Capturing and reporting on data provides critical insights that ultimately drive the business. However, the myriad of data points, tools and technology and stages of business make choosing an approach for analytics a complex question. In this talk, Sergey will use a primary case study and other examples of SaaS companies to: Explore how you can collect and analyze key metrics such as Customer Acquisition Cost (CAC), Lifetime Value (LTV), Churn, Monthly Recurring Revenue (MRR), Average Sales Price (ASP). Consider different technical approaches to capturing, integrating data, reporting including various types of tools and internal data Examine a framework for selecting your own stack from project inception, through evaluation process and eventual implementation and discuss how we evaluated what to use in this particular case and why Consider how these choices likely will vary based on stage of the business Discuss approaches to more complex issues like cross device and attribution

1. SaaS Analytics
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SERGEY SUNDUKOVSKIY, CO-FOUNDER, CTO/CPO
RAKEN

2. Why?
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3. Agenda
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4. Lies, Damn Lies and Statistics
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5. Who Are You?
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6. What Are You Selling?
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7. What Type of Business Are You?
B2B SaaS
Land and Expand
Seat Based Pricing
Feature Based Packaging
Bottom Up Adoption
Top Down Purchasing
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8. Who Needs Analytics and Why?
Management – Potential Energy and Trip Wires
Investors – Opportunity Development
Board – Strategy Validation
Employees – Kinetic Energy
Customers – Actionable Insights
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9. Know Your Units
CAC – Cost of Customer Acquisition
LTV – Lifetime Value
Churn
Break Even Point
Expansion
Contraction
MRR – Monthly Recurring Revenue
ARR – Annual Run Rate
ASP – Average Sales Price
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10. Here Is What They Mean
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11. Analytics and Reporting Stack
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12. Trial and Error Process
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13. Evaluation
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14. Stage and Scale Matters
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15. Web Click Stream Analytics
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16. Mobile Click Stream Analytics
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17. Mobile Heatmapping
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18. Web Heatmapping
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19. Funnel Analysis
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20. Content Analytics
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21. Complex Analytics and Attribution Scenarios
Cross Device
Cross Channel
Cross User
Cross Company
Cross Account
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22. Marketing Attribution
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23. Mobile A/B Testing
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24. Unit Economics
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25. Modeling
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26. Dashboarding
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27. Stakeholder Analytics
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28. Unit Economics
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29. Customer Metrics
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30. Marketing Metrics
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31. User Journey – Marketing and Sales Analytics
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32. Product Funnels
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33. Analytics Architecture
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34. Analytics Data Lifecycle
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35. Data Pipeline
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36. Event Centric Model
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37. Stateful Data
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38. 3rd
Party Data
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39. Simple Do It Yourself Simple Solution
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40. Self-Managed Open Source Solution
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41. Managed Proprietary Solution
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42. Post Display
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43. Decision Making
Outsourcing Ignorance
ROI Considerations
TOC Considerations
Scalability
Automation
3rd
Party Integrations
Data Volume
Support
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