CRM Chapter 9 Analytics

Analytics  Collection, extraction, modification, measurement, identification, and reporting of information designed to be useful

Types of Analytics  Descriptive – Historic look at customer behavior, organization performance, or customer segment’s habits  Predictive – “Could be if”, Models of possible and scoring likelihood of achieving possibility by individuals, Projecting by combining past performance and other factors

Segmentation  An early step in creating “actionable intelligence” from data  Analytics help better understand customers, target appropriate segments with offers, and make efficient decisions

Modeling and scoring  Modeling – Not profiling, Descriptive focus on predictive behavior  Compares customer profile to behaviors of similar profiles then makes weighted prediction  RFM Analysis is common marketing model

Scoring  Getting results from predictive analytics  Explores customer history, behavior and other factors to make prediction  Eg FICO (Fair Isaac Corporation rating)  Payment history (35%), Amounts owed (30%), Length of credit history (15%), New credit (15%), Types of credit used (10%)

Validation  Tests the predicted results to ensure the sample was not biased  Risk Analysis: Potential for disease, bankruptcy, eating a banana with adverse reaction, walking down a street and collapsing {credit, life insurance, health insurance}

Measurement and tracking  How to collect data  Humans love stats  Compare results with objectives/expectations

Technology  3 software types:  OLAP – data from different dimensions  Query – Ask questions about patterns or details in data  Data mining – Searches for patterns or correlations

Business Intelligence (BI)  Ability to understand and influence customers, products, services, etc to increase performance and income