Credit from Tetiana Boichenko
Machine learning is making significant inroads in a wide range of industries, including retail. In fact, Gartner predicts that by 2020 over 80% of all customer interactions will be run by AI.
First of all, let’s clarify what machine learning is, and how it works.
What is Machine Learning?
Machine Learning is a subset of Data Science. It uses statistical models to get actionable insights, find dependencies, and make predictions. Data scientists train machine learning models and then apply those well-trained models to real-life cases. One of major values of machine learning solutions is that they learn from experience and can be retrained as many times as needed.
Let’s take a look at top 8 most promising use cases of machine learning in retail
Demand prediction and stock optimization
It is one of top applications of machine learning in e-commerce as it helps businesses to be always in stock and be ready to meet the demand and customer needs.
Companies can use it to get product insights and inventory analysis and predict if they are going to be overstocked or understocked. However, demand prediction is a tall order as there is no universal model that can work for all types of products, from apples to vacuum cleaners.
Companies can use it to get product insights and inventory analysis and predict if they are going to be overstocked or understocked. However, demand prediction is a tall order as there is no universal model that can work for all types of products, from apples to vacuum cleaners.
Recommendation engine and personalization
Another critical problem to be addressed by e-commerce businesses is tailoring their offers, messages, and customer experiences. In fact, a study by Janrain showed that 73% of customers are annoyed by being presented with irrelevant content. Thus, personalization will make them happier buyers.
For example, one of our British companies has cooperated with N-iX engineering teamto develop a next-generation one-to-one CRM marketing automation platform. The platform uses NLP and machine learning to deliver more engaging and personalized email content to customers and helps to considerably increase conversion rate.
Visual Search
E-commerce companies can use it to facilitate online purchasing. For example, Pinterest Lens is a solution that is like a visual Shazam for real world objects: you just point your camera at an object you are looking for, and the Machine Learning Model helps to find it online. It is really helpful as we don’t always know what exactly we are searching for and how to translate it into words.
Sentiment analysis
Sentiment Analysis can help companies enhance their products and services by providing insights into how their offering impact customers. Also, it helps to measure customer satisfaction and define what should be improved or changed.
Fraud detection
The Machine Learning models can assess each action a cardholder takes and examines if an attempted activity is characteristic of that particular user. Such model helps to flag fraudulent behavior. The algorithms can assess and check a transaction in just a few seconds, and the speed helps to prevent fraudulent behaviour in real time.
Churn prediction
Churn prediction helps to see that an individual customer’s buying trend is going down and they may soon stop buying here. That concerns products and services that a customer buys quite often as it provides more data and more accurate picture of the current customer behaviour.
Price optimization
There are many factors you should take into account when setting certain price. They include competition, market positioning, production costs, distribution costs, the period of the year, the current state of the market and many others. Thus machine learning algorithms can help you find the right balance to optimize your profits.
Customer support
Regarding human customer support in e-commerce, there may be such problems as long waiting time, unqualified help, stressed out or upset employees. Machine learning can help to automate and streamline it. It can provide assistance in answering phone calls, e-mails or providing support via chat-bots. In fact, recent progress in speech recognition and natural language processing via deep learning have enabled us to have a more natural and effective interaction with AI than it used to be a few years ago. Especially, that concerns taking into account contextual information and improving the accuracy of Machine learning models.
How to implement machine learning in e-commerce
If your retail company decides to use machine learning, you do not need to develop new algorithms and models.
Most machine learning projects are about solving problems that have already been treated by such tech powerhouses as Google, Microsoft, Amazon, and IBM. And these companies often sell machine learning software as a service. Google offers a wide range of recommendation systems that can fit your specific purpose and product category.
Thus, to adopt machine learning in your retail business you will need a machine learning engineer that can tune up the models and implement the system concentrating on your specific data and business domain. The specialist needs to retrieve the data from different sources, transform and clean it, receive and visualize the results.
If you are planning to apply and lead the trend of the future, pick your phone and call us now!
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