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Basho Technologies: Revolutionizing SaaS Sales Strategies

Explore how Basho Technologies is eradicating subjective sales with a new reference architecture for SaaS, addressing crucial problems affecting revenue growth and forecasting methods for enterprises.

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Basho Technologies: Revolutionizing SaaS Sales Strategies

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  1. Monitor 9/16 Sell More. CONFIDENTIAL DO NOT DISTRIBUTE

  2. Basho Technologies - Agenda • The Problems • Eradicating Subjective Sales • A New Reference Architecture for SaaS • Questions Mission StatementBasho Technologies creates premium products and services that accelerate massive and predictable revenue growth.We fuel the engine of our innovation with a passion for sales. Sell More. CONFIDENTIAL DO NOT DISTRIBUTE

  3. Obligatory Introduction Basho Technologies • Founded in 2007 • Incorporated IP from Basho Strategies • Taught non-analytic form of heuristic • I got involved in July 2007 • formerly a VP at Akamai and a peer of Ross Seider’s • gained an appreciation from Ross for the values process, in proper doses, brings to startups • Basho entered BETA with two customers in June; three more 9/08 Sell More. CONFIDENTIAL DO NOT DISTRIBUTE

  4. Basho Delivers Analytics and ROI Sell More. CONFIDENTIAL DO NOT DISTRIBUTE

  5. Basho Technologies – The Problems • Problem One – Enterprises rely on subjective information generated by the least qualified source to plan and forecast • Problem Two – SaaS reference architectures rely on RDBMS which make true fault-tolerance, per the CAP theorem, impossible Sell More. CONFIDENTIAL DO NOT DISTRIBUTE

  6. Basho Technologies – The Problems • Problem One – Enterprises rely on subjective information generated by the least qualified source to plan and forecast • Problem One (a) – Lack means of obtaining objective data • Problem One (b) – Entrenched workflows biased to subjective methods are hard to displace • Problem One (c) – Sales representatives hate CRM/SFA Sell More. CONFIDENTIAL DO NOT DISTRIBUTE

  7. Forecasting Methods Sell More. CONFIDENTIAL DO NOT DISTRIBUTE

  8. Forecasting Methods Sell More. CONFIDENTIAL DO NOT DISTRIBUTE

  9. Current Forecasting – Unaided Judgment, ETE • Use own subjective opinion of reporter plus minimal deal knowledge to modify • Law of large numbers protects big enterprises • No insight into WHY something closes subjective • Use own subjective opinion of reporter plus moderate deal knowledge to modify • May rely on late gates (e.g. T’s & C’s) to forecast • Spend 25% of time doing forecast reviews insight • Least qualified – incentives not always on accuracy • Lack process and therefore repeatable successes • Cannot prioritize data • Use an ordinal sales stages Sell More. CONFIDENTIAL DO NOT DISTRIBUTE

  10. The Implications • Missed sales forecasts • Inefficient Manufacturing • Failure to grow revenue and deliver on investor expectations • Inefficient use of sales resources - Over- or under-hire sales - Slow ramp times for new hires • Marketing dollars misaligned with sales objectives Sell More. CONFIDENTIAL DO NOT DISTRIBUTE

  11. Basho Sell Sell More. Sell More. CONFIDENTIAL DO NOT DISTRIBUTE

  12. The Heuristic – Addresses Problem 1(a) The Scorecard Template Capture your process –scorecard method A balanced two-column list – gives ordered by “cost,” while gets ordered by general order sought NOT Ordinal – can get in any order you want or you are asked Score – integer pair resulting from addition of numerical value beside each give and each get “scored” Can go in ANY order Customer and preferably rep designs Now we are free to solve: Problem 1(b) Problem 1(c) The larger Problem One Sell More. CONFIDENTIAL DO NOT DISTRIBUTE

  13. Solving problems 1(b) – Entrenched Workflows • Entrenched processes are co-opted • Capture how enterprises currently sell • Institute and easily share best practices • Get reps focused on what really matters in deals • Get reps selling fast, especially new ones • Show rapid rewards – REPS MUST SEE CONNECTION TO CLOSED BUSINESS • BIG PROBLEM – Managers trained to inspect in CRM • Integrates Into Existing Sales Workflows • Complements and enhances CRM usage • integrates into SFDC • Siebel in Q4 • Adds vast new toolset to Opportunity management • Reps spend less time in CRM Sell More. CONFIDENTIAL DO NOT DISTRIBUTE

  14. Solve Problem 1(c) – Reps Hate CRM • 73% of sales reps visual learners/visually analytical • create a sense of feedback and reward for interaction • Reduce time in CRM Sell More. CONFIDENTIAL DO NOT DISTRIBUTE

