Tom Koch, associate director of enterprise sales, Xandr
In a crowded digital advertising landscape, success comes down to proven results. Marketers are increasingly seeking to deploy unique and differentiated insights to buy more effectively, while agencies, ad tech players, and trade desks are looking to gain a competitive advantage by implementing tailored, intelligent, cross-channel media buying strategies that create stickier offerings for advertisers.
By using data science and artificial intelligence to plan, target and measure the effectiveness of digital campaigns?, marketers can automate many day-to-day tasks?, saving time, reducing costs and allowing teams to focus on higher-value activities. Custom bidding strategies, in particular, can support a future-proofed identity solution by providing marketers more choice in how to reach their audience with the power of AI.
Unpacking custom bidding strategies and why they’re necessary
While this makes sense in theory, marketers and agencies are each pressured to achieve more with fewer resources. When this happens, there’s often a race to the bottom during which data can become so commoditized it loses most of its value. If everyone is pulling from the same pool, each company and its competitors are in the same boat. Imagine if Coca-Cola and Pepsi used the same recipe; their branding might differ, but they would lose differentiation and competitive advantage by offering identical products.
So, what exactly is custom bidding, and why is it important now? Essentially, custom bidding empowers media buyers to package their own data science and unique insights as model-based algorithms, which can be vastly more detailed and expressive compared to out-of-the-box optimization performance settings provided by a DSP. And, this will be more important than ever in the cookieless world.
As identity evolves, custom bidding strategies offer a future-proofed solution
Changes in the identity landscape are causing seismic shifts in the ways marketers find and reach their desired audience. In this evolving landscape, advertisers are looking to future-proof themselves for a world without third-party cookies with scaled, viable solutions that reach audiences with relevant advertising in effective ways.
As user identity is increasingly limited, context is becoming more valuable. AI-enabled custom bidding is another alternative to third-party cookies, offering marketers the ability to apply contextual targeting in a scaled way. When done well, contextual targeting is as effective, if not more so, than user-based audience targeting but achieves the scale and efficiency marketers demand through the power of AI.
The ability to understand and accurately value the context of an advertising placement has never been more paramount in driving performance. Marketers and agencies alike are looking to differentiate themselves by deploying intelligent, proprietary, cross-channel programmatic media activation strategies in a DSP landscape that is increasingly commoditized.
In short, AI-enabled custom bidding reaches audiences and contexts at scale despite a changing identity landscape. It should be considered a must-have for media buyers to keep pace with the competition.
Leveraging an experienced partner unlocks a competitive edge for marketers
Media buyers have looked toward customized buying with proprietary bidding algorithms to stay relevant. While this may solve some problems, designing and activating custom bidding strategies can be difficult for companies to execute effectively on their own, especially those with already scarce resources, such as those lacking data science and engineering expertise.
The writing on the wall suggests that teaming up with an experienced partner is key to making custom bidding possible and practical for marketers. The combination of advanced customization and unique, actionable insights from a partner can unlock a competitive edge, especially when tailored to advertisers’ specific needs.
Furthermore, the importance of unique insights to inform bidding strategies by leveraging proprietary data can’t be understated. This is why third-party partners are even more critical. Since no single partner can solve for every intended outcome, cultivating a robust partner ecosystem is critical. Data science partners understand the right levers to pull at the right time to unlock true, sustainable competitive advantages. For instance, this might include CRM and offline data, which can be used to optimize bidding and exploit budget delivery to inventory and drive the most lift, based on a source of measurement truth that marketers have defined.
How one brand implemented a custom bidding model to supercharge performance
Multi-channel marketers at MBuy used this approach to optimize the unique performance requirements of a pharmaceutical client, which operates in a niche and regulated industry, to surpass performance goals and satisfy the customer while maximizing operational efficiency for the traders.
MBuy teamed up with Scibids, a leader in AI-generated customizable algorithms and strategic partner for Xandr’s Data Science Toolkit, to build a custom bidding model for the client. By enhancing what Xandr’s optimization capabilities offered to the client out of the box, Scibids and the client were able to build and execute an intelligent buying model. The custom bidding model was able to identify the best inventory for the specific campaign goals based on the pharmaceutical client’s unique measurement requirements and bid the appropriate price for every impression?. The result was a 90% decrease in cost-per-acquisition and a 95% drop in cost-per-click.
While the individual goals of marketers, agencies, ad tech players and traders vary, the advantage in differentiating media buying by incorporating advanced customization with unique insights is palpable. More importantly, a partner that can leverage a robust network of collaborators has the potential to generate meaningful ROI. While custom models may appear intimidating on the surface, they can empower media buyers and enable agencies to influence each factor feeding into a final bid.
Sponsored By: Xandr