What is the best way to choose an attribution model for your campaign?

1 Attribution models overview

Attribution models can be classified into two broad categories: single-touch and multi-touch. Single-touch models give all the credit to one interaction, such as the first or the last click. Multi-touch models distribute the credit among multiple interactions, such as linear, time decay, or position based. Single-touch models are simpler and easier to implement, but they ignore the complexity and diversity of the customer journey. Multi-touch models are more realistic and holistic, but they require more data and analysis, and they may involve some arbitrary assumptions.

2 Business goals and KPIs

One of the most important factors to consider when choosing an attribution model is your business goals and key performance indicators (KPIs). Depending on what you want to achieve and measure, you may prefer a different model. For example, if your goal is to increase brand awareness and reach, you may want to use a first-touch model, which gives credit to the first interaction that introduced the customer to your brand. If your goal is to optimize conversions and sales, you may want to use a last-touch model, which gives credit to the last interaction that led to the purchase. If your goal is to balance both awareness and conversions, you may want to use a multi-touch model, such as position based, which gives more credit to the first and last interactions, and less to the middle ones.

3 Data availability and quality

Another factor to consider when choosing an attribution model is the availability and quality of your data. To use any attribution model, you need to have accurate and consistent data on your customer interactions across different channels and platforms. However, this can be challenging, as there may be gaps, errors, or discrepancies in your data sources. For example, you may not be able to track offline interactions, such as phone calls or store visits. You may also face issues with cross-device tracking, cookie deletion, or ad blockers. These factors can affect the reliability and validity of your attribution model. Therefore, you need to assess your data quality and availability before selecting a model, and use methods such as data cleansing, validation, and integration to improve it.

4 Customer journey complexity

A third factor to consider when choosing an attribution model is the complexity of your customer journey. The customer journey is the sequence of interactions that a customer has with your brand before making a purchase. The complexity of the customer journey depends on factors such as the number of touchpoints, the length of the sales cycle, the type of product or service, and the customer behavior and preferences. The more complex the customer journey, the more likely you will need a multi-touch attribution model, as a single-touch model will not capture the full impact of each interaction. However, the more complex the customer journey, the more difficult it will be to assign credit to each touchpoint, as there may be more uncertainty and variability. Therefore, you need to understand your customer journey and its complexity before selecting a model, and use methods such as customer journey mapping, segmentation, and personalization to enhance it.

5 Attribution model testing and evaluation

A final factor to consider when choosing an attribution model is the testing and evaluation of your model. No attribution model is perfect, and each model has its own strengths and weaknesses. Therefore, you need to test and evaluate your model regularly, and compare it with other models, to see how it affects your results and insights. For example, you can use tools such as Google Analytics or Facebook Attribution to compare different models and see how they change your conversion rates, return on ad spend, or cost per acquisition. You can also use methods such as A/B testing, incremental lift analysis, or media mix modeling to measure the causal effects of your campaigns and channels. By testing and evaluating your model, you can identify its limitations and opportunities, and optimize it accordingly.

6 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?