4 Critical Areas Where Big Data Impacts Ecommerce

Without a doubt, big data can be the next major game-changer in eCommerce. By finally giving online retailers an opportunity to learn about their target customers from terabytes of data, it’s well on its way to becoming a major source of competitive advantage. 

Business leaders understand that. According to this Business Wire report, the global big data analytics in the retail market is already estimated at an impressive $3.45 billion and it’s projected to reach $10.94 billion in less than five years. This means one thing: more and more companies are building the capacity to take advantage of big data. 

By “taking advantage of big data,” I mean the following:

• Utilize the analytics to determine business opportunities that are worth pursuing
• Interpreting the findings of big data analysis to design better products and meet more needs of target customers
• Deliver the products and services more effectively to the online marketplace.

While the eCommerce industry stands to benefit from big data in all areas, some of them surely stand out. In this article, we’re going to talk about them and define where big data adds the most value for online retailers.

1.Personalization and Localization of Customer Experience

Customers want personalized services and are willing to pay more for them. In fact, on average, 36 percent of consumers are interested in buying personalized products and services; moreover, 48 percent say they’re even willing to wait longer to get them.

Since basic personalization is no longer effective at engaging customers these days, eCommerce businesses need to use consumer data intelligently to create truly unique and relevant experiences. Only in this case they can achieve higher customer engagement rates and increase the number of loyal buyers in the long-term. 

This is exactly what many retail giants are doing. 

“For example, even though Macy’s has a reputation of being traditional in many ways, customer data analysis is certainly not one of them,” says Marie Fincher, a tech blogger from Trust My Paper. “The company evaluates a number of data points, including product promotions and stock levels, and matches these results with product availability and customer activity data at specific physical locations.” 

Another example, Amazon, uses an algorithm to analyze tons of customer-related data to generate a personalized website browsing experience. Thanks to this technology, registered Amazon visitors always have great product recommendations; in fact, the website’s product recommendation engine generates 35 percent of sales.

2.Customer Segmentation

This also falls into the relevancy category and helps to learn how specific customer groups react to certain offers and trends. Knowing how to properly segment your target audience is tremendously important for eCommerce, as it gives the ability to deliver more relevant offers as well as communications. 

These days, businesses have access to various types of unstructured and structured data, the analysis of which can help them improve customer segmentation. According to Reggie Bradberry, a digital marketing expert from Writing Judge, Some of the most important data types here include: 

• Social sentiment data, i.e. customers’ interactions with a business on social media (likes, views, comments, reviews, service records, etc.)
• Activity data, i.e. website browsing data, purchasing history, customer service requests, response to specific content, offers, sales, and other incentives
• Social media platform data, i.e. group memberships, history of employment, etc.

All of this data can be fed into a customer segmentation engine whose main purpose is to sort through it and identify valuable insights for businesses. Some of them may be the following, according to this Oracle report:

• Identification of customers who are likely to buy specific products as well as those who may need more nurturing
• Creation of more accurate and richer customer profiles through the analysis of their interactions with a business; the ultimate benefit is more effective cross/up selling messaging
• Identification of irrelevant products and services that hurt the positive customer experience
• Introduction of more opportunities for customer re-engagement with more relevant marketing messaging and recommendations.

3.Pricing Optimization

Building consumer trust, authenticity, and consistency requires eCommerce businesses to have a good understanding of each and every interaction with customers and utilize this data in real time. Pricing is one of the areas where insights from big data analysis can improve real-time customer engagement. 

“It’s a known fact that eCommerce businesses should be able to find the optimal price for a customer, but traditional manual practices of setting prices make it impossible to get access to pricing patterns,” writes Samantha Whittaker, an eCommerce analyst at Studicus. “Even worse, companies develop prices based on such factors as prices of similar products, market trends, and standard margins.” 

On the other hand, using big data to make more effective pricing decisions is a better way to sell. As this example graphic from McKinsey shows, the analysis can point to opportunities for differentiated pricing at a very specific level (customer-product), based on their willingness to pay. 

Here are the essential things that eCommerce businesses should do to optimize pricing with big data, according to McKinsey:

• Monitor and analyze data. Big data can help to reveal the factors that drive prices for products as well as the response from customer segments
• Automate pricing. There are already real-time pricing analytics tools that utilize big data and set prices according to analytics without the need for manual work.

4.eCommerce Fraud Prevention

Fraudulent behavior is a major concern for eCommerce businesses, with the global cost of fraud payments being billions of dollars (and increasing every day). By using big data analysis, companies can add another layer of security with real-time detection and prevention of fraud.

For example:

• By integrating all online payment options into one platform, an algorithm based on big data analysis can detect anomalies and other signs of fraudulent behavior as they occur
• Big data analytics can identify fraud in real-time; for example by analyzing the history of purchases and other customer data, it can highlight repeated patterns, thus allowing to distinguish between legitimate and suspicious financial transactions
• Big data is known to highlight online laundering operations, including those that appear to be legitimate.

Alibaba is one of the big players in eCommerce that rely on big data to minimize the risk of online fraud. The company’s own Cloud Fraud detection system uses big data to identify “fraud behavior and cheating users during marketing campaigns efficiently, quickly, and precisely,” according to their website. 

The system captures fraud signals from massive volumes of customer data and uses an innovative system. It’s based on real-time big data processing and different risk models; the full structure is shown in the below image.

By using similar models, eCommerce businesses make large-scale fraud detection possible; in turn, this ensures that the technology will continue to draw interest from online retailers in the future. 

Conclusion

Big data has a bright future in eCommerce. For the first time in history, businesses can finally dig into the goldmines of data and actually find something they can base their decisions on. From improving the personalization of customer experience to preventing online fraud, there are great benefits to obtain. Undoubtedly, this is an advantage that no business can afford to ignore.