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

1. The true meaning of data
Data Science meets Marketing
Maciej Dabrowski
Chief Data Scientist, Altocloud
1

2. Altocloud
2

3. 3

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

8. 8

9. Data Science
9

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

18. Presentation
Label your axes!
18

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

22. Correlation vs. causation
22

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