  15. Basho SaaS Cheap. Distributed. Fault-tolerant. Sell More. CONFIDENTIAL DO NOT DISTRIBUTE

  16. Problem Two: Current SaaS is not cheap, fault-tolerant, or particularly scalable • Multi-tenant • level 0 – chaos – one instance per customer, differing instances • level 1 – managed chaos – one instance per customer, identical instances • level 2 – multi-tenant hi-rise –instances scale to a point -- MOST COMPANIES STUCK HERE • level 3 – multi-tenant build-out –scale limited by economics, vs. technology • level 4 – Utopia – scalable, customizable instances • Databases limit fault-tolerance and scale Sell More. CONFIDENTIAL DO NOT DISTRIBUTE

  17. Problem Two: Current SaaS Reference Architecture The “A” in AIDA Sell More. CONFIDENTIAL DO NOT DISTRIBUTE

  18. At the Heart of The Problem: RDBMS • To understand why RDBMS is now a barrier, one must take a step back into theory. • You can only have, at most, two • RDBMS sacrifices everything at the altar of consistency • Stonebraker/Helland paper – End of Architectural Era • RDBMS a 1970’s technology • enormous money spent mimicking good Partition Tolerance and, to a lesser extent, Availability behavior Sell More. CONFIDENTIAL DO NOT DISTRIBUTE

  19. What’s wrong with Oracle? (…or MySQL, or Postgres, etc) Consistency-Obsession! Sell More. CONFIDENTIAL DO NOT DISTRIBUTE

  20. Brewer’s Theorem Consistency Availability Partition-Tolerance pick any two! Sell More. CONFIDENTIAL DO NOT DISTRIBUTE

  21. The Solution: RIPPLE + WEB MACHINE + ERLANG Any Application DATA CENTER - IAD DATA CENTER - LON WEBMACHINE: A RESTful web server written in ERLANG DATA CENTER - BOS RIPPLE: an Open-source version of Amazon’s Dynamo key value store Sell More. CONFIDENTIAL DO NOT DISTRIBUTE

  22. The Solution: RIPPLE + WEB MACHINE + ERLANG Any Application DATA CENTER - IAD DATA CENTER - LON WEBMACHINE: A RESTful web server written in ERLANG DATA CENTER - BOS RIPPLE: an Open-source version of Amazon’s Dynamo key value store Sell More. CONFIDENTIAL DO NOT DISTRIBUTE

  23. Ripple A decentralized highly-available easily-scaled lightweight key-value store. What? Sell More. CONFIDENTIAL DO NOT DISTRIBUTE

  24. What’s a key-value data store? It’s just data with names! “/person/10b4cag8” -> (information about Jeff Hoffman) It enables linked data. “/scorecard/b473ad99” -> (Jeff’s SYMC deal in progress) Sell More. CONFIDENTIAL DO NOT DISTRIBUTE

  25. What else works? The Web Works! Independent linked data is key. Sell More. CONFIDENTIAL DO NOT DISTRIBUTE

  26. Hash Ring Sell More. CONFIDENTIAL DO NOT DISTRIBUTE

  27. Hash Ring – Select the Bucket Sell More. CONFIDENTIAL DO NOT DISTRIBUTE

  28. Hash Ring – ID and share location of next N dissimilar buckets N=3 Sell More. CONFIDENTIAL DO NOT DISTRIBUTE

  29. Hash Ring – ID and store next N dissimilar buckets store “x” ok. store “x” ok. store “x” ok. N=3 Sell More. CONFIDENTIAL DO NOT DISTRIBUTE

  30. Hash Ring – ID and store next N dissimilar buckets Ok, store complete! store “x” ok. store “x” ok. store “x” ok. N=3 W=2 Sell More. CONFIDENTIAL DO NOT DISTRIBUTE

  31. Hash Ring – Node Failure Sell More. CONFIDENTIAL DO NOT DISTRIBUTE

  32. Hash Ring – Fail over and store hint Sell More. CONFIDENTIAL DO NOT DISTRIBUTE

  33. Vector Clock: Data Versioning for Eventual Consistency Set X=7 X=2 (v1) X=7 (v2) X=2 (v1) Sell More. CONFIDENTIAL DO NOT DISTRIBUTE

  34. Vector Clock: Data Versioning for Eventual Consistency Set X=4 X=4 (v3) X=7 (v2) X=7 (v2) X=2 (v1) X=2 (v1) X=4 (v3) X=7 (v2) Fail! X=7 (v2) X=2 (v1) X=2 (v1) Sell More. CONFIDENTIAL DO NOT DISTRIBUTE

  35. Vector Clock: Data Versioning for Eventual Consistency Read X, get X=4, X=4, X=7…eek? X=4 (v3) X=7 (v2) X=2 (v1) X=4 (v3) V3 descends from V2, so X=4. X=7 (v2) X=7 (v2) X=2 (v1) X=2 (v1) Sell More. CONFIDENTIAL DO NOT DISTRIBUTE

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