ACCT10003 Lecture Notes - Lecture 11: Customer Relationship Management, Predictive Analytics, Lego Ninjago
19 Jun 2018
School
Department
Course
Professor
Accounting Processes and Analysis
Lecture 11: Data Analysis and Decision Making
Information ecosystems of business and society have been fundamentally transformed by technology
(non-financial data and big data). It has the potential to create value for a business through predictive
analytics. Significant practical in terms of skills and computer programs and ethical challenges exist.
#LO1: Outline Key Predictive Analytics Techniques
Analytics = Pairing data with predictive models to arrive at a conclusion
Business knowledge + Data Mining = Predictive Analytics Value
Basic Analytics
- Slicing and dicing- breaking the data into smaller units to be easier managed, descriptive statistics
and data visualisation.
- Basic monitoring- monitor large volumes of data in real time
E.g. social media response to a new product
- Anomaly identification- actual observation differs from expectations
E.g. higher rates of defects in one machine
Advanced Analytics
Include algorithms for complex analysis of either structured or unstructured data, predictive modelling,
text and voice analytics and other statistical and data mining algorithms
- Classification trees, logistic regression, neural networks, clustering techniques like K-nearest
neighbours.
Operational Analytics
Analytics become part of the business process.
E.g. insurance company builds a model that predicts the likelihood of fraudulent claims; incorporate
in claims-processing system to ‘red flag’ claims
E.g. predict customers who are good targets for ‘upselling’; sales staff are alerted to specific
additional products/services during interaction. Ninjago upgrade when pressing the small combo.
Monetizing Analytics
Beyond businesses using analytics to increase revenues from their own operations, datasets may be
valuable to other companies.
E.g. Customer data of credit-card providers, financial institutions, telcos.
#LO2: Describe examples of how predictive analytics can be used in business to create value
Some major applications:
- Customer Relationship Management (CRM)
E.g. Analysing products with maximum demand now & in future, cross-selling and up-selling and
predicting the buying habits of customers.
- Supply chain management
- Collection analysis
For financial institutions and other business, improved prediction of defaulting customers
- Credit risk analysis
- Operational Management
E.g. inventory management, airlines and hotels forecasts
- Insurance underwriting
Fraud Investigation
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find more resources at oneclass.com
find more resources at oneclass.com
Document Summary
Information ecosystems of business and society have been fundamentally transformed by technology (non-financial data and big data). It has the potential to create value for a business through predictive analytics. Significant practical in terms of skills and computer programs and ethical challenges exist. Analytics = pairing data with predictive models to arrive at a conclusion. Business knowledge + data mining = predictive analytics value. Slicing and dicing- breaking the data into smaller units to be easier managed, descriptive statistics and data visualisation. Basic monitoring- monitor large volumes of data in real time. E. g. social media response to a new product. E. g. higher rates of defects in one machine. Include algorithms for complex analysis of either structured or unstructured data, predictive modelling, text and voice analytics and other statistical and data mining algorithms. Classification trees, logistic regression, neural networks, clustering techniques like k-nearest neighbours.
