7 Ways Big Data is Changing E-commerce

According to an IDC report, it’s estimated that the digital universe of data will grow 61% to 175 zettabytes by 2025. E-commerce represents a large chunk of this digital universe — accumulating customers’ social media activity, geolocation services, web browser histories, and abandoned online shopping carts.

Although gathering consumer data is great, analyzing the data is what gives e-commerce companies a distinct advantage. E-commerce companies leveraging big data analytics can understand their customers’ purchasing behavior in the context of current market trends. In turn, these companies tailor their marketing directly to customer preferences, create new products that meet customer needs, and ensure that employees provide the level of service customers expect.

Clearly, big data can have a significant effect on e-commerce. 

This article will highlight seven ways big data can foster positive change in any e-commerce business:

1. Elevated shopping experience
2. More secure online payment
3. Increased personalization
4. Optimized pricing and increased sales
5. Dynamic customer service
6. Generate increased sales
7. Predict Trends, forecast demand

1. Elevated shopping experience

E-commerce companies have an endless supply of data to fuel predictive analytics that anticipate how customers will behave in the future. Retail websites track the number of clicks per page, the average number of products people add to their shopping carts before checking out, and the average length of time between a homepage visit and a purchase. If customers are signed up for a rewards or subscription program, companies can analyze demographic, age, style, size, and socioeconomic information.

Predictive analytics can help companies develop new strategies to prevent shopping cart abandonment, lessen time to purchase, and cater to budding trends. Likewise, e-commerce companies use this data to accurately predict inventory needs with changes in seasonality or the economy. 

Lenovo, the world’s largest PC vendor, serves customers in more than 160 countries. Striving to enhance the customer experience and differentiate the company from the competition, Lenovo needed to understand customer needs, preferences, and buying behaviors. By collecting data sets from a variety of touch points, Lenovo used real-time predictive analytics to elevate the customer experience and achieve an 11% increase in revenue per retail unit.

2. More secure online payments

In order to provide a peak shopping experience, customers need to know that their payments are secure. Big data analysis can recognize atypical spending behavior and notify customers as it happens. Companies can set up alerts for various fraudulent activities, like a series of different purchases on the same credit card within a short time frame or multiple payment methods coming from the same IP address. 

Likewise, many e-commerce sites now offer several payment methods on one centralized platform. Big data analysis can determine which payment methods work best for which customers, and can measure the effectiveness of new payment options like “bill me later”. Some e-commerce sites have implemented an easy checkout experience to decrease the chances of an abandoned shopping cart. The checkout page gives customers the ability to put an item on a wish list, choose a “bill me later” option, or pay with multiple various credit cards. 

3. Increased personalization

Besides enabling customers to make secure, simple payments, big data can cultivate a more personalized shopping experience. 86% of consumers say that personalization plays an important role in their buying decisions. Millennials are especially interested in purchasing online, and assume they will receive personalized suggestions. 

Using big data analytics, e-commerce companies can establish a 360-degree view of the customer. This view allows e-commerce companies to segment customers based on their gender, location, and social media presence. With this information, companies can create and send emails with customized discounts, use different marketing strategies for different target audiences, and launch products that speak directly to specific groups of consumers.

In fact, many retailers cash in on this strategy, giving members loyalty points that can be used on future purchases. Sometimes, e-commerce companies will pick several dates throughout the year to give loyalty members extra bonus points on all purchases. Typically, this is done during a slow season, and increases customer engagement, interest, and spending. Not only do loyalty members feel like VIPs, they give information companies can use to deliver personalized shopping recommendations.

4. Optimized pricing and increased sales

Beyond loyalty programs, secure payments, and seamless shopping experiences, customers appreciate good deals. E-commerce companies are starting to use big data analytics to pinpoint the fairest price for specific customers to bring in increased sales from online purchases. Consumers with long-standing loyalty to a company may receive early access to sales, and customers may pay higher or lower prices depending on where they live and work.

Otto, Germany’s biggest online retailer for home furnishing products, is one of Europe’s most successful e-commerce companies. To maintain that title, Otto has to compete against giants like Amazon. Otto conslidated its many data silos into one database, making it easier to develop 360-degree customer profiles, analyze competitor data, and determine what sales channels perform best. Otto can now easily use big data to optimize pricing, produce more tailored marketing campaigns, and sharpen their strategy for onsite ad bidding.

5. Dynamic customer service

Customer satisfaction is key to customer retention. Even companies with the most competitive prices and products suffer without exceptional customer service. Business.com states that acquiring new customers costs 5 to 10 times more than selling to a new customer. What is more, loyal customers spend up to 67% more than new customers.

Companies focused on providing the best customer service increases their chances of good referrals and sustains recurring revenue. Keeping customers happy and satisfied should be a priority for every e-commerce company. So how does big data improve customer service? Big data can reveal problems in product delivery, customer satisfaction levels, and even brand perception in social media. In fact, big data analytics can identify the exact points in time when customer perception or satisfaction changed. It is easier to make sustainable change to customer service when companies have defined areas for improvement.

Shoe retailer ALDO recognized that the millennial generation — which drives much of their sales — recognizes the importance of customer service. Not only do ALDO customers want to interact on e-commerce sites, consumers also want to hear and read about ALDO on social media, and other channels. ALDO needed to leverage big data to understand customer behavior and provide excellent customer service. 

Although ALDO was already collecting customer data, it was challenging to connect customer profiles to transactions and interactions across all channels. Using a big data tool that was agile, fast, scalable, and flexible to capitalize on variable cost, ALDO can now easily expand global reach — supplying a localized experience for each customer. ALDO continues to use big data to create innovative products and deliver a delightful customer experience.

6. Generate increased sales

Big data helps e-retailers customize their recommendations and coupons to fit customer desires. High traffic results from this personalized customer experience, yielding higher profit. Big data about consumers can also help e-commerce businesses run precise marketing campaigns, give appropriate coupons, and reminding people that they still have something sitting in their cart.

Domino’s Pizza is an extraordinary example of an e-commerce business using big data to boost sales. Domino’s “AnyWare” ordering program allows customers to purchase pizza via their smartwatches, TVs, cars, and social media. Making sales so easy and convenient was a critical advantage for Domino’s pizza sales. However, combining data from disparate sales channels in real time was inconceivable without modern technology.

Using a big data platform, Domino’s easily integrated information from 85,000 unstructured and structured data sources. With a single view into customers and global operations, Domino’s can now collect, cleanse, and standardize data from all point-of-sale systems and supply centers. This data is fed into Domino’s data warehouse, where it is combined with USPS, competitor, and demographic information.

7. Predict trends and forecast demand

Catering to a customer’s needs is not just a present-state issue. E-commerce depends on stocking the correct inventory for the future. Big data can help companies prepare for emerging trends, slow or potentially booming parts of the year, or plan marketing campaigns around large events.

E-commerce companies compile huge datasets. By evaluating data from previous years, e-retailers can plan inventory accordingly, stock up to anticipate peak periods, streamline overall business operations, and forecast demand. For example, e-commerce sites can advertise large markdowns on social media during peak shopping times to get rid of excess product.

To optimize pricing decisions, e-commerce sites can also give extremely limited-time discounts. Understanding when to offer discounts, how long discounts should last, and what discounted price to offer is much more accurate and precise with big data anlytics and machine learning.

Get started with big data in e-commerce

Big data has already greatly impacted the e-commerce industry and will likely continue to do so. 99 Firms expects that 95% of all purchases will be made via e-commerce by 2040. To prepare, companies can use big data analytics to upgrade their copy, strengthen their self-service customer support articles, and interpret surveys. Not only that, e-commerce businesses can prepare for seasonal influxes, new trends, and customer preferences.

Although big data may be an e-commerce business’s most powerful tool, only 0.5% of big datasets are being used. Traditional on-premises solutions cannot store or process data sets of today’s scale and complexity. So what is a promising e-commerce company to do?

To harness the power of big data, e-commerce companies are turning to cloud-based big data analytics. Cloud-based tools can house, transform, and examine data quickly and effectively. Talend Data Fabric is a comprehensive suite of apps that automates integration, making it easy to analyze all data sources concurrently and at the speed of your business.

Become an e-commerce leader today — try Talend Data Fabric for free.