How tech startups can leverage data analytics and visualization
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The document discusses how tech startups can effectively utilize data analytics and visualization to make informed business decisions. Key topics include understanding the importance of meaningful data, collecting the right metrics based on the startup's goals, and common analytics mistakes to avoid. Best practices for analytics are also outlined, emphasizing the need for balance between short-term and long-term strategies.
How tech startups can leverage data analytics and visualization
- 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?