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You open your favorite online shopping app, and there it is: a curated selection of products that perfectly align with your style and preferences. It’s almost like your digital genie, knowing exactly what you’re looking for before you do. This isn’t by mere coincidence or luck; it’s the brilliance of product recommendation engines at work. These clever systems work by sifting through data, and bringing you those perfect picks. And, they’re the reason your online shopping feels less like searching and more like discovering.

If you listen to music often, I’m sure you know of the Apple Music vs. Spotify rivalry in the music streaming industry. And usually, you’re on one side or the other—but not both. In this multi-billion dollar industry, companies like Apple Music and Spotify are forced to regularly produce updates and keep their subscribers happy. Then, in 2016, some researchers looked into what makes each company successful, and they found that Apple Music had a churn rate that was three times higher than that of Spotify. But what exactly does that mean, and what made this information useful for the researchers at the time?

When a leading supplement brand was in need of an easy way to send out emails right when a customer was most likely to have run out of a product, they came to Cotera. Their retention team had tried exploring different ways to optimize email schedules manually, but estimating the right timing on their own was near impossible. This meant that there was so much wasted potential for campaigns sent to their top customers — but they knew they couldn’t make the needed predictions on their own.

Recently, we had a large food & beverage e-commerce company come to Cotera looking for a strong segmentation model. The Head of Marketing needed to bring down a high one-and-done and churn rate. She knew they were facing an abundance of customer churn but that her team wasn’t proactively identifying these at-risk customers until it was too late. With a company-wide focus on retention, she knew they needed a dedicated tool that specialized in customer segmentation.

Around a year ago, an apparel and accessories brand came to Cotera looking for a new, strong recommendation model to replace its current one. The Head of Retention knew they should be generating higher incremental revenue from moving customers across different products. Yet they found themselves stalled leveraging their ESPs’ recommendation engine, as it was unable to adjust for the nuances of their business. And any attempts at testing stand-alone recommendation engines lacked the turnkey integration with their ESP.

Whether you still use Pinterest or no longer do, I think we can all agree that their content recommendations are absolutely spot on. But how does their algorithm work, and why is it even important in the first place? Pinterest’s mission as a company has always been to deliver the right content to the right people — meaning that a strong content recommendation system is essential to the success of their brand. But this is not to say that content recommendation models aren’t useful for B2C companies selling services or tangible products either. In fact, it’s just as important.

It’s one thing to get someone to purchase your product, but how do you get them to keep buying? Most companies send out occasional email campaigns, abandoned cart reminders, or special promotions to re-peak their customers’ interest, but there’s still another approach we haven’t touched on just yet: replenishment.

I’m sure at some point, you’ve gone through Yelp, or Trip Advisor, or any other review platform of some sort before committing to a purchase or a visit. I know I do (and I can’t live without them). But what you may not have thought about is how these reviews help that company just as much as they help you as a customer. But how? Sentiment analysis.

Now, we all know and love Netflix the same way Netflix shows that they know and love us. And why is proving their love toward their customers so important? For any subscription company, the key to success is making sure their LTV (lifetime value) stays relatively high. But it’s not just about how they increase those numbers that makes Netflix such an outstanding company - it’s even more-so about how they measure those very numbers and how deeply they analyze them.

In Las Vegas, CA, tourists from all around the country line up outside the doors of the Coca-Cola Factory right before it opens. Why? To try Coca-Cola’s ATW (Around the World) Tray. On each ATW Tray, customers get to taste a cup each of the various flavors of Coca-Cola offered in different countries across the world. What makes it so special is that each country’s Coca-Cola drink has a distinct flavor catered to the tastes, preferences, and sometimes even the traditions of the people living there. But why is this effective?

Everyone knows that these days, if you’re NOT using data and integrating tech into your company, you’re doing it wrong. On the other hand, there’s also a pretty big misconception here. Sure, we’re in the digital era, but that doesn’t mean we can just brush away the importance of human judgment. Instead of letting new technologies take over your entire business, you can use data more effectively by complementing existing human judgment and decision-making processes with it. And Whole Food’s recent merger with Amazon tells you exactly why.

It’s pretty clear that in the dynamic world of marketing, success today is driven by data. When we look at trends in the consumer retail industry especially, companies are beginning to up their digital game to keep up with their competitors. In other words, many traditional methods simply don’t cut it anymore. This is where data enablement steps in. Let’s dive into what exactly data enablement is and how it’s changing the way companies go about marketing.