Opinion
Successful planners are those who embrace data and technology to help perform their roles more effectively.
Media planning involves reaching the right people with the right message at the right moment.
Machine Learning (ML) has been a part of the media planning toolkit for a long time, and most agencies have been using a mixture of predictive analytics and algorithms to plan, set-up, and optimize campaigns for several years. However, in recent years, the explosion of data and computing power has meant that the sophistication of the ML models has improved significantly.
And that’s good news for media planners. Where ML really excels is speed and efficiency. For a human, processing huge data sets to serve the right content to the right person at the right moment is not only time consuming, it is impractical. ML models are able to process data in fractions of a second that would take humans weeks.
With most technology there are always apocalyptic predictions that the machines are (at best) out to replace us. When it comes to media planning however, successful planners are those who embrace data and technology to help perform their roles more effectively — as it is the blend of human and machine intelligence that will succeed in the long-term.
Machine learning fits perfectly into the media planning ecosystem
For example, a Marketing Mix Model (MMM) could plan a perfect campaign with an optimum media mix, and we’d need to augment that with knowledge of whether that media is actually available to purchase. Furthermore, MMM uses past data to help predict the future, so again we’d need planning expertise to supply parameters to help “guesstimate” the unknown and plan new campaigns, touchpoints, audiences, and/or creatives.
That’s why ML fits so perfectly into the media planning ecosystem. It provides direction at speed within the parameters set and frees up humans to do the creative and strategic thinking that only humans are capable – including designing tests & experiments to continually learn and improve performance.
Making decisions quicker
So, what’s next? The use of ML is growing within Media Planning, be it to effectively plan spend across channel touchpoints through to reaching the optimal audiences with the right content – and in real-time.
At the same time, as clients’ media spend becomes more digitally focused and with the depreciation of third-party cookies and app tracking transparency, there is a challenge to make decisions quicker and with chunks of data around a customer’s journey not being available.
This is where ML becomes more important in the media planning toolkit. Especially as clients start acquiring and using more of their first-party data alongside second and third-party data — as it allows planners to develop more personalised strategies and tests using a breadth of data sources — and in turn effectively deliver the right message at the right moment to the right people, leading to better performance.
Seena Samani is Mindshare’s head of integrated analytics
