In the age of big data, information and self-improving technology, eCommerce websites have access to a lot of personal data including demographics, exact geographic location, and personal preferences. This technology, which falls under the umbrella of machine learning, carries enormous value and can be applied to many facets of the eCommerce industry.
Online sales have doubled in just two years and are predicted to quadruple during the next few years, according to Apiumhub. This major boost can be partly accounted for by the development, continuous improvement, and application of new technology in the eCommerce industry. Global eCommerce companies like Netflix and Amazon use machine learning and artificial intelligence to assist their teams in manual tasks. For instance, the Metricstory team relies on machine learning to help our partners make strategic business decisions without spending any time on manual data analysis. Although now machine learning may seem like only a trend, we believe it has the power to impact eCommerce in ways we cannot imagine. Here are just a few ideas.
Generally, eCommerce websites suffer a certain amount of separation or disconnection with their customers. When we talk to each other in person, body language plays a vital part in understanding what our conversation partner is really feeling. In the digital world, however, we’ve only scratched the surface of personalization possibilities.
Artificial intelligence and machine learning can significantly ease this issue by studying the personal preferences of customers (yes, each customer’s preferences) and guiding them to what they might like in the online store. Not only does this technology create more meaningful and pleasant experiences for the customers, but also drives traffic, sales, and revenue to the store.
Every business is susceptible to fraud, but digital shops are even more so. For instance, in 2016, the cost of chargeback fraud via digital channels across the US was $6.7 billion dollars. When dealing with fraudulent activities, companies can suffer a lot more than just a loss of money, but also a hurt reputation and loss of customer trust.
Applying tedious data analyses to transaction logs can significantly help detect and prevent fraud on a large scale. For instance, PayPal, a global online payment system, used machine learning to analyze customers’ behavior, spot fraudulent transactions and lower PayPal’s fraud rate to 0.32% (as compared to the industry average of 1.47%; Source).
It’s no secret that investing in search engine optimization (SEO) and marketing is one of the proven ways to increase your eCommerce website’s ranking and show more content to readers. At the same time, SEO managers spend their days doing manual research and data entry to see results. Machine learning can save the routine labor and find valuable search results from not only long-form content but also purchases, reviews, and even product tags.
Product recommendations may also fall into this category. Amazon’s Recommendation Engine is responsible for 35% of its sales. It is managed entirely by AI technology and finds the right patterns of products to suggest to its customers.
In the age of massive automation and the desire for strong personalization, customer support plays an important role in the success of a brand. In fact, 7 out of 10 consumers say they’ve spent more money on brands with great digital customer service. In addition to that, millennials are willing to spend an additional 21% of the price for good customer care.
Technologies like artificial intelligence and machine learning can help brands optimize their customer care departments. Through intelligent automation and tools like natural language processing (NLP), AI can run self-support systems, encourage well-informed actions, and most importantly, provide uninterrupted services to customers all over the world. As a one-time investment, technologies like this can carry a high ROI over the years following its implementation.
Strategic Business Decisions
Lastly, when running an eCommerce business, all actions and analyses come down to making strategic business decisions. These decisions rely on strong data which not only provides the raw numbers but also tells a story to the decision maker. AI can support regular data retrieval, analyses, and predicting on a regular basis, thus clearing the way for company team members to focus on strategy, rather than data analysis.
If you are considering implementing AI in your business, we suggest you start small and work your way up. Analyzing existing data can be a smart first step and a light investment.