NATC 2013 - Global SaaS offering that serves 1.3 M+ users in 150+ countries by Anshu Verma, Intuit


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NATC 2013 - Global SaaS offering that serves 1.3 M+ users in 150+ countries by Anshu Verma, Intuit

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  • A good global product is when it meets the local needs of everyone local that it feels it was made for them …We have big goals globally and have realized that if we follow the standard approach for building products that and have will not work .. So we had to try something different .. In this session I will talk about how we using some different approaches (whether innovative or not, I would leave it on you .. But different they are)While making strategic shifts and changes in the 2M lines of code, we needed
  • Facebook is in 216 countries .. Plane analogy .. Link humbled with the scale ..
  • Why are we going after customers worldwide ? We believe we can simplify the business of life : Intuit’s mission to use technology to change the business life so profoundly that they cannot imagine going back to older ways of doing things.To improve our customers’ financial lives so profoundly, they can’t imagine going back to the old way.We all have financial tasks in our personal or business lives that just feel like work. This is the “business of life.” By making these activities easier, faster and more efficient, we free people up to do the things that really inspire them. More time for personal and professional passions; more money for the present and future; more control over their destiny.
  • Any guesses on Intuit’s share ??
  • Under no illusion that we have a long way to go but we feel that by getting
  • Will share building blocks of a global platform that you should try and build. Customer Care, Back-Office, Marketing SitesWill talk about how and what we did in each.
  • 20 months, 8 weeks, What was 20 months , call it a leap because it was after 10 years
  • 20 months, 8 weeks, What was 20 months , call it a leap because it was after 10 years
  • Story … we were localizaing (16 months, 9 months, <=3 months) … but it was hard work (localizing, QA, ) .. .and at the same time turning away a large side of population .. So we decided a new approach Pull each other, not that it was not working .. It was .. But it was taking time … it was taking us 3-4 months (e2e to launch) .. And that rate it would take us 60 years to localize for the world20 months, … UK 16 months, SG – 9 months, CA – 3 months,
  • Story … we looked at what we needed to localize and then , we were also thinking of creating teams , outsourceI think your message of "we thought there'd be a better way" and the tee up to talk about rapid experimentation is great. This was transformational …
  • India needs Tin# on invoice, AU needs bank routing #, SG needed iRASTax on Tax , cess, Tax , 1000 of rates, , how do you let users setup ??Good luck modulifying !!
  • It gets unwieldly very fast and difficult to scale …
  • 59.8 year jump in just 2 monthsWhile I thought I am done and dusted w/ it … came the reality check …
  • Delight, relevance missingWe love to give choices, some got it and some people don’t get it .. Dropping ff ..
  • We want to give delightful experience , Ok, we translated, what, … and this is result of the world we live in .. Personalization is key
  • So many differences across folks …
  • Combination of w
  • Thinking of a better way and while experimenting with many things …
  • Innovation for language quality testing gave us idea that we can use the same principles for our customers. Hence we launched, Transpedia. Machine translation for languages, user contribution to fill in where we fall short , taxpedia, <<< will use some picture here >>
  • And we have the data …peta-bytes of it ..
  • Concept isUsing the peta-bytes of data … our customers have used our product
  • Biggest challege is pile of data and making sense out of it .. So we started to build a machine learning system
  • Self-servicable,It is not a mere reco, we are changing the productUnlike reco systems where reco is taken or not taken - our feedback system is key to how the system learns and adaptsstandard technologies for domain not working - example offerings or offering (what do we apply?), so we are using mix of approaches and building a domain specific thesuarus
  • People were customizing & localizing (we had gone to 150 countries)People were setting it upNow it was time to go deeperObviously many were dropping .. So a few and we thought we can now use power of manyBut we didn’t want to go back to our earlier country by country approach as that was time consuming. We brainstormed on what will Millions of One offering automagically customized into 1000s Waze ,,, example
  • speaks, their
  • Under no illusion that we have a long way to go but we feel that by getting
  • NATC 2013 - Global SaaS offering that serves 1.3 M+ users in 150+ countries by Anshu Verma, Intuit

    1. 1. Lessons learnt while building a Global SaaS product that serves 1.3M+ users, 150 countries Anshu Verma, Architect, Intuit, India
    2. 2. What will I talk about ? • Innovative technology transformations to fuel GLOBAL growth
    3. 3. What is QuickBooks ?
    4. 4. Facebook of Accounting #1 accounting solution for small businesses, worldwide 150 countries 1.3M+ users Used by businesses ranging from “Cradle to Grave” 2000+ industries 46 languages 143 currencies Commerce of 11B , 100K transactions/minute Developer APIs Available via Web, mobile & tablets
    5. 5. Why are we globalizing ? Why do we really need a different approach ?
    6. 6. Vision – Simplify the “business of life” via, Products are So LOCAL that it feels it was tailor-made for them.
    7. 7. Opportunity Balance Sheet > 500M SBOs worldwide INTUIT’S SHARE 5M 7
    8. 8. Opportunity Balance Sheet SBOs use Intuit’s Products, worldwide SBOs in the world are our opportunity !! 8
    9. 9. The steep curve Subscribers 500M Tech. transformations to “LIFT” the curve 5M in 10 years Today TIME
    10. 10. Equation to solve Relevance Do it right Scale Do it for many ? Velocity Do it fast
    11. 11. Durable Technology – 3 tenets Durable 3 Network Effects via “Power of Many” 2 Flexibility, Configurability 1 Table Stakes
    12. 12