This session focuses on Lean analytics and data visualization utilizing Pirate Metrics Model for tech startups.

1. How tech startups can leverage
data analytics and visualization?

2. Today's key discussion points
1. What is data?
2. Importance of having meaningful data?
3. Tech startup - Data driven business decision
4. Lean Analytics
5. Importance of data visualization
6. Let’s get technical
7. Conclusion
8. Share your experience

3. Who are we?
Vish
● Data scientist and software
developer at Explorate.
● Masters in Data Science at
Queensland University of Technology
(QUT).
● Current interest - Statistical data
analysis in R programming language.
www.linkedin.com/in/vishanthbala
Abi
● Business analysis consultant at
BAPL.
● 6 years experience as Business
analyst and consultant.
● Current interest - robotic process
automation (RPA) and data
management.
www.linkedin.com/in/abisachi

4. 56% of SMEs rarely or infrequently
check their business’s data, while
3% have never looked at it at all.
(One Poll, 2018)

5. 40% of major decisions are
still based on your Manager’s
gut feeling.
(Accenture, 2019)

6. Data

7. Data analytics can uncover hidden opportunities,
identify trends and patterns, problem areas and
successes.

8. Good data visualizations should place meaning
into complicated datasets so that their message
is clear and concise.

9. Q1

10. ANSWER
“87” - 86, 87, 88, 89, 90, 91

11. Q1

12. Importance of having meaningful data
On a high level, you can achieve two things with meaningful data;
1. Understanding your audience better. Learning about their needs, their
struggles, their motivations, their habits and their relationships to your
product or service.
2. Using this understanding to create a better product or service and turning
that into profit.

13. Importance of having meaningful data
The order is important!!
Understand
your customer
Create a better
service or
product
Turn that into
profit

14. Tech Startup

15. Pirate Metric Model (Dave McClure)

16. Collect the Right Data
What do you look for when buying a car?

17. Collect the Right Data
● Data is the foundation of your data analytics.
● Even the best analysts in the world won’t be able to do much for you if they
don’t have good data to work with.
“Deciding what data to collect is something you should research
and implement as early as possible….
….because the more good data you’ve collected, the more
effective your analysts can be in their analysis”.

18. What are the 5 metrics every start-up
should measure?

19. “The most important metrics depend on
the stage of your product”.

20. Sample metrics

21. At the end of the day, there are no five
metrics that are relevant to every start-up.
It’s impossible!!
Every start-up is different, every entrepreneur
is different. We all have different goals and
different plans for achieving those goals.

22. Techniques
Top down approach
Bottom up approach

23. Data sources
● Applications logs
● Google Analytics
● Third Party Integrations
● Local Spreadsheets
● Anything and Everything

24. Identifying ideal customer profile using
Machine Learning Algorithm

25. Decision Tree vs Cluster Analysis

26. Decision Tree - Pros and Cons
● Pros
○ Easy to understand
○ East to generate rules
● Cons
○ Sometime the tree can get very long
○ Easily can overfit (Pruning)
○ Not effective for continuous variables (May lose information)

27. ● R
● Tableau
● Python
● SAS

28. DEMO

29. Q2

30. ANSWER
“2” - Number of holes in the
numbers

31. What do we do when the dataset is
small?
N-Fold Validation

32. Top 3 Start-up Analytics Mistakes
1. Playing in Success Theatre

33. Top 3 Start-up Analytics Mistakes
2. Focusing Too Much on the Long-term or Short-term

34. Top 3 Start-up Analytics Mistakes
3. Collecting Data and Neglecting Action

35. Start-up Analytics Best Practices
1. Embrace Lean Start-up Analytics
2. Balance between short-term and long-term
3. Follow Dave McClure’s Start-up Metrics for Pirates (AARRR)
4. Deal only with important metrics
5. Ask WHY?