What are the main challenges and pitfalls of measuring ROAS in analytics?

1 Data discrepancies

One of the most common issues with measuring ROAS in analytics is data discrepancies. This means that the data you get from your ad platforms, such as Google Ads or Bing Ads, does not match the data you get from your analytics tools, such as Google Analytics or Adobe Analytics. This can happen for various reasons, such as different attribution models, different conversion windows, different tracking methods, or different time zones. Data discrepancies can lead to inaccurate or misleading ROAS reports, and make it hard to compare and optimize your campaigns across different channels.

2 Attribution challenges

Another challenge with measuring ROAS in analytics is attribution. Attribution is the process of assigning credit to the different touchpoints that influence a customer's journey and purchase decision. For example, a customer may see your ad on Google, click on it, visit your website, leave without buying, see your remarketing ad on Facebook, click on it again, and then make a purchase. How do you attribute the sale to the different ads and channels that contributed to it? There are different attribution models, such as last-click, first-click, linear, or data-driven, that can affect how you calculate and report ROAS. Choosing the right attribution model for your business goals and campaign objectives is crucial for measuring ROAS accurately and fairly.

4 Data interpretation challenges

Data interpretation can be a challenge when measuring ROAS in analytics. This process requires technical skills, business knowledge, critical thinking, and creativity. To avoid data interpretation challenges that affect your ROAS measurement, it is important to define clear and relevant goals and KPIs for your campaigns. Segmenting or filtering data to identify patterns, trends, and outliers is also crucial. Additionally, using benchmarks or industry standards to evaluate your performance and testing or experimenting with different variables such as ad copy, keywords, bids, or landing pages are essential. Lastly, it is important to consider the context and limitations of your data such as sample size, margin of error, or confidence level.

5 Data actionability challenges

A fifth and final challenge with measuring ROAS in analytics is data actionability, which is the ability to use data to make informed decisions and actions that improve results. This can be difficult to do as it requires data interpretation, communication, collaboration, and execution skills. Data actionability challenges that can affect your ROAS measurement include not communicating data findings clearly to stakeholders, not aligning data insights with business strategy and priorities, not prioritizing or allocating resources based on data insights, not implementing or monitoring data-driven actions and recommendations, and not measuring or evaluating the impact of data-driven actions on ROAS and other metrics.

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?