The true meaning of data by Maciej Dabrowski
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The document discusses the intersection of data science and marketing, emphasizing the importance of real-time analytics and understanding customer behavior to drive conversions. It outlines the need for appropriate metrics, the common pitfalls in data analysis, and effective presentation techniques for data insights. Additionally, it highlights that successful data science in marketing does not necessarily require advanced tools, with a focus on simplicity and accuracy in data interpretation.
The true meaning of data by Maciej Dabrowski
- 1. The true meaning of data Data Science meets Marketing Maciej Dabrowski Chief Data Scientist, Altocloud 1
- 2. Altocloud 2
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- 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. Analytics 5
- 6. Predictive Analytics Q: Which customers currently on my site are likely to convert? 6
- 7. This talk What is Data Science? Common traps in data analysis Data Science and Marketing 7
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- 10. Data Scientist Human (storytelling) vs. Machine analytics (Machine Learning) Type A (analytical/statistician) vs. Type B (builder/engineer) 10
- 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. 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. Metrics 13
- 14. Metrics Common metrics: number of sessions/visits number of unique visitors total sales time on site Other metrics conversion rate (percentage) 14
- 15. Is the metric accurate? Monthly visits 15
- 16. Is the metric accurate? Daily visits 16
- 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
- 19. Presentation Label your axes correctly! 19
- 20. Tricks to make your data look better 20
- 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
- 23. Correlation vs. causation Conclusion: Science is depressing! 23
- 24. Correlation vs. causation Conclusion: Cheese makes you more likely to get killed by your bedsheets 24
- 25. Correlation vs. Causation Conclusion: Eating margarine will get you divorced! 25
- 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. 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. Big ML 28
- 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. 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 macdab@altocloud.com
Editor's Notes
- #6: aggregations - slicing a bit like