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. Building SaaS Analytics Platform
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SERGEY SUNDUKOVSKIY, PH.D.
CO-FOUNDER, CTO/CPO RAKEN

2. Quick Bio
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Dr. Sergey Sundukovskiy
Co-Founder, CTO and Chief Product Officer at Raken, Inc.
Before Co-Founding Raken, Dr.
Sergey Sundukovskiy served in
capacities of Chief Technology
Officer, Chief Information
Officer, and Chief Product
Officer. Sergey is a serial
entrepreneur co-founding
multiple successful Startups
focused on Small Business
Marketing and eCommerce. The
last Startup co-founded by
Sergey was acquired by Capital
One in 2014.
Sergey specializes in
implementation of subscription-
based high volume SaaS
platforms, with a strong
emphasis on early stage product
development, product marketing,
and customer acquisition. Specific
areas of expertise include User
Testing, A/B Testing, Marketing,

3. Why?
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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. What’s The Problem?
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10. Solution Stack
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11. Stage and Scale Matters
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12. It Is Complicated
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13. Analytics and Reporting Stack
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14. Trial and Error Process
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15. Evaluation
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16. Web Click Stream Analytics
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17. Mobile Click Stream Analytics
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18. Mobile Heatmapping
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19. Web Heatmapping
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20. Funnel Analysis
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21. Content Analytics
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22. Complex Analytics and Attribution Scenarios
Cross Device
Cross Channel
Cross User
Cross Company
Cross Account
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23. Marketing Attribution
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24. Mobile A/B Testing
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25. Unit Economics
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26. Modeling
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27. Dashboarding
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28. Stakeholder Analytics
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29. 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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30. Here Is What They Mean
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31. Unit Economics (Finance)
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32. Customer Metrics
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33. Marketing Metrics
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34. Sales Metrics
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35. Product Metrics
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36. Analytics/BI Architecture
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37. No Magic
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38. Analytics Data Lifecycle
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39. Data Pipeline
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40. Event Centric Model
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41. Stateful Data
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42. Simple Do It Yourself Simple IaaS (AWS)
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43. More Complex Do It Yourself IaaS (GCP)
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44. Open Source BI/Analytics PaaS
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45. Data Sources
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46. Managed Proprietary SaaS
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47. Post Display
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48. Decision Making
Outsourcing Ignorance
ROI Considerations
TOC Considerations
Scalability
Automation
3rd
Party Integrations
Data Volume
Support
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