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If you follow the luxury industry at all, you might’ve heard a little something about 2023’s best performing company in the industry, Hermés. Now, luxury brands aren’t necessarily known for their retention efforts. Most customers buy one or two luxury products from a brand and say that’s enough. But Hermés isn’t just a winning brand because they play it safe when it comes to pricing and strategize carefully when it comes to marketing. They’re also well known for their discrete loyalty and retention frameworks that keep their customer loyalty rate sky high.

Customer experience, CX, is not customer service. Yes, that’s right. Though they seem to imply the same thing, customer service is actually just one part of what customer experience entails.

In prehistoric times when things like AI, ChatGPT, and models weren’t quite as hot, data collection and analysis was – and often still is – very manual. We’ve all probably seen massive datasets filled with hundreds of thousands of rows and thought, “Geez, how am I gonna get through all of this?” Now, though, the data analysis landscape has massively improved with the rise of AI! But don’t worry, you don’t need a computer science degree in order to keep up. If you’re interested in learning more about basic AI tools and how they can be used to unlock your customer insights, you’ve come to the right place.

When starting a business, the main goal is to bring in as many customers as possible, probably through extensive marketing and campaigning. But, at some point, the next biggest hurdle becomes keeping these customers so that your business doesn’t limitlessly expend what may be a limited budget on acquiring new customers.

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.