Big Data Applications in the Ecommerce Industry: 5 Remarkable Use Cases

The analytical capabilities that big data brings to the table are disrupting all industries, including ecommerce. Online vendors engage ecommerce development services to weave big data analytics tools into their e-shops and marketplaces and tap the ensuing benefits that include:

Let’s dive into how ecommerce vendors can attain these goals with big data.

At the core of big data lies data analytics, i.e. the practice of utilizing data analysis technologies and statistical models to create predictions and evaluations underlying informed decision-making.

Predictive analytics is used to assess the likelihood of various events occurring in the future based on a variety of factors, but big data systems can also provide insights into past events (through descriptive analytics), enhance decision-making by collating predictions and possible outcomes, and come up with the suggested course of action (prescriptive analytics).

Data analytics has a wide range of applications, covering clinical decision support systems, enhancement of marketing campaigns, fraud detection, and project management. In ecommerce, big data is the cornerstone of demand forecasting.

When items in stock don’t generate sales, they incur considerable losses by taking up space in a warehouse and wasting the time of the company staff who still have to account for them in the inventory. Not to mention that these products can also expire over time or go out of fashion. Moreover, the longer they are sitting dormant on shelves, the greater the chances of their getting damaged.

Small ecommerce merchants may be able to handle demand forecasting with basic ERP systems or Excel spreadsheets, yet the amount of data processed by medium to large enterprises is too vast to rely on legacy solutions when developing an accurate demand planning strategy. That’s where big data proves invaluable.

Business-focused demand planning solutions based on big data-driven processing collect and analyze multiple factors in real time to pull out insights about the future demand. These factors include the market state, historical sales data, customer preferences, geo-location, competitors’ landscape, and so on. By doing so, these platforms take the guesswork out of stock planning and help ecommerce enterprises improve demand management on a large scale. Thanks to handpicked algorithms, companies can predict with high probability which items are going to be in demand, and stock them up properly.

Today, supply management wouldn’t exist without big data, as supply chain operations are becoming more complex and involve an increasing number of products, processes, and agents. This is especially true for ecommerce businesses that operate in the global market, as their supply chain gets stretched in time and complexity. Thanks to its powerful ability to draw meaningful insights from overwhelming datasets, big data solutions provide a wealth of process improvement opportunities for ecommerce in the area of supply chain management.

Every production process starts with planning and scheduling. Big data solutions provide ecommerce owners with high-quality decision support when it comes to the entire continuum of supply chain operations, planning included. The application of powerful statistical methods to production data sources creates insights that are instrumental in the improvement of supply chain planning and scheduling, offering an accurate evaluation of production volumes.

Predictive analysis of real-time telemetry data captured from smart sensors distributed along the production line can also help suppliers prevent failures and minimize downtime, both of which have a direct impact on ecommerce store deliveries. Should a failure occur, big data models can act retroactively to pinpoint the cause and prevent similar glitches from reoccurring in the future.

Since we’re talking about deliveries, big data solutions can also help minimize delays. They can do so by collecting key data points such as weather and traffic data, GPS position, fleet information, and driver performance, and analyzing them to suggest optimal routes to avoid traffic.

Ecommerce vendors do their best to make buying online easier than ever for consumers. They add new purchase channels, such as mobile apps or social media marketplaces, to reach out to their audiences wherever they are. They also strive to simplify the buying process by investing in user-friendly interfaces, reducing the number of steps to finalize the purchase, and providing support for diverse payment methods.

Unfortunately, along with these innovations comes an increasing number of associated threats. The broader choice of paying options creates more opportunities for cybercriminals to find weak spots and vulnerabilities.

Online customers expect ecommerce to be secure. The fundamental ecommerce security strategies that guarantee safe transactions involve the use of HTPPS protocols and SSL certificates and the installation of a firewall and transaction monitoring. Big data analysis adds another layer of security to e-stores with real-time fraud detection, analysis, and prevention.

Analyzing streams of real-time big data helps ecommerce businesses identify anomalies that may indicate fraud in order to detect suspicious transactions as they happen. Big data solutions delve into customer information and past purchases and trace repeated patterns to discern between legit and suspicious transactions and suggest the most fitting course of action to combat cybercrime.

Predictive big data engines also allow online vendors to look proactively for potential threats and implement preventive strategies to mitigate the risk of a future cyberattack. Thanks to big data solutions, modern organizations can identify and prevent fraud related to credit cards, product returns, and identity stealing threats.