Ecommerce personalization is certainly not a new concept, but there are many new advances in personalization that are helping retailers and consumer brands create deeper relationships with shoppers through meaningful, relevant and contextual marketing.

Personalization Through the Years

Ten years ago, Amazon and Netflix were the poster children for personalization. Amazon was applauded for its ability to show different home pages for different customers based on their past clickstream paths or previous purchase behaviors (something they continue to do to this day). Other retailers’ personalization efforts, however, simply greeted returning customers by name or enabled them to save website preferences. And then there were those that took a one-to-many approach, such as versioning their site for entire segments of visitors.

These rudimentary approaches are now considered table stakes for today’s retail marketers. Consumers are far savvier than they were ten years ago. They not only want personalization, they expect it! According to a study by Magnetic and Retail TouchPoints, more than half of internet users said that information shared with them should be relevant to what they’re currently interested in or looking to buy, as well as pertinent to their personal taste, style, age group or location.

How Do We Get There?

We know what consumers want, but how do retailers deliver true one-to-one marketing messages? How do they get to know every single shopper and understand what they want, when they want it? The simple answer is data. Retailers have a wealth of data (though often siloed) at their fingertips, which can help start to paint a picture of each shopper. Together with third-party data, this data can act like a series of filters to help retailers narrow down their targeting and improve their relevance.

This is where the market is currently. Many of the retailers and brands we speak with are simply trying to figure out what data they have and how to use it to gain insights into their customers. Then they need to figure out how to make those insights actionable.

Sounds like a lot of work, right? Well, it is. That’s why partnering with data specialists can help bring retailers up-to-date on the personalization efforts.

What’s Next?

Bringing these organizations up-to-date is simply going to level the playing field. For retailers and brands that want a true competitive edge – to stand out from the hundreds of advertising and marketing messages shoppers see on any given day – they need to go one step further.

Enter artificial intelligence (AI). Many people have a hard time thinking about AI without conjuring up images of robots from sci-fi movies. Add to this, AI is now the biggest buzzword across multiple industries. Within the context of retail data, AI is all about really sophisticated math.

AI lets us harness powerful algorithms to find patterns in the data and then look for repetitions in these patterns. There are three core underpinnings of AI that are transforming retail personalization:

Machine Learning: Stated very simply, machine learning is about solving problems using statistics. Used in the context of personalization, machine learning will continually adjust the data sets until the right message for each individual shopper is presented. A relevant message that makes them buy – whether that is online or a store – wherever they happen to be at the right moment for the message.

Knowledge Discovery: More popularly known as data mining, this is focused on the methodologies for extracting useful information from large data stores. Most retailers have been doing this since data was first collected.

Natural Language Processing: This is focused on understanding human speech – what the contextual meaning is behind the speech. This is incredibly important in personalization – bringing an awareness of tone, context and meaning. NLP is allowing retailers to use social media streams as a way to understand individual buyers, understand what motivates them, and what drives them to purchase.

This can all seem very overwhelming for retailers who are still trying to understand how to access and harness their own data sets. That’s why we created Genie, an AI-powered recommendation engine that uses these three key aspects of AI to empower retail marketers to target their customers and prospects with the right message, at the right time, in the right channel.

With Genie, we’re able to get retailers’ data in order and into our system in a matter of weeks. Not only will they be able to meet customer expectations, but also exceed them, leaving the competition in the dust.

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