BI Consultancy - Data, Analytics and Strategy
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The document outlines a data strategy focused on leveraging data to enhance business objectives, optimize outcomes, and improve user experiences. It emphasizes the importance of understanding not only what data is collected but also its implications for decision-making, involving both quantitative and qualitative analyses. Various methodologies, such as A/B testing and user feedback, are discussed as ways to inform decisions and measure key performance indicators (KPIs).
BI Consultancy - Data, Analytics and Strategy
- 7. DATA Planning Media Planning Social Listening Search Queries USER INTENT Research Web Analytics A/B testing UX & Usability USER VOICE Returns User Satisfaction Goal & Conversion ROI & Revenues RESULTS
- 8. Behavior DATA Planning Media Planning Social Listening Search Queries USER INTENT Research Web Analytics A/B testing UX & Usability USER VOICE Returns User Satisfaction Goal & Conversion ROI & Revenues RESULTS
- 9. Behavior DATA Planning Media Planning Social Listening Search Queries USER INTENT Research Web Analytics A/B testing UX & Usability USER VOICE Returns User Satisfaction Goal & Conversion ROI & Revenues RESULTS
- 10. Behavior DATA Planning Media Planning Social Listening Search Queries USER INTENT Research Web Analytics A/B testing UX & Usability USER VOICE Returns User Satisfaction Goal & Conversion ROI & Revenues RESULTS
- 11. Behavior DATA Planning Media Planning Social Listening Search Queries USER INTENT Research Web Analytics A/B testing UX & Usability USER VOICE Returns User Satisfaction Goal & Conversion ROI & Revenues RESULTS
- 15. DATA on its own is NOTHING
- 16. It’s not just about DATA
- 17. but what it MEANS
- 18. &howitRELATES
- 19. totheOUTCOME
- 25. The DATA that drives our DECISIONS
- 26. The DATA that drives our DECISIONS
- 27. High Volume High Quality The DATA that drives our DECISIONS
- 28. High Volume High Quality Behavioral Attitudinal The DATA that drives our DECISIONS
- 29. High Volume High Quality Behavioral Attitudinal Actual Artificial The DATA that drives our DECISIONS
- 30. High Volume High Quality Behavioral Attitudinal Actual Artificial systematic observations semi-structured collection The DATA that drives our DECISIONS
- 31. High Volume High Quality Behavioral Attitudinal Actual Artificial systematic observations semi-structured collection The DATA that drives our DECISIONS
- 32. THAT’S A LOT OF DATA
- 33. Quantitative WHAT happens and WHEN HOW it happens and WHY Qualitative
- 34. Quantitative WHAT happens and WHEN HOW it happens and WHY Qualitative
- 35. It is not much use to know WHAT is happening if you don’t know WHY
- 36. It is not much use to know WHAT is happening if you don’t know WHY You can’t know WHY things are happening if you don’t know WHAT is happening
- 38. What we want to measure KPIs / Key Metrics Collection of Data Analyze Metrics Benchmark Improve Quantitative Top Down Analysis
- 39. What we want to measure KPIs / Key Metrics Collection of Data Analyze Metrics Benchmark Improve Quantitative Top Down Analysis Bottom Up Analysis Qualitative
- 40. Quantitative Top Down Analysis Bottom Up Analysis Qualitative What we want to measure KPIs / Key Metrics Collection of Data Analyze Metrics Benchmark Improve Prove Hypothesis Collect Data Devise Experiment How can we improve? Why is this happening? Analyze existing trends
- 41. What we want to measure KPIs / Key Metrics Collection of Data Analyze Metrics Benchmark Improve Prove Hypothesis Collect Data Devise Experiment How can we improve? Why is this happening? Analyze existing trends Quantitative Top Down Analysis Bottom Up Analysis Qualitative
- 42. What’s in the Middle? What we want to measure KPIs / Key Metrics Collection of Data Analyze Metrics Benchmark Improve Prove Hypothesis Collect Data Devise Experiment How can we improve? Why is this happening? Analyze existing trends
- 43. What we want to measure KPIs / Key Metrics Collection of Data Analyze Metrics Benchmark Improve Prove Hypothesis Collect Data Devise Experiment How can we improve? Why is this happening? Analyze existing trends What’s in the Middle?
- 44. DECISIONS What we want to measure KPIs / Key Metrics Collection of Data Analyze Metrics Benchmark Improve Prove Hypothesis Collect Data Devise Experiment How can we improve? Why is this happening? Analyze existing trends What’s in the Middle?
- 46. Till now, > How Business Intelligence works > BI approach and methodologies towards DATA > What informs our decisions
- 47. IT WAS SIMPLE
- 48. IT WAS SIMPLE
- 50. What we want to measure KPIs / Key Metrics Collection of Data Analyze Metrics Benchmark Improve Prove Hypothesis Collect Data Devise Experiment How can we improve? Why is this happening? Analyze existing trends What’s in the Middle?
- 51. REPORTS What we want to measure KPIs / Key Metrics Collection of Data Analyze Metrics Benchmark Improve Prove Hypothesis Collect Data Devise Experiment How can we improve? Why is this happening? Analyze existing trends What’s in the Middle?
- 52. APPROACH DATASOURCE Adapted from work of Christian Rohrer 2008 X Multivariate (live A/B) testing X Eye Tracking X Usability Lab Studies X Customer email Feedback X Intercept Surveys X Data Analysis / Mining X Live UX assessments X Participatory Design X Focus Groups X Card sorting X Desirability studies X social Listening solutions X Message board mining X Email Surveys
- 54. ?
- 55.
- 56. BI Solutions Directing Metrics to Strategy
- 57. SHIVAM
- 59. Parting Notes Connect with me asking ‘How do we… Bridge cultural gaps Design and Manage integrated web teams Develop DATA reliance with reliable data Generate analytical awareness Improve on tools, methodologies and standards
- 60. THANKS Follow me @ShvmDhwn More Here… Connect -