What you need to know: 

• Artificial intelligence (AI) and Machine Learning (ML) are certain to trend as shiny new buzzwords in the marketing toolkit
• Most digital media already use AI and ML behind the scenes to optimise targeting and ROI
• Much like the martech bubble, AI will not be a silver bullet, but will become an important new component for marketers to understand benefits and risks

Here is something I thankfully haven't heard so far in 2021: “This will be the year of mobile”. And what a horribly over-used prediction it was, bandied about by every self-proclaimed media expert from around 2000 to 2020. 

And now that the year of mobile has well and truly passed, I suspect that “this will be the year of AI” will become the new phrase du jour. Without question, just like the promise and hype that surrounded mobile, AI will be touted as the solution to all the marketing world’s problems – the silver bullet.

With this in mind, and after testing the waters with several clients in this space, it seems like an appropriate time to lift the hood and offer my thoughts on the positives and negatives of artificial intelligence (AI) and machine learning (ML). Yes, there is plenty of promise. But a silver bullet? Probably not. 

AI and ML: How did we get here?

AI and ML are used synonymously with growing velocity and are staking their claim on the market. In 2018, an EConsultancy survey showed that under 50 per cent of marketers were using AI for digital ad tactics. Fast forward 2.5 years, and I suspect the adoption is + 90 per cent, given the digital buying tools used by most media agencies and advertisers have quietly increased their reliance on AI for most of the optimisation processes.

Facebook and Google use AI and ML to shape the ad experience for the user and optimise budgets to goals. Newscorp, Stan, Netflix use it to suggest content, and Ooh Media uses it to optimise ads in shopping malls.

AI and ML are also being adopted in media planning, media mix modelling and yield optimisation, with early take-up generating positive results. 

Thankfully, the hype and the hyperbole have remained relatively low key. So far. So, in advance, I want to plant a flag in the sand, and provide some clarity around the opportunities and pitfalls we have discovered through our recent workings with ML and AI in media planning and buying.  

AI and ML without doubt represent important milestones in the evolution of media planning. We are backing these developments at Atomic212 to provide scientific rigour which complements our planning, reporting and optimisation. We have learned that it requires deft handling and lives up to its promise in most parts, but requires caution in others.

Before exploring the opportunities and pitfalls, I will digress for a moment on terminology. AI is the more lively terminology, used to sound futuristic and impressive.  It conjures images of robots taking over mankind and is indeed the field of science that wants to create intelligence that mimics the neural activity of the human brain. 

Machine learning on the other hand is a field of maths that programs algorithms to figure out patterns and produce forecasts from these patterns. It is ML that is most prevalent in media buying. This is an important starting clarification because we do not want to solve or end humanity in the pursuit of stronger media plans. Our core task is to produce media plans that will be more effective at creating demand for goods and services. 

AI and ML: Opportunities ripe for the plucking

1. ML is scientific

ML in particular provides an undeniable science to support or counter a media recommendation. It typically runs from three years or more of data and finds a prediction that is + 90 per cent accurate to a ‘hold our period’ (a period of data that is not provided to the model, but known and used for comparing the prediction).

The model outputs can produce curious insights, but it is hard to quibble with a mathematical equation that has no bias, states its confidence levels and has many decades of statistical surety built into it. It is the opposite of gut feel, a cold hard look in the mirror, as only a robot can demand. It is not swayed by trends, client sensitivities or fear of being shown the door.  Herein lies the core value. Science over human experience. The tension and conversion produces a competitive scenario that benefits the ultimate decision maker.

2. ML provides a contrary viewpoint

The results from a ML model provide the basis for deeper, more valuable discussions with the media agency. No matter which side wins the discussion, the contrary or complementary evidence of a model is merited as a sense check to other media planning methods. Better to measure twice and cut once when there is so much at play.

3. ML can be done in-house

Once the data is assembled (ideally more than three years of media, competitor, price, product, place data), the tech programs that run machine learning are available at affordable costs. Taking this data in-house empowers the marketing department to analyse and build the data for deeper and deeper purposes.  

I see a future where a business collates its media, creative and marketing data within a larger data lake of all other business information, activating machine learning algorithms to suggest the next promotion, price point, product and media plan.   

4. ML is quick and always improving

Unlike media mix models of the past, all the calculations are executed within the program and take less than four hours to run. The learning programs also get quicker and more accurate every time they run, as their neural networks learn, allowing more data sources to be brought in for improved accuracy.

AI and ML: Beware the hidden pitfalls?

1. ML is based on historical data

For all the scientific methods that ML models employ, they are based on historical data. This means that they are very good at predicting the future based on the past, but, for instance, they could not hold the human judgement as to the impacts of COVID-19 on audience behaviour. For this very reason, the data anomalies in 2020 were so significant that all modelling will be impaired through 2021. With regard to media planning specifically, they do not account for new media opportunities, or medium.

2. ML needs a lot of data to provide any real value

The computational power behind these models is awesome. They can crunch through thousands of permutations in minutes. Feeding the beast is often the most challenging aspect of the initial set up. Media plans, competitor spends, creative, and sales data are the basics. We typically spend two to six months finding, cleaning and organising these data sets for future use.

3. Data for ML models is difficult to source and collate

Businesses are now generating vast data lakes, driven by a vision where every decimal place of operating, financial, product, pricing, placement and promotion data is stored in a unified manner for analysis. It is a nirvana state, and it is still very early days. Unless you are a data behemoth like Facebook or Google, I suspect it will still be many years before most brands are able to source and collate the necessary data.

4. ML struggles with brand metrics

The modelling will want to work through direct correlation and causation, i.e., when we spent in radio, we saw a direct, same day increase in sales. It is a truism of marketing that awareness activity in media takes time to trickle down the funnel to manifest as sales.

5. ML will make us all the same

AI and ML will likely homogenise businesses because similar data sets will produce similar insights and recommendations. I would question whether the unique elements of a client’s data will yield seismic business insights such as Nespresso’s “The Choices We Make” campaign, which capitalised on shifting global environmental sentiment to address consumer concerns around ethical production of coffee; or the long-running #Likeagirl campaign which has smashed cultural and gender boundaries and inspired female empowerment.

The future of AI and ML

I have no doubt that AI and ML will become integral value-adding components of media planning. I am excited that businesses will unify data, both marketing and non-marketing, to enable deep learning and AI-driven insight at a speed that traditional agency methods could never hope to match. 

My concern and wariness relate to the hyperbole that is surely soon to come. Much like with the overly optimistic and somewhat misguided promises that martech could independently solve customer acquisition, marketing professionals should approach AI and ML with a healthy level of scepticism and an attitude of test-and-learn.