PRACTICAL EXAMPLES OF ARTIFICIAL INTELLIGENCE IN MARKETING

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Artificial intelligence isn’t new, but as a technology it’s still very much in its infancy. The term was first coined in 1956, but AI as we know it didn’t officially begin until the first neural networks emerged in the mid 80s. Even then, it was another decade before concepts like machine learning and data mining emerged, culminating in the Deep Blue chess program famously beating Garry Kasparov in 1997. Fascinating as the history of AI is, from a marketing perspective artificial intelligence didn’t truly begin until the 2000s. That’s when the technology moved from intriguing concept to workable reality and AI, along with related buzzwords such as ‘machine learning’ and ‘neural network’, entered common usage.

Due to the newness of AI, there’s a tendency to think of it in a future sense: ten years from now AI will do this and will achieve that. These predictions may well prove correct, but there’s no need to look to the future to picture AI in action. In the business world, AI is already proving its worth, helping marketing departments work smarter and more efficiently. Here are 10 examples of AI in B2B marketing.

1. TARGETING ADS

There are many tasks that humans still perform better than computers. When it comes to mundane and repetitive tasks however, the machines hold sway. Machine learning is particularly good at detecting subtle differences between products and optimising them to produce more favourable outcomes. It’s good for adverts in other words. Machine learning has been shown to reveal which adverts perform best and which placements are most effective. This technology is especially useful when serving ads to individuals who are at a pivotal stage in the buying process. Based on historical data, computers can make extremely accurate assessments about when, where and how ads ought to be placed. You make them, they’ll optimise them.

2. RETARGETING

Remarketing is getting smarter, but it’s a process which is still reliant on humans doing the curation and ad creation. Instead of guessing what’s likely to bring visitors back to your site and cause them to re-engage with your products, why not leave all that head-scratching stuff to a machine? Computers are very good at discerning the sort of content that’s likely to appeal to an individual based on content they’ve previously consumed. AI-driven retargeting is more personal and more effective.

3. PROPENSITY MODELLING

Propensity modelling is all about using past behaviour to predict future behaviour. It’s the sort of data-heavy process which is suited to machine learning. Feed in vast amounts of data about customers or prospects and propensity modelling can identify which organisations and individuals are likely to purchase a certain product. From a marketing perspective, this sort of information is extremely valuable.

4. PREDICTIVE ANALYSIS

The sibling of propensity modelling, predictive analysis combines statistical algorithms, customer data and machine learning to predict a wide range of outcomes. In other words, it’s a broader tool which can be combined with your existing data to predict market trends, customer behaviour and to forecast business outcomes.

5. CHATBOTS

Chatbots receive a mixed reception from users, but then the same could be said of automated customer service lines. In other words, love, hate or tolerate them, chatbots are here to stay. For engaging with B2B prospects, they can prove effective as a standalone technology or as a lead-in to a live chat service on your site that can get visitors talking before seamlessly switching from machine to human operator.

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