Post on 03-Jan-2016
description
3/1/2011
Team Members Alan Chiu
Product management, enterprise software, storage, distributed systems
Danielle BuckleyProduct management, business development,
management consulting Evan Rosenfeld
Machine learning, mobile / web app architecture Gabriel Yu
Enterprise software development, web systems
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
Cloud compute marketplace
Build a cloud Build a cloud marketplacemarketplace
Direct sales Direct sales to both to both
buyers and buyers and sellerssellers
Many Many different different customer customer segments segments on buy-on buy-side and side and sell-sidesell-sideHuge Huge
dependency dependency on technical on technical
platformplatform
We got out of the building…
Interviewed potential buyersZynga, Xambala, Greplin, Pulse,
KISSMetrics, SumoLogic, Zencoder, Desktone, All Covered…
Interviewed potential sellersSavvis, AWS, Azure, Yahoo, Addepar…
Interviewed industry expertsVMware, Zuora, NetApp, SolarWinds, telco
consultant…
… And found a challenging missionary market Diverse IaaS products Non-trivial switching costs Amazon default for many Long-term vendor relationships
dominate Enterprise IAAS
Cloud Services Match Maker
Pivot Pivot away from away from technical technical platformplatform
Help buyers Help buyers find the best find the best
providerprovider Removed Removed financial, financial, consumer consumer segmentssegmentsAct as Act as
channel for channel for sellerssellers
We ran AdWords campagns and talked to customers… Ran Google AdWords campaign to test
landing pages and copy Talked to more customers
… 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
Low search traffic implies “missionary” sales effort
Automated Cloud Capacity Planning
Pivot 1:Pivot 1:Capacity Capacity PlanningPlanning Pivot 2:Pivot 2:
Focus on Focus on enterprises enterprises
with with variable variable workloadworkload
We focused on demand creation and sales… Researched demand prediction models Explored sales models with experts Talked to more customers
… And came up with a 2-tiered model Found traction for capacity planning
business Identified sales strategy
Field sales model to large enterpriseInside sales model for lower end offering
Inside sales model for entry level customer
$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
Sales Model Estimated Customer LTV
Field sales model for enterprise level customer
$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
Sales Model Estimated LTV
Enterprise sales process
Capacity Planning· High variability in usage Service Matching• Companies new to cloudSLA Monitoring• Companies with high SLA requirements
· 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
· For enterprise, higher touch model with field sales
Customers· Reduced cloud infrastructure cost· Increased visibility on service level
Integrators:· Increased revenue
• Develop capacity planning algorithm•Develop IaaS vendor relationships•Marketing and sales
· Technology partners – cloud vendors, management tools· System integrators / Consultants
· IP– prediction · Developers· Inside sales force· Field sales force · Biz dev (channel and technology partners)
Agora – FINAL
Cloud Lifecycle Management
Partner with Partner with IntegratorsIntegrators
Leverage Leverage both inside both inside and field and field
salessales
Position Position product for product for
lifecycle lifecycle managementmanagement
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
…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
Agora Evolution
Addressing $5.4B market
Stage 1:Demand Prediction
Stage 2:Service Matching
Stage 3:Usage Monitoring/Control
Stage 4:Lifecycle Management
Relevant Category
IT Capacity Planning, Job Scheduling
Lead-gen on cloud spend
Server Management
BSM/ALM
Sizing Estimate
Capacity Planning: $258M (2008) -> $392M (2011)
Job Scheduling:$1.2B (2008) ->$1.6B (2011)
Forrester
10% affiliate fee on $13.1B cloud spend = $1.3B
IDC 2010
$430M (2008) -> $500M (2011)
Forrester
$637M (2008) -> $1.6B (2011), accelerating 36% YoY growth rate
Forrester
Total $2B $1.3B $500M $1.6B
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
Thanks!
Appendix: Canvases
Week 1
Week 2
Week 3
Week 4
Week 5
Week 6
Week 7
Capacity Planning· High variability in usage Service Matching• Companies unfamiliar with using cloud infrastructureSLA Monitoring• Companies with high SLA requirements with their customers
· 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
· 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
· Technology partners – cloud vendors, management tools· System integrators / Consultants
· Intellectual property – prediction algorithm· Developers· Inside sales force· Field sales force · Biz dev (channel partners and technology partners)
Agora – V8
Week 8