Published June 6, 2019
Before we dive into leveraging machine learning to improve the customer experience, let’s make sure we understand the foundations of this concept.
Artificial intelligence completes tasks by mimicking human cognition. Machine learning is a subset of artificial intelligence that improves performance of a task by analyzing patterns and making conclusions rather than following explicit instructions.
What does it mean for ecommerce?
In the battle between ecommerce and brick-and-mortar, online stores have always been at a disadvantage when it comes to the flexibility of having a human associate.
When a customer walks into a store, there’s someone ready to suggest products, answer questions, and close the purchase. Well-trained associates instantly adapt to the unique needs of the customer in front of them and match them with the product that best fits their needs.
But online stores have always operated more like a catalog, unable to do more than display products that a customer searched for and show information that was put into product feeds.
Machine learning changes the game, allowing online stores to offer the personalization, adaptability, and “human” touch that brick-and-mortars have used to dominate the retail industry.
Here are five ways ecommerce companies can leverage machine learning to improve their customer experience.
Personalization is a huge advantage of using machine learning – we wrote a whole post about it. Take a look at Ecommerce Personalization: Products They Want to Buy to dive into the four main categories of personalization throughout the customer experience – website personalization, product recommendations, social proof notifications, and triggered emails. You’ll also see how you can incorporate them into your processes.
2. Customer Service
Unlike a brick-and-mortar store, ecommerce stores are open 24/7. That’s a lot of hours to have someone manning a chat window. But for most companies, an instant and helpful response is essential to avoiding customers choosing a competitor’s product. With millions of online sellers, 84% of customers leave a site if their question isn’t answered within two minutes.
Fortunately, customer service chatbots are able to provide instant answers at any hour of the day.
Chatbots are already a huge asset simply because of their ability to respond quickly and accurately to queries. With machine learning, chatbots can take things a step further by learning more about specific customers and providing personalized answers that increase the chance of conversion – almost mimicking an in-store associate.
This technology also allows you to program bots with your company’s values and voice so they become an extension of your brand. This is important because customers don’t want to feel like they’re talking to a computer.
3. Search Results
In the past, search engines have ordered search results based on keywords. While this may work for many products, it isn’t the most effective tool.
Here’s an example of where keywords can fail you.
Let’s say I’m looking for a T-shirt for my brother, who is a huge Game of Thrones fan. If I search a clothing website for “Game of Thrones,” it may show you shirts with board games, or video games, or even the British throne.
Machine learning allows the search engine to connect what results get clicked on with which keywords were searched and learn that when I type “Game of Thrones,” I’m talking about a TV show.
A search engine can also learn about a specific customer by looking at their former searches, personal preferences, and purchase history. Using this data, the engine can generate search results that are most relevant to the user and most likely to make a conversion.
Traditionally, we price products based on competitors, costs, demand, etc. For brick-and-mortar stores that don’t have malleable prices, this may be good enough. But online stores now have the ability to show different customers different prices based on geographic location, purchase history, and search behavior.
Machine learning has the ability to:
- Collect in-depth information on pricing trends, competition, and demand for products.
- Combine this information with information on the customer surfing your site.
- Come up with a price that will meet your goals.
5. Fraud Reduction
There are two types of fraud that machine learning can protect against: payment and returns.
Retailers, online and offline, have been struggling with customers making large purchases using stolen cards or even canceling their payment after the order has been delivered. Machine learning can prevent this by collecting data, identifying patterns, and detecting outliers.
Another issue for large ecommerce companies lies in customers returning their orders with fake items. For companies that don’t handle their own packaging or that have poor tracking capabilities, determining whether the buyer or seller inserted the false product can be difficult. Machine learning combats this by analyzing consumer behavior and determining the probability of each return being false.
Through the reduction of customer service issues and increase personalization, machine learning can reduce cart abandonment and increase sales. These are just a handful of the capabilities of these technologies. As they become more accessible, thought leaders will come up with innovative and impactful ways to leverage these tools.
To stay up-to-date on this year’s digital marketing trends and technological developments, download our 2020 Trends + Tech Brand Growth Guidebook. This industry evolves so quickly, and that isn’t changing anytime soon. Don’t let your brand get left behind!