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Agora Final Presentation

3/1/2011. Agora Final Presentation. Team Members. Alan Chiu Product management, enterprise software, storage, distributed systems Danielle Buckley Product management, business development, management consulting Evan Rosenfeld Machine learning, mobile / web app architecture Gabriel Yu

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Agora Final Presentation

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  1. 3/1/2011 AgoraFinal Presentation

  2. Team Members • Alan Chiu • Product management, enterprise software, storage, distributed systems • Danielle Buckley • Product management, business development, management consulting • Evan Rosenfeld • Machine learning, mobile / web app architecture • Gabriel Yu • Enterprise software development, web systems

  3. Hypotheses needed for cloud compute marketplace • Cloud IaaS has become a fungible commodity • Large supply of excess capacity • Willingness to purchase from various providers • It’s possible to create a cloud marketplace

  4. Cloud compute marketplace Build a cloud marketplace Many different customer segments on buy-side and sell-side Huge dependency on technical platform Direct sales to both buyers and sellers

  5. We got out of the building… • Interviewed potential buyers • Zynga, Xambala, Greplin, Pulse, KISSMetrics, SumoLogic, Zencoder, Desktone, All Covered… • Interviewed potential sellers • Savvis, AWS, Azure, Yahoo, Addepar… • Interviewed industry experts • VMware, Zuora, NetApp, SolarWinds, telco consultant…

  6. … And found a challenging missionary market • Diverse IaaS products • Non-trivial switching costs • Amazon default for many • Long-term vendor relationships dominate Enterprise IAAS

  7. Cloud Services Match Maker Pivot away from technical platform Help buyers find the best provider Removed financial, consumer segments Act as channel for sellers

  8. We ran AdWords campagns and talked to customers… • Ran Google AdWords campaign to test landing pages and copy • Talked to more customers

  9. … And struggled to identify a “hair on fire” problem • Low search volume for IaaS comparison • Interest from public sellers in new channel • Private seller IT not revenue-driven • Variable workloads impact opex

  10. Low search traffic implies “missionary” sales effort

  11. Automated Cloud Capacity Planning Pivot 1: Capacity Planning Pivot 2: Focus on enterprises with variable workload

  12. We focused on demand creation and sales… • Researched demand prediction models • Explored sales models with experts • Talked to more customers

  13. … And came up with a 2-tiered model • Found traction for capacity planning business • Identified sales strategy • Field sales model to large enterprise • Inside sales model for lower end offering

  14. Inside sales model for entry level customer Sales Model Estimated Customer LTV • $1,000 / mo • 5% attrition rate month-to-month • 20 month average lifetime • $20,000 LTV • Annual Sales Cost (inside sales): $1.3M • Leads cost: $8.3K • MarComm: $240k • Advertising: $37k • 5 Inside sales reps: $1M • 2 Tradeshows: $200K • Annual New Revenues: $4.8M

  15. Field sales model for enterprise level customer Sales Model Estimated LTV • $20,000 / mo • 2% attrition rate month-to-month • 50 month average lifetime • $1M LTV • Annual Sales Cost (Field Sales): • 3 Field Sales Reps: $1.5M Cost • Annual New Revenues: $3M

  16. Enterprise sales process

  17. Cloud Lifecycle Management Agora – FINAL • Develop capacity planning algorithm • Develop IaaS vendor relationships • Marketing and sales · Technology partners – cloud vendors, management tools · System integrators / Consultants Partner with Integrators • Capacity Planning • · High variability in usage • Service Matching • Companies new to cloud • SLA Monitoring • Companies with high SLA requirements · For enterprise, higher touch model with field sales Customers · Reduced cloud infrastructure cost· Increased visibility on service level Integrators: · Increased revenue Position product for lifecycle management Leverage both inside and field sales · IP– prediction · Developers· Inside sales force · Field sales force · Biz dev (channel and technology partners) · IaaS Integrators / consultants • Inside and field sales· Development Costs· Infrastructure costs – AWS· Support costs • Subscription charge to buyers • Pricing table scales based on # of servers and # of seats, with tiers

  18. We got out of the building, and built a business model… • Decided to use two-tier sales model • Attended AWS meet-up • Interviewed IT consultants • Analyzed competitor and comparable models • Selected strategic direction

  19. …and validated a 2-tier sales model with integrator support • Ecosystem of cloud IT consultants / integrators willing to engage • Our product makes integrators money • Concerns about 2-tier sales model, though some examples of success • Income statement passed test of reason

  20. Agora Evolution

  21. Addressing $5.4B market

  22. We came a long way • Key Lessons • Early days for compute market • Opportunity for tools to support move to PaaS/ SaaS adoption • Customer engagement crucial • Our product now: a tool set for managing cloud compute usage • Service matching • Capacity planning • Usage monitoring & control • Targeting ~30% savings for customer • Potential for a viable business

  23. Thanks!

  24. Appendix: Canvases

  25. Week 1

  26. Week 2

  27. Week 3

  28. Week 4

  29. Week 5

  30. Week 6

  31. Week 7

  32. Week 8 Agora – V8 · Technology partners – cloud vendors, management tools · System integrators / Consultants • Capacity Planning • · High variability in usage • Service Matching • Companies unfamiliar with using cloud infrastructure • SLA Monitoring • Companies with high SLA requirements with their customers · For enterprise segment, higher touch model with field sales force · Reduced cloud infrastructure cost· Better compute needs matching · Increased visibility on service level Integrators: · Increased budget for consulting services • Design and refine capacity planning and match making algorithms • Develop and maintain cloud infrastructure vendors relationships • Develop brand as go-to place for cloud lifecycle management · Intellectual property – prediction algorithm· Developers· Inside sales force · Field sales force · Biz dev (channel partners and technology partners) · Integrators / consultants specialized in cloud infrastructure • Inside sales and field sales· Development Costs· Infrastructure costs – AWS· Support costs • Subscription charge to buyers • Pricing table scales based on # of servers and # of seats, with tiers

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