Data management is a key ingredient for a good return on ad spend. But many companies can’t analyze the full slate of data using traditional software. Thankfully, you can find new ways to maximize your return on ad spend with artificial intelligence and machine learning.
Our article shows how AdTech companies apply machine learning to automate programmatic auctions, detect anomalies, and target high-value customers. The examples we’ve prepared will show you how to spend less and earn more for each ad. So, let’s get going!
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How to calculate the return on ad spend?
Return on ad spend is the revenue you earn for every dollar you invest in a marketing campaign. You can calculate it by dividing ad revenue by its cost. For example, if you invest $100 into a campaign and get $300 in return, your return on ad spend will be $3.
This performance indicator is similar to return on investment (ROI), which shows the ratio between net income and investment. Companies usually apply return on investment to measure the long-term profitability of marketing campaigns. In contrast, return on ad spend is most useful for granular tracking (short-term strategies, channels, and specific ads).
To calculate return on ad spend, most companies use analytics software to measure costs per impression, click-through rate, conversions, revenue per visitor, and other metrics. However, these tools present information via reports or dashboards, meaning you need to monitor and analyze this data constantly.
This creates a new problem: keeping track of numerous metrics across multiple ad campaigns, channels, and platforms. According to Invoca’s report on marketing data, about 31% of companies can’t optimize their ad performance efficiently because they have too much data to analyze.
And that’s where artificial intelligence enhanced by machine learning algorithms can help you.
Managing return on ad spend with artificial intelligence and machine learning
Machine learning models analyze data to forecast results, optimize campaigns, and automate tasks. They can examine thousands of factors that affect your return on ad spend.
All machine learning algorithms can be divided into two classes:
Supervised learning. Algorithms in this class train using labeled datasets. It makes your systems better at solving classification problems (assigning data into categories and predicting discrete variables) and regression problems (understanding relationships and predicting continuous variables).
Unsupervised learning. These algorithms analyze unlabeled datasets to detect hidden patterns and data groupings. It uses methods like clustering, anomaly detection, pattern mining, and feature modeling. For instance, audience segmentation is a prime example of using unsupervised learning in AdTech.
We should note that machine learning algorithms depend on high-quality data. You must feed your systems with months’ worth of marketing information to let it train. This also requires an established process for storing, organizing, and filtering your data. Consequently, you need a sizable upfront investment to implement this technology.
On the other hand, machine learning systems improve as they process more information. They become more accurate and therefore help increase profits. So, the technology will pay for itself over time.
But just how practical are these algorithms for publishers and advertisers? Let’s look at a couple of examples.
How to apply machine learning to improve ROAS
Machine learning (ML) algorithms have many applications in AdTech. Here’s how companies use ML to increase ad revenue and lower expenses.
Anomaly detection software can monitor ad performance without burdening your human resources. The machine learning models learn from past campaigns and historical data to produce an expected performance model. This lets you detect and resolve any deviations (like changes in traffic and behavior metrics) that interfere with your campaigns.
Trained algorithms can also detect ad fraud to reduce marketing expenses. Beat, a popular ride-hailing company, used anti-fraud software to monitor suspicious, reflected, and protected traffic sources. It allowed the company to decrease fraudulent activities by 92% and save over $54,000.
Enhanced programmatic bidding
Advanced machine learning platforms can understand which bids and ad impressions deliver the most revenue per dollar. For example, Google’s Smart Bidding platform optimizes auctions in real time based on strategies with higher conversion rates.
Manny’s, an independent musical equipment store, ran a campaign that used the Dynamic Creative platform and Google’s machine learning algorithms to optimize ads for users with strong buying interest. As a result, the company reduced the cost per acquisition by 14% and increased conversions by 18%, which led to a 66% improvement in return on ad spend.
Automated ad creatives
Dynamic creative software can modify and produce ad creatives. The system analyzes device type, product interests, installed apps, and other parameters that reveal the user’s preferences. Afterward, the machine learning algorithms create combinations of color schemes, ad formats, interactive elements, and text to engage audiences.
Here’s how Day 7 Interactive, a mobile game developer, improved its return on ad spend with an artificial intelligence platform from Meta. First, the company prepared the elements and text messages for the ads. The algorithm then produced new creatives and placed them on Facebook services. The results were astounding, they experienced 252% uplift in return on ad spend and 92% increase in conversions in two months after launching.
Machine learning platforms analyze a wide range of parameters to tailor campaigns for different user categories. These parameters include geographical location, purchase timing, browsing history, in-app behavior, session depth, and click tendency. A system then groups visitors in cohorts to let you target them with the most appealing offers.
Bukalapak, one of Indonesia’s most prominent eCommerce companies, automated ads to Indonesian users with the help of the Smart Shopping platform. The system analyzed best-performing products and matched them to relevant audiences while also taking care of ad placement. It improved Bukalapak’s return on ad spend fivefold and drove four times more conversions.
The algorithm helps you target audiences that didn’t show explicit buying intent. To clear things up, it’s not about advertising products that people abandoned in a cart (or, even worse, items they already bought). Instead, it’s about identifying a similar product or better deals that visitors are likely to buy.
Solutions like Session Quality and Conversion Probability use machine learning to estimate a proximity to convert. For instance, it can identify users who have studied smartwatches or added them to carts. The well-placed ad with a list of better-priced alternatives can be a final nudge for a conversion.
User experience optimization
User experience isn’t the least important factor for achieving a good return on investment on marketing spend. Page speed, for example, is a critical search ranking and conversion factor for websites.
Here, machine learning tools like PageSpeed Insights or Thor Render Engine help you optimize HTML structure, media, and style to optimize your page speed. Some models can pre-load the pages users are likely to visit next to improve their experience. This makes the browsing much more pleasant, making the user more likely to click on ads.
Machine learning lets you automate your data analysis to maximize your return on ad spend. It does so by making your system better at understanding audiences, optimizing user experience, and delivering relevant ads to visitors. Algorithms can also automate auctions and prevent fraudulent activities. Do you want to exploit all available data in your ad campaigns? Postindustria’s artificial intelligence and machine learning solutions can help you out. We’re always happy to share information if you want to learn more!