AI/ML

Media Planners: Three Strategies for Personal Survival in the Age of Machine Learning

Media Planners: Three Strategies for Personal Survival in the Age of Machine Learning

If you’re currently employed as a media planner or media buyer, there’s a very good chance that you won’t finish this decade in the same role. The reason?

Robot eye Pixabay/PeteLinforth

The unstoppable rise of Machine Learning.

In recent years, those working in the delivery of ad campaigns have witnessed dramatic growth in the use of programmatic technologies. Over the next couple of years, we can also expect automation to extend its reach into the planning of campaigns, with Machine Learning replacing much of the manual effort currently associated with the task.

Machines can now quantitatively evaluate the consumer journey of millions of individual customers more quickly, efficiently and effectively than any human planning team could ever hope to replicate, producing valuable insights into consumer behavior at a depth and level of detail that was never previously possible. These insights, in turn, can then be used by the machine to automatically deliver relevant messaging to consumers at a frequency appropriate to their stage of the consumer journey.

As the benefits of Machine Learning become more widely understood, brands are likely to demand change inside their agencies. There still are plenty of agencies out there with teams of 20 people buying media on behalf of a single client and another 20 focused on delivery. For agency leaders, keeping this level of resource in place for large clients can be problematic, with continuous churn often leaving a significant open headcount. Rather than keep trying to fill a leaky bucket, many agencies are now recognizing the operational benefits and efficiencies that machine learning can deliver. Those with majority online paths to conversion will push furthest, fastest.

Machine Learning will supplement and overtake audience planning and manual campaign optimization thanks to its ability to automatically adjust delivery based on the consumer journey, driving higher-frequency messaging to high intent users and delivering lower-frequency to users showing enough interest signals to justify targeting with brand awareness messages.

The job of the marketing campaign at that point becomes getting your brand (rather than some other competitor brand) to truly ‘connect’ with the consumer – which is good news for humans, as we are still better at understanding the subtleties of human communication than any machine.

So, how can media planners currently facing the sharp-end of automation successfully re-position themselves to safeguard their professional future? Here are just three recommendations:

Rusty Robot Pixabay/sferrario1968

Spend more time thinking about the creative message and the brand offer to each audience identified – Personalization-at-scale has been difficult historically but is now within reach.  While it is true that, as Machine Learning becomes prevalent, there will be fewer people employed by media planning agencies to manually select targets for their clients’ campaigns, there will be an increased opportunity to closely craft the message for each user that the machine has chosen. The agencies who will thrive will be those who retain human resources to work closely with vendors to extract insights from their data, then feed those insights back into the overall creative and marketing strategy.

Spend more time helping clients to access and interpret ad server data – As the algorithmic complexity of automated planning increases and results improve, it gets hard to explain at a forensic level why the automated decisions are being taken. Smart agencies will continue to require human experts to interpret data to help unpick and understand why and how media is being placed.

There will also be a need and an opportunity for increased collaboration between media agencies and other service vendors, e.g. working with a multi-touch attribution vendor in a weekly or bi-weekly cycle to help identify opportunities to optimize the overall plan between channels and vendors. Currently, where this type of collaboration happens at all, it tends to happen far too late in the cycle and too far from the planning process to be useful.

Spend more time working directly with clients – As the use of Machine Learning becomes the norm across the industry, the quality of customer service provided by an agency will become the key differentiator for clients. Enlightened agency leaders will redeploy the human resources freed-up by Machine Learning to deliver enhanced, personalized customer-service for clients. This may take the form of increased face-time or be working onsite with clients, helping them to an instrument and optimize their desktop, mobile sites and apps with pixels, tag managers, and DMPs.

 

The war is over. Machine Learning has triumphed.

As a result, media planners and delivery teams may get smaller but data scientists will continue to rise to the top. For individuals, it’s time to brush up on maths, basic coding, and data visualization tools.

My advice for media buyers and planners everywhere is to embrace the benefits that automation can deliver for their clients and agency alike and start positioning themselves in roles where the human touch can still make a real difference.

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  1. Pingback: Battle for Machine Learning Dominance Intensifies. Is it Worth the Cost? — MarTechSeries

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