Online retail is one of the fastest-growing industries in the world and it keeps booming year after year. According to the report, the global eCommerce market is expected to reach
How is this possible?
Online stores grow because they use state-of-the-art technologies such as predictive analytics. It is the only surefire way to stay ahead of the competition and keep expanding the business in the long run.
There are a number of different ways eCommerce stores can use predictive analytics to improve their performance. In this post, we will explain the concept of predictive analytics and show you six ways to use it for eCommerce purposes.
Predictive analytics is the use of data, statistical algorithms, and machine learning techniques to identify the likelihood of future outcomes based on historical data. It works by using the existing data to create models that predict new business outcomes.
As such, predictive analytics has a wide range of applications. For example, banks can predict how likely someone is to default on a loan based on their credit history. This allows financial institutions to make more informed decisions about who to lend money to and helps them avoid potential losses.
The same logic applies to online retail - predictive analytics is able to predict which customers are most likely to churn or which products are most likely to be returned. But that's far from being the only benefit of predictive analytics for eCommerce businesses. Keep reading to learn about the six most important use cases.
Let us be clear about one thing here - the possibilities to use predictive analytics in eCommerce are almost countless. The technology is still evolving and it's impossible to say what it will lead to in the coming years, but we can already identify the most valuable use cases. Here are our top picks.
Predictive analytics can improve inventory management by interpreting historical data to create models that predict future demand for a product. This information can then be used to make decisions about how much stock to keep on hand, when to order more products, and so on.
For example, if the model predicts that demand for a product is going to increase in the coming weeks, the company might want to order more of that product sooner rather than later in order to avoid running out.
Conversely, if the model predicts that demand is going to decrease soon, the company might want to sell off excess stock or return it to the supplier.
Do you know that there is an 80% increasein revenue for businesses that focus on improving customer experience? This is exactly why it's vital to use predictive analytics in online retail.
Predictive analytics can improve customer experience because it helps you learn what a customer might want or need. This allows businesses to provide a more personalized journey for each individual customer, which can result in a better overall customer experience.
Additionally, predictive analytics can identify potential problems with the customer's shopping experience before they even happen.
For example, if it appears that a customer is about to abandon their cart, predictive analytics could be used to send them a warning message or offer them a discount on their purchase in an effort to keep them from leaving. You could even trigger a personalized discount animation with a countdown to indicate it's a limited offer they should take advantage of right now.
Your employees make the business strong or weak, which is why you have to pay close attention to their behavior. Predictive analytics can help identify employees who are at risk of leaving eCommerce companies and assist these companies in taking steps to minimize turnover.
For example, predictive analytics can be used to pinpoint employees who have a high probability of quitting, based on factors such as job satisfaction, tenure, and performance.
But that's not all.
Predictive analytics can also help eCommerce companies identify which employees are most likely to be successful in certain roles, and match them with positions that are a better fit for their skillset. This can lead to increased job satisfaction and decreased turnover.
Predictive analytics can enable accurate price formation in eCommerce by utilizing customer data to predict what customers are likely to buy and how much they are willing to pay for it.
This is done by analyzing past purchase behavior, browsing patterns, demographic information, and other relevant data in order to create a profile of each customer. An accurate consumer profile can then be used to predict what that customer will buy in the future and set the price accordingly.
Predictive analytics can also determine when customers are most likely to make a purchase so that prices can be lowered during those times in order to increase sales.
Personalized marketing is yet another product of predictive analytics. With huge consumer-related databases, eCommerce companies can identify common patterns and trends. After that, you can easily automate campaigns and place highly targeted marketing messages.
For example, if a retailer knows that a particular customer is likely to buy a certain product in the near future, they may send that customer a promotional email or SMS message with a discount code for the product.
Predictive analytics can also help retailers identify customers who are at risk of abandoning their purchase funnel. That way, you can pay special attention to those customers by offering targeted promo deals and relevant messages.
The worst thing that can happen to an eCommerce business is to fall into the trap of scammers or hackers. In such circumstances, it is necessary to use predictive analytics to enable fraud prevention in your online store.
How is this possible? It turns out that predictive analytics can spot risk factors and warning signs of fraudulent activity. It can recognize patterns that may indicate fraudulent behavior.
This allows eCommerce stores to develop strategies for preventing scams, such as implementing stricter verification measures for high-risk customers. Predictive analytics also helps you monitor customer behavior in real-time, so you can flag suspicious activity and thwart attempted fraud.
Predictive analytics is an essential tool for eCommerce businesses eager to stay ahead of the competition and keep their customers happy. It handles a lot of repetitive tasks that would take too much time from your team, all while making much more accurate conclusions and decisions.
From customer experience to fraud prevention, predictive analytics has the potential to improve your online store in many different ways. Are you ready to give it a try already?