The term “Big Data” has been used since the 1990s but only experienced explosive growth in the last 10 years. According to Gartner, Big Data can be used in many fields such as retail, banking, healthcare service, telecommunication, entertainment, insurance, transportation, education…in four general areas: optimizing operations, improving customer experience, creating new services and managing risks.
Dynamic Pricing:. Instead of traditional method based on supply, demand and expiry date, Big Data enables price adjustments in accordance with factors such as weather, location or the purchase history of customers. Amazon uses Big Data to change prices every 10 minute, Walmart changes prices 50000 times a month for an increase of 26% in sales.
Basket analysis:. Previously this is based on the order history of customers. For example if users often purchase Merries Baby Diapers with Glico Milk, retailers can put the Merries section next to the Glico section. Alternatively they would recommend buying milk when customers buy Merries diapers. Big data can add other factors to the analysis such as the time of the day, the shopping time, the weather, or even the music in the supermarket or the waiting time at check-out.
Shopping cart defection: According to some research, only 57% of users click to select products on a website and only about 5% of them add items to their carts. Half of those do not even make the payment. Big data technology can predict shopping cart abandonment from the collection of items that users view or add to cart. In those cases discounts or vouchers can help reduce shopping cart abandonment rate.
Improving customer experience
Recommendation System: Based on purchase history, these systems make recommendations for products which customers could be interested in. Frameworks such as Spark MLLib or graphical databases such as Titan, Neo4j enable the deployment of recommendation algorithms by distributed computing and graph analysis to identify hidden relationships among customer groups.
Maintaining customer loyalty: The growth of social networks, forums and review websites makes it easy to reward customers with loyalty points and discounts if they have positive comments on products and brands.
Creating new Services
Flexible price adjustment: Big Data can implement counter-dynamic pricing which helps customers decide when to buy for the best price. For example, Farecast (integrated into Bing search) analyzes about 200 billion airfares to find the time to buy ticket at the best prices.
Retail Data: Retailers can sell data to suppliers because suppliers need it to change their marketing strategies or production strategies.
Fraud detection: Big Data tools can detect fraud in real time, for example, using stolen credit cards to make purchases.
Collecting and analyzing information about careers, wages, social status and graduated students in different fields to suggest proposals to improve courses.
Improving customer experience
Personalizing online education: analyzing student history, favorite subjects, study time etc. to personalize lessons.
Creating frameworks for analysis and predictive reporting, for example predicting dropouts.
Creating new services
Training data scientists: with the explosion of Big Data, all professions will need data scientists; therefore it is necessary for educators to design data science programs.
Fraud detection in scientific articles: Thanks to developments in natural language processing (NLP), big data technology can detect fraud in scientific articles by referencing online scientific libraries.
Above are some examples of Big Data application in education and retail sectors. Similarly, Big Data applications in any other fields can be classified by the four general areas above, in order to find suitable applications in each scenario.
|Nguyen Viet Cuong|
Specialist level 3 – FPT Technology Innovation Department.
He graduated VNU University of Engineering and Technology – Vietnam National University. He has professional experience in Big Data. Currently he is leader of DMP project, deployed for internal customers as Sendo, FPTShop, ANTS, FU and certain external customers. He achieved the FSU1 Best performance of the Year.
Vu Quang Chien
Specialist Level 3 – FPT Technology Innovation Department.
He is praised as a young talent by colleagues. He has extensive knowledge of Big Data, Cloud Computing, Web development and analytics. He owns certificates such as AWS Cloud (AWS Certified Solutions Architect – Associate) certification and Big Data Cloudera (Cloudera Certified Developer for Apache Hadoop – CCDH) Currently he is managing the project DMP, deployed for internal customers as Sendo, FPTShop, ANTS, FU and certain external customers.
(This article is published in FPT Technology Magazine, FPT TechInsight No.1)