Machine Learning can help eCommerce retailers offer intelligence-powered shopping experiences to customers, increase conversions & curtail cart abandonment.
The average customer of today owns and uses at least four devices – mobile, tablet, laptop and a desktop on a daily basis. At least 50-60% of customers use their smartphones and tablets to make online purchases.
Every single action of the customer online can be tracked and archived for analysis and behavior prediction. Traditional systems enabled retailers to deduce insights from these data. But, they are proven to be slow to respond to customer preferences that change within a day.
The need of the hour is the real-time intelligence of what customers are looking at, what they looked at in the past and what they would be interested in looking at in the future.
That is where machine learning can pitch in and give eCommerce a competitive edge.
Now, let us look at some real-life examples of machine learning is reshaping customer experience in eCommerce.
Chatbots for personalized product recommendations
The prime motive of equipping eCommerce with machine learning is to make the task of buying easier for the customers. That is how the idea of chatbots and the era of conversational commerce as Gartner calls it, took form. Chatbots are AI programs that enable brands to interact with their customers through virtual avatars. Machine learning enables chatbots to learn from customer input and feedback and fine-tune their responses for accuracy.
Stephanie Baghdassarian, Research Director at Gartner says, “Bots can go as far as enabling transactions, handling payments, ensuring delivery and providing customer service.”
Facebook launched its Messenger Bot platform in April 2016 which enables businesses to interact with their customers through bots. Machine Learning is the technology the powers these bots to collect input from users, fetch relevant information from databases and provide contextually correct answers that satisfy customers.
Needless to say, it will replace the complacency and delays that are inherent in manual-assistants based customer service. It won’t be long before chatbots will replace the “Contact Us” page forever.
Every online store collects user reviews from customers for completed deliveries. Exploring the text reviews submitted by customers can help an eCommerce store know how exactly their customers feel about the product.
Machine learning can be used for real-time sentiment analysis of customer reviews to spot top-rated and worst-rated products. Each text review can be run through an ML system to determine the positiveness or the negativeness of the review.
The same can also be replicated for social media. This would be of immense benefit to an online store which could be collecting thousands of reviews in a single day.
Faster Product Discovery
A typical online store would have thousands of products. eBay, for instance, has about 1 Billion products in their catalog. Even with few thousands of products on the store catalog, helping customers find the right product from such an expansive catalog can be extremely difficult. Machine learning with its ability to connect the dots between the customer profile criteria like age, location, gender with previous search terms, previous product views, buying habits, etc. can be a great value addition.
Google is a classic example of how the search function can be optimized to perfection with the help of machine learning. The same benefits that Google and other search engines gain from machine learning can be attained by eCommerce stores too.
Machine learning can detect patterns from the most common products searched by customers, the price or brand filters they apply, location-wise features of the searches, etc. Retailers can use such insights to provide tailor-made product suggestions that will help improve conversions.
For instance, a generic product search can be provided with most preferred brand choices that previous buyers opted for. This would simplify product selection for the customer helping them complete the purchase quickly. Amazon, Walmart, Flipkart, etc. have already set up ML-based search type-ahead feature.
McKinsey in its blog states that Retailers like Amazon are using machine learning algorithms to make customers spend more. The ML algorithm helps Amazon recommend alternative products that with a higher markup price that the customer might be interested in buying.
For instance, assume a customer is searching for a cube of orange sticky notes. The regular price charged by Amazon for a cube of orange sticky notes could be $6.72. Amazon algorithm would combine various datasets to recommend alternative colors of sticky notes that the customer would be interested in.
These alternative recommendations lure the customer to spend more, thus earning Amazon a higher profit. ML-based product recommendation systems can replicate the same success story for other existing or newly launched eCommerce businesses too.
Market-driven dynamic pricing is one of the biggest challenges of eCommerce retailers. Analyzing competitor pricing, estimating market demand, offering holiday-specific discounts all require enormous amounts of manual effort. Machine learning can simplify that part for eCommerce retailers and help achieve market-right pricing that will improve their bottom line.
In fact, Financial prediction using Artificial Neural Networks has been helping traders and investment bankers maximize their RoI through intelligent trading. eCommerce can leverage the same capability to predict the right market-price at which customers would buy more. However, there are several factors to be considered before opting for an ML-algorithm based predictive pricing model, like the premium status of a brand, whether to allocate more weight to competitor pricing or customer spending behavior, forecasted inventory levels, etc.
Machine Learning will reshape the eCommerce experience. It will equip retailers with advance intelligence that is drawn from real-time data instances. The end result would be a far more customized customer experience based on granular level data collected from everyday search terms, product searches and ad click-throughs.