Cloudslate Berkeley Final Presentation


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  • The most rewarding course I’ve taken at Haas! The slides summarize our learning journey of 10-weeks. The idea that we converged to is to recommend new sales and marketing leads based on mining a company’s existing customers base. Those existing customers are the ones who resonated the most of the product – and they represent the customers’ archetypes of the product-market fit. We model them based on accessing big data (public and third parties), and then we generate look-alikes. Similar to Pandora, based on what you listened to, we recommend new music/leads that are personalized to your taste/product. In effect, we replace the Sales & Marketing funnel from an exercise of finding a needle in a haystack into ultimately a haystack full of needles. Large-scale data-driven algorithms will prescribe the next best action on what to do – that’s what we call the Prescriptive innovation wave of Sales & Marketing 3.0.
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  • Look-alike recommendationExisting & New Leads Scoring (prediction)Data-driven Customer Archetype ModelingBehavioral ModelingPersonalization
  • Google, Cisco, LinkedIn,Cloudera, Salesforce, Marketo, Oracle, SAP, Intel
  • Cloudslate Berkeley Final Presentation

    1. 1 Lean LaunchPad Spring 2014 Recommendation Engine for Lead Generation Leading the wave towards Sales & Marketing 3.0
    2. 2 Team Mukesh Ranjan Designer • EW MBA 2015 • PhD , CS – U. Cincinnati • Program Manager, Intel Mohamad Charafeddine Picker • EW MBA 2015 • PhD, Elec. Eng. – Stanford U. • Principal Solutions Architect, ASSIA Inc Kartik Shah Hacker • EW MBA 2014 • MS, CS – USC • Software Engineer, Cisco Swaroop Sayeram Hacker, Hustler • EW MBA 2015 • MS, CS – U. Wisconsin - Madison • Group Product Manager, McAfee Steve Adelman Managing Director Nexus Partners Peter Lee VP, Bessemer Venture Partners Mentors Dheera Prabhakar Hacker MIMS Student, UC Berkeley
    3. Innovation Time Sales Automation: Salesforce Marketing Automation: Marketo, Eloqua, Hubspot, Act-on 2.0 | Descriptive What’s happening? Vision | Sales & Marketing 3.0 4 CloudSlate, Infer, Mintigo, Lattice Engine,.. 3.0 | Prescriptive What to do? Big Data Explosion Cloud Computing Large Scale Machine Learning
    4. 5 • 102 Interviews in 10 weeks • 62 Unique Companies • 39 CEOs/Founders, CTOs, (S)VPs, Directors • 4 Interested in a paid high-volume trial • 1 Pilot
    5. Value Prop | Learning & Evolution 6 Database of companies and installed technologies Lead Management and Scoring of Leads in the Funnel New Leads Recommendation “Lookalikes” based on existing customer base Finding Influencers and Decision Makers 15+ players in Lead Management
    6. 7 MVP | Applied to McAfee Security product Actual real data McAfee’s data is obfuscated
    7. Cust. Segments | Learning & Evolution 8 SalesOps Dept Demand Gen within Marketing Mid-market & including big companies with specific product segments (like Cisco) Sales Dept Marketing Dept for B2B focusing on mid-market & excluding big oligopolies Week 1 Week 10 Archetypes in Appendix
    8. Channels | Evolution & Learning 9 Week 1 Own website Sales partnering with marketing automation companies Week 3-10 SalesforceAppExchange Marketo LaunchPoint EloquaAppCloud Salesforce takes 15-25% Rev share. Eloqua and Marketo channels are free – in order to attract adopters.
    9. 10 Cust. Relationship | Learning Network Effect Algorithm performance improves the more the customer uses it. Performance also improves as other “similar” customers use CloudSlate – CloudSlate owns the models and does clustering on its own customers to run parallel learning experiments Vs serial.
