So, your business is humming along. Thriving, even. But you’re not content with leveling off – you want to boost your e-commerce conversions even higher. What’s the best way to make that happen? Introducing the recommendation engine.
Let’s start with the basics. What is a recommendation engine, and why would you want it? The answer is easy: A recommendation engine helps you offer the perfect personalized customer experience.
Think of it as a way to harness the power of prediction – it offers you a chance to speak directly to your customers by offering them things they want. Recommendation engines take an educated guess about what those things might be, and they get smarter – and make your site stickier – as they go along.
Showing Customers You Understand Them
The mechanics of recommendation engines can vary.
Some are based on a method called collaborative filtering, which is a fancy way of saying they guess what a person might like based on what other users have liked.
Content-based filtering is another approach; it focuses on the attributes of your product and recommends other products that have similar characteristics. There are demographic-based recommendation engines (categorizing users based on things like geographic location, income, etc.), knowledge-based recommendation engines (making suggestions based on individual users’ interests and needs) and more.
No matter what’s under the hood, recommendation engines are an effective vehicle for showing your customers you understand them, which keeps them happy and boosts your bottom line.
The upside of recommendation engines is huge. One of their core potential benefits is their ability to improve customer retention. Because they are continuously calibrating user preferences, they improve with use.
Think of it this way – the more Netflix shows you watch, the better Netflix will understand what you like and suggest other shows you might enjoy just as much. The end result: more time spent watching Netflix! It’s win-win because you feel seen, and Netflix gets to deepen its relationship with you and your pocketbook. With a recommendation engine, it’s just the same: Your user has a better experience, which boosts sales, drives customer engagement, enhances customer loyalty and brand reputation and, ultimately, generates additional traffic.
Let’s return, for a moment, to the Netflix example. According to McKinsey, 75 percent of what users watch on the platform comes from product recommendations. The numbers speak for themselves.
Today’s most successful digital enterprises rely heavily on recommendation engines. Case in point: Amazon. The company uses recommendation algorithms to personalize the online marketplace for each individual customer. Amazon, of course, has transformed the global economy – and recommendation engines continue to play a critical role in its success, generating (according to a McKinsey estimate) roughly 35 percent of the company’s revenue.
Spotify is another trailblazer in its ability to leverage recommendation engines. Through Release Radar, Spotify sends out a personal playlist, every week and to every subscriber, based on a proprietary algorithm. The engine combs through more than two billion playlists created by its users, collates this information with the company’s own playlists and makes predictions by comparing a user’s listening preferences to those of other subscribers with similar tastes. Release Radar is a key part of what’s enabled Spotify to grow its number of monthly users from 75 million to 100 million – and this at a time when the company is facing off with muscular competitors like rival streaming service Apple Music.
Recommendation Works at Any Scale
Not every online retailer is going to be an Amazon, but the benefit of recommendation engines can be brought down to scale.
King Arthur Flour, the oldest flour company in America, turned to SLI to create a smart search system – making sure visitors find everything they are looking for, from recipes and tips, to videos and products. After implementing SLI Learning Search™, the company saw a spike in conversion for search users from 3.7 percent to six percent. And revenue from search users skyrocketed, from 17 percent to an astonishing 47 percent!
So why choose SLI?
Okay – it’s clear you need a recommendation engine. But which one to choose? We’re biased – but with good reason. What it boils down to is this: SLI’s recommendation engine is better. In fact, you can see your predicted ROI for yourself.
The longer version, is that we support our customers with a superior deployment and operating experience. Our product suite is flexible, designed to fit into any e-commerce environment. And our systems are lightning fast – fast enough to help you influence your customers’ shopping decisions. We house every client’s product discovery and personalization engines in 11 separate (and highly redundant) data centers globally, which means they’re up and running 24/7 – day or night.
At SLI, we’re also dedicated to improvement – making our products stronger so our customers can soar. We use state-of-the-art machine learning to optimize customer conversion, AOV and customer retention. We also perform all data feed preparation and platform integration.
Our customers constantly tell us how happy they are they’ve outsourced their recommendation engines. The alternative is developing it in-house, which carries considerable costs (less efficiency, a new department to support, etc.). Simply put, investing in SLI is worth it, trimming your operating expenses over the long term and getting you set up for sustainable success.
Not sure if we’re a good fit for you? Drop us a line and we’ll be happy to chat (or just click the chat icon there on the right side of the screen).