What's the revenue for every dollar spent on a marketing campaign? This question leads to understanding return on advertising spend (ROAS). Marketing teams should expect a profit, but how much? And how does this knowledge shape strategies?
ROAS helps in budget allocation, campaign optimization, and future planning. Though it seems simple, it's complex to measure and doesn't always reveal the whole picture.
This article will cover what ROAS is, its significance, calculation methods, ways to enhance it, and its limitations for marketing teams.
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ROAS is a ratio that is easy to comprehend. Marketers can divide the revenue that an ad campaign generated by the cost of the ad campaign and get a return on ad spend. The higher the ROAS, the greater the return on investment from that individual campaign.
To calculate ROAS, marketers need to document the amount of spend and measure conversions and sales per campaign. They also need to determine a reasonable payback period for the ad campaign – in other words, the amount of time allotted to measuring the return on investment after spending on the campaign.
One of the most difficult parts of measuring ROAS is measuring and attributing the conversions in the first place.
Depending on the business model, there may be multiple steps involved in calculating a dollar value for the return. Additionally, for multi-channel marketing teams, assigning credit to a given touchpoint is not straightforward.
As for measuring conversions, marketers typically use website analytics in conjunction with tracking pixels (such as the LinkedIn Insight Tag) from the advertising platforms themselves. Often, marketers also create UTM parameters (text strings added to a web link to track where visitors come from) that allow them to precisely define attributes of their campaigns like source, medium, campaign name, and specific content in the ad.
From there, marketers need to choose an attribution model. This can be very complex, but basically, an attribution model is a system and set of rules for assigning credit to a given touchpoint in the customer journey.
Common models include the last click model, which credits the final touchpoint before conversion, and the First Click model, which credits the initial interaction. More complex models like linear, time-decay, and position-based distribute credit across multiple touchpoints.
Advanced attribution tools and software can help track these journeys, providing a more accurate picture of how each campaign contributes to sales or conversions.
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