Over the past few decades, shopping has become more comfortable and faster than ever. Our preferred products seem to magically find their way into our shopping cart, and personalized recommendations intuitively cater to our unique tastes.
Moreover, stores have become so smart that they anticipate our needs even before we do. Guess who transformed eCommerce that much? The answer is machine learning.
If you operate in the retail industry and want to know how to elevate your business to new heights with machine learning technologies, we have crafted a comprehensive article with all the answers. Dive in to find out the core opportunities that ML can offer for your eCommerce solution.
How Machine Learning Shapes the Retail Industry
Now let’s delve deeper into the innovative world of ML technologies and understand a bit its essence. Basically, ML in retail is all about using smart self-improving algorithms that learn from huge datasets, identifying patterns, anomalies, and cause-and-effect relationships among variables. It leads to a deeper understanding of the industry dynamics and the environments in which retailers operate.
As a result, machine learning for retail empowers business owners with valuable insights to remain competitive and drive their businesses forward. In particular, it can assist in enhancing customer experiences, improving pricing strategies, building better-targeted marketing campaigns, and much more.
These are just a few benefits that ML has to offer in eCommerce. Leveraging machine learning in your retail business can open up a world of opportunities. Let’s discuss them in detail in the next chapter.
13 Cutting-Edge Use Cases and Opportunities of Machine Learning in Retail
If you aim to build a successful eCommerce store that will lead the market by implementing ML technologies into your business, then you are on the right track. Now, as we promised, we will explore the most groundbreaking opportunities that machine learning presents for the retail industry.
1. Product Dynamic Pricing
To stay competitive and capture customer attention, it is vital to set reasonable pricing. If your products are priced higher than similar ones in the market or if you miss out on sales, customers may opt for your competitor’s offerings to avoid overpaying.
Yet, monitoring numerous competitors and tracking pricing can be a daunting task. Fortunately, with the help of machine learning, there is no need to bore yourself with such tasks. ML excels in data analytics, making it easier to process vast amounts of data.
By analyzing factors like demand, competition, inventory levels, and historical data, ML algorithms can seamlessly assist retailers in real-time price adjustments.
2. Inventory Predictions
Did you know that inventory distortion can cost retailers almost 1.8 trillion USD a year? Therefore, inventory management is a crucial component of a successful retail business. Ideally, you need to keep a balance between stocking up on popular items to meet customer demand and avoiding overstocking, which can lead to unnecessary costs.
Machine learning is widely used in the retail supply chain for demand forecasting. By analyzing historical sales data and assessing current purchasing trends, ML algorithms provide retailers with accurate inventory predictions.
For instance, if you manage a sports shop and want to introduce a new collection of hiking shoes, ML can analyze customer preferences and demand for these products. This empowers you to make more informed decisions about the quantity to order. As a result, it is possible to maintain the right stock levels to meet customer demand without overstocking or understocking.
3. Customer Segmentation
No matter how perfect your product is, if you do not target the right audience, there is no long way to success. By providing insights into customer segmentation, machine learning algorithms may help you get to the point.
With the important insights at hand, you can divide your clients into groups, thereby facilitating the creation of more effective and personalized marketing strategies.
Today, numerous retail businesses harness the power of ML for this very purpose. Take Amazon, one of the industry giants, as an example. Though it offers thousands of diverse products, it exactly knows whom and what to suggest.
When crafting marketing campaigns, Amazon also takes into consideration factors like location, age, gender, and the choice of web or mobile app usage.
This customization makes sure each customer gets the relevant content. And this is just one example of how machine learning empowers the retail industry to achieve better customer segmentation.
4. Market Basket Analysis
Typically, when retailers analyze customer data, they don’t merely focus on promoting current products. They aim to encourage users to buy other related goods.
Let’s consider the same example with a sports shop. If you find a potential buyer interested in tennis, you will probably recommend to this person other relevant tennis items, instead of just suggesting a racket. Here ML and market basket analysis come to the rescue. They empower you to provide a holistic shopping experience and suggest other complementary goods.
As a result, you can create more lucrative cross-selling opportunities and streamline your sales process.
5. Customer Sentiment Tracking
Thriving businesses are those that always know what their audience thinks about them. Only by being aware of your customers’ preferences can you keep them satisfied. ML algorithms, along with NLP technologies, can analyze data from different sources, like social media posts, reviews, and customer feedback. So you will have a bigger picture of customer sentiment.
With such important data at hand, it becomes easier to take proactive measures and keep buyers satisfied. Additionally, you may adapt your marketing strategies based on clients evolving sentiments.
6. Personalized 24/7 Customer Support
Another option to keep customers happy and satisfied is to provide around-the-clock customer support. Typically, when shopping online, people expect quick results, whether they are placing orders for products or seeking answers to their queries. Empowering retail solutions to provide customers with 24/7 support, machine learning and artificial intelligence can serve as perfect tools.
Moreover, many businesses today reap the benefit of GenAI solutions. Thanks to this innovative technology, it is possible to resolve more customer queries. Additionally, it elevates the overall quality and accuracy of responses provided in customer service.
Watch our webinar to uncover how to integrate GenAI for improved productivity and decisions.
Let’s take for example Amazon or Walmart. They use GenAI assistants to provide immediate support to customers. This approach helps generate answers to customer inquiries within a few seconds.
Obviously, it’s beneficial for both customers and retailers. With these technologies, you can increase efficiency and cost savings in your customer support operations. Customers, on the other hand, can enjoy quicker and more personalized assistance.
Furthermore, algorithms continuously learn from each interaction, which enables them to become increasingly proficient at resolving complex issues. Thus, ensuring an ongoing and exceptional level of support over time.
7. Churn Rate Prediction
Preventing customer turnover is another purpose of using machine learning in retail. ML algorithms can analyze historical customer data and provide patterns that lead to churn. By predicting possible turnovers, retailers can take action in advance.
Recognizing that customers are at risk of leaving, they may craft personalized offers and promotions to encourage their continued loyalty, thereby mitigating turnover. Afterall, it can be twice as challenging to engage new customers as it is to retain existing ones.
8. Contextual Commerce
One of the core reasons ML is being used in retail is its ability to provide contextual shopping. Simply put, it is the process of integrating buying opportunities into customer everyday activities. Let’s discuss an example to understand the essence of this shopping experience.
Potential buyers scrolled through a social media feed and saw the ad for sunglasses they wanted to purchase. There is no need to go to the brand’s website to order it. Users can click the button on the ad and simply order the desired product without even leaving the app. Obviously, shopping has never been so simple and comfortable.
However, to make people order products, you need to target a relevant audience. The key player here is ML, which analyzes a wide range of data about user browsing history, location, past purchases, etc. As a result, retailers gain powerful insights to offer personalized product recommendations.
9. Voice Commerce
How many times a day do you find yourself typing out your routine messages, and how often do you prefer the convenience of sending voice messages as an alternative? In this hectic world, many of us have probably become too busy to type and prefer to send voice messages. But this habit is not limited to our daily conversations. It has extended to the way we shop online as well.
That is the reason why many popular eCommerce companies prioritize voice commerce. Typically, these businesses leverage AI and natural language processing (NLP) technologies in their retail solutions to promptly understand and respond to spoken requests and inquiries.
One of the vivid examples of voice commerce is Google Assistant. It makes it possible to create a shopping list, voice a shopping request, make payments, and more.
10. Visual Search
Voice commands are not the only alternative to typing. Another innovative option is visual search. With AI and ML-powered technologies, retailers can help customers find the goods of their dreams. Let’s see how it works. When customers are unsure about where to find their desired products, they can simply use a photo or a screenshot and ask a search engine to find similar items.
So, by using ML and AI algorithms retailers can gather data on the styles, colors, and patterns that customers are searching for. As a result, companies may align their offerings more closely with what their customers want, thus boosting sales.
11. Delivery Optimization
Today, many retail businesses utilize machine learning in their solutions for delivery route and conditions optimization. In particular, it is quite beneficial for perishable goods delivery, which require special conditions.
Typically, ML employs Internet of Things devices to gather data on various parameters like location, current traffic, weather conditions, temperature, and even humidity. Thus, ML helps both identify the most effective delivery routes and ensure the freshness of products at their optimum level.
Some modern companies took it a step further. They use machine learning for retail to provide delivery with autonomous cars or self-driving vehicles. The integration of ML with robotics in retail operations works by equipping autonomous vehicles with advanced sensors and cameras to continuously capture data from their surroundings. ML algorithms process this data to navigate, identify obstacles, and make real-time decisions.
Amazon Scout is a vivid example of this trend. It operates around the clock, offers faster deliveries, and proves to be cost-effective.
12. Video Surveillance
If you want to enhance your retail store’s video surveillance, then it is worth implementing machine learning in your retail solutions. ML improves video surveillance by enhancing the analysis of large video datasets for better security and monitoring.
In particular, ML algorithms may detect suspicious behavioral patterns. For example, when they detect that someone has picked up an item in their pocket, they will send a notification to the security department.
With this advanced technology, even if your security staff overlooks any fraudulent activities, ML-powered systems stand as an unyielding barrier, preventing potential theft.
13. Safe eCommerce Platform
The final key point on our list is the ability of ML to enhance the overall security measures of your platform. Only with a safe eCommerce solution can you provide a secure shopping environment and build trust among your customers.
Machine learning boosts eCommerce platform safety in several ways. For example, ML algorithms can perform real-time analysis of transaction data, which helps identify fraudulent activities like unauthorized credit or debit card usage, identity theft, or attempts to take over user accounts.
Additionally, ML can be employed in authentication processes to recognize suspicious activities, thus adding an extra layer of security to your eCommerce ecosystem.
Enthusiastic to Leverage Machine Learning in Your Retail Business?
Obviously, today, technologies like ML have become an inseparable part of the retail sector. They unlock a realm of opportunities for this industry. Therefore, to stay ahead in this highly competitive field, you need to embrace the innovative trend.
By adopting ML in your retail and eCommerce software, you can enhance efficiency, cut costs, build customer loyalty, improve your platform security, and much more. However, machine learning development may appear to be challenging.
You won’t have to navigate this journey alone. With our proven expertise, we can become the skilled team that will help you create a cutting-edge solution. Feel free to contact us for consultation.