Wayne Blodwell, Founder and General Manager of The Programmatic Advisory & The Programmatic University, battles through the buzzwords to explain why custom machine learning can help you unlock differentiation and regain a competitive advantage.
In the past, just being programmatically on schedule was enough to give you a competitive advantage and no one asked questions. But as Programmatic has grown and matured (84.5% The UK is said to be on track to programmatically buy US digital display spending in 2020 92.5%) What’s next to gain advantage in an increasingly competitive landscape?
Using and developing computer systems that can learn and adapt without following explicit instructions, using algorithms and statistical models to analyze and draw conclusions from patterns in data.
(Oxford Dictionary, 2020)
You are likely to be the leaders of machine learning, as present on many demand-side platforms in the form of “automated bidding”. The automated bidding feature does not require manual CPM bid entry or further bid adjustments. Instead, bids are automated and adjusted based on machine learning. Automated bids are based on destination entries, e.g. B. “Achieve a CPA of x” or simply “Maximize conversions”. These inputs control machine learning to prioritize certain requirements within the campaign. This tool is immensely helpful in removing the guesswork from bidding and the need for continuous bid intervention.
These are so-called standard algorithms, as all buyers have access to the same tool within the DSP. There’s a lot of confidence in this automation at the time of purchase, and many even forego traditional tweaks for fear of disrupting and holding back learning – but how do we know this approach will really maximize our results?
Well we don’t. What we do know is that this machine learning will be kept reasonably general to suit the wide range of buyers who are active on the platforms. In most cases, the functionality is limited to a single, low-context success metric that can be used to isolate campaign KPIs from their actual overall business goals.
Custom machine learning
Instead of using out-of-the-box solutions, possibly the same as your direct competitors, custom machine learning is the next logical step to unlock differentiation and regain an advantage. Custom machine learning is simply machine learning that is tailored to specific needs and events.
Off-the-self algorithms belong to the DSPs. However, custom machine learning belongs to the buyer. The possibilities for applications increase as leading DSPs open their APIs and consoles so that custom logic can be built on top of the existing infrastructure. There are also third party machine learning partners available such as B. Scibids, MIQ & 59A who develop custom logic and add a layer to the DSPs to act as a virtual trader and develop detailed strategies and approaches.
With this ownership and customization, buyers can consider custom metrics like measuring visibility and enter their first-party data to align their purchase and success metrics with specific business goals.
This degree of automation not only offers a competitive advantage in terms of the correct assessment of inventory and prioritization, but the transparency of the process also enables confidence in the automation.
For custom machine learning to be effective, there are a handful of basic requirements that can be used to determine whether this approach is relevant to your campaigns. It is important to have discussions with vendors about minimum event thresholds and campaign size to understand how much value you can get from this path.
In addition, a custom approach won’t fix a bad campaign. Custom machine learning is designed to run a well-structured and well-managed campaign and maximize its potential. Data needs to be inline so that it can be properly ingested and real insights and benefits can be gained. Custom machine learning can’t just take care of itself. It can ease the normal daily stress of a trader, but requires maintenance and close monitoring to achieve maximum impact.
While custom machine learning has numerous benefits – transparency, flexibility, targeting – it is not without maintenance and disruption to the workflow. The level of operational engagement may vary depending on the vendors selected to facilitate this customization and their functionality. In general, however, buyers need to be willing to adapt to maximize the potential of custom machine learning.
Learn more about machine learning in one session Programmatic University is partnering with Scibids to host the Future of Campaign Optimization on September 17th. Sign up here.
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