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The true meaning of data


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A story of how Data Science can or should be used to tell the true story about the data.

Published in: Data & Analytics
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The true meaning of data

  1. 1. The true meaning of data Data Science meets Marketing Maciej Dabrowski Chief Data Scientist, Altocloud 1
  2. 2. Altocloud 2
  3. 3. 3
  4. 4. Real-time analytics Real-time for us is under 1-5s Q: How many customers are currently on my website? Q: How many customers are looking at the new article? Q: How many people from Dublin who spent over 20 minutes on a star wars product page end up spending over €100? 4
  5. 5. Analytics 5
  6. 6. Predictive Analytics Q: Which customers currently on my site are likely to convert? 6
  7. 7. This talk What is Data Science? Common traps in data analysis Data Science and Marketing 7
  8. 8. 8
  9. 9. Data Science 9
  10. 10. Data Scientist Human (storytelling) vs. Machine analytics (Machine Learning) Type A (analytical/statistician) vs. Type B (builder/engineer) 10
  11. 11. Data Science Select a question and a metric Who is likely to convert? (purchase/conversion rate) Collect relevant data User behaviour (page views) and demographics (device) Analyse the data and discover patterns 10% of returning customers who visit my website on their iPhone after 8pm and spend over 20 minutes end up buying. 11
  12. 12. Common problems Am I using correct metrics to answer my question? What is the quality/accuracy of my data? Do I use correct visuals and draw the right conclusions? 12
  13. 13. Metrics 13
  14. 14. Metrics Common metrics: number of sessions/visits number of unique visitors total sales time on site Other metrics conversion rate (percentage) 14
  15. 15. Is the metric accurate? Monthly visits 15
  16. 16. Is the metric accurate? Daily visits 16
  17. 17. Metrics Make sure that you understand how your metric works How are the visits counted? Always challenge the quality of your data What events can influence my metrics? Use the right metric for the job absolute value vs. percentage 17
  18. 18. Presentation Label your axes! 18
  19. 19. Presentation Label your axes correctly! 19
  20. 20. Tricks to make your data look better 20
  21. 21. Less is more Overloaded dashboards may hide important facts about data. Focus on what you want to know Use charts when you care about trends Use numbers when you care about absolute values Use pie charts when you care about percentages Simplicity allows you to understand data quicker and easier. 21
  22. 22. Correlation vs. causation 22
  23. 23. Correlation vs. causation Conclusion: Science is depressing! 23
  24. 24. Correlation vs. causation Conclusion: Cheese makes you more likely to get killed by your bedsheets 24
  25. 25. Correlation vs. causation Conclusion: Eating margarine will get you divorced! 25
  26. 26. Data Science for Marketing Content marketing Which content has the potential to go viral Marketing success Predict the success of marketing campaigns Customer analysis Predict churn Segment your customers 26
  27. 27. Amazon Machine Learning Easy to start Does not require complex knowledge of Machine Learning techniques and algorithms Require to move your data to the cloud 27
  28. 28. Big ML 28
  29. 29. R Project Free desktop tool Very powerful for advance statistics Can work with Big Data platforms (Spark) Requires more knowledge about stats 29
  30. 30. Summary Make sure that you understand your data and metrics Less is more in analytics dashboards Correlation is not causation Data science does not require very complex tools! 30