Online shopping is on the rise. During the early onset of COVID-19, eCommerce sales increased by 43%, causing many retailers to either open new online stores or strengthen their existing ones.
As online retail sales continue to scale new heights, there’s more demand for hyper-personalization for delivering tailored experiences, content, and incentives.
Machine learning is one way of optimizing the output of personalized recommendations. However, it has many different applications in the eCommerce world.
In this blog, we’ll explain how machine learning can take your eCommerce business to the next level.
What is machine learning?
Machine learning is a branch of artificial intelligence (AI) that uses data and algorithms to make predictions.
Retailers can apply machine learning algorithms to make accurate assumptions on what each customer needs throughout their journey.
Let’s say you’ve just added a certain product (item 1) to your online shopping cart. Machine learning can study previous purchases of item 1 to see whether it’s typically bought with something else. If there’s a product (item 2) that tends to purchased with item 1, the technology will recommend item 1 to customers checking out with item 2, and vice versa.
As you might have guessed, machine learning uses data to learn from every journey. The more data it gathers, the more it learns, and the better it gets.
How does eCommerce and machine learning work together?
Machine learning can mimic human behavior to complete tasks quickly and without supervision. This role is key to integrating hyper-personalization into your marketing strategy.
Some of the tasks performed by machine learning include processing large amounts of data, monitoring repetitive patterns, and building files to record and optimize engagement rates. The outcomes for eCommerce brands are better conversion rates and order values by improving the success of your recommendations.
Now we know how machine learning works in an eCommerce context, let’s look at some ways of applying it.
Machine learning application #1: Recommendation engines
The first example of how machine learning can improve your eCommerce business is through product recommendation engines.
Here, machine learning allows you to look over past data (i.e. previous sales, interactions) and assist with creating customer profiles. These can help you serve accurate recommendations to shoppers that exhibit familiar behaviors.
Recommendation engines process lots of different information. Some examples include:
- Items in cart
- Cart value
- Customer behavior
Using machine learning to analyze this data can help you assign keywords to products and track preferences from sales. When someone fits the profile of a ‘lookalike’ customer, you can then make recommendations based on successful outcomes of similar shoppers.
Machine learning application # 2: Pricing optimization
Instead of creating a static price strategy, you can use machine learning to build a dynamic plan based on multiple factors. Machine learning helps you understand whether to go lower or higher by predicting the response from your customers.
You could change the price of a product for multiple reasons. Machine learning considers factors such as:
- Production costs
- Special events
The demand for your products will go up and down throughout the year. Machine monitors past purchases, sales trends, and demand/supply data to devise the right pricing strategy.
Machine learning application #3: Trend analysis
To stay ahead of your competition, you must be up-to-date with current trends. It would take employees hours to gather enough data to compare trends across the industry. However, machine learning can do it within a few minutes.
For busy times of the year like Black Friday and Christmas, machine learning helps you to prepare in advance and ensure you have the right items for customers.
Think of it as forecasting what’s about to be popular during a major sales event.
Where RevLifter comes in…
Ultimately, the benefits of machine learning are clear for any eCommerce businesses. With RevLifter, you can drive your goals by using data to decide precisely what your customers need to convert.
Let us know if you have any questions on how this works, or request a demo today.