One of the primary roles of emerging technology is to make humans more efficient and more effective. Technological advances help us to do more while using fewer resources, enhancing products and services and fueling innovation.
Ecommerce retailers are no different, having seen a wealth of evolution since the industry matured. This includes the introduction of machine learning, which has spun out of artificial intelligence to become a key application of that technology.
Specifically, ecommerce machine learning is the creation and refinement of applications and algorithms by artificial intelligence that “learns” from a steady flow of data. It involves artificial intelligence not just executing on a data set, but using data to test and change reactions to data.
It’s an evolving field for online retailers and ecommerce stores, but is starting to impact customers and users and new ways.
Much of the ad targeting on Amazon is handled by machine learning and even Netflix is using the technology. Approximately 75 percent of suggested viewing on the platform is fed to consumers by machine learning.
By definition, humans are not involved in machine learning once a system is deployed. The technology is primarily machine learning algorithms that become more accurate and effective as more data is introduced. The application analyzes the data and outcomes and applies these “learnings” to new data sets.
Much like humans, machine learning involves trial and error before optimization is achieved. Unlike humans, machines aren’t hindered by little things like sleep and can operate 24/7.
Machine learning offers ecommerce companies new opportunities for providing an optimized customer experience for online stores. From the public-facing site to the logistics of fulfillment, machine learning is helping the ecommerce industry better meet customer needs.
Machine learning may boost conversion rates by optimizing on-site search engines and delivering informed product recommendations on ecommerce websites. Applying AI to both create “smarter” results that are more in line with how the customer acts and thinks.
Natural language processing more accurately predicts what customers are searching for. Product recommendations can be altered based on a customer’s purchase history. Both lead to customers finding what they want — quickly.
Ecommerce platforms have more data than ever before to draw from. This is data that can be fed to an algorithm that shows what interests different customers or visitors to your website. That allows for more accurate customer segmentation. You can split your prospects based on their interests. That lets you target them with far more relevant marketing material.
Algorithms can also deliver real-time insights to help you make your other operations more efficient. This is often found within logistics or supply chains where stock levels and inventory are monitored in real time. Machine learning can monitor customer demand to determine what products need to be ordered and when and can even predict future demand.
At the center of machine learning is analysis. Understanding data and how it impacts your business is key to fully leveraging the power of machine learning. A quality algorithm processes and understands large amounts of data quickly. This leads to better decision making.
Machine learning can significantly enhance the online shopping experience, for the customer and the company. From product selection to inventory to security, machine learning makes the shopping experience smoother for all.
Modern consumers don’t want to be made to feel like just another number. They want a shopping experience personalized to their tastes. Machine learning can do this by analyzing past behavior to better predict specific products customers may be interested in.
A tailored experience means more eyeballs on products that customers have a proven affinity for. This can mean higher conversion rates.
Consumers are used to getting the quality experience that Google provides — although many ecommerce site search experiences fall short. Machine learning can shrink that gap by using intelligent algorithms to include a recommendation engine or visual search, making it more likely to connect customers with what they’re looking for.
Inventory management and optimizing supply chains is hard — but machine learning can ease some of that burden. Accurately forecasting demand for certain products based on a variety of data-driven factors means you keep up with consumer demand, without wasting resources on unnecessary warehouse space.
Churn prediction is the rate at which consumers abandon a brand. It’s easier to sell to existing customers than new customers, so having an effective retention marketing strategy is key. Machine learning can optimize customer churn by accurately predicting when customers may be on the verge of leaving your platform.
Fraud detection and protection are essential parts of any ecommerce platform. The amount of data collected that is stored or in motion is massive and makes for a valuable target for hackers. Machine learning can better analyze transactions to understand what are genuine transactions and what are potentially fraudulent.
Ecommerce platforms are open 24/7 and expect customer support to do the same. That’s, of course, not always possible, but machine learning can make that possible by offering automated responses to basic customer service inquiries before referring more complicated issues to humans. This increases customer satisfaction and encourages purchases.
Pricing is not constant and, with the ever-fluctuating state of global markets, can change quite frequently. A machine learning solution that takes changes in materials, shipping and production costs into account and automatically alters pricing ensures that profit margins are maintained.
Customer data is invaluable for ecommerce companies. Leveraging purchase history, social media, searches or cart activity enables platforms to recommend similar products and encourage visitors to make additional purchases.
As mentioned above, staffing customer service 24/7 isn’t realistic for most companies, although customers expect answers to questions around the clock. A chatbot powered by machine learning is a good solution to give customers a quality user experience without placing a major burden on staff.
It’s one thing to understand machine learning on a surface level. It’s another to execute on an effective business strategy. This process will help do just that.
Before you can leverage machine learning effectively, you must fully understand its capabilities. That means putting in the time researching the present state of the technology and understanding automation and deep learning.
Look into the AI-enabled solutions others are using and how they’re leveraging these platforms.
It’s very likely that you don’t have the requisite experience on staff to deliver a fully optimized machine learning solution. You may need to bring in outside experts — either a consultant or new staff member — to manage adoption across the organization.
Problems should be specific and easily identifiable. Having a grand problem you want to solve (“increase sales”) isn’t enough. They should be more granular, like “increase retention rates among first-time buyers.”
Applications of machine learning should be well-reasoned and well-researched.
This step is best taken in concert with the previous one. When defining your machine learning goals, take your organization’s capabilities into account. Don’t dream bigger than your staffing or tech resources allow.
A project of this size must have dedicated resources, including manpower. Assembling the knowledge and skill sets to execute on a vision is a crucial step in ensuring a project delivers on its goals.
It’s advisable to start small and scale as more data is compiled that shows what works and what doesn’t. Once the initial implementation meets expectations, further applications can be explored.
How machine learning was initially viewed was the realm of science fiction. Although it’s not quite as grandiose as some of those depictions, machine learning is now a part of everyday life. Ecommerce companies that don’t embrace this technology are in danger of falling behind competitors that do.
It doesn’t solve all your problems, but it can make experiences and operations more efficient and, ultimately, deliver a better customer experience.
Although not full proof, a properly implemented machine learning system with sufficient data points can make reasonable predictions on if an online shopper will make a purchase, based on customer behavior while on your site.
AI is the ability of computers to emulate human thought and do tasks in real life. Machine learning is a subset of AI and refers to technology that uses data to improve systems and identify problems.
Yes. Machine learning is becoming increasingly common in ecommerce, with the biggest players having fully mature operations. Not implementing the technology is risky and can actually negatively impact ecommerce sales.