    10. 11 Revenue & Pricing | Evolution & Learning Revenue Strategy Subscription • Predictable budget forecast Conversion based: End-to-End • Unpredictable budget forecast • Complex Conversion based: stage-wise • Unpredictable budget forecast • Complex Per Lead • Low quality perception PricingTactic ConversionRate w/ CloudSlate w/o CloudSlate CloudSlate ValueAdd 30% Commission to CloudSlate Avg.C.A.C($) w/ CloudSlate w/o CloudSlate C.A.C Saving Computed from backtesting on SalesForce data & QuarterlyA/BTesting Value-based dynamic pricing
    11. 12 Partners & Data Suppliers | Evolution Quality Quantity
    12. 13 Partners & Data Suppliers | Learning Data Suppliers (Content) Algorithms Layer Negotiation power now Data Suppliers (Content) Algorithms Layer Negotiation power in the future Data will get more restricted or more expensive in the future strategic plan to create our own data (similar to Netflix’s content strategy).
    13. Activities & Resources | Learning 14 Algorithms Data Sources Delivery Phase I: High-Volume MVP in 3 months Phase II: SalesforceAppExchange and Marketo LaunchPoint
    14. Metrics that Matter Annual Revenue per Customer $50k-$120K Annual COGS per Customer $33K 15 CloudSlate Amplificatio n Factor 3x Y1 4x Y3 Annual Churn Rate 20% Initially 10% Steady State LTV > $250k ($50K gross profit x 5 yrs) C.A.C for CloudSlate $5K(Salesforce) to $16K (Marketo) (less in steady state)
    15. What’s Next? We believe strongly there is real opportunity! 1. Develop high-volume prototype over the next 3 months working closely with McAfee 2. Then, re-engage with the early adopter pipeline - Cisco,Adaptive Insights, and Gainsight 16
    16. THANK YOU! Teaching Team Ryan Jung Mentors Awesome Classmates! 17
    17. APPENDIX 18
    18. Lookalike Modeling Company: size, location, Vertical, Growth, etc. Professionals: Names, Titles, Dept, Email, Phone # Company: Technologies used/installed , Products, Solutions, Patents, “About Us”, Full website content Company: Financial health, Financial trend, key events/triggers Company: Social sentiment, news stories Professionals: What they said, liked, read, followed, attended, and their influence Klout Customer 19
    19. Existing Customer Base Attributes Augmentation Segmentation/ Clustering Archetypes Extraction Discovery of new Leads w/ similar Archetypes Attributes Bank of augmented leads (Dynamic, constantly evolving) Data supplied by our partners and key data resources New Look-alike Leads Marketing & Sales Funnel 20
    20. High Level Overview 21 Cloud Slate
    21. Customer Archetypes Account Manager (B2B company) SVP Marketing (B2B company) Sr Dir Sales (B2B company) Demand Gen Manager (B2B company) Pain Quote Pain Meter 5 4 3 4 User Buyer Influencer Decision Maker Dave Jill Dan Susan Cust. Segments | Archetypes Most leads in the system are useless ROI from lead gen campaigns is weak Pressure to meet Sales target Collects leads through laborious methods, but Sales never seem to convert them “Only 40% of the leads I close are provided by Marketing. I have to hunt for the other 60% myself” “My marketing budget is just $250K/quarter, and I can get very little done with it” “I don't want my sales reps wasting their time chasing dead-ends” “Difficult to satisfy Sales”
    22. Finance and Operations TimelineCashDev. Channel& Customer Financial Year 1 Q1 Q2 Q3 Q4 Year 2 Q1 Q2 Q3 Q4 Year 3 Q1 Q2 Q3 Q4 Seed $1M Series A $3M Amplification = 3x Salesforce AppExchange Demographic,Tech Install Data Marketo Launchpoint Behavioral, Business News & Financial Data Amplification = 3.5x Amplification = 4x Jobs posts DevelopTech Install Database Microsoft Dynamics, Oracle CRM,Website First 5 cust., $50k Rev Next 10 cust., $500k Rev Next 25 cust., $1.5M+ Rev Eloqua, SugarCRM Next 40 cust., $5M+ Rev; Profitable 23
    23. Why we chose the Lean LaunchPad 24 Not because it is easy, but because it is hard, because that goal will serve to organize and measure the best of our energies and skills, because that challenge is one that we are willing to accept, one we are unwilling to postpone, and one which we intend to win… Borrowed from John F. Kennedy “We Choose to go the Moon” speech in Sept 1962
    24. 25 Week 1
    25. 26 Week 2
    26. Week 3 27
    27. Week 4
    28. Week 5
    29. Week 6
    30. Week 7
    31. 32 Week 8
    32. 33 Week 9
    33. 34 Week 10