5 Machine Learning Use Cases That Transformed eCommerce
For the longest time, the term machine learning resided in the domain of wild speculation. In the recent decade, it has made its way from incomprehensible scientific papers to real-world applications.
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For the longest time, the term machine learning has resided in the domain of wild speculation. However, in the recent decade, it has made its way from incomprehensible scientific papers to real-world applications. Perhaps its biggest success story to date is e-commerce, where machine learning solutions were effectively used by large enterprises and small companies to create value for customers while at the same time spearheading growth and pushing the limits of innovation.
What is Machine Learning?
Machine learning, or ML for short, is a technology in the field of artificial intelligence that focuses on the systems capable of automatic self-improvement. In simplest terms, it works in the following way: the algorithm receives some inputs and a goal, after which it analyzes the available data to achieve the desired result. The technology is often referred to simply as artificial intelligence, although the latter is a broader concept that includes other types like deep learning.
Depending on the way the inputs are structured, machine learning falls under one of three categories:
- Supervised learning: The data is selected and labeled, then expected results are outlined so that AI can compare its results with the intended ones.
- Unsupervised learning: The inputs are unstructured, so the AI has to find patterns on its own.
- Reinforcement learning: The AI interacts with a dynamic environment and receives positive feedback for accomplishing the desired goal.
As a rule, the more guidance is involved in machine learning, the more accurate the results are. Unguided ML, on the other hand, takes longer and requires more resources but can discover patterns that are more efficient than those suggested by “teachers.”
Influence on eCommerce Transformation
Initially, machine learning was viewed primarily as a tool for data analysis. As the technology progressed, and more resources became available for powering ML-based systems, it turned out that they can accomplish many tasks far quicker and with higher precision than humans. In many cases, such as image analysis and voice recognition, AI-driven solutions outperformed humans by a wide margin. This opportunity was quickly recognized in the business sector, leading to the rapid adoption of the technology by companies. Digital-first services like e-commerce were a particularly suitable domain for this, where a clever idea brought to life by a mobile application development company from Singapore could topple an industry giant.
Currently, the overwhelming majority of organizations in the for-profit sector use machine learning in some form. According to one survey, 85% of employees agree that it makes their work simpler and more productive, and 75% believe that it plays a major role in the company’s digital transformation. As the technology matures, it is reasonable to expect the adoption rate to increase.
Benefits of Machine Learning for eCommerce
Commercially available ML-based solutions started as sophisticated and expensive tools available only for large enterprises. Eventually, as the technology became more accessible and eCommerce app development cost went down, smaller companies got the chance of experimenting with it, revealing a much broader range of advantages. Here are the most interesting ones:
- Spot-On Marketing: Crafting a marketing campaign for a specific audience is a tricky process that needs to account for a multitude of factors. Many of these factors, such as individual values, views, and beliefs, are difficult to quantify — to a human, that is. A well-trained AI can spot patterns in consumer behavior that are buried deep under heaps of seemingly unrelated data.
- Improved Performance: As mentioned above, ML-driven apps can optimize company operations. Not only does it drive performance up, but it also alleviates the load from employees who can then focus on more creative activities.
- Insights and Predictions: The ability to process large volumes of information quickly opens up the possibility to provide insights on relevant operations in real-time. This adds flexibility to corporate decision-making and offers more accurate strategic predictions.
Effective Use Cases of Machine Learning by Businesses
The potential of machine learning in e-commerce is tremendous. It has already been used in the ways it was never intended for, and more creative approaches will probably be discovered in the oncoming years. Meanwhile, here are the most interesting use cases for machine learning to date.
On the surface, the search couldn’t be simpler — you type a phrase and the algorithm matches it to the content on the website. So why use machine learning for it? Well, for starters, customers may not know the name of the product they need, so the AI can help them find it by analyzing the request. Taking it one step further, with machine learning it is possible to match the request with the available database and cater personalized recommendations to better match the customer expectations.
Speaking of personalized recommendations, machine learning can make many aspects of service laser-focused on individual preferences. In fact, ML has become powerful enough to detect the intricacies of consumer behavior that people are not aware of, exceeding the expectations of visitors. In the age where a unique experience is key, this becomes a crucial advantage to build a loyal customer base on.
The power of ML doesn’t end with figuring out people’s desires. Another way to enhance customer experience is through offering high-quality support that is available 24/7. Doing it the old-fashioned way — by hiring employees — is quite expensive, which is where machine learning comes in. AI-driven chatbots, for instance, are capable of basic assistance like directing the customer to a specific item in the shop, which not only reduces the waiting time but also optimizes the use of human resources.
No matter how good the service is, it won’t get you far once you run out of goods. While modern inventory management tools do a good job of monitoring the supply, they cannot account for unpredictable shifts in demand. One way of making your supply chain management proactive is to implement machine learning that would track fluctuations in demand and forecast oncoming shortages.
One of the scary properties of AI is its ability to generate imagery that is indistinguishable from real-life photographs. The fidelity of these images is so high that only AI can spot fakes generated by AI. This opens up interesting possibilities for detecting forged documents and fraudulent transactions that still plague the eCommerce industry. Not only that, the process is nearly instantaneous, so no customer experience is sacrificed to security.
The power of machine learning has captured the imagination of entrepreneurs for a long time. Now that it has become accessible and mature enough, we are seeing more and more of its creative applications that bring value to both businesses and customers. Time will tell how much of its potential still remains undiscovered, though it’s probably reasonable to say that what we see now is just the tip of the iceberg.
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