Wayne Blodwell, founder and chief exec 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 edge.
Back in the day, simply having programmatic on plan was enough to give you a competitive advantage and no one asked any questions. But as programmatic has grown, and matured (84.5% of US digital display spend is due to be bought programmatically in 2020, the UK is on track for 92.5%), what’s next to gain advantage in an increasingly competitive landscape?
The use and development of computer systems that are able to learn and adapt without following explicit instructions, by using algorithms and statistical models to analyse and draw inferences from patterns in data.
(Oxford Dictionary, 2020)
You’ve probably head of machine learning as it exists in many Demand Side Platforms in the form of ‘automated bidding’. Automated bidding functionality does not require a manual CPM bid input nor any further bid adjustments – instead, bids are automated and adjusted based on machine learning. Automated bids work from goal inputs, eg “achieve a CPA of x” or simply “maximise conversions”, and these inputs steer the machine learning to prioritise certain needs within the campaign. This tool is immensely helpful in taking the guesswork out of bids and the need for continual bid intervention.
These are what would be considered “off-the-shelf” algorithms, as all buyers within the DSP have access to the same tool. There is a heavy reliance on this automation for buying, with many even forgoing traditional optimisations for fear of disrupting the learnings and holding it back – but how do we know this approach is truly maximising our results?
Well, we don’t. What we do know is that this machine learning will be reasonably generic to suit the broad range of buyers that are activating in the platforms. And more often than not, the functionality is limited to a single success metric, provided with little context, which can isolate campaign KPIs away from their true overarching business objectives.
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 edge. Custom machine learning is simply machine learning that is tailored towards specific needs and events.
Off-the-self algorithms are owned by the DSPs; however, custom machine learning is owned by the buyer. The opportunity for application is growing, with leading DSPs opening their APIs and consoles to allow for custom logic to be built on top of existing infrastructure. Third party machine learning partners are also available, such as Scibids, MIQ & 59A, which will develop custom logic and add a layer onto the DSPs to act as a virtual trader, building out granular strategies and approaches.
With this ownership and customisation, buyers can factor in custom metrics – such as viewability measurement – and feed in their first party data to align their buying and success metrics with specific business goals.
This level of automation not only provides a competitive edge in terms of correctly valuing inventory and prioritisation, but the transparency of the process allows trust to rightfully be placed with automation.
For custom machine learning to be effective, there are a handful of fundamental requirements which will help determine whether this approach is relevant for your campaigns. It’s important to have conversations surrounding minimum event thresholds and campaign size with providers, to understand how much value you stand to gain from this path.
Furthermore, a custom approach will not fix a poor campaign. Custom machine learning is intended to take a well-structured and well-managed campaign and maximise its potential. Data needs to be inline for it to be adequately ingested and for real insight and benefit to be gained. Custom machine learning cannot simply be left to fend for itself; it may lighten the regular day to day load of a trader, but it needs to be maintained and closely monitored for maximum impact.
While custom machine learning brings numerous benefits to the table – transparency, flexibility, goal alignment – it’s not without upkeep and workflow disruption. Levels of operational commitment may differ depending on the vendors selected to facilitate this customisation and their functionality, but generally buyers must be willing to adapt to maximise the potential that custom machine learning holds.
Find out more on machine learning in a session The Programmatic University are hosting alongside Scibids on The Future Of Campaign Optimisation on 17 September. Sign up here.