How AI can help provide marketers with the 'right moment' for consumer purchase
For today’s marketing professionals, it’s all about data and differentiation. Get the best insights out of the data you’ve got, and put something together that differs from the crowd. Artificial intelligence (AI) and machine learning (ML) can help in this quest – but it is not as simple as flicking a switch and ‘being AI.’
This gap between data collection and data utilisation is where Zeta Global, a data-driven marketing solutions provider, comes in. Ron Sadi, senior director of business development of Zeta’s data cloud division, explains the challenges he’s seeing in the market, regardless of where organisations are in their data journey.
“Some customers that come to us are just building out their data strategy, and then there’s others who are ultra-sophisticated in the way they monitor data, but there’s still challenges in deriving high value insights and actionable understanding for their customers,” he tells IoT News. “It’s about clearly defining business objectives and then building smart tooling, specifically AI and machine learning, around those very clearly defined use cases.”
Sadi notes a pet store as an example of a business which is getting it right. They decided to differentiate on products and services around pet grooming and animal care – which has to be administered at a physical location. By analysing straightforward behavioural patterns – finding that owners who were more active with their pets were more likely to need a pet grooming service, for instance – they built it up further to put together a behavioural profile analysis. “Literally to the point of identifying those who were going to visit national parks in the next 90 days,” Sadi notes.
Ultimately it is about getting the right moment – anticipating when a consumer might move on to purchase. Sadi shares his sympathies with marketers struggling with this shift. “Frankly, these technologies are complicated, and it’s very difficult to start integrating a lot of the high volume data with a model that really makes sense for the specific use case of that customer or brand,” he says.
“Traditionally, big data has been focused around data at rest – demographic data, age, income, household earnings,” Sadi adds. “But those things are somewhat unchanging. We really believe that behavioural data is really the key to identifying users and really understanding the patterns of those users to ultimately derive intent… and then activate marketing against that intent.”
Sadi came to Zeta through the company’s acquisition of commenting platform Disqus, where he had led data partnership initiatives. As a result, it was a natural fit for him to move into Zeta’s data cloud division.
“Zeta’s been developing this leading solution in the marketing stack for over 10 years, and natural language processing (NLP) and machine learning is really at the heart of us being able to decipher the data we have available to us at scale,” he says. “Of course, marketers are moving towards trying to personalise messaging to be what we call right-people-based, one-to-one marketing. Doing that at scale is very difficult if you don’t have some proprietary AI solution in place.”
Sadi will be speaking at IoT Tech Expo North America later today on how to leverage NLP and ML for ‘high-value’ consumers – and the overriding theme will be of not having a second to lose. “The session we’ll be talking about is around how marketers can do that, and how they can leverage and really get a deeper understanding of their consumers through the data that’s available to them – whether that’s first party data that they’re collecting, or leveraging other data available to them”, he says.
Interested in hearing industry leaders discuss subjects like this? Attend the IoT Tech Expo World Series events with upcoming shows in Silicon Valley, London, and Amsterdam.
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