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How Retailers Can Mine Online Shopping Carts for Creative Gold

by Amobee, November 21, 2016

While holiday shopping, you will see tons of those “you may also like” recommendations on ecommerce sites. What you may not realize is that now, even if shoppers abandon a shopping cart, retailers can cash in on ecommerce insights to drive their creative strategy.

And the right strategies can lead to more helpful, meaningful brand interactions and influence two integral parts of the new customer journey: the engagement and loyalty stages. Marketers can look to ad tech partners who use creative data science and apply association rule learning in an innovative way: digging into on-site activity to develop better product-specific online creatives to more effectively target shoppers during the holidays.

It all starts with a retailer using site tags and then centralizing their online, offline and CRM data through a data management platform (DMP); tying in an analytics platform to spin off insights; and then using the layer of expertise from a smart data science team.

Association rules are nothing new. Every retailer knows insights can be gleaned from mining large data sets. That’s why bread displays are often set up near the butter in the dairy aisle of your local supermarket and why perusing cell phones on Amazon almost guarantees that you’ll be shown cell phone cases before you check out. However, it’s not as simple as it appears at first glance, because some patterns could be happening solely by chance. A data analyst has to take into account the issue of support (the number of transactions in the dataset that include the items in question) and confidence (the reliability of the inference drawn).

If You Like Our Hoodies, You Might Like Our Polos

A luxury retailer recently wanted to learn which categories of items online customers purchased, the relationship between those categories and others, and which items were leading to high shopping cart values. Again, this was not a simple matter because the retailer dealt with many types of merchandise. Within women’s apparel, for example, there were dresses, skirts, coats, and so on.

Using our DMP and analytics capabilities, an algorithm showed the retailer, for example, that users interested in hoodies and sweatpants are 14.5% more likely than other users to also be interested in casual T-shirts and polo shirts, so those are the items they should be shown next — on any site relevant to reach the consumer.

The Prediction Machine

What’s exciting is that the marketer can see that the information compiled was highly actionable off-site and that they could use it to drive creative. In other words, they aren’t locked into site-side personalization through a recommendation engine, and they aren’t looking solely at what creative had gotten the greatest response from users who clicked on it. Instead, they have the opportunity to preemptively figure out creative based on patterns that are found.

This is an important distinction on a few levels, and it has great implications for the customer journey. If a user has purchased a coat, for example, a recommendation engine is going to generate suggestions comprised mainly of other pieces of outerwear. But if they’ve gone ahead and bought the coat, they probably don’t need another right away. They might be interested in an entirely different category of item, and because the marketer is looking at data from the point of view of categories, they’re poised to act on that. Reaching off-site is very effective in these scenarios because once a shopper finishes making a purchase, he or she will rarely return within moments to make another. This way, the shopper can be redirected back at a later time, encouraging loyalty.

There are implications for this technology in-store as well. Consider the case of an in-store shopper who doesn’t necessarily visit the e-commerce site. If the CRM data shows that they’ve consistently purchased dresses and skirts at one of the retailer’s brick-and-mortar locations, and if it’s known that people who buy dresses and skirts often buy handbags, they can be shown digital ads for handbags.

Making New Associations

Creative applications of a non-bleeding edge technology like association rules can yield exceptional insights. If a marketer already has a great recommendation engine in-house, and they’re comfortable with it, that’s wonderful. But working with an ad tech partner, they can see a totally new application that will allow them to get greater value out of all that valuable data. Next time you see “recommended for you” when you buy shoes online, just think of all the new ways to use that kind of insight to power your marketing.

For more on how marketers can mine their data for insights, see our post on Advanced Audience Insights, a solution that makes it possible to understand customers at a molecular level in real-time.

 

About Amobee

Founded in 2005, Amobee is an advertising platform that understands how people consume content. Our goal is to optimize outcomes for advertisers and media companies, while providing a better consumer experience. Through our platform, we help customers further their audience development, optimize their cross channel performance across all TV, connected TV, and digital media, and drive new customer growth through detailed analytics and reporting. Amobee is a wholly owned subsidiary of Singtel, one of the largest communications technology companies in the world.

If you’re curious to learn more, watch the on-demand demo or take a deep dive into our Research & Insights section where you can find recent webinars on-demand, media plan insights & activation templates, and more data-driven content. If you’re ready to take the next step into a sustainable, consumer-first advertising future, contact us today.

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