How can you simplify multi-touch attribution models for marketing analytics?

1 Choose a suitable model

There are many types of multi-touch attribution models, such as linear, positional, time-decay, algorithmic, and custom. Each model has its own advantages and disadvantages, depending on your business goals, data availability, and customer behavior. You should choose a model that aligns with your marketing objectives, reflects your customer journey, and accounts for your data quality and limitations. For example, if you want to emphasize the first and last touchpoints, you can use a positional model. If you want to give more credit to the recent touchpoints, you can use a time-decay model. If you want to use data-driven methods to assign weights to each touchpoint, you can use an algorithmic model.

2 Simplify your data sources

One of the main challenges of multi-touch attribution is to collect and integrate data from multiple sources, such as web analytics, CRM, email marketing, social media, and offline channels. This can result in data silos, inconsistencies, and gaps that affect your attribution accuracy and reliability. To simplify your data sources, you should use a common identifier, such as a cookie or a customer ID, to track and link your customer interactions across different platforms and devices. You should also use a consistent naming convention and format for your campaigns, channels, and touchpoints. Additionally, you should clean and validate your data regularly to remove duplicates, errors, and outliers.

3 Focus on key metrics and segments

Another challenge of multi-touch attribution is to analyze and interpret the results and derive actionable insights. This can be overwhelming, as you may have to deal with a large amount of data and metrics that vary by model, channel, and touchpoint. To simplify your analysis, you should focus on the key metrics and segments that matter most for your marketing performance and decision making. For example, you can use metrics such as return on ad spend (ROAS), customer lifetime value (CLV), and cost per acquisition (CPA) to evaluate your marketing efficiency and profitability. You can also use segments such as customer personas, lifecycle stages, and product categories to understand your customer behavior and preferences.

4 Test and refine your model

Finally, you should not treat your multi-touch attribution model as a static or final solution. You should test and refine your model periodically to ensure that it reflects your changing marketing environment, customer behavior, and business goals. You should monitor your model performance and validity using methods such as holdout tests, incrementality tests, and sensitivity analysis. You should also update your model parameters and assumptions based on your data feedback and insights. By testing and refining your model, you can improve your attribution accuracy and relevance and avoid bias and errors.

5 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?