Artificial Intelligence and Data Science applications and technologies have penetrated our society so deeply that they are now being used in every industry let alone the eCommerce industry.
In some cases, the usage of AI and Data Science are so seamlessly integrated into the picture that you might not even be noticing them. Without further ado, here are the seven interesting applications of Data Science in the e-commerce industry.
#1 Recommendation systems
The first example of Data Science being used in e-commerce is that of recommendation systems. It is quite obvious that these systems largely rely on data to make their recommendations, so Data Science pretty much lies at the foundation of the recommendation systems used in e-commerce.
Every time a customer makes a purchase (or even simply checks out a product page), their activities are recorded and then used by the system to make personalized recommendations. This way, businesses can sell more products. Such systems pretty much offer exactly the kinds of products specific customers are interested in.
Likewise, the data collected and analyzed by recommendation systems can be used by your marketers and customer service managers to create special offers for individual customers. You can then send these offers by email, SMS, etc. to directly reach the customers and increase the chances of them making a purchase.
Learn in detail about data driven marketing for better ROI
#2 Predictive customer segmentation
Another popular usage of Data Science in e-commerce is that of predictive customer segmentation. Every e-commerce store has its own target audience, but to work with this audience in the most effective and efficient way, you need to segment it and target each segment separately.
In most cases, this segmentation is done manually (or to a large extent manually). However, when you are using predictive customer segmentation, the system helps you segment your target audience. By gathering data and using AI technologies, you can predict customer interest in your offer and identify different groups of customers accordingly.
Moreover, with the help of predictive customer segmentation, you can also identify the types of users who likely won’t become your customers. You can then exclude them from your target audience and avoid wasting part of your budget in vain. Essentially, you will be making smarter decisions in terms of targeting and segmentation.
#3 Pricing optimization
Pricing optimization is one more way of using Data Science in e-commerce. There are so many factors that are being considered when deciding the price of a specific product. From the cost of materials to the quality of the product to its competitive edge when compared to alternatives – all of these need to be taken into account when pricing it.
For example, you can hire an experienced writer from a writing services reviews site like Best Writers Online who will perform market research and provide you with a report on other similar products. However, you will still need to pay attention to a number of other factors to determine your product’s price. One of these (often overlooked factors) is the demand for your product.
Pricing optimization solves this issue for you because the system will consider the demand for your product when setting its price. Similarly, it will consider the supply (i.e. the number of items available) when displaying the price. This way, you can sell your products at a higher price when you know your customers are willing to pay more.
#4 AI chatbots and assistants
AI chatbots and virtual assistants have been using AI and Data Science for what feels like ages now even though truly smart chatbots are relatively new. Such chatbots and assistants can help your customers by providing them with a more engaging and enjoyable buying process and improving their overall experience.
For example, when a customer has questions about the products, they don’t need to send an email and wait for a response or contact the call center and wait for someone to pick up the phone. All they should do is send a text to the chatbot on your website and get an instant answer to their question or concern.
Of course, AI chatbots are still limited, but they are already quite advanced in what they can do. A lot of chatbots use past customer data to give them suggestions, guide them in their choices, answer their questions, and so on. As this technology continues developing, chatbots will likely become even more common and helpful.
Read more about how you can improve customer service using data science
#5 Inventory management
While this is not something you were likely thinking of when you were considering Data Science, inventory management is still an aspect of e-commerce where Data Science is extremely helpful. This is because managing your inventory efficiently takes more than simple manual management.
For instance, you can hire a professional writer from the custom writing reviews site Writing Judge to create templates for product descriptions for the inventory. But you will need to be the one filling them out. With AI and Data Science, this process of filling them out can be automated which will significantly simplify and accelerate everything for you.
#6 Customer sentiment analysis
Just like target audience segmentation can be made easier with the help of AI and Data Science, so can be customer sentiment analysis. To put it simply, customer sentiment analysis is about analyzing the conversations online between your current and potential customers to determine what their opinions about and experiences with your brand are.
Customer sentiment analysis is most commonly performed on social media platforms where conversations are abundant, but you can also perform it on forums and even by analyzing media outlets (though in this case, it will be non-customer sentiment analysis). Once you have performed the analysis, you can make smarter decisions about your product design, marketing, customer service, and so much more.
#7 Lifetime value prediction
Last but not least, Data Science is also being used in e-commerce for predicting the lifetime value of customers. Essentially, the customer lifetime value is the total value of the profit your get from a specific customer over your entire relationship with that customer.
Of course, making such predictions accurately is extremely difficult, but it isn’t completely impossible. Different systems and algorithms are used to collect and analyze a lot of data about your customers and then make such predictions about their lifetime value. Then, you can make further decisions based on these predictions about your customers.
AI and Data Science applications revolutionizing eCommerce industry
At the end of the day, the way the e-commerce industry operates will likely continue changing in the near future. And even the way AI and Data Science applications are being used in e-commerce will eventually evolve. For now, it’s worth using these two technologies to their fullest and reaping the benefits they provide to online store owners.