Machine learning is more than the new buzzword on Buzzfeed; it has the potential to spearhead another digital upheaval, transforming the way humans interact with technology, and the way ecommerce does business.
Any business that relies on ecommerce needs to know about digital trends — how Google’s Panda updates changed SEO, how social media changed digital advertising. But machine learning is a new and even more influential beast than either of these, because it will impact everything.
We take a look at some of the most impactful results of machine learning, real groundbreaking algorithms, some of which have already shaped the world of ecommerce today, some of which are only beginning to spread to the far reaches of the web in 2017.
Product Recommendations
The simplest form of machine learning has been around since PageRank. The premise of PageRank was to learn from human behavior. Amazon translated the idea for ecommerce applications with its product recommendations engine. Just about everyone has seen the Amazon “Customers Who Bought This Item Also Bought” section underneath a product they’ve clicked on. Recommendations such as these are generated via machine learning and predictive analysis, which analyzes user purchase patterns, product attributes, and the performance of different products. This data helps generate the most relevant product recommendations that also have the highest probability of generating a sale from a particular customer. The premise is much simpler than the types of machine learning at work today.
Customer Service Chatbots
Since the Turing Test was invented, the ability for robots to communicate with humans has been the purview of science fiction. Now… it’s real. Providing good customer service online involves being available to your customers, both via your website and social media. Unfortunately, small and mid-sized eCommerce companies alike struggle to staff and maintain a team of customer service representatives around the clock. This is where machine learning comes in.
Intelligent customer service chatbots can field basic customer service questions, while learning how to help customers in ways that are tailored for a specific business’ products. These chatbots can handle the most commonly asked questions or problems through on-site chat sessions, as well as respond and maintain conversations via Twitter direct messages and Facebook messenger conversations.
Predictive “Market Right” pricing
Before the use of machine learning pricing algorithms, online sellers would often resort to margin slashing price wars with competitors in order to remain competitive. With the rise of predictive pricing, ecommerce companies have been able to use data regarding pricing trends, product rates, and customer behavior to find “just right” prices for a particular product and particular customer. This helps the retailer ensure they are optimizing the price point to entice the buyer to make a purchase, while maximizing profits. Some Customer Relationship Management (CRM) software already “employs” machine learning algorithms to help their clients act quickly on real-time information.
Better Customer Segmentation
Customer segmentation is essential to maximize the reach of any ecommerce site. By identifying patterns among customers or potential customers, ecommerce retailers can run their eCommerce marketplaces in a more profitable manner. The implementation of machine learning provides the information needed to easily identify additional customer segments, allowing the opportunity to create ads specifically targeted for each of these segments.
Trend Forecasting and Analytics
Online retailers are often blindsided by chaotic and severe shifts in trends, leaving purchased inventory on the shelf, resulting in wasted investment and reduced profitability. Already, ecommerce merchants analyze as much data as is available to anticipate these seismic shifts in purchase behavior, for instance, internal data on the performance of previous seasons’ products lines and current trends across their industry. But before machine learning, there was no concrete data on how to improve upon the assortment of products they sell.
Data analysis and machine learning gives retailers the ability aggregate trends and sales information from a wide variety of sources around the globe, including retail sites, blogs, and social media, and then make that information accessible, understandable, and useable in real time.
Product Inventory Forecasting
With the ability to analyze huge datasets to identify patterns that would be nearly impossible for human analysis to uncover, machine learning is the perfect technology for inventory forecasting. By applying machine learning to inventory forecasting, companies are able to better identify the products that are most likely to sell and therefore the products they should stock, which results in using the company’s capital more efficiently.
Machine learning can also help a company understand consumer demand, which allows for the efficient management of the supply chain. The information analyzed includes planning and forecasting, sourcing, fulfillment, delivery and returns. If a retailer has the ability to predict the revenue for a specific product or product line over a set period of time, it results in better inventory management, optimal use of warehouse space, better usage of capital, and the ability to avoid marketing out of stock items.
Fraud Detection and Prevention
In 2016, the annual cost of fraud in the ecommerce sector reached $40 billion, which is a 33% increase from 2015. The level of fraud as a percentage of revenues also inched upwards from 1.32% to 1.47%. On average, ecommerce retailers in the U.S. reported an 8% increase over last year in cost per dollar fraud losses, from $2.23 to $2.40. This means that for every dollar in sales, merchants are losing $2.40 based on chargebacks, fees, and merchandise replacement.
This increasing trend of fraud committed against online retailers shows that fraud detection and prevention have become more crucial than ever. Machine learning is as the forefront in the fight against those that commit fraud. Since detecting and identifying fraud requires the constant monitoring of online activity, data mining using statistical analysis and machine learning is essential for combating fraud.
Machine learning has already become a significant asset to large ecommerce, and it is poised up the stakes for all sizes of online retailers. The examples listed above, while far from exhaustive, are just the beginning of a technological wave that will have a positive impact on all aspects of the eCommerce industry – from suppliers to retailers to end consumers, and from the frontend to the backend.
With the use of machine learning and its applications, ecommerce retailers are finally able to deliver the right products at the right place and the right time and at the right price, by providing intelligence-powered shopping experiences. The retailers who understand and can implement machine learning to their greatest advantage will be the winners of tomorrow.
Ron Dod is Partner and CEO of Visiture, LLC