Retail personalization is certainly not a new concept, but there are many new advances in personalization helping retailers create deeper relationships with shoppers through meaningful, relevant and contextual experiences.
Personalization through the years
Ten years ago, Amazon and Netflix were the poster children for personalization. Amazon was commended for its ability to show different home pages for different customers based on their past clickstream paths or previous purchase behaviors. Other retailers’ personalization efforts simply greeted returning customers by name or enabled them to save website preferences. 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!
Today’s retailers are rising to the occasion. Retail personalization is having a major resurgence, thanks to the proliferation of big data, as well as the implementation of machine learning across distributed platforms.
Stitch Fix’s AI success
Some retail organizations have built entire businesses around AI. Take Stitch Fix, for example. The styling subscription service uses AI to tailor clothing and accessories to busy women’s personal tastes, budgets and lifestyles. Stylists work with a team of more than 60 data scientists to choose tailored items for each shipment. By applying machine learning to the process, the computers become smarter as they handle more and more data.
This strategy of marrying humans and machines has been wildly successful for Stitch Fix. Over 80 percent of clients return within 90 days for a second order, and a third of clients spend 50 percent of their clothing budget with the subscription service.
While we’ll continue to see businesses in and out of the retail space launch with AI at the core, this approach certainly won’t make sense for all retailers. Instead, established retailers can apply AI to different business units. Recently, Macy’s announced its customer service unit is testing a “mobile companion” tool using AI. The tool enables shoppers to get answers based on the store that they are physically shopping in rather than having to find a sales associate. While this is a great way to leverage AI to engage with current shoppers, what about using the technology to acquire new ones?
AI’s role in marketing
Applying AI to marketing not only helps retailers acquire new customers, but also encourages repeat business. As recommendations and offers become more tailored, shoppers’ loyalty will continue to deepen as they associate the brand with personalized and relevant experiences. For retail marketers that want to stand out from the hundreds of advertising and marketing messages shoppers see on any given day, AI-driven marketing is a must.
AI enables marketers to harness powerful algorithms to find patterns in internal and third-party data, and then look for repetitions in these patterns. One of the core underpinnings of AI that is transforming retail personalization is machine learning. Stated very simply, machine learning is about solving problems using probability and statistics. Used in the context of personalization, machine learning can continually adjust the data sets until the right marketing message for each individual shopper is presented at the moment, and through the channel that matters most.
This can all sound very daunting for marketers. For those just getting started in AI-powered personalization, keep the following in mind:
1. Don’t be overwhelmed by data. Many retail organizations have data everywhere and have no idea how to consolidate it, let alone make sense of it all. If you don’t have internal resources to organize disparate data sets, simply outsource this important task to a personalization technology partner.
2. Pull in as much third-party data as possible. From POS to loyalty data, retailers have a lot of information about customers. But to get a true 360-degree picture, it’s important to pull in as much third-party data as possible. I’m not talking simple demographics here (though that’s helpful as well). Your data combined with factors such as location, time, social media activity and price sensitivities can really make the difference in understanding how, why and when customers shop, as well as the right purchase triggers so retailers can present the most relevant marketing messages.
3. Determine the best output channel. Many retailers have seen the power of email personalization for driving traffic and sales to their stores, both online and off. But there are many other channels where personalization works well — in particular mobile. As we saw in the Macy’s examples, mobile can be used to augment in-store service, but it’s also a great vehicle for timely promotions and offers for shoppers on the go. In addition, the emergence of AI-powered chatbots is something to keep an eye on, as retailers look to follow their audience onto the appropriate channels, including social.
You don’t have to be a personalized subscription service like Stitch Fix to provide personalized service to prospects and customers. A huge impact can be made by applying AI to one area of your business at a time — from marketing to customer service to merchandising. A once over-hyped technology, personalization has arrived and is the future of retail thanks to the power of artificial intelligence.
Originally published on Retail Customer Experience http://www.retailcustomerexperience.com/blogs/how-artificial-intelligence-is-transforming-retail-personalization/