Why the future of marketing is led by AI and data

A survey of 2,000 North American consumers found a significant level of frustration with generic or inconsistent communication (58 percent); also, 32 percent of respondents said that a brand that sent irrelevant messages would drive them away. This is bad news for consumer brand companies for whom achieving consistent and relevant messaging across channels is the topmost challenge.

AI is right on target

The good news, however, is that advances in AI are rapidly enabling marketers to hyper-personalise their campaigns like never before. Endowed with impressive analytical and natural language capabilities, AI models can, based on a fine understanding of customers, provide relevant recommendations to personalise engagement on every channel. Sportswear brand, Under Armour, mixes brick and mortar with digital by deploying an AI-based solution in their stores that customers can use for scanning their feet and getting suitable footwear recommendations.

Machine learning and analytics solutions offer real-time visibility into marketing operations enabling marketers to understand what’s working well in their campaigns and what needs to be fixed, while the campaign is still running. Brands can also leverage AI tools to effectively target advertising, optimise email send-times, and estimate the probability of conversion.

A great example of the successful use of AI for marketing comes from Mastercard, which uses a proprietary AI engine to analyse billions of social media conversations in real-time and extract micro trends that it selectively targets. If the marketing team catches a trend of interest, they pull relevant content from their library (relevant posts and targeted ads, for example) and use it to participate in the discussion. When Mastercard (along with an airline company) used the engine in a campaign promoting a particular destination, it improved click-through and engagement rates by 37 percent and 43 percent, respectively.

Gen AI goes one better

The emergence of generative AI is taking such possibilities to a new level. For example, consumers are using technology to produce their personalised marketing collateral: at the French Open, fans could choose a memory of Rafael Nadal and write a prompt in a simple interface to instantly create a poster of the tennis star.

By leveraging generative AI to ingest massive datasets, marketers can granularise customer understanding down to the type of messaging preferred by each customer. Then, using specialised generative AI tools they can create personalised ad copy and layouts in several languages to boost click-through rates and engagement. Further, they can tune into (gen AI-enabled) campaign performance insights to make timely improvements.

Marketers who make full use of this technology stand to gain substantial value: it is estimated that the technology could create $2.6 trillion to $4.4 trillion of annual value, 75 percent of it in four areas, including sales and marketing. Things look promising going by the findings of an April 2023 survey of 200 CMOs from around the world, nearly 90 percent of whom confirmed trial or usage of generative AI in their respective organisations.

It is expected that by 2025, AI will generate 30 percent of the marketing messages sent out by large companies. AI tools will also assist marketers with routine tasks, including conceptualising and generating content, to free up 5-10 percent of marketing bandwidth. Specialised tools can not only be trained to build “creatives” in conformance with brand guidelines (colour, font, layout, tone of voice etc.) but also fed with context, such as the content attributes sought by different roles and personas – simple, or original and attention-grabbing – to generate exactly the kind of content the user is looking for.

Use AI fully and responsibly

To unlock the full range of benefits, brand marketers should consider investing in a set of AI-amplified marketing services, solutions and platforms; equally important is that they use AI responsibly. Apart from protecting customer data privacy and confidentiality, they should adhere to ethical principles, such as clearly indicating when content is AI-generated, using customer data to train algorithms only after seeking consent, creating transparent, explainable models, and making sure training data is clean, complete, accurate and free of bias.

John Premkumar

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John Premkumar is Vice President & Service Offering Head – Digital Experience at Infosys.