Driving E-commerce Growth Using Big Data Analytics
In recent years, the growth of internet-based technologies has provided to e-commerce firms a set of higher customer service capabilities, including dynamic pricing, real-time service, and the ability to provide personalized offerings.
One of these technologies is Big Data Analytics (BDA) that has been drawing the attention from researchers and from the e-commerce industry. Some of the advantages of BDA include identifying the optimal price, the most loyal and profitable customers, perceiving quality issues, or decide on the ideal level of inventory.
A recent study reports that, through a more efficient use of data, improved decision-making and by empowering customers, BDA resulted in more than 10% of growth for 56% of e-commerce firms.
As shown in Figure 1, there is an increase in the BDA market, which is based on the number of global e-commerce customers and their per capita sales.
The article of Shahriar Akter and Samuel Fosso Wamba at Electronic Markets aims to identify different dimensions and applications in the use of big data in e-commerce and its relevance to enhance commercial value.
Big Data and E-commerce
Today’s web technologies have given rise to a huge amount of relevant data in the e-commerce environment. These data are broken down by the following dimensions, known as the 4Vs:
- Volume — Huge amount of data driven by business;
- Variety — Various types of data from multiple sources, mostly unstructured data;
- Velocity — How fast that data is generated and needed to be analyzed;
- Veracity — Data accuracy and reliability for forecasting.
Among these, big data veracity should be highlighted. Strict verification of data demand requires compliance with quality and safety issues. Accurate and reliable data are important requirements of big data analytics for better predictability. Each dimension has their own implications to e-commerce and they are applied in different ways, resulting in business value creation.
Big data types used in e-commerce
E-commerce is an online transaction for buying or selling goods and services through the use of technology, where data plays an important key role to track consumer shopping behavior. Data are collected using consumer browsing and transactional points.
The various types of data are divided in structured and unstructured data and can be classified into four types, illustrated in the table below.
Big Data Analytics Business Value in the E-commerce
Accurate customer retention strategies can be created by building a customer sample from big data and apply analytic algorithms to forecast at-risk customers, being able to interact in real time with them. We can set six different dynamics to retrieve business value in big data and increase sales:
1. Personalized service and customized offers
Consumers may use multiple channels from a retailer, so firms gather big data from several sources. By using real-time analytics, they can design personalized services and specific promotions to loyal customers and new ones.
2. Dynamic pricing to attract new customers
Pricing is critical in a highly competitive market. Dynamic pricing system used by Amazon.com, monitors prices from the competition and send alerts every 15 seconds, resulting in a 35% sales increasement. They process big data like competitors’ prices, product sales, customers actions and geographical preferences.
3. Customer service and proactive maintenance
BDA can increase CRM value, when e-commerce firms use contact forms and chatting in their online stores. By using data collected by sensors in their products, firms can also offer proactive maintenance.
4. Supply chain management
The supply chain process involves several third parties, so big data analytics by obtaining information from various parties, can be helpful in this process. When customers places orders on an online platform, they expect to be able to check their exact availability, current status and location.
5. Fraud detection and security issues
In order to identify fraud in real time, it’s necessary to analyze data at an aggregated level. Softwares like Hadoop do this through the combination of customer´s transaction data with their purchase history, web logs, social feed and smartphone geospatial location data.
6. Predictive analytics
When used together, big data and decision science tools enable firms to predict individual customer’s potential value and future sales patterns, helping to better forecast inventory requirements.
In conclusion, we can say that Big Data analysis is increasingly valuable to e-commerce firms, by transforming data into business insights and contributing to solid decision making to maximize the commercial value of your business. This can only be done with the correct application of human resources, processes, and sophisticated technologies.
This post is based on the article “Big data analytics in E-commerce: a systematic review and agenda for future research” and was written by António Monteiro, Duarte Stock and Joana Valente. It is also currently published on NOVA IMS’ Digital Marketing Magazine. For further information, click here.
