This is a Sponsored post from Olga Ezzheva – Oxagile.com
In a digital ad space, programmatic advertising is rapidly growing and becoming the industry mainstream. In fact, Emarketer predicts that automated ad buying will account for 86.2% of all digital display ads by 2020.
Programmatic advertising, also known as programmatic buying, refers to automated and data-driven buying and selling of digital ad inventory across multiple digital media channels including display, search, social, video, and mobile. In layman’s terms, programmatic uses advanced algorithms to automatically serve personalized ads to customers in the right context.
To navigate the programmatic landscape, brands and advertising agencies invest in powerful adtech solutions fueled by machine learning, which helps digital marketers make the most of their advertising effort. How? Well, let’s see.
1.Reduced infrastructure costs
An umbrella term, programmatic advertising covers an array of media buying means, including real-time bidding (RTB) that allows advertisers to compete for ad impressions in an auction setting, and Programmatic Direct where marketers can buy guaranteed ad inventory from specific publishers in advance.
To become a part of the programmatic ecosystem, brands and agencies use Demand-Side Platforms, or DSPs. In a nutshell, a DSP is a software suite that automates media buying process and helps ad buyers efficiently bid on ad impressions across multiple ad exchanges.
Recently, the volume of bid requests flowing to DSPs has exploded, due in no small part to header bidding when a publisher offers the same ad space to multiple ad exchanges. This tactic may be winning for publishers, but it puts a gigantic strain on buyers. In two years, the industry-leading DSP platform The Trade Desk has leaped from processing 1 million impressions per second to 5.7 million.
To cope with this explosion in traffic and reduce infrastructure costs, DSPs leverage machine learning capabilities to determine which bids they are most likely to win and which bids to avoid. Publishers, in their turn, use advanced AI algorithms to better categorize their inventory and offer ad buyers the most relevant bids.
2.Optimized Digital Ad Spend
In real-time bidding, a transaction takes less than 100 milliseconds. A lot happens within this fraction of time: a user visits a website triggering a bid request which, accompanied with collected user data, goes further to an ad exchange. There the ad impression becomes available for advertisers to bid on, and the highest bid wins the ad placement.
Deciding on a bid value is a balancing act between spending all your digital ad dollars to offer the highest bids and losing lucrative ad space. DSPs rely on robust ML algorithms to evaluate ad impression parameters and available user information — including proprietary and publisher-provided data — for an optimal bid value on a per-impression basis.
Above, we have touched on multiple sources of data available to marketers and advertising agencies: first-party data aggregated directly from their audiences, second-party data from business partners (e.g. publishers), and any third-party data purchased from outside sources.
A Data Management Platform (DMP) brings all these disparate data sources together in a holistic, centralized view, providing digital marketers with greater visibility and better control. Underpinning a DMP, machine learning programs comb through these vast amounts of data to build user profiles, provide behavioral insights, and model look-alike segments.
With this deep understanding of customers, marketers can target their audiences more precisely and serve personalized ads to respective groups of users. Real-time insights into ad campaign performance enable brands to adjust their targeting criteria on the fly for maximum efficiency.
Rich real-time user and market data coupled with available historical stats power predictive capabilities of machine learning algorithms. These capabilities prove invaluable to digital marketers who leverage ML-supported marketing tools to predict ad campaign performance, user behavior, and purchase intent.
In a programmatic environment, machine learning is used to fuel a predictive algorithm that crunches millions of data inputs to predict what combinations produce the best results in terms of higher customer lifetime value, lower cost per acquisition, and higher lead conversion. And as ML algorithms learn and improve over time, the predictions for future programmatic ad campaigns will beсome more accurate.
5.Right Context for Ad Placement
Going the programmatic path, advertisers may feel like losing the grip over the context their ads are shown in. Just last year, the media giant YouTube lost millions when major brands pulled out their budgets over inappropriate ad placements. Concerned about their brand integrity, many marketers choose Programmatic Direct that secures premium placements and inventory control.
Natural Language Programming (NLP), a subset of AI, puts control back in the hands of advertisers. By using NLP and sentiment analysis, programmatic solutions can understand the content of a page and correctly gauge the sentiment. This can help to avoid any bad ad placement that may hurt brand reputation, and find the best match for your ad creative.
On a final note
In today’s fast-paced world, programmatic help advertisers and digital marketers to effectively compete over users’ attention. Supported by machine learning, programmatic advertising tools allow marketers to optimize their digital ad budget, target audiences with unparalleled precision and better control the context of their ad creatives. Are you leveraging programmatic to take your digital marketing strategy to the next level?