10 Big Data Trends That Can Change Ecommerce Industry In 2021

According to IDC, the worldwide datasphere is slated to grow to 175 zettabytes by 2025 from a mere 45 zettabytes in 2019. I was astonished to know that this total of the world’s data is increasing at a compounded growth rate of a staggering 61% annually.

Many eCommerce businesses assume that collecting information is enough. However, as an e-retail technology expert, I know that gathering data is only half the battle. Big data’s real power can give eCommerce stores the competitive advantage only after analyzing this chunk of data accurately.

Of late, I have seen businesses leveraging big data analytics to usher in improvements to the way their business is done. Everyone is leaving their digital footprint in the datasphere, and this vast volume of incredible information is becoming a goldmine for data analysts and brands. When eCommerce companies start gaining strategic details on customer’s shopping behavior, they will be better equipped to develop better products, forecast trends and demand. With experience, I know that e-retailers can directly tailor their entire marketing efforts, target particular customer preferences and make sure their employees provide the right degree of customer service to their consumers.

As data collection is increasing rapidly, software and applications are also increasing their data processing speed. And eCommerce businesses are moving to the cloud to store such vast datasets. With big data going mainstream, the latest technologies such as Machine Learning and Artificial Intelligence have created critical environmental implications. Many eCommerce industry heavyweights have ensured data-driven decisions to be the norm in the digital world. The case is clear- big data analytics is expected to change the eCommerce industry in 2021.

10 Big Data Trends in 2021

These are the 10 big data trends that can change the eCommerce industry in 2021

1. Augmented Analytics is Getting More Real

Augmented Analytics

Augmented Analytics is considered by many to be the game-changer for the eCommerce industry. Global big data analytics in the retail market are expected to reach US $17.85 billion by 2027. It is a projected compounded annual growth rate of 20.4% from 2020 to 2027. Augmented analytics relies a great deal on big data. The process of digital transformation in the eCommerce industry has been enhanced with the use of sophisticated tools such as Natural language generation and Machine learning. As the complexity and quality of information gathered increases each year, the tools used for data analysis are also increasing. Such tools have also helped data scientists sifting through the vast datasets, exploring the information, and focusing only on the relevant facts that help in decision-making.

2. AI helping in Getting Personal with Shoppers

With Artificial Intelligence, it is possible to understand customer’s buying preferences and also predict trends. Many eCommerce businesses are already applying big data predictive analytics to track and analyze user behavior. Organizations that have decided to use predictive intelligence have registered a 40.38% increase in revenue after only 3 years of adoption.

Predictive analytics will help eCommerce businesses accurately track everything about customers, from their shopping preferences to their personal information. It will help them identify products that will better resonate with those customers. Big data will also help in creating better lead scoring. Lead scoring helps in predicting whether a prospective customer will turn into a loyal customer.

3. Big Data Providing Personalized Shopping Experience

By working complex algorithms on big data, eCommerce businesses can understand consumer behavior and shape their purchasing patterns. It means they can detect trends, identify anomalies in trends and make business decisions based on hard facts and not instincts. For example, cart abandonment is a massive problem for eCommerce businesses. Companies try their best to deter customers from leaving their stores without actually making a purchase. It is why online stores should develop better recommendations based on engagement rather than simply assuming customer choices.

Personalization is not new, but big data helps companies understand how customers interact with their brand across multiple channels. We have all used marketing tools such as questionnaires, surveys, focus groups to gather data on customer needs and preferences. But these ancient forms of information collection have always suffered from inadequate sample size or bias in group selection. But with big data, eCommerce stores can effectively do away with such inaccuracies and capture customer information across multiple channels. According to a study, 84% consider customer experience as necessary as the products and services. And one extraordinary customer experience raises the expectations bar for nearly 73% of customers.

By bringing together information from various datasets such as demographics, product usage, websites, social media, psychographic data, marketers will have all the necessary information to improve customer value.

4. IoT and Big Data

Big Data and IoT, although developed independently, have mainly become interdependent. Without the critical information provided by big data, IoT devices would not fully utilize their resources. For instance, edge computing is at the forefront of running data analytics that helps data to be stored in local storage devices closer to the source of data gathering rather than in the cloud. It will help manage data better and use it in near-closer real-time.

Big data and IoT have transformed the entertainment industry and are set to bring drastic changes to the eCommerce industry. Netflix has influenced 80% of content viewed by its subscribers by accurately using data insights. The disappointing fact is that less than 50% of structured data collected by IoT devices is used in decision making. And astonishingly, 99.5% of data collected is not used or even analyzed. Much of the potential information is wasted by not being analyzed. This apathy is slated to change in the coming years, with many eCommerce companies adopting big data in a big way.

5. Enhanced payment methods and Security

Another reason for abandoned carts is the lack of payment security and a variety of payment methods. If I was your customer, I wouldn’t be interested in going ahead with the purchase if I can’t pay how I prefer to pay. When eCommerce stores offer multiple digital payment methods, it can help improve conversion rates. Furthermore, eCommerce stores in the future might also accept crypto-currencies. I think cryptocurrencies such as Bitcoin will help the industry and customers by having lower transaction fees and almost nil reverse transactions.

Enhanced payment methods and Security

The future can see big data helping improve payment security as well. As a customer, I prefer to have safe and secure transactions and have a variety of payment methods. It is where big data will come into play by recognizing fraudulent activities. Ecommerce stores can set up alerts for any out-of-the-ordinary transactions on the same credit card or if multiple transactions using various payment methods are coming out of the same IP address.

Nowadays, many eCommerce stores are using a centralized platform to offer multiple payment methods. With big data, it will be easier to identify the right payment method that will suit the right customer. A new payment option that can take the eCommerce shopping experience a notch higher is the ‘bill me later option. It wasn’t possible to have such an option earlier since we lacked the right data to understand customers and their buying behaviors. With the abundance of information at hand, customers can now put a product on a wishlist, or choose the ‘bill me later’ option, or even pay for the product using multiple cards.

6. Forecast Demand

Data analytics lies at the very core of big data. Using the wealth of information in big datasets to predict future events based on various factors accurately is one method of forecasting. Using descriptive analytics, eCommerce stores can gain insights into historical events, which will help predict the future. Stores can now use machine learning and statistical algorithms to use historical data to develop accurate predictions and feasible outcomes and courses of action for the future.

I think big data is not only going to be helpful in forecasting trends and customer purchasing predictions. It will also aid in inventory optimization, reducing the risks of having surplus stock or stock-outs. If you are a small eCommerce merchant, you can still forecast demand and trends using simple ERP systems because the information is not too huge. However, for large eCommerce stores, the data processing happens on a large scale and legacy solutions won’t be able to satisfy the need. That’s where big data-driven demand planning involves using historical sales information, geo-location, buyer preferences, market and competitor’s market share and more.

7. Reduced Cost for Marketing and Sales Team

We all know about Domino’s Pizza using big data analytics to boost their sales. Their famous ‘AnyWare’ ordering program allows pizza enthusiasts to order from any internet-connected device such as their smartwatches, their cars, TVs, and even using social media. I have always believed that big data can make it easier for the sales and marketing teams. Combining all the information from disparate channels into actionable data would not be easier if not for big data analytics. It is possible to collate hundreds of strands of structured and unstructured information from various sources and provide the information to point-of-sales centers.

8. Improve Operational Efficiency

Ecommerce businesses that operate on a global level need big data to improve their operational efficiency. For instance, eCommerce businesses depend heavily on their supply-chain management. The need for big data analytics is especially true in the case of eCommerce businesses that operate on a global level. Since their supply chain operations are entirely complex, stretched over resources, time and products, such companies need to derive meaningful insights from vast datasets.

For example, smart sensors present along the production line can easily spot gaps or flaws in production. The captured telemetry data can help inform suppliers about an impending production failure or even downtime. Any glitch in production can have a considerable impact on store deliveries. Moreover, big data can pinpoint the exact cause of the failure and ensure such eventualities do not occur in the future.

Additionally, when you think of supply chain and eCommerce, one of the first things that come to mind is deliveries. 80% of customers would not continue shopping with an eCommerce store if they had had a negative delivery experience.

With big data, it is easier now to predict and prevent delivery delays. Information on weather conditions, traffic situation, driver information and GPS location can predict delays and suggest alternatives to avoid such holdups.

9. Dynamic Shopping Experience

Customer satisfaction has a significant impact on customer retention. I have seen many companies provide competitive prices, grab-worthy coupons, and attractive products and still lack customer service’s competitive advantage. According to a study in business.com, businesses have to spend 5 to 10 times more money in acquiring new customers than selling to them. It means it is getting tougher to attract new customers into the stores. The battle doesn’t stop with acquiring new customers; it starts with influencing them into spending. Customers who are loyal to the store tend to spend nearly 67% more than new customers.

That is why I suggest eCommerce stores keep their focus on providing better customer service by increasing referrals and customer engagement. So how does big data help improve customer experience? With big data, finding the fault lines in customer satisfaction levels, delivery issues, and brand perception are possible. Big data has the potential to spot the exact time when customer satisfaction levels changed or alter their perception.

10. Dynamic Pricing

Hitting the pricing bull’s eye is a tough job for any e-retailer. The right price is determined after careful consideration of the market, competitor analysis and other sales strategies. Tangible data is needed to come up with a dynamic price that satisfies both the consumer and the store’s needs. Using big data’s predictive capabilities, you can pick the right price that meets the customer while also maintaining a healthy profit margin. These calculations are done using advanced algorithms in real-time so that stores can make data-backed decisions.

Another aspect of dynamic pricing is using big data to offer products at different prices to different customers. You have already experienced such a strategy while booking flight tickets. But surprisingly, we would still go in for the same flight, because we find the price to be fair or find a better alternative wasn’t available. Anyway, dynamic pricing offers products to customers at the highest price they are still willing to pay.

Wrapping Up

The bottom line is that eCommerce stores have always been data-centric. It is possible to offer the right products at the right time to the right people only when e-stores have accurate information on customer expectations. With big data, eCommerce stores are better equipped to handle massive loads of structured and unstructured data in real-time and extract reliable and actionable insights from it. ECommerce companies should harness the power of big data solutions to drive changes across their organization, improve personalization, enhance operational efficiency and maximize profits.

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