17 Examples of How to Use Machine Learning and AI in E-Commerce
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Machine learning and AI present e-commerce companies with several opportunities. By eliminating the human element, you can do more with your time and know that the work is done without error.
As these technology tools continue to improve, there are more and more areas you can leverage their power. From integrating personalization and automating your marketing efforts to accurately forecasting supply and demand -- there’s a use case for machine learning and AI in almost everything you do as a business.
In this article, we’ll dive into 17 different examples of how you can use machine learning and AI in your everyday process to improve efficiencies, reduce waste, and increase revenue.
1. Personalization, Segmentation, and Campaign Targeting
When it comes to running an e-commerce site, it can be challenging to provide each of your customers with a personalized experience at scale. But in today’s day and age, your customers expect the brands they follow to provide them with a personalized experience -- and they reward the ones that do.
80% of consumers are more likely to purchase a brand that provides a personalized experience. Knowing this, you need to find ways to make creating these relevant, personalized experiences easier.
That’s where AI and machine learning come into play. With these tools, you can quickly drive a more personalization experience by using customer data to dynamically integrate relevant content onto your website, automatically segmenting similar customers, and developing highly targeted campaigns.
2. Always Have the Best Pricing
Customers who choose to purchase products from your brand are rarely ever just looking at your website. They’re generally doing their research and comparing prices between your products and the competition to find the best deal.
This is also true for the in-store shopper. 65% of shoppers look up price comparisons on their mobile devices while they’re in a physical store.
With the help of machine learning, you can stay ahead of your customers’ needs and provide them with the best pricing possible. Machine learning will take various things into account, such as competitor pricing, customer type, and product demand -- and then change and adjust your pricing based on its findings.
Optimized pricing will allow you to stay ahead of the competition and increase sales quickly.
3. Make the Shopping Experience Simple With Image Processing
Image processing can help make the shopping experience more straightforward for your customers. This AI capability reads an image that a customer uploads and then provides a similar output.
So, for example, if a customer uploads an image of a shirt they are looking to purchase, whether they saw it on a celebrity or another store, the AI technology will process that image and provide them with similar recommendations from your one brand.
This technology provides you with another way to meet your customers’ needs and quickly provide them with the product insights they’re looking for.
4. Integrate Personalization Across Your Website
We’ve already hit on how important personalization is to your customers now. Let’s talk about how you can integrate AI and machine learning to provide more personalized content across your website.
By processing all of the data you have on your audiences, machine learning techniques will allow you to use this information to serve the most relevant content based on each customer’s wants and needs.
Some of the data points that come into play for website personalization include browsing and purchasing history, favorite style of product, and personal preference.
5. Improve A/B Testing Capabilities
A/B testing is a powerful tool that can help you learn how your customers interact and engage with your content, but it can be challenging without the right technology.
With the help of machine learning and AI, the testing process can be simplified. It can pinpoint what elements on your pages should be tested and dynamically change page elements based on the test findings to display different content based on the customer needs.
Using these tools to drive your A/B testing capabilities will allow you to quickly identify which elements are performing better without having to spend hours analyzing data and site performance.
6. Predict Customer Lifetime Value
Knowing how much money your customers are likely to spend is a valuable thing. It can help you determine what customers to focus your hard-earned time and money on -- and which ones might not be as likely to purchase.
Customer lifetime value can be determined by finding the average order total, multiplying that by the average number of purchases in a year, and then multiplying that by average retention time in years.
When thinking about doing this for all your customers, that looks like a lot of math and hours combing through historical customer data. With AI and machine learning capabilities, you can simply pull a report and have the data right there in front of you -- saving yourself a lot of wasted time.
7. Manage Supply and Demand Needs
Understanding supply and demand is an important part of running a successful e-commerce business. To find the right balance between ordering too few or too many products, you need to have insights into your site’s historical data.
Machine learning can help you know exactly how much inventory you should purchase based on the metrics from the last several years. This will allow you to make data-driven decisions on how to manage your supply and demand needs effectively.
8. Provide Fraud Protection
Fraudulent activity can cause e-commerce businesses to lose a large sum of money. It’s expected that e-commerce fraud will rise to over $250 billion in 2021 -- that’s a huge impact.
So as you’d expect, many businesses have fraud prevention and protection on their minds.
Machine learning can help to identify fraudulent activity and flag it before it becomes a problem. This could save you substantial monetary savings and better protect your business from falling victim to fraudulent attacks.
9. Predict and Decrease Customer Churn Rates
When a customer has become inactive for a specified period, they are at risk of churning. This simply means that they’ve been inactive long enough to assume they no longer are using your products or services.
Knowing what customers have been inactive for an extended period will allow you to develop retention campaigns to bring back these inactive users.
Why is it important to try and win back these inactive customers? Because it’s anywhere from 5 to 25 times cheaper to retain a customer than to acquire a new one. That means that keeping those inactive customers can have a significant impact on your bottom line.
AI and machine learning can be used in this scenario to identify who these customers are. By setting specific criteria, such as inactive in the last 30 or 60 days, you can easily identify who is at risk of churning and develop automated campaigns to win them back.
10. Improve Customer Service with Chatbots
Keeping up with the needs of your customers can be challenging for any business. When your audience asks you a question, they expect a response within a reasonable amount of time.
Unfortunately, if you’re managing every single inquiry personally, then you’re probably not going to meet their expectations or make a dent in the number of messages you receive every week.
Chatbots are a great way to solve this problem. They can quickly and efficiently respond to common questions and provide your customers with a response within moments.
It’s as simple as identifying the most asked questions, developing standard answers for these, and then programming the chatbot to determine when and how to answer them. Of course, not all inquiries can be resolved by a chatbot, but if you can eliminate even 50% of the number of questions, you’ll feel the impact.
11. Optimized Site Search Capabilities
Have you ever searched for a product on a website but weren’t 100% sure how to spell the product you were looking for? Did that site still find the items you were looking for, even though your input was way off?
If so, that website may have been using machine learning to identify what you were looking for.
While some websites simply use keywords to identify what someone is looking for, machine learning can learn search patterns and then present users with the most relevant content to what they input.
It also takes into consideration the previous purchasing and browsing behaviors of the shopper to present relevant results. This means that the content that populates will be highly relevant to the customer’s needs based on previous engagement and interactions.
12. Autocomplete Tool via Site Search
Another site search capability that can be helpful for users is autocomplete.
This is done through machine learning and natural language processing. Together, these things can understand your customers’ phrases, how often they use them, and if the results are what they’re looking for.
Using autocomplete on your site will improve the customer experience and allow them to easily identify the products they’re looking for -- even if they’re not exactly sure what to search to find them.
13. Share Relevant Product Recommendations
Product recommendations are a great way to drive an increase in sales and provide more relevant content to your audiences.
While personalized product recommendations may only account for 7% of visits, they account for 26% of revenue. That means that those who visit your site spend a substantial amount of money on the products you’re sharing with them.
Once again, machine learning and AI technology can help you make the most out of this knowledge. By quickly mining your data, identifying trends, and injecting dynamic content, you can easily share relevant product recommendations to your customers on an individual or segment basis.
14. Improve Omnichannel Marketing Efforts
Omnichannel marketing is an excellent opportunity for businesses to provide customers with a comprehensive shopping experience from your physical store locations to your website and everything in between.
Having an omnichannel marketing strategy is vital for e-commerce businesses because customers no longer shop just in-store or just online. 73% of people use multiple channels during their purchasing journey.
Utilizing AI and machine learning can help you achieve a more effective omnichannel experience and make the process a lot less manual on your end. With the mass amounts of data these tools can filter through, you can easily automate campaign enrollment, email marketing, SMS messages, social posting, and so much more.
15. Creating More Relevant Marketing Campaigns
Going back to the idea of providing your customers with personalized experiences, you should also be curating content for your audience based on what is relevant to their wants, needs, and interests.
By segmenting your audiences, you can easily identify the most relevant content for each of your audience groups. This will allow you to develop more relevant marketing campaigns that drive higher engagement and conversions.
Machine learning and AI will allow you to automate your segmentation efforts, making the process much simpler for your teams. From there, your audiences can then be enrolled into your marketing campaigns that are most relevant to their needs,
16. Sales Forecasting and Inventory Management
Tracking historical sales and inventory statistics can help you to better plan and manage the supply and demand of your products. This will allow you to forecast future needs better to ensure you’re not drastically under or overestimating the number of products you need to order.
In addition to helping with forecasting purchasing needs, these technology tools will allow you also to have the ability to track and manage your inventory levels. If you’re running low on certain products, you’ll receive a notification that you need to restock.
Having detailed insights into your product inventory will help you stay ahead of the demand of your audiences and always have the products your customers need in stock.
17. Identifying Dishonest Ratings and Reviews
Ratings and reviews are valuable tools for e-commerce businesses. Nearly everyone who makes an online purchase spends some time reading what other people had to say about the products they previously purchased.
If you have a large number of poor reviews out there, then you’ll see an impact from this. Knowing that 94% of people say an online review has convinced them to avoid a business, it’s easy to see why you need to focus on driving positive feedback.
Sometimes some reviews are posted that are dishonest or maybe from someone who didn’t purchase your products. Luckily, there are ways for AI and machine learning tools to identify test reviews and notify you of the issue. From there, you can take corrective action to keep your company’s good name and reputation.
Tap Into the Power of Machine Learning and AI for Your E-Commerce Business
As you can see, machine learning and AI truly can solve your business problems across a number of different areas. By automating processes and processing data faster than any human, these technologies will allow you to do more with your time and money.
The challenging part is determining where you should invest for the most significant impact on your business. No matter what you decide, whether it’s to improve your marketing efforts or drive to improve your site search -- it will all pay off in the long run.