Why AI in Marketing Is Not Overhyped

A survey from Resulticks’ Marketing Flab to Fab challenge a year ago showed Artificial Intelligence to be one of the most overhyped marketing buzzwords. The results don’t just show disillusionment of a lot of marketeers but also the lack of understanding of what ‘Artificial Intelligence’ is the opportunities it brings along for the world of marketing.

AI in marketing does not mean we are going to see embodied structures running around doing our chores for us, charting out strategies at board rooms, networking at conferences or having ‘apolitical’ water-cooler conversations.

Artificial Intelligence doesn’t do all our jobs for us (yet). It enables us to do our jobs better.

And it has already made its way into marketing with predictive modelling, chatbots, re-targeting, dynamic pricing. With varied applications of AI for a marketeer, it would do us good to understand the three different kinds of AI on which several applications are built:

1.      Machine Learning

2.      Propensity modelling

3.      Natural Language processing

Machine Learning

Machine learning gives computers the ability to learn from data and create accurate predictions. What machine learning is doing today for marketeers is that it is able to quickly find hidden patterns in massive amounts of consumer data and then learn from it. Once these historic pattern sets are created, they can be then fed into propensity modelling. 

Propensity modelling

A propensity model is basically a statistical scorecard that can be used to predict the behaviour of a customer or a prospect. Predictive analytics can today help us cut out the guess work and identify our key target segments, moreover also tell us what they like, dislike and are likely to ‘like’ in future. This can help us hugely in smart content curation and personalized marketing.

Natural Language processing

In its early stages of development now, natural-language processing as the name suggests tries to create AI interfaces which appear more ‘natural’ or ‘human-like’ by learning the most powerful techniques used by humans for accurate communication – language. NLP is what powers chatbots and voice assistants.

The good thing? These 3 underlying technologies are not ‘out-of-reach’.

Even the NLP component and most of the algorithms applied in ‘machine learning’ and ‘propensity modeling’ are easily available for everyone: built into messaging platforms, rentable in the cloud for just a few bucks, or simply available as open source toolkits.

However, good AI is powered by good data. What ‘Big Data’ your company owns makes a ‘Big Difference’. Which is why such rage in data collection has picked up in the recent years. ‘Collecting data’ and ‘collecting the right data’, will both be the biggest challenges for marketers and strategists. And since companies have realized this, regulators have also stepped up the data security and privacy norms.

The balance between AI and human intervention in marketing is what can power the next generation of innovations in marketing. Humans bring context and relevance that may still elude machine learning algorithms. On the other hand, AI brings greater mathematical accuracy and speed to take several problems head on at a time. The collaboration is already working wonders.

Our call-for-action in the next few years would be to take this collaboration to its full potential!

Your thoughts on AI in marketing? Feel free to share them in the comments!