It was not long ago that Google sent shockwaves across the entire advertising industry by making a move to block third-party cookies from Chrome, the most popular browser in the world. Fearing that the adtech sector would crumble, many investors pulled away.

But recent adtech mergers and acquisitions paint a far more optimistic picture. Last year, deal volume among adtech and martech companies rose a whopping 82% year-on-year. If anything, this growth means that businesses are excited about a new chapter of efficient, brand-safe digital advertising.

And data-driven analytics is exactly what helps adtech solutions deliver on that promise. Given the unprecedented volume of data that the advertising industry deals with, advanced analytics enable publishers, advertisers, and marketers to make informed business decisions, improve ad inventory efficiency, create personalized campaigns, all while staying on top of ad spend. Now, let’s see how.

Programmatic advertising

Programmatic advertising is at the core of the adtech industry. It refers to the use of technology to automate the process of buying and selling advertising space as opposed to manual processes that relied on negotiations, insertion orders, and requests for proposals.

Back in 2012, the share of programmatic advertising was slightly above 10%. Last year, 72% of global digital display ad spend was transacted programmatically, and the number continues to climb up.

Marketing Charts

Source: Marketing Charts

Programmatic advertising ecosystem consists of many components: demand-side platforms (DSP), supply-side platforms (SSP), ad networks, ad exchanges, private marketplaces, data management platforms (DMP). These solutions rely heavily on data analytics to support better, more efficient digital advertising:

  • SSPs use data-driven analytics to define price floors based on historical data;
  • DSPs leverage analytics to forecast impression bids;
  • DMPs rely on machine learning algorithms to analyze first-, second-, and third-party user data and enable in-depth insights into audience behavior;
  • Ad networks utilize sophisticated, ML-powered real-time bidding (RTB) models to help advertisers get the most conversion value.

Ad performance measurement

Management guru Peter Drucker is often quoted as saying “You can’t improve what you don’t measure”. Indeed, the importance of efficient and unified measurement and key ad metrics monitoring cannot be overestimated. But since the advertising ecosystem has become very segmented — think social, search, direct, programmatic, connected TV, and more — the challenge is to effectively track these multiple environments.

Adtech analytics tools bring disparate sources of data under one roof and enable marketers to track relevant advertising metrics and KPIs from a single dashboard. Armed with the holistic view of their marketing efforts across platforms and channels, businesses can improve performance and maximize reach, while reducing operational pains.

What are your adtech challenges?

Leverage our multi-year adtech expertise and machine learning know-hows to build a tailored adtech solution that solves your unique business needs, whether it is automated ad buying, efficient ad inventory management, granular audience segmentation, or any other challenge.

Intelligent audience targeting

No matter how much you have invested in your ad campaign, it will bring results only when shown to consumers who are most likely interested in your product, i.e. your target audience. To deliver more tailored and personalized messages, you can utilize data analytics to further segment your target audience into subgroups based on shared characteristics like demographics, location, online behavior, interests, and more.

But in-depth audience insights is not all that advanced adtech analytics can provide. By leveraging computer vision and machine learning capabilities, adtech solutions can accurately identify context and enable smarter contextual targeting, which is making a strong comeback as third-party cookies are phased out.

Ad yield optimization

In digital advertising, ad yield refers to the revenue that publishers get from selling advertising inventory. To maximize revenue, publishers need to sell at best prices based on market demand. Although this concept seems rather simple, managing ad inventory is not — publishers today are juggling multiple ad exchanges, ad networks, individual advertisers and media companies. Also, ad yield optimization is much more subtle than selling every bit of inventory at the maximum price. This is a variable pricing strategy that aims to take into account both short-term and long-term implications.

Yield optimization is the effort to maximize the value of advertising by delivering the right inventory at the right time to the right viewer.

Big data analytics can help publishers optimize pricing on programmatic markets in real time. With the ability to analyze millions of data points like competitors’ pricing, user’s location, time of day, the device type, etc., machine learning-powered adtech solutions help publishers predict the buyer’s willingness to pay for an ad impression and then dynamically adjust the price. This yields a significant revenue improvement for publishers.

The bottom line

After the pandemic-induced recession and budget cuts, the advertising industry is finally making a major comeback. Last year already saw record ad spendings, according to industry leaders. To improve return on ad spend, businesses need scalable adtech analytics solutions that can handle vast amounts of complex data, learn patterns for different KPIs, and deliver actionable insights and data-driven predictions.