5 Compelling Cases for Machine Learning in Ecommerce
Not to brag but…
Segmentify was named one of Forbes top 25 Machine Learning Companies to Watch. Forbes writes:
“Segmentify’s vision is to create a better online shopping experience personalised for each visitor and increase the conversion rates for online retailers. The startup does this by creating a unique online shopping experience that is relevant to each visitor by utilising advanced machine learning technology.”
While this was a huge honour which we are very proud of, we still get the same question when we speak to potential customers. What can machine learning do for growth and retention when it comes to ecommerce businesses?
We thought this would be the perfect opportunity to start to explain how machine learning has a huge place in the future of online retail and how it’s changing the landscape of how we interact with each other online.
But like, what is machine learning?
Machine learning has become somewhat of a buzzword these days and while we enjoy being a part of this new revolution in technology; we understand that it’s not the easiest concept to wrap your head around.
Machine learning is an approach to data analysis which automates analytical model building. It is a part of artificial intelligence formed around the concept that systems can learn from data, determine patterns and make decisions with little human interference. To clarify, machines that are able to complete certain tasks by mirroring human cognition is AI, while machine learning is a method used to improve performance through experience over a specific duration of time.
So while machine learning is an innovative advancement in technology, how does this play a major role in the world of ecommerce business? Well, we have identified five key applications for machine learning in ecommerce businesses below.
One element of ecommerce business that has always been common but somewhat elusive has been forecasting. Nowadays, with more data than previous years, ecommerce organisations are choosing to work with more advanced business intelligence tools. Machine learning technology can find particular insights hidden within your company data because it can process data much faster than before.
Processing speed and workflows utilise data science insights with accurate and transparent results. This allows ecommerce businesses to make data-driven decisions that will ultimately drive growth within the company.
By analysing patterns in shopping behaviour, machine learning can be used to recommend ecommerce products to both new and repeat website visitors. By evaluating data from various channels, the algorithm can identify purchasing patterns and buying behaviours used to predict your customer needs, preferences and wants.
Amazon is a great example of successful use of recommendation engines for growth. 35% of Amazon.com’s revenue is generated by its recommendation engine while the company recently reported a 29% sales increase. “Recommended for you”, “Frequently bought together” and “Recently viewed items” are staple sections within the Amazon platform that personalise the visitor’s shopping experience.
The connection between personalisation on the platform and personalisation through other types of communications (such as email) are vital to Amazon’s success. With all recommendations set in real-time, any visitor can now feel like they have their very own personal shopping assistant which enhances your customer’s overall user experience and drives sales.
Segmentation & Personalisation
In-store salespeople have always had the advantage of assessing shoppers behaviours, languages and rhythms in-person to understand more about how to offer the best service and get them to the till. Online shops however, don’t have the same luxury and rely on huge amounts of data to try to understand their customers in more depth.
This is where customer segmentation becomes integral for ecommerce organisations because it allows companies to customise their outreach. It is estimated that 80% of consumers are more likely to do business with a company that offers personalised experiences while 44% of consumers say they would most likely become repeat buyers after a personalised shopping experience with a business.
Also, machine learning technology allows for proper segmentation within personalisation for your ecommerce company. This can help brands to understand behaviours such as price sensitivity, batch ordering, and indecisiveness in your visitors – which in turn can help retailers shape offerings for their online visitors.
Omnichannel marketing is extremely important in retail, so you can expect artificial intelligence to use not only customers’ digital data, but also analyse their in-store behavior. Omnichannel in ecommerce refers to a multichannel sales approach that provides the customer with an integrated shopping experience. The customer can be shopping online from a variety of devices and the experience will be seamless across them all.
Machine learning technology is a large component to driving successful remarketing campaigns. They can reach out to people who previously interacted with your website through email or push notifications to offer up discounts, offers and general touchpoints.
Machine learning algorithms can help you gather information regarding pricing trends (and those of your competitors) but can also analyse that data to suggest best pricing and demand on a product level. Machine learning also enables ecommerce businesses to develop more complex strategies that work and help them easily achieve their goals.
This technology can analyse huge numbers of products and help optimise prices on a global level which is ideal for companies who are larger or who don’t have the time and resources to do it manually.
Is it a must-have?
While machine learning, AI, IoT and Blockchain have been making headlines in the way they are shaping the future of ecommerce business, it is important to understand what will work specifically for your business. Within the scope of ecommerce business, we have seen the major positive results from our customers including increased AOV, increased revenue and an increase in overall customer satisfaction.
A happy customer means a loyal customer and machine learning is helping us to understand how to cater to our customers in the best possible ways. One way to assess your overall website health is to try our free website health check which can help you identify any usability issues your customers may be experiencing! For more about Segmentify and our personalisation products, schedule a quick chat with one of our success team.