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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 โ cashflow, profitability, sales forecasts Market analytics โ market size, market trends, marketing channels Customer analytics โ customer lifetime values, social media, customer needs Employee analytics โ capacity, performance, leadership Operational analytics โ supply chains, competencies, environmental impact Bare business analytics โ 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. Review: Nice one to have to refer - Nice one to have to refer Review: Handy - Useful key concepts
| Best Sellers Rank | #43,123 in Analysis & Strategy |
| Customer Reviews | 4.3 out of 5 stars 48 Reviews |
M**S
Nice one to have to refer
Nice one to have to refer
N**A
Handy
Useful key concepts
A**R
content of the book
Summary of content in the book key business analytics : 1. Bare analytics business experiments / experiemntal design / ab testing visual analytics corelational analysis sccenario analysis forecasting / time series analysis data mining regression analysis text analytics sentiment analysis image analysis video analysis voice analysis monte carlo simulation linear programming cohort analysis factor analysis neural network analysis meta analytics literature analysis 2. analytics input tools or data collection methods quantitative surveys qualitative surveys focus groups interviews ethnography text capture image capture sensor data machine data capture 3. financial analytics predictive sales analytics customer profitablility analytics product profitability analytics cash flow analytics value driver analytics shareholder value analytics 4. market analytics unmet need analytics market size analytics demand forecasting market trend analytics non customer analytics competitor analytics pricing analytics market chanel anlytics brand anlytics 5. customer analytics customer satisffaction customer life time customer segmentation sales channel web social media customer engagement customer churn customer acquisition 6. employee anaytics capability capacity employee churn recruitment chanel competency acquisition employee performancec corporate cultur e leadership 7. operational analytics fraud detection core competency supply chain lean six sigma ccapacity utilisation project and programe environmental impact corporate social responsibility
B**B
An excellent bullet point
A bullet point of ideas about problems inside a company and how to go deeper in case of need. Obviously, as the author anticipates, no technical explanation of techniques, but links and references, if you need to deepen. The title says what will be inside the book and no bad surprizes, but inspiring ideas.
A**R
love it
a great deal of material, very helpful and easy to read
D**Y
A good concise book providing an overview of business analytics
A good concise book providing an overview of business analytics. A little light in places only giving a brief explanation and then saying there are resources/software available but providing full details. Certainly worth the money for initial insight into a fascinating topic.
S**A
Don't hesitate to buy your copy
Don't hesitate to buy your version. It's one the books recommended for booth students.
S**S
Nice intro in sentiment and text analytics but overall simplistic ...
Nice intro in sentiment and text analytics but overall simplistic views in many points and far-fetched use of the term analytics. Also, some points are blatantly wrong and very bad practice, for example, what is the point of measuring the time developers spend programming and using that as a KPI in 'performance analytics'? It is as inefficient a metric as it can get. A data scientist, a developer or any other coder can spend 80% of the time reading and solving on paper before actually coding something, how is that less efficient than just writing comments in the screen? The book should have contained 1/3 of the chapters, be more concentrated and cost 1/3 of the price. Analytics is a fantastic new world in the business, but it's not panacea and we can't label everything analytics.
Trustpilot
2 months ago
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