The ad tech industry has been on a journey; applying more technology to their audience targeting and optimisation strategies than ever before. Developments in technology, such as sophisticated machine learning, have changed the process of data collection and analysis, enabling it to grow massively in the last few years and shifting from a predictive approach to a cognitive one.
The advanced programmatic technology that machine learning provides is improving campaign effectiveness, insights, relevance and refining brand strategies for today’s marketers. Yet, many within the adtech industry are still hesitant to embrace this technology within their campaigns. Marketers who don’t use these machines to uncover new insights are in danger of missing valuable opportunities to find new audiences and gain more customers.
A key challenge for marketers is finding the right audiences for their campaigns. It goes without saying that placing your ads in front of the wrong people is a waste of time and money. For decades marketers have been relying on intuition and assumptions when it came to targeting the right audience. However, as audiences connect through an ever-growing variety of devices it has become increasingly more difficult to keep track of who you are addressing.
Machine learning is able to provide the industry with real observations on how people are going about their digital lives. This enables them to analyse their audiences in more detail and gain more insight into their interests and passions.
Machines are able to analyse and evaluate more web pages in a minute than a human can in a week, so it would seem to be common sense to let the technology do the work for you.
Can A Machine Be Creative?
Critics have claimed that a machine is not capable of learning the spark of creativity that marketing demands. However, last summer a partnership between M&C Saatchi, Clear Channel and Postercope made progress as they launched what they called “the world’s first ever artificially intelligent poster campaign”.
Whilst the campaign didn’t silence critics, it no doubt left some temporarily muted. Using Kinect technology to read audience reaction, the poster was able to adapt itself accordingly. It could then assess the success of each displayed ad and continue creating copy and selecting images for the next.
The ad wasn’t without its flaws and with many pointing out the randomised text, it’s safe to assume that copywriters and art directors’ jobs are secure. However, the technology demonstrated is a step in the right direction. Rather than machine learning completely replacing the creative, we should look to utilise the technology to work alongside it in order to better the audience experience.
Machine Learning And The Battle To End Ad-blocking
Within the last two years the ad tech industry has seen a dramatic increase in the rise of ad blocking. A recent report by IAB/YouGov found that it has increased by 22 per cent within the UK. Furthermore, the report found it is highly popular amongst 18-24 year-olds, with 47 per cent using them.
These figures can look disheartening to those within the industry. Of course, for a customer who is being constantly bombarded by the same ads on their devices, ad blocking may seem the only option to stop this. But if marketers embrace machine learning they will gain a richer understanding of when to buy impressions to prevent their ads from becoming annoying to the user.
Machines can help stop poorly placed and repetitive ads that might annoy the user and instead draw down targeted and personalised ads. This might be through optimising what product mix to display when retargeting, or what ad copy is best suited to a particular demographic.
Machine learning has the ability to understand nuances of a page and the context of recent events to judge what ad should appear and how. The result of this is that audiences become less inclined to use ad blocking because the ones they see are of more relevance to them.
In recent years machines have changed the ecosystems of advertising, and machine learning promises to go further. Not only will machine learning ensure audiences are receiving hyper-targeted ads and less wastage – these machines have the ability to run without human assistance, ensuring that when a marketing team is out of the office, their campaigns will still be just as efficient in driving engagement to their brand.
If machine learning is used more and more, then the future of advertising will be faster, more efficient and more bespoke, resulting in an improvement of ROI for advertisers and driving a better online experience for all users.