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How Is Ecommerce Predictive Analytics Revolutionizing The Retail Industry?

Ecommerce sites have grown in popularity. An estimated 44% of US online shoppers purchased from these sites in 2015 and 71% of eCommerce sales came from mobile devices. According to Bloomberg, this trend has continued to have ecommerce firms spending more than $1 billion on such tools since 2019. The results are only set to keep rising with each financial year! With statistics like these, it’s not hard to see why ecommerce predictive analytics are becoming so essential to the retail industry.

The retail industry has traditionally been slow to adapt to the changes taking place in digital and predictive analytics. However, that’s changing as more and more companies embrace these technologies to improve their ecommerce presence. How can you use predictive analytics in your ecommerce business? What are the key benefits that you’ll see from implementing these solutions? Find out in this article about how ecommerce predictive analytics are changing the retail industry!

What is predictive analytics?

predictive ecommerce Machine learning qualities

Predictive analytics is a set of mathematical and statistical techniques used to analyze current and historical data. This is used to make predictions about future trends, for example in retail. Ecommerce predictive analytics is a particularly specialized subset of predictive analytics and it looks at shopping data—both online and offline. Ecommerce sites collect data through search, browsing history, demographics, customer profiles, social media activity and more. Once those numbers are collected, eCommerce predictive analytics predicts customer behavior based on those past behaviors. This is to help retailers make better business decisions going forward. Rather than using traditional categories like age or geographic location for segmentation purposes (as has been done in marketing since time immemorial), an eCommerce site can now use purchasing patterns.

Why does it matter to the retail industry?

use of Predictive Anatytics in Retail

Ecommerce predictive analytics services are incredibly important to retail. This is mainly because it gives companies insight into their business in a way that hasn’t been possible before. This information can be used for so many different applications. Right from identifying which products are most likely to sell to which customers are most likely to buy them. Companies can use predictive analytics services as a basis for understanding consumer trends. This way they can ultimately make better decisions about their inventory, product mix, pricing, marketing efforts, and more. Many retailers who have implemented ecommerce predictive analytics tools report tremendous increases in revenue. Imagine what you could do with your business if you had access to that kind of information!

Types of predictive analytics tools

preferred ecommerce analytics tools

The two main types of ecommerce predictive analytics tools are customer relationship management (CRM) software and recommendation engines. CRM is an all-in-one platform that helps businesses organize customer data. Also, it helps manage interactions between salespeople, customers and other stakeholders within a company. Recommendation engines provide suggestions on how to improve customer experience. They do this by helping businesses predict buyer behavior based on past purchases or even demographic information like age or location.

Many retail companies choose to implement both types of services for a complete view of their customer base.

What kind of questions can we answer using predictive analytics?

Today, businesses have to contend with an endless stream of information. Finding ways to process and make sense of that data is a challenge. Luckily, technology has given us tools we didn’t have before. Using predictive analytics can be one way in which ecommerce can improve. For example, understanding what your customer might buy next—from you or someone else—can help you boost sales and keep customers happy. Basically, analyzing historical data can reveal your customers’ buying habits so you know what they want before they do! The analytics software collects data from all channels in order to create a coherent view of everything. More importantly, this is based on events happening in real time for a company’s different products and services.

For example, we can track how many shoppers abandon their carts on an ecommerce site and optimize our efforts based on what they wanted to buy. We can also analyze when people tend to make purchases. This can be during a specific time of year, which is critical for forecasting inventory.

Today’s predictive analytics tools give companies real-time feedback about what’s happening across their platforms. With new ways to get information out of data at a rapid pace, there are endless opportunities for retailers. Particualry, for those looking to improve processes and boost profitability. And it’s only going to get better as analytical technology improves over time. In summary, it is safe to say that predictive analytics has transformed the e-commerce industry. It has made way for smarter decision making in retail. The Internet has become one of most influential media channels available today, but how do you leverage its power to reach your target audience?

Understanding customer behavior using descriptive analytics

Predictive Anatytics in Retail

To understand customer behavior and trends, it’s vital to employ descriptive analytics to interpret your data. This is where a predictive analytics tool comes in—it’s helpful for analyzing historical sales data and viewing behavioral patterns. For example, you can use a predictive model to forecast sales based on past seasons’ sales history, predict which products are likely to sell out, or analyze consumer behavior from browsing an ecommerce site.

Ecommerce businesses often have massive amounts of raw data at their fingertips. Analytics tools help sift through that information and uncover valuable insights about their customers’ preferences. By combining sales figures with demographic information, companies can get a better idea of who are their typical customers. Also, what they’re looking for when they visit their website. As ecommerce grows more competitive than ever before, it’s important to leverage every advantage possible to make sure you’re staying ahead of your competitors.

The information from descriptive analytics can help you understand things like your customers’ purchasing patterns and which strategies are working for different types of products. This information can help you create more effective ad campaigns and put together sales funnels that are tailored to your customers’ needs—for example, recommending related products or upselling those who don’t purchase immediately. This strategy alone can have positive effects on your bottom line but still won’t tell you where your industry is going.

Pinpointing patterns in trends using inferential statistics

Ever wonder how a website like Amazon can predict that you will prefer a certain product, even before you’ve searched for it? The answer is machine learning. As an increasingly large number of products are now digitally available, retailers have access to more information about what people like and purchase than ever before.

Predictive Models

Personalized product line up

Ecommerce businesses now analyze this data to provide recommendations for products that customers will most likely prefer. By using their own data to predict what customers want based on past purchases, e-tailers can curate product offerings that might not exist otherwise—not just things they know they can sell quickly. In addition to making sure products are available when customers want them, predictive analytics also help companies figure out which items should go together (think Customers who bought X also bought Y). This helps improve customer satisfaction and conversion rates.

Cross-Selling

With predictive analytics, you can predict future outcomes to prepare for them. For example, businesses use predictive analytics to identify which customers are likely to buy an extra product or service and then pitch it to them before they’ve finished their current purchase. (This is referred to as cross-selling.)

Fraud Detection

Some retailers also use predictive analytics in order to prevent fraud. By analyzing customer data, they can identify patterns that indicate fraudulent activity. If a transaction seems suspicious, a company might ask for additional information from a customer or even block a purchase entirely.

Conclusion

Overall, predictive analytics helps companies tailor their products and services to individual consumers based on what those consumers have done in the past—which means more personalized offers for customers and more money for companies. The possibilities of predictive analytics are endless because each business has its own unique set of data that it can analyze using these tools.

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