Chapter 3 of Loyalty 3.0: How to Revolutionize Customer and Employee Engagement with Big Data and Gamification is a great primer on the concept of Big Data.
Some of my notes chapter 3: How Can We Use Big Data?
Big data collection and processing tools enable you to listen and react. (41)
Cluster Analysis – a classification technique that partitions a diverse set of objects into smaller groups so that objects in the same cluster are more similar to each other than to objects in other clusters.
A/B Testing – a technique in which a control group (A) is compared with a test group (B) to determine what treatments (changes) will improve a given objective (typically referred to as the conversion rate.) Conversion can be any success condition. Multivariate testing is a variation of A/B Testing that lets a business run several A/B tests at the same time.
Crowdsourcing – outsourcing work to a distributed group of people who aren’t known ahead of time.
Predictive Modeling – refers to a set of mathematical model-techniques created to best predict an outcome. It goes farther than clustering by trying to predict what a group might do under certain circumstances based on current and historical facts and data.
Sentiment Analysis – applies natural-language processing and other analytic techniques to large quantities of source text material to identify and extract subjective information.
Stream Processing – the continuous and real-time analysis of data streams from a variety of sources.
Outlier detection and similarity search – outliers are deviations from the norm. Outliers can help identify problems, lend insight to your product-design process, and expose bad behavior.
Cohort Analysis – by dividing users into cohorts, businesses can compare the relative value of each cohort. Example – source – where did they come from or date of acquisition – when did they join
How are Businesses Using Big Data?
Microsegmentation – big data takes existing data that can be collected and inferred about a consumer and then supplements it with online browsing behavior, shopping patterns, social-networking activity, mobile access, and more data based on actual user behavior to create microsegments.
Targeted advertising and cross-selling – microsegmentation enables businesses to craft the perfect cross-sell/upsell offer to close or expand a sale in real time.
In-store behavior analysis – real-time navigation analysis can provide insight into customer behavior.
Real-time pricing optimization – retailers can change their prices dynamically to reflect demand.
Social-media monitoring – social customer relationship management (SCRM).
Recommendation Engines – predict things that customers might be interested in.