Machine Learning Use Cases in Retail and Other Industries
We are in the midst of the fourth industrial revolution (or industry 4.0), where the introduction of innovative technologies such as Artificial Intelligence (AI) and Machine Learning (ML) transforming the way companies across various industries operate their businesses.
In particular, businesses organizations that are in the retail industry or e-commerce companies have been using advanced AI-powered and machine learning applications including Recommender system, Chatbot, Predictive Analytics system, etc. to innovate and enhance their business processes.
A number of big Retail and E-commerce players like Amazon, Alibaba, Wal-Mart, Flipkart have successfully incorporated AI and Machine Learning technologies across their entire sales cycles from logistics to sales to post-sales services, thus improve results as well as business processes.
Now, many other Retails and E-Commerce companies might question whether they can adopt AI and Machine Learning for their business needs. The answer is YES, and you do not have to be a large enterprise in order to take advantage of the immense benefits provided from using Machine Learning technology.
In this article, we explore how both e-commerce and retails companies of any size can use Machine Learning applications to enhance their business – in order to increase sales and reducing costs – staying ahead of the competition.
Examples of Machine Learning Use Cases in Retail and Ecommerce:
1. Recommender Systems
One of the most popular examples of machine learning in retails and e-commerce is the application of a recommender system to increase sales by offering the relevant items for purchase that users could be highly interested in.
By leveraging the data collected across systems, many retailers and e-commerce (e.g. Amazon) companies have successfully implemented recommender systems providing highly personalized offers and a custom-tailored online shopping journey to users via their websites.
Not only recommender system makes it easier for customers to search for the content they’re interested in, but it also provides suggestions to users on the offers they would have never searched for in the first place. Moreover, companies are able to enhance their marketing activities by sending out personalized emails offering recipients special or relevant items that suit their purchase profiles.
Once customers begin to feel like they are understood and paid particular attention to, they will likely purchase more products or consume the services more.
More importantly, by understanding what exactly your customers want or currently looking for and providing it to them right away, it will be less likely that they will leave your platform looking elsewhere. This means higher conversion rates while reducing the chance of losing to competitors.
By integrating recommendation systems into their e-commerce websites, retailers are able to provide added value to their customers, and at the same time enhance their sales process, staying ahead of the competition.
2. Pricing Optimization
Pricing is one of the critical factors ensuring business success and profitability. Retailers and e-commerce companies can leverage the immense power of machine learning to build an effective automation pricing solution.
As you may already know, a machine learning algorithm can learn patterns from data, instead of being explicitly programmed.
In the case of pricing optimization, a developed algorithm take into account a number of pricing variables to determine the best prices for items sold by retailers, as well as understanding how customers react to different pricing of products and services.
Retails companies can consider various factors such as demand, supply, competition, and other external factors which affect their businesses in order to create an automatic pricing system that efficiently using Machine Learning Technology to adjust and optimize prices.
Additionally, the algorithm can search for business data related to the company products’ pricing (e.g. competitors’ products history, or future promotional programs, etc.) in order for businesses to have better information and make informed decisions.
3. Predicting Customer Behavior
Imagine if businesses can predict their customers’ behavior – such as particular interest in purchasing a special kind of product, or switching to competitors for a better price – having access to this information could open up so many sales opportunities for your companies.
Based on data collected regarding customers’ previous behaviors, a system developed based on machine learning can analyze to predict how customers will behave in the future. Such a system enables businesses to carry personalized marketing activities that are more effective than the traditional approaches.
For example, being able to predict which customers are more likely to convert to a paid subscription after the trial period ends, or to know which ones are more likely to purchase a company’s product in the next holiday season, etc. allow businesses to send out very personalized offers, or provide special customers support to focus on those users who are more likely to make a purchase, result in a better conversion rate.
Performing sales and marketing activities that are based on predicted customers’ needs also help increase loyalty and retention rate.
4. Social Media: Tracking Brand & Customer Sentiment
As mentioned in our previous blog post, Social media has changed the way people shop with almost all major retailers' brands have an active online presence these days.
More than just for social networking, customers can actually browse products and services, send inquiries, and/or place orders directly through social media.
Furthermore, many also use social media as an official contact channel providing customers care to their customers. Thus, tracking customers’ sentiment and monitoring brands through social media is highly important to retailers.
Thanks to AI and machine learning technologies, companies can now monitor their social media on a large scale, automatically obtaining an analysis of business data about what is driving traffic, engagement, and customers’ sentiments.
Besides, from insights gathered, retailers can generate social media content that is relevant to the current social media trend, marketing to their customers and prospects at the right time.
In addition to tracking the mentions of their brands automatically through social media in the text form, retailers can now also monitor to see how they are being portrayed through other media forms such as images and videos thanks to image recognition.
5. AI-Powered Chatbots and Virtual Assistants
A chatbot is another popular example of applying AI and Machine learning technologies to retail businesses. A typical chatbots application can communicate and interact with customers, simulating a conversation that is human-like providing answers to frequently asked questions (FAQ) by customers.
For big e-commerce companies with a large catalog of many items, sometimes it would be difficult for customers to search for the specific item they need.
More particularly, many customers want and need to search for items based on item attributes (e.g. color, size, etc.) without knowing the exact search term. An advanced chatbot application can help customers with their requests fairly easily, similar to a human sales associate does.
Additionally, a chatbot can provide added value, enhancing customer's shopping experience by suggesting additional items for purchase and handle a significant part of your company’s online customer service.
Above are some of the common examples of applying machine learning technology in retail, business organizations in other industries such as manufacturing, healthcare, and transportations, etc. are also using machine learning today to better serve customers, improve their business process and innovations.
Healthcare: One example of the use of AI in healthcare is to help detect abnormalities in X-rays and MRIs or application of a HealthCare bot which is an AI application patients can interact with via a HealthCare website or via telephone to receive help with their requests.
Transportation: Autonomous Vehicles such as self-driven cars and trucks have been of high interest in the last several years. The leading examples include big players like Uber and Tesla who have successfully built self-driving cars and trucks solutions to save time and increase productivity.
Finance: AI and Machine Learning technologies can be used in finance to detect and prevent frauds automatically. By giving the algorithm large datasets about real and fraudulent activities, machine learning models can train themselves to make better guesses on which transactions are most likely to be fraudulent and such.
Data Required For Using Machine Learning In Your Businesses:
In the various examples described above, machine learning models are trained with business data in different formats (e.g. text, images, numerical, etc.) regarding customers, products, competitors, etc. gathering from various sources. Without data, machine learning models wouldn’t be able to train themselves.
In case your businesses are newly established (e.g. start-ups) or do not have the data required yet, you may need to consider ways to collect such data by crawling the web or using services from machine learning consulting companies.
Generally speaking, the bigger the volume of data gathered, the better the results (although in some cases, small data sets still provide good and exploitable results).
However, the more important thing is to understand your business well and put yourself in the perspective of customers so that you can provide precisely what they want anytime, anywhere in a very personalized way.
TP&P Technology is a Software Development Company based in Vietnam providing AI & Machine Learning Consulting Services and build Data-driven solutions to help customers solving their unique business challenges in different industries such as retail, E-commerce, Finance, and Trading & Investment.
We can work as an extension of your team to consult & build custom Machine Learning applications, thereby helping businesses achieve their goals and improve KPIs. All so companies can deliver innovation faster and provide unique, tailored made, and personalized experiences to their customers.