How Predictive Analytics Supports ROAS

Return on ad spend (ROAS) is the marketing metric that measures the revenue returned on the amount spent on advertising campaigns. ROAS differs from return on investment (ROI) because ROAS specifically focuses on the return on advertising expenditures. ROAS can help measure the effectiveness of this spending. In addition, ROAS can be segmented at a campaign, channel, or ad level.

How Do Businesses Calculate ROAS?

Calculating return on ad spend is simple. First, sum up the revenue attributed to the marketing initiative, ad campaign, channel, or ad. Then, find the total cost associated with the same initiative, campaign, channel, or advertisement.

Next, divide the total revenue driven from the initiative, campaign, channel, or ad by the associated cost. When using this ROAS formula, businesses typically express the metric as a percentage.

return on ad spend (ROAS) formula
Return on Ad Spend (ROAS) formula

In most cases, ROAS captures the out-of-pocket advertising costs associated with a campaign, ad channel, or individual advertisement. However, it can include additional costs of ads to provide a holistic sense of the total investment in an initiative. These costs can include, but are not limited to, the following:

  • Marketing Salary Costs: Associated salary cost of in-house teams or contracted personnel who manage the ad campaign.
  • Agency or Vendor Costs: Fees or commissions from vendors and agencies that manage campaign delivery and/or serving.

Why Is ROAS Important for Marketing Teams?

ROAS is a valuable metric for marketers when evaluating how their campaigns and advertising efforts impact their overall return. This metric allows marketers to measure, evaluate, and optimize their efforts. As a result, they may decide to end underperforming initiatives or increase investments in programs that drive considerable return.

What Is a Good ROAS?

When planning a marketing initiative or campaign, you should always set a baseline goal. This goal should reflect the return the media should generate within a specific time frame. This goal will allow campaign managers to determine whether the campaign is a success.

BigCommerce mentions that a standard benchmark for ROAS is 4:1. This ratio means that for every $1 of ad spend, a campaign will generate $4 in revenue. However, this ratio will vary widely on an industry and advertiser basis.

For new initiatives without historical data, there is no industry benchmark for ROAS. However, for new initiatives, a ROAS of 1.2 can be viewed as a great baseline. This means that for every dollar spent on advertising, you generate $1.20 return. This ratio is a sign that the programs are being well received.

What Are Common Issues of Using ROAS Metrics?

Return on ad spend is a great metric when evaluating the return associated with ad campaigns. However, there are a few shortcomings to consider when communicating campaign success and optimizing decisions.

  1. Attribution Limitations: ROAS usually relies on clickstream attribution. When looking at ROAS on a channel level, it is impossible to determine how other channels impacted a conversion event. As a result, teams will under-report on other influential touchpoints in the customer buying journey.
  2. Organic or Free Media Coverage: Due to the nature of the metric, ROAS does not consider ‘free,’ earned, or owned media. The cost associated with email programs, SEO, or a website is not properly represented when using ROAS to evaluate channel results.

How Does Predictive Analytics Support ROAS Goals?

Predictive analytics supports ROAS goals by allowing campaign managers to act faster. They are also better informed by using customer-level predictions based on the future value of a customer. With predictive analytics, marketing teams can optimize ad campaigns sooner after launch instead of waiting 2-4 weeks for a campaign to mature.

  1. Customer Lifetime Value: AI and machine learning make it possible to predict customer lifetime value for new customer relationships. Predicting and optimizing on the future lifetime value of a campaign conversion allows marketing teams to rely less on short-term clickstream data. They can focus more on revenue metrics that will drive future profits.
  2. Conversion Rate Modeling: Predictive analytics supports ROAS goals best by supporting acquisition strategies and driving new conversions. Predictive analytics can help businesses create tailored conversion strategies that emphasize offline interactions and are tailored to their specific needs.
  3. Look-alike Modeling: Look-alike modeling is another excellent way to support ROAS goals with predictive analytics. With predictive analytics, you can create audience segments that look like your highest-value customers. This modeling will help lower your customer acquisition cost in finding new customers and raise profit margins.

If you’re looking to get started in predictive analytics, we created a helpful guide. The guide leads you and other stakeholders through gathering information and making decisions about your data-driven strategy.

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