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Key Business Analytics: The 60+ tools every manager needs to turn data into insights: - better understand customers, identify cost savings and growth opportunities

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Key Business Analytics: The 60+ tools every manager needs to turn data into insights: - better understand customers, identify cost savings and growth opportunities


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  • Copyright 2016
  • Dimensions: 234X156
  • Pages: 272
  • Edition: 1st
  • Book
  • ISBN-10: 1-292-01743-0
  • ISBN-13: 978-1-292-01743-3

Key Business Analytics will help managers apply tools to turn data into insights that help them better understand their customers, optimize their internal processes and identify cost savings and growth opportunities.

It includes analysis techniques within the following categories:

  • Financial analytics – cashflow, profitability, sales forecasts
  • Market analytics – market size, market trends, marketing channels
  • Customer analytics – customer lifetime values, social media, customer needs
  • Employee analytics – capacity, performance, leadership
  • Operational analytics – supply chains, competencies, environmental impact
  • Bare business analytics – sentiments, text, correlations

Each tool will follow the bestselling Key format of being 5-6 pages long, broken into short sharp advice on the essentials:

  • What is it?
  • When should I use it?
  • How do I use it?
  • Tips and pitfalls
  • Further reading

This essential toolkit also provides an invaluable section on how to gather original data yourself through surveys, interviews, focus groups, etc.

Sample Content

Table of Contents


The Raw Material - Data   

Data Types and Format

How to use this book

Part One: Bare Analytics   

1: Business Experiments / Experimental Design / AB testing

2: Visual Analytics  

3: Correlation Analysis

4: Scenario Analysis 

5: Forecasting / Time Series Analysis  

6: Data mining

7: Regression Analysis

8: Text Analytics

9: Sentiment analysis

10: Image Analytics 

11: Video Analytics

12: Voice Analytics

13: Monte Carlo Simulation  

14: Linear Programming

15: Cohort Analysis  

16: Factor analysis

17: Neural Network Analysis

18: Meta Analytics - Literature Analysis   

The Part II: Analytics Input Tools or Data Collection Methods

19: Quantitative Surveys  

20: Qualitative Surveys

21: Focus Groups  

22: Interviews

23: Ethnography

24: Text Capture

25: Image Capture

26: Sensor Data

27: Machine data capture  

Part Three: Financial Analytics  

28: Predictive sales analytics

29: Customer Profitability analytics 

30: Product Profitability analytics

31: Cashflow analytics

32: Value Driver Analytics

33: Shareholder value analytics

34: Risk Reward Analytics

35: Unmet need analytics  

36: Market Size Analytics  

37: Demand Forecasting

38: Market Trend Analytics  

39: Non-customer analytics 

40: Competitor analytics

41: Pricing analytics 

42: Marketing channel analytics

43: Brand Analytics  

Part Five: Customer Analytics

44: Customer satisfaction analysis 

45: Customer Lifetime Value Analytics

46: Customer segmentation analytics  

47: Sales Channel Analytics 

48: Web Analytics  

49: Social Media Analytics

50: Customer Engagement Analytics

51: Customer Churn Analytics  

52: Customer acquisition analytics  

Part Six: Employee Analytics

53: Capability Analytics  

54: Capacity Analytics

55: Employee Churn Analytics  

56: Recruitment channel analytics  

57: Competency acquisition analytics

58: Employee Performance Analytics

59: Corporate culture analytics

60: Leadership Analytics   

Part Seven: Operational Analytics

61: Fraud Detection Analytics

62. Core Competency Analytics

63: Supply Chain Analytics  

64: Lean Six Sigma Analytics

65. Capacity Utilisation Analytics

66: Project and Program Analytics 

67. Environmental Impact Analytics

68: Corporate Social Responsibility (CSR) Analytics



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