Big Data
Business Intelligence

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

Every day, we send 294 billion emails, 500 million tweets, and 65 billion messages over WhatsApp. What can ecommerce organizations do with that rising sea of information? We have some answers.

Every day, we send 294 billion emails, 500 million tweets, and 65 billion messages over WhatsApp alone. These numbers are humongous to the point of being unimaginable. The question is, what can ecommerce companies do with that rising sea of information? This article offers some answers.

Big Data Is Instrumental to Ecommerce Growth

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:

  • Optimization of back-office processes
  • Enhancement of customer-facing operations
  • Streamlined supply chain management
  • Fraud identification and prevention in customer transactions
  • Increased competitiveness with dynamic pricing

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

The Application of Big Data in Ecommerce

Let’s take a look at some of the most relevant and promising applications of advanced analytics and big data in ecommerce. These big data use cases can help ecommerce companies like yours drive operational change and inspire some future implementations.

Ecommerce and retail businesses investing in AI by 2021
Big data is only getting bigger, and here's how ecommerce businesses can get their hands on it

1. Demand Forecasting

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.

Predictive analytics for sales 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.

2. Pricing Optimization

Price is one of the main factors driving purchase decisions among online shoppers, which makes it one of the key success enablers for ecommerce companies. The right pricing can be determined based on different analytical techniques, including market segmentation, competitor analysis, or targeting strategy. In order for these approaches to work and bring more profit, they have to rely on tangible data. The larger the data pool, the greater the chances of hitting the bull’s eye with pricing.

By extending big data’s predictive capabilities, digital retailers can find their pricing sweet spot. Big data analysis engines extract price-affecting data from a variety of consumer touchpoints and other sources to calculate the price that offers customers the best deals while maintaining the highest possible margin. They do these calculations automatically and in real time based on defined algorithms and parameters, which helps e-store owners make more strategic, data-backed pricing decisions.

Attitudes of retail buyers toward pricing, 2018

Another aspect that feeds into the use of big data in ecommerce when it comes to pricing strategy is dynamic pricing. E-retailers can take advantage of tremendous predictive capabilities of big data to boost sales by offering products at a price that varies from one customer to another or fluctuates hour by hour, depending on a set of variables.

This strategy is already a standard practice on flight and accommodation booking portals. Have you ever checked a flight ticket price one day only to find out that it went up by 50% a few days later? Chances are, you still followed with the purchase because, after all, the final price was still within reason or you couldn’t find any better. That’s exactly what dynamic pricing is about—adjusting the price depending on various factors to offer the customer the highest price they are still willing to pay.

The business goals to be achieved through dynamic pricing can vary. It’s not always about capitalizing on market demand or selling as much as possible; some companies may want to use this mechanism to get return on their investment, get rid of excess stock, attract new buyers, or draw them away from the competition. Anyway, it is a great way for ecommerce companies to gain a competitive edge, and big data has been a massive enabler in this process.

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3. Supply Chain Management

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.

It would take a lifetime to manually analyze the data produced by a single sensor on a manufacturing assembly line

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.

  • 64% of customers say delivery experience is an important factor determining their choice of an e-retailer
  • 80% of customers won’t shop with an e-store following a negative delivery experience
From planning to scheduling and delivery, big data tech can bring new efficiencies to ecommerce

4. Customer Experience

There’s no doubt that personalized experience is critical to positive customer perception of any ecommerce store. Businesses that meet or, even better, exceed, customer expectations, demonstrate higher retention and conversion scores and drive greater profit. The competition in the ecommerce market is stifling, and it is encouraging more sophisticated consumer demands. Online brands that excel at engaging their buyers and prospects are the ones that precisely know what consumers need and deliver that to them quickly. To achieve that aim, they need to gather, analyze, and understand relevant data.

Tools delivering customer insights have been around since the beginning of marketing (surveys, interviews, questionnaires, focus groups), yet they traditionally involved various challenges, such as sample size and selection bias. Big data solutions allow companies to eliminate these inefficiencies and capture customer needs, feedback, and expectations competently and automatically.

Customer experience and buyers' expectations

Thanks to in-depth big data analytics, ecommerce stores have a chance to discover customer needs that so far remained unrecognized. Big data provides marketers and e-store owners with detailed information about customer interactions and preferences, collected from multiple channels of consumer engagement with a brand, such as websites, social media, browse searches, online questionnaires, email campaigns, and many others. All of these touchpoints deliver precious information about customer buying behavior, most popular brands, most searched for products, shopping trends, and so forth, to allow ecommerce owners to identify repeatable patterns and adjust their offering accordingly.

By extracting the right details from the right channels, ecommerce stores can find precise answers to the questions below. This is the first step to establishing and maintaining a strong personal bond with customers that will lead to repeat purchases and increased customer retention.

  • How do your customers engage with your store?
  • What products are they looking for?
  • What related products are they investigating (as an opportunity for upselling and cross-selling)?
  • What happens after they place an order?
  • What is their paying experience?

5. Secure Online Payments

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 retailers are set to lose $130 billion in card-not-present fraud between 2018 and 2023
Juniper Research

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.

The Bottom Line

The success of any retail enterprise, be it offline or online, has always depended on data-centricity. Only by knowing what exactly customers expect and understanding how their organizations work in every aspect, can e-vendors offer the right products to the right people at the right time.

Big data took data analysis to a whole different level: from manual to automated; from small batches of data at a time to massive data volumes handled within minutes; from arduous and error-prone manual processes to almost instant and highly-reliant insights and feedback.

By harnessing the power of big data solutions, ecommerce providers can drive change across every tissue of their organizations, empowering personalization, enhancing the effectiveness of internal operations, and optimizing profit.

The big data market will be worth $103 billion by 2027 and ecommerce may well be among the key drivers of this growth
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