Let’s talk numbers. Americans spent – brace yourself – 11,000 years shopping online just during Cyber Monday 2018. If that doesn’t tell you everything you need to know about how many and how often people are shopping online, you should also know there were 7.9 billion dollars in sales during that day.
What does this mean? It means that depending on the website, it could be a hacker’s paradise.
Machine learning can help curb the risk of fraud while customers are shopping on your website. Take PayPal, for example. They recently added new algorithms to their transaction process to prevent money laundering to and from unsuspecting customers.
The algorithm looks for data points that spark the system, such as first-time transactions, the amount of money sent, sender/receiver location and more. PayPal has an automated, filtered system that flags specific transactions so humans can double-check them for fraud.
It’s astounding to think about how far online businesses have come over the past few years. At one time, you had what was on the storefront and that was that. Now, with machine learning, your system is constantly running data points on customers based on their profile and the type of things they search for on your site/store.
You can change your algorithm to target customers who view certain content pages in your blog. For example, if you sell pet products, you can use machine learning to sell cat products to people who view your articles with cats in the title.
This is a win-win situation, because you can link to your products within your blog post. Machine learning is expanding in leaps and bounds and able to offer customers the right products at the right time.
On the fly pricing or dynamic pricing, is another feature found in the world of machine learning. In the old days, we had to offer everyone a flat rate. This takes the cross-selling aspect to a brand new level. Now, not only can you offer the right product at the right time, but you can offer it at the right price based on your user.
Airbnb is a great example of this type of machine learning, and it’s carrying over to airlines and ride-sharing services like Uber. The artificial intelligence built into Uber’s program analyzes each person, their route, other routes in the area and how they can offer their customers amazing deals that will keep them coming back for more.
The pricing structure can change depending on a variety of factors, including location, time, customer frequency and more. You can apply this type of pricing structure to your product through machine learning and build a business that really caters to every customer’s needs.
Finally, you may want to consider using machine learning to help build your customer service presence. There are many businesses that use online chatbots to help answer customer questions.
There’s no doubt that chatbots are boosting sales. Sites like Sephora, which sells beauty products, saw an 11 percent increase in sales when they switched to a bot to help customers on their Facebook page.