderStack Reverse ETL. The article will then demonstrate how AWS SageMaker was utilized to run the Jupyter Notebook and outline the results from multiple models being sent back to the warehouse for reporting.

The article will provide a comprehensive data collection flow from the different platforms, enriched user journeys, conversion and user features using dbt, and the process of running the models and their output to the warehouse and downstream tools. Through these processes, RudderStack empowers teams to generate various views of attribution that answer different questions, offering insights that are not available in basic first touch or last touch models. The enriched user journey data can expose powerful insights in multi-touch attribution, where modeling becomes critical for understanding the influence of different campaigns at various touchpoints throughout the customer journey.

In the article, we will delve into how data engineers can effortlessly capture every touchpoint from the stack without the pain. The concept of user journeys is broken down into a linear sequence of all user touchpoints and how RudderStack collects data from behavioral events, cloud apps, and proprietary systems. The customer’s advantage of having a flexible stack is highlighted, showcasing how data engineering, identity stitching, and warehouse integration allowed them to accurately evaluate marketing impact, factors, and costs that were previously impossible with third-party tools.

Strengthen your attribution modeling by embracing the comprehensive data collection, incorporation of diverse data sets, and advanced statistical modeling enabled by RudderStack.

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