Demystifying AI for Personalization

Horizontal's Robert McGovern's Article Featured in MediaPost

What comes to mind when you think of artificial intelligence and machine learning? If you’re picturing HAL 9000 from the sci-fi classic “2001: A Space Odyssey,” you might be getting a bit ahead of yourself. AI and ML are hot topics these days, with some of the discourse centering around the potential negative consequences of unregulated AI advancements: computers gaining sentience and taking over the world. While this is a scary thought, the reality of how this technology is being used in the real world is a little more prosaic.

 

The Reality of AI: Personalization


While advanced use cases like image recognition and autonomous driving are often praised, the most common use cases for AI and machine learning from a commerce point of view revolve around personalization. In this context, like many other types of personalization, it basically comes down to taking signals from a particular data set or some past behavior, and using that to inform a future action.

For example, if a system finds out over time that the users that interact with content about car maintenance tend to end up buying more car insurance, then it can prioritize showing more car insurance product suggestions to users that interact with car maintenance content, thus streamlining the journey.

This action can also be done manually of course, but with the help of AI / ML it can be done without a human having to trawl through a sea of data to find the insights and set up an action. AI / ML can uncover patterns that humans may not see, and can be set up to automatically act on them without explicit go-ahead. Look at it like a helping hand in implementing personalization, letting you free up your marketing team to concentrate on other creative tasks like creating campaigns or copy.

 

Identifying Consumer Segments


Another particularly helpful role AI / ML can play is to detect customer segments and help create personas. This technology can be very effective at finding ways to group customers together that might not be overly apparent to the human eye.

Creating personas can be a difficult thing for brands to get right. Much of the time, true insights can be hidden behind the data, and marketers can end up relying on basic demographic-based characteristics like age, gender, or geography when creating their personas. AI / ML can help discover nuanced segments that human analysts might have missed.

 

AI won’t work without data


When done well, personalization can have an outsized benefit for retailers, increasing conversion, cross-selling and brand affinity, and AI / ML is making it easier for companies to get it right.

All this comes with a caveat, though. To really utilize AI / ML, you need data -- the more, the better.

It’s never been more important for brands to take their first-party data strategy seriously and it’s not just to take advantage of AI / ML. Bolstering your first-party data can have benefits across your whole business, strengthening your ability to sell directly to your customers, increasing margins and lifetime customer value.

In a post-third-party-cookie world, brands that rely on middlemen to reach their customers will be increasingly at a disadvantage while brands that take their first-party data seriously and use technology like AI / ML to utilize it will be well placed for tomorrow.

 

Read the original article on MediaPost here


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