Over the past decade marketing has evolved due to the advance­ments in ad tech. It has been every mark­eter’s dream to target the right user at the right time with the right message. Historically, it has been difficult to know what customers want. Marketers tackled this problem by diversifying their product portfolios, modifying their pricing strategies and using research data and promotions to create value for their consumers. But thanks to the rise in applications and tools that can process complex sets of data, compared with traditional processes that cannot deal with these complexities, we have been able to get closer to the consumer than ever before. And when you factor in advancements in targeting capabilities through multiple online channels, the explosion of smartphone and social media usage and the power of analytics platforms, marketers now have all the necessary combat tools at their helm.

I have been lucky to witness the digital media scene explode in MENA, and it has been a phenomenal ride so far. From experiencing the launch of price-per-click campaigns in 2008 during my time at Bayt.com, to recently building robust data strategies by integrating segmented customer relationship management data with platforms like Facebook and Google and reaching consumers with personalised and targeted messages. Add to that the emergence of programmatic buying, which helped digital experts bridge the gap between targeting capabilities of giant ad tech providers and your average local supplier.

As a performance marketer, it almost feels like you have the world at the tips of your fingers when you are able to use data sources to deliver highly customised messages to your users at scale. After all, we are in the era of intent-based marketing, where attention spans are shorter and context matters more than ever. This is the bare minimum that your average consumer would expect. And yes, digital experiences could get annoying, so please, Booking.com, stop retargeting us with ads about destinations that we have already booked via your platform. You could probably start mining through our historic booking data patterns to help us plan our routine summer trips instead. Sometimes you wonder, are advertisers doing enough, given that they have all this technology at their disposal?

And the real question is, are advertisers and marketers adopting the right technologies to analyse large sets of data that would enable them to make better and smarter decisions by predicting which marketing actions are more likely to work and which are more likely to fail? The answer in my opinion is that we are not even close.

Predictive analytics tools allow business units to apply historic data to future events so that they can maximise profitability while saving a lot of time. This encompasses the essence of predictive marketing. Now, imagine if we could combine predictive analytics with different data sources, CRM and marketing automation systems. This would allow any business to identify prospects that they never thought existed, project their return on ad spend and ultimately anticipate the creation of new product advancements and lines way ahead of time.

Just imagine how awesome it would be if a consumer packaged goods company that sells sanitary pads would know when to stop serving ads to pregnant women and immediately divert those unique consumers to their diaper brand to educate them about pregnancy, while cross-selling them baby products.

Even though single-customer view can be achieved through advanced data and analytics, learning from this data and successfully identifying new prospects through machine learning resulting in the launch of new products and services that could make a mother’s life easier is why predictive marketing tools have been working on improving their predictive engine algorithms. After all, it would create win-win situations that help businesses identify new revenue streams, while ultimately building a personalised relationship that lasts. Another example would be an e-commerce website that would use predictive marketing tech to successfully identify and convert existing or new prospects at anticipated success rates that are considerably higher than your standard remarketing and look-alike audience strategies.

That is to say, if an e-commerce website can accurately predict the likelihood a specific type of a customer to buy, like whether a customer who is simply browsing the site is offered a unique discount that helps convert him/her as a result of machine learning, then marketers will feel empowered to take bigger business decisions. After all, advertisers want to maximise their customer lifetime value while delivering meaningful experiences for their customers. In fact, some sources report that giant retailers like Amazon generate about 35 per cent of their revenues by focusing on predictive marketing and they have business units in place that help them improve their machine learning algorithms.

All this sounds awesome and exciting, right? Now let us summarise in a few points what is required to get there for advertisers in MENA.

First of all, advertisers need to ensure that they master standard data measurement, in terms of capturing and analysing basic sets of data. There are two types of advertisers in our region. The first group sit on a wealth of data and are not doing anything with it and the second, more worrying type, are are not collecting or measuring data correctly.

Secondly, advertisers need to be more open to the idea of sharing data with their partners, especially in this era of data-driven marketing. What is even more important is for agencies and tech providers to focus more on creating a servicing model that is focused on purpose and transparency rather than selling.

Last but not least, all parties involved must invest in existing predictive marketing technologies and in resources that trust machine learning and marketing automation tech to help businesses make better decisions. After all, combining performance acquisition strategies with predictive analytics tools will unlock endless business opportunities.