The high volume and high rate of transactions in the adtech market pushes vast amounts of data through the entire ecosystem, 24×7. Regardless of its place in the market – advertiser, ad exchange, ad network, or publisher – each has thousands or even hundreds of thousands of metrics that measure every aspect of the company’s business.
Monitoring these metrics can prevent incidents from impacting the business. Even a short-term outage of some aspect of selling or serving ads can result in significant revenue loss.
Today, adtech businesses need to go beyond simply monitoring KPIs, they need to be able to react to the story the data is telling, the moment it shows up.
The best way to do that is using machine learning (ML) and artificial intelligence (AI) to automate the process of monitoring critical metrics and spotting issues as soon as they appear.
Some metrics are more important than others, mainly because they have an outsized impact on revenue. In such cases, analysts want to know as soon as possible if the metric is deviating from the normal baseline in a negative way.
Some of these metrics are enormously complex, with multiple dimensions that make them quite impossible to monitor without a sophisticated ML/AI system. Let’s look at a few examples of metrics that adtech firms have told us that, in their experience, are absolutely critical to monitor closely.
Publishers need to closely watch fill rates for their placements
Fill rate is a critical metric for publishers on the supply side of advertising. This metric refers to the rate at which a specific ad placement area is utilized. The more time that the space is populated with ads that are seen by visitors to the webpage, the higher the fill rate. Optimally, a company would like to have a fill rate of 100% or as close to that as possible.
Take the example of a news website that provides free access to content. Since there is no paywall, advertising revenue is vitally important, making fill rate a critical KPI to monitor.
If the fill rate suddenly drops for a particular region, browser, advertiser, or reader profile, the company is going to miss out on some revenue. And this isn’t the only placement area the company offers up; there may be hundreds of placements.
One reason it’s difficult to measure fill rate is that the metric experiences seasonality. Different placements may have different fill rates at different times of the day for different locations. Only a machine learning model that accounts for seasonality can keep up with the variations in the data patterns for this very important metric.
If the drop in fill rate is associated with a specific advertiser, the publisher can reach out to let them know there’s a problem where ads aren’t appearing as they should. This is bad for both the advertiser and the publisher, so the sooner the issue is resolved, the better.
Advertisers must keep an eye on ad spend to optimize their budgets
Another crucial KPI for advertisers on the demand side to pay close attention to is paid impressions (ad spend). An advertiser wants to reach as many eyeballs as possible and typically pays to place ads where they are most likely to be seen (and hopefully clicked on). This is another metric that can get complicated very quickly, making the ad spend hard to manage.
The first assumption is that the advertiser wants to spend the entire budget. There is little value in not spending the full amount because that means ads aren’t being served, people aren’t seeing them, and sales may not occur due to prospects’ lack of awareness of the product or service.
The next question is how to allocate the funds to maximize impressions. Even a very simple example shows how this can get complicated quickly. Suppose an advertiser has a monthly budget of $30,000 for online ads. A simple plan might be to spend $1,000 each day on placements. But traffic isn’t equal across every day of the week and there may be seasonal events that cause spikes or drops in traffic.
Now imagine a company with a very large ad spend budget, many different campaigns, and an array of target content platforms. It’s easy to see how planning and tracking ad spend can get complicated. This is where ML and AI is critical to understanding the business context and impact of seasonality of your key metrics that impact ad spend.
The example alert below shows an unexpected drop-off in ad spend that would certainly warrant investigation.
There’s one more thing that can throw a monkey wrench in the ad spend monitoring process. What if the company knowingly spends all the money set aside for a campaign in the first 10 days of the month, i.e., the campaign is capped? On the 11th day, and every day of the month after that, a machine learning system might flag the day’s $0 spend as an anomaly, flooding the advertiser with false positive alerts.
Anodot has solved this issue by having the advertiser send a metric indicating the campaign is capped. Then the ML system ignores the ensuing $0 daily spends, thus preventing false positive alerts. Learn more about how Anodot is reducing false positives in ad campaigns.
Proactive monitoring of ad requests and bid requests
There are other important KPIs that should be monitored closely, including ad requests and bid requests.
Ad requests are calls from a publisher into an exchange to sell their placement inventory. It’s important to monitor this metric in real time because, if it decreases dramatically, it would indicate that there is low inventory in order to sell. Therefore the revenue to the publisher and/or exchange would decrease. Conversely, if ad requests spike up too much, it could cause capacity issues in an exchange’s data center volume.
Bid requests are calls to bid on inventory in an exchange from the demand side. This metric should be monitored in real time because decreases in the number would affect ad spend and potentially create unsold inventory for the publisher. The graphic below illustrates an alert on this metric showing an unexpected drop in bid requests.
Anodot can autonomously monitor your critical KPIs to maintain your revenues
Buying and selling ads at scale triggers exponential complexities. Anodot’s AdTech analytics monitors 100% of data and metrics, including the backend process, data quality, continuity and ad load time, to ensure smooth platform performance and to protect the user experience.
Anodot helps adtech companies monitor changes in traffic volume, quality and conversion rates. See which campaigns have “gone silent” or are at risk of churn. Use these insights to reach out to customers and resolve issues before they escalate.