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The Future is Here: AI in Digital Marketing

February 18th 2020

By Matt


AI is not just a buzzword anymore, it is cheaper and more accessible than ever. With the Big Data boom, lower cost of computational power and development of new techniques, AI’s popularity is constantly growing. As consumers, most of us are used to technology preceding our every digital action. We get personalised playlists on Spotify, clothing recommendations based on previous purchases and social media feeds full of potentially likeable content, not to mention the humble predictive text feature. It is all there for our convenience and less overtly – our money.


The business case.

From the business perspective, it is estimated that in 69% of cases AI can improve the performance. It varies across industries, with the top 3 being travel, transport and retail. However, the most impactful use of AI remains in marketing and sales, followed by supply-chain management and manufacturing.

AI use in marketing can be defined as “the development of artificial agents that, given the information they have about consumers, competitors, and the focal company, suggest and/or take marketing actions to achieve the best marketing outcome” (Overgoor et al., p.157). Since collecting feasible data is facilitated in the digital environment, the future of digital marketing is looking particularly interesting.


Use of AI in digital marketing.

It is especially apparent in online sales of goods and services, where all data is collected in real-time directly from customers, including personal details, demographics and purchasing behaviour. Thanks to this information, marketers can understand them and their shopping behaviour better. Adding social listening with tools such as Brandwatch on top of the company’s data can give a fuller picture of industry trends and brand sentiments.

All this knowledge is then used to improve the value proposition. Here, Amazon remains one of the best examples, with their always-changing pricing, which reportedly boosts sales by an impressive 25%. Moreover, programmatic advertising tools such as good old Google Ads and Facebook Ads enable marketers to target specific audiences with their offering and adjust, based on performance to improve Return on Advertising Spend. AI can seamlessly find your current customers’ lookalikes and serve them the perfect ad. On a side note, it is interesting how such tools offer a more inclusive way of utilising AI for smaller businesses, without an army of data tech experts.


Focus on personalisation.

One of the most significant trends in digital is personalisation. Firstly, it can enhance the customer experience and secondly, generates 5-15% more revenue. It also comes with a dilemma of simultaneously providing 1-on-1 marketing experience to a lot of customers. Without the proper technology, it is not possible, or at least not viable, for the majority of goods and services due to operational costs. Yet technology alone is not a sufficient driving force of personalisation – the human element is required as well, to understand customers’ needs.

One way to tackle personalisation is by segmenting the data by demographics and customer lifecycle stage. Then, AI can take over and do most of the heavy lifting. An example could be the help with keeping the engagement high with content suggestions (all the “recommended for you” and “you might also like”) or even with independent content creation, as well as with reacting to changes in the customer lifecycle stage in real-time with appropriate marketing automation.



Needless to say, the use of AI in digital marketing is constantly increasing, with a wide availability of facilitating tools. There are still many barriers, including difficulty in harvesting a large volume of quality data and high costs of developing custom business solutions, which only big companies can afford. One of the most significant barriers is privacy and customer’s concerns over the collection and use of data. It creates a trade-off between personalisation and data protection – you cannot have personalised solutions without sufficient data. However, research indicated that with honest data use practices that bring value to consumers 90% of is willing to share behavioural data. Undoubtedly, this decade will bring a lot of innovation in this domain.



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