Rajat Paharia’s (2013) chapter three of How to Revolutionize Customer and Employee Engagement with BIG DATA and GAMIFICATION Loyalty 3.0 is all about big data. Paharia's equation for Loyalty 3.0 is based on three parts “Motivation + Big Data + Gamification = Loyalty 3.0” (Paharia, p.39). Big data is what helps companies and brands get to know what their consumers want.
The majority of big data that companies collect come from clickstreams, the most colloquially known clickstreams being, internet protocol (IP) addresses, Global Positioning System (GPS), and radio-frequency identification (RFID) chips. The big question that comes with big data is what you do with it. The majority of companies have more collected data than they know what to do with it. Analyzing and interpreting the information collected requires more money and manpower than some brands can supply. Paharia suggests eleven important forms of data collection techniques and analysis such as Cluster analysis, A/B testing (split testing), Crowdsourcing, Threadless, CrowdFlower, Stock Market, Predictive modeling, Sentiment analysis, Stream processing, Outlier detection and similarity search, and Cohort analysis. Each of these recommendations gives valuable information for brands to upgrade their business to the next level.
Through big data, we can create informed campaigns to help boost our brands, however, to be able to use this data, we have to crunch the numbers. Technological industries have created many large-scale data processing systems, Paharia (2013) defines two of them NoSQL, Not Only Structured Query Language and Hadoop. NoSQL is a programming language specifically made for inputting and receiving data from large web-scale databases. Most SQL uses the relational model ACID, atomicity, consistency, isolation, and durability, which confirms the verification and reliability of data within transactions. However, NoSQL was specifically made to accommodate large data stores and run on commodity hardware (Paharia). Hadoop, created by Doug Cutting, is an open-source software framework made for processing large data stores across and distributed hardware system (Paharia). Big data has made widespread innovations in technology to accommodate its presence.
Big data, once analyzed, can be used in a number of different ways, one of those ways being defining target audiences within your consumers and learning how to specifically cater to them. Paharia defines six ways to utilize big data to increase revenue for your business Microsegmentation, Targeted advertising and cross-selling, In-store behavior analysis, Real-time pricing optimization, Social-media monitoring, and Recommendation engines. Through these lenses of research and data-collection, you can improve both customer and employee experience (Paharia, 2013). From the process of hiring new employees to how their day-to-day schedule is planned big data is how many big companies decide and justify these decisions, for better or for worse. Paharia describes some of the workforce analytics used to provide insight into employee behavior and incentives. “Progress toward goals, Live performance reviews, Employee skills, Employee influence networks, Benchmarking, Predictive analytics, and Personalization” (Paharia, p. 59-61) are different ways to observe employee productivity and loyalty. By understanding the physical and emotional wants an employee desires you can find how to spur good employee loyalty for your brand.