Apr 18 2022
Basis Technologies

Campaign Optimization Using Digital Advertising Automation


The following is adapted from Basis Technologies’ guide, Meeting the Moment with Advertising Automation. To get even more advertising automation-related insights and statistics, download the guide today.

Data complexity is sky-high. Competition is tougher than ever. Expectations around privacy are growing. A brand’s social values must be front and center. To connect with today’s consumer, advertisers need to get just about everything right, and optimizing manually is simply no longer effective.

Modern campaign optimization should never be a set-it-and-forget-it task, but with the immense challenges of placing a winning programmatic bid, marketers can increasingly benefit from automating a wide-ranging set of actions across a number of different parameters. Every advertising activity has an end goal—from increasing brand awareness and lifting social engagement, to expanding lead generation and driving sales, and everything in between. Through various means, automation can help advertisers achieve those objectives in the most efficient way possible.

Let’s break some of them down:

Optimizing Bids

Automated bidding solutions help advertisers harness data patterns to raise the odds of reaching consumers when they are most likely to respond. By leveraging the latest advances in data science—including machine learning algorithms, Bayesian modeling, predictive performance methodology, and natural language processing—automated engines can tailor bids for every auction to ensure media buyers are spending the optimal dollar amount for each impression. The result is less waste and increased returns on campaigns that are data protected from the outset and set up to help advertisers achieve their goals faster and more efficiently.

Accurate Forecasting

AI forecasting engines turn the art of planning into a data-driven science. AI analyzes historical performance and bid landscapes to surface trend data, which is subsequently fed into predictive models that help map strategies across channels and devices. Armed with this technology, advertisers no longer need to send trial balloons out into the real world to gauge interest, so say goodbye to costly designated learning budgets.

Instead, machine learning algorithms continuously learn from past campaigns to provide increasingly accurate forecasts, all so advertisers can predict the most lucrative investments before they spend a single dollar of their budget. Essentially, they take the guesswork out of the media buying equation.

Controlling Budget Pacing

Depending on a company’s size and its program ambition, the amount of money flowing through a digital media buyer’s hands can be daunting. The larger the budget, the greater the impact of even the smallest mistakes. This is where AI-powered budget pacing comes in.

Automated budget pacing can play a critical role in monitoring the movements of digital advertising spend and keeping campaigns on course to meet desired performance targets. Should advertisers encounter any volatility in the form of significant overspend or underspend, they will receive automatic alerts that allow them to rectify the issue and regain control over the budget.

Anomaly Detection

Successful anomaly detection hinges on a capacity to accurately analyze time-series data in real-time. Considering the number of relevant metrics in any given campaign and the speed at which market trends shift, doing this manually—and at scale—is just not practical.

Algorithmic anomaly detection systems, on the other hand, can alert advertisers instantaneously if a campaign’s performance is meaningfully deviating from forecasted models, while also providing actionable next steps so buyers can quickly make any requisite changes. This is a critical (yet oftentimes neglected) facet of campaign optimization. Imagine the cost per click on a certain ad is suddenly soaring—or, on the flip side, that sales volume is unexpectedly skyrocketing. If advertisers want to amend and optimize their campaigns, they need to be able to act on these valuable insights in the moment. With automated anomaly detection, they can do just that.

Managing Inventory Effectively

Inventory management refers to the process by which media buyers can track the availability of their products or services and, if necessary, avoid bidding on ads that direct consumers to an offering that is unavailable. The costs of disregarding this process are well-documented—just Google “Nike i2 fallout,” or “how Best Buy stole Christmas,” or “Target Canada merchandise management.”

One of the most rudimentary goals for any advertiser is to target the right people at the right time in the most profitable way possible, and they certainly can’t do that if they’re wasting money on redundant ads. Automated inventory management nullifies that risk.

Safeguarding Against Advertising Fraud

By 2023, worldwide digital advertising fraud could cost companies over $100 billion. It is a problem lurking ominously, as measurement remains difficult and transparency standards like ads.txt are still nascent and not yet widely adopted.

Armed with the right tools, however, advertisers can automatically detect and abate fraudulent activity—and, in turn, ensure they are spending their budgets on real impressions. At higher levels of functionality, automated solutions include built-in systems designed to mitigate advertising fraud in addition to both allowlisting and blocklisting sources and publishers in paid media campaigns.

Your Guide to Digital Advertising Automation

By embracing automation for granular campaign optimization, digital marketing professionals are better positioned to overcome the myriad of challenges facing modern advertisers. Simply put, automated campaign execution can help businesses realize what humans alone cannot.

Want to learn more about advertising automation? Check out our guide to see why digital advertising automation is essential to the future success and long-term growth of the media buying industry.

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