January 17, 2024

How Data Is Reshaping eCommerce: Big Data Trends In Online Retail

Nadya Bakhur

Researcher, Technical Writer

Ecommerce

eCommerce Big Data
January 17, 2024

How Data Is Reshaping eCommerce: Big Data Trends In Online Retail

Nadya Bakhur

Researcher, Technical Writer

Ecommerce

eCommerce Big Data

The practice of using ecommerce Big Data is already quite common among large foreign retailers. Thanks to Big Data, Amazon, the world’s largest ecommerce company,  has built its own dynamic pricing algorithm, and H&M has optimized the assortment, avoiding closure.

Below, we’ll overview why retail needs ecommerce datasets, and big data and how online stores can use it in business.

Big Data In eCommerce: How Does It Work?

Let’s start with the fact that is necessary for a general understanding of the process: building technologies based on Big Data is relevant when a company is faced with a huge amount of information that is physically impossible for a person to process.

For example, the Walmart chain, another company using Big Data, includes 20,000 stores located in 28 countries and has to process 2.5 petabytes of data every hour. The retailer receives information from 200 sources – these are metrological, economic data, Nielsen data, telecommunications, social networks, even gas prices and local news. To cope with such a flow, Walmart created an analytical hub Café data: every hour it processes about 25 thousand requests, 90% of which are analyzed within 2 seconds.

How does this happen?

The retailer collects all possible information about customers: its own data – both about checks and about customers registered in the loyalty system, other people’s data – from partners. The entire gigantic amount of acquired knowledge is in a single repository, where it can be enriched and further analyzed. Therefore, it is important to approach the issue of its creation wisely. Specialized tools allow you to take all the information available about a person and build an analytical model based on it.

The data received by the retailer must be stored for as long as necessary. This means that historical information can be used to develop the model. Therefore, as a result of the analysis, a qualitative model with high reliability is obtained.

The second stage is the implementation of the resulting model in the ongoing process of interaction with the client. If with the help of classical tools it was possible to influence buyers only through the site or through email newsletters, then Big Data allowed for building more complex scenarios: an offer can be sent or shown to the client at the most opportune moment. For example, when a person came to a store or entered its website and started looking for something on it. Analytical algorithms begin to be applied to the real-time story that is being formed, which calculates what can be offered to a particular visitor.

According to an IDC report, the digital universe of data is estimated to grow 61% to 175 zettabytes by 2025. eCommerce accounts for a large chunk of this digital universe: it accumulates customer activity on social media, geolocation, web browser history, and abandoned online shopping carts.

While collecting consumer data is already an important element, it is the data analysis that gives ecommerce owners and/or marketers a distinct advantage. Companies leveraging Big Data analytics for ecommerce can understand the buying behaviour of their customers 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 deliver the level of service customers expect.

7 Ways Big Data Using Companies Can Improve Their eCommerce Businesses

Big Data can have a significant effect on ecommerce.

NEKLO experts in custom ecommerce development highlight seven ways Big Data can drive positive change in any ecommerce business.

The excellent online shopping experience

eCommerce companies have an endless supply of data to be able to carry out predictive analyzes 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 cart before checking out, and the average time between a homepage visit and a purchase. If customers sign up for a rewards or membership program, businesses can analyze demographic, age, style, size, and socioeconomic information.

Predictive analytics can help businesses develop new strategies to prevent shopping cart abandonment, reduce the time to purchase, improve customer service in ecommerce, and meet budding trends. Similarly, ecommerce companies use this data to accurately predict inventory needs as seasonality or the economy changes.

Lenovo, the world’s largest PC supplier, serves customers in more than 160 countries. Seeking to improve the customer experience and differentiate the company from the competition, Lenovo needed to understand customers’ needs, preferences and buying behaviour. By collecting datasets from a variety of touchpoints, Lenovo used real-time predictive analytics to improve customer experience and drive an 11% increase in revenue per retail unit.

Safer online payments

To offer an optimal shopping experience, customers need to know that their payments are secure. Big Data analytics can recognize atypical spending behaviour and alert customers as soon as they occur. Businesses can set up alerts for various fraudulent activities, such as a series of different purchases on the same credit card in a short amount of time or multiple payment methods originating from the same IP address.

Similarly, many ecommerce websites now offer different payment methods on one centralized platform. Big Data analytics can determine which payment methods work best for which customers and can measure the effectiveness of new payment options such as “bill later”. Some companies using Big Data have implemented a simple checkout experience on their websites to reduce the chances of an abandoned cart. The checkout page gives customers the option to add an item to a wish list, choose a “bill later” option, or pay with multiple credit cards.

Increased customization

In addition to enabling customers to make simple and secure payments, Big Data can create a more personalized shopping experience. 86% of consumers say that personalization plays a big role in purchasing decisions. Millennials are particularly interested in online shopping and assume that they will receive personalized suggestions.

Using Big Data analytics, ecommerce companies can establish a 360-degree view of the customer. This view allows ecommerce companies to segment customers based on gender, location, and social media presence. With this information, companies can create and send personalized discount emails, use different marketing strategies for different target audiences, and launch products that directly appeal to specific groups of consumers.

Many retailers cash in on this strategy, giving members loyalty points that can be used towards future purchases. Sometimes, ecommerce companies will choose different dates throughout the year to give loyalty members bonus points on all purchases. Typically, this is done during a slow season and increases customer engagement, interest, and spending. Loyalty members not only feel like VIPs, but they also provide insights that businesses can use to provide personalized buying recommendations.

Optimized prices and increased sales

In addition to loyalty programs, secure payments, and seamless shopping experiences, customers appreciate good deals. eCommerce companies are starting to use Big Data analytics to find the fairest price for specific customers to drive more sales from online purchases. Consumers with long-standing loyalty to a company can receive early access to sales, and customers can pay higher or lower prices depending on where they live and work.

Otto, Germany’s largest online retailer of home furnishing products, is one of the most successful ecommerce companies in Europe. To keep that title, Otto must compete against such solutions as Big Data Amazon. Otto has brought together its many data silos into a single database, making it easier to develop 360-degree customer profiles, analyze competitor data, and determine the best-performing sales channels. Otto can now easily use Big Data to optimize pricing, produce more personalized marketing campaigns, and refine its strategy for on-site advertising deals.

Dynamic customer service

Customer satisfaction is the key to customer retention. Even companies with the most competitive prices and products suffer without outstanding customer service. eCommerce solutions providers say it costs 5 to 10 times more to acquire new customers than it costs to sell to a new customer. Additionally, loyal customers spend up to 67% more than new customers.

Businesses focused on providing the best customer service increase their chances of good referrals and sustain recurring revenue. Keeping customers happy and satisfied should be a priority for every ecommerce business. So how does Big Data improve customer service? Big Data can reveal issues in product delivery, customer satisfaction levels, and even brand perception in social media. In fact, Big Data analytics can identify the exact moments when customer perception or satisfaction changes. It’s easier to make sustainable changes 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 engage on ecommerce sites, but consumers also want to hear and read about ALDO on social media and other channels. ALDO needed to leverage big data to understand customer behaviour and deliver excellent customer service.

While ALDO was already collecting customer data, it was difficult to link customer profiles to transactions and interactions across channels. Using an agile, fast, scalable and flexible Big Data tool to capitalize on variable costs, ALDO can now easily expand global reach while providing a localized experience for every customer. ALDO continues to use Big Data to create innovative products and deliver a pleasant customer experience.

Improved personalized offers

Big Data helps online retailers tailor their recommendations and coupons to meet customers’ wishes. High traffic results from this personalized customer experience, resulting in higher profit. Consumer big data can also help ecommerce companies conduct accurate marketing campaigns, provide appropriate coupons, and remind people that they still have something in their cart.

Domino’s Pizza is an amazing example of ecommerce using Big Data to drive sales. Domino’s “AnyWare” ordering program allows customers to buy pizza via smartwatch, TV, car and social media. Making sales so easy and convenient has been a key benefit to 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 information is easily integrated from 85,000 unstructured and structured data sources. With a single view of customers and global operations, Domino’s can now collect, cleanse and standardize data from all point-of-sale systems and supply centres. This data is fed into Domino’s data warehouse, where it is combined with USPS, competitor and demographic information.

Trend predictions

Satisfying a customer’s needs is not just a problem of the current state. Ecommerce depends on storing the right inventory for the future. eCommerce consulting companies offer to use Big Data to prepare for emerging trends, slow or potentially booming parts of the year, or plan marketing campaigns around major events.

eCommerce companies compile huge datasets. By evaluating data from previous years, online retailers can plan inventory accordingly, stock up to anticipate peak periods, streamline overall business operations, and forecast demand. For example, ecommerce sites may advertise big discounts on social media during peak shopping hours to get rid of excess products.

To streamline pricing decisions, ecommerce sites may also offer extremely limited 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 analytics and machine learning.

Wrapping Up

Big Data has already had a big impact on the ecommerce industry and will likely continue to do so. It is predicted that by 2040, 95% of all purchases will be made online. To prepare, companies can use Big Data analytics to update their copy, strengthen their self-service and interpret surveys. Not only that, ecommerce businesses can prepare for seasonal flows, new trends, and customer preferences.

If you want to start benefiting from Big Data, you can always ask an ecommerce development company for help. NEKLO can help you with the clear preprocessing of Big Data so that you’ll obtain valuable information and get insights. Just fill in the form on our website – and our ecommerce website design company will work out a strategy for fruitful cooperation.