How small businesses leverage AI services to compete with the giants and increase customer satisfaction
Product designers agonize over interface design and UX flows to ensure that first-time users will find their websites and apps intuitive enough to enjoy the first time, and sticky enough that they keep using them. In recent years, artificial intelligence has played a key (and complementary) role by optimizing the content that is presented to users. We can learn a lot from how the biggest brands have used AI throughout their apps and services to personalize offerings and bolster customer loyalty. Let’s take a closer look at how Amazon, Starbucks and Netflix have used AI and see how we can apply those lessons to our own projects.
Amazon: The Ultimate Tipster
In its early days, Amazon began experimenting with ways that a digital bookstore might surpass a physical space in terms of customer experience. They began suggesting items based on A) previous purchases, and B) previously viewed items. This was an early form of artificial intelligence that would, over time, build a personality profile for 300 million Amazon users. As recommendations got better, Amazon users would consider shopping around less and less.
“Amazon is truly the ‘one-stop shop’ for consumers”
– Claudia Hoeffner, VP of Global Marketing, Feedvisor
Speaking to Forbes, Claudia Hoeffner, VP of Global Marketing for AI Optimization company Feedvisor described the addictiveness of Amazon: “One of the most interesting things we found in the study is that Amazon is truly the ‘one-stop shop’ for consumers. Consumers go to Amazon for almost every activity possible in their shopping experience. And, for activities like browsing for new deals and discounts, comparing prices, and checking availability, Amazon is head and shoulders above other marketplaces and retailers.”
It’s not only Amazon’s in-site recommendations that are effective; their email notifications are even more impressive, with a nearly 60% conversion/sales ratio.
Item recommendations are based on your purchase history, browsing history, related items, frequently bought together data, and more. Amazon has conceived and tested a wide range of recommendations using their massive data set in concert with artificial intelligence. On Amazon, you are what you buy. While Amazon continues to iterate over its UI with traditional research and A/B testing, its personalization of custom content is truly the result of brilliant AI. Over time, data-backed customization and personalization serve to cement customer loyalty.
How might you apply this to your own business?
The first step to implementing AI starts with looking at how a recommendation engine (there are many, and Amazon itself has offerings in this area) might convert casual visitors on your site, or looking at how a platform might upsell loyal customers by predicting their intent with AI.
Starbucks: Using AI to delight longtime coffeeholics
It’s a cliché by now, but hardcore coffee lovers all have their favourite Starbucks drink and get tired of explaining it repeatedly. When Starbucks decided to build their own mobile app, it was designed around simplifying the ordering of your go-to drink. The app makes this a breeze by letting you save your drink order down to the very last detail.
A loyalty/ordering mobile app by itself wouldn’t be an AI story. The AI comes in when Starbucks takes your saved drinks and purchase history and combines it with a slew of other factors: your home city, your favorite time of day to order, and even the weather when you order. They use this data to build a profile so they can intelligently suggest other items – in other words, the smart upselling on snacks or new drinks that are ordered by users with a similar behavioral profile. Starbucks knows how exciting it is to discover new favorites, so they use AI to suggest items they know you’ll love.
Also included in the app are exclusive discounts and promotions, which further solidifies loyalty to the Starbucks brand. Haven’t been to a Starbucks in a while? You might get a seductive email pulling you back in.
“[Starbucks] evaluates massive amounts of data, such as proximity to other Starbucks locations, demographics, traffic patterns and more, before recommending a new store location”
– Bernard Marr, Author “Data Strategy”
Lastly, one of the smartest things they’re doing is taking GPS data from mobile users and computing which geographical areas are most in need of a new Starbucks location. There is a store locator in the app, and it registers your distance to the nearest store. With enough queries, the company can prioritize new locations accordingly.
“[Starbucks] evaluates massive amounts of data, such as proximity to other Starbucks locations, demographics, traffic patterns and more, before recommending a new store location. This system even predicts impact to other Starbucks locations in the area if a new store were to open.”
The lessons learned from Starbucks are less about recommending products and services, and more about identifying untapped markets and opportunities. By utilizing AI in creative ways one may determine the ways in which customers can be better served.
Netflix: Using Big Data to Entertain the World
Finally, we come to Netflix, which is using AI to reinvent 21st century entertainment. They’re not just using AI for content delivery at a near-subatomic level; they’re using it for content creation. Netflix plans their shows, architects their season arcs and episodes, and then markets that show to the ideal viewer – all based on user behaviour. It is truly remarkable.
Netflix’s goal isn’t just subscription revenues, but increasing the time spent watching Netflix, which translates into long term customer satisfaction and loyalty. And they do it with AI personalization. Netflix knows, for example, where you dropped off in the second season of Stranger Things. They consider the actors, story arcs and the lighting of the scene to determine exactly what show you want to watch–even if it hasn’t been created yet. Then they create that show. This is an algorithmic approach to art, which some find unromantic -but you can’t argue with the results: Netflix has used this big data to achieve success rates on new TV series of 80%, much higher than the 30% or so for the average series.
Justin Basilico, research and engineering director at Netflix, sat down with the AI Podcast to talk about the company’s core values:
“Because we’re a subscription company, our North Star is really whether people stay a Netflix subscriber over time, and we have a monthly subscription, so that means it typically takes a month or two to be able to measure that,” Basilico said. “But we see that as the ultimate signal that people see enough value to stay with Netflix. And if we can move that with the recommendation algorithm, we know that we are making people happier and making a better user experience and improving their satisfaction.”
“Our North Star is really whether people stay a Netflix subscriber over time…if we can move that with the recommendation algorithm, we know that we are making people happier”– Justin Basilico, Netflix
These three companies all started off with a simple recommendation engine which leveraged AI to promote items or actions of relevance to their users.
From there, they observed macro trends, and then applied analysis to those trends to forecast product/service demand.
AI optimizes user convenience and “surprises” users by presenting exactly the product/service they were looking for (whether consciously or subconsciously). The integration of artificial intelligence enables a business to determine which markets to get into and which to ignore.
Featured Image | Freepik
Michael is Design Lead at Convergence. From drawing to design to code, he loves the creative problem solving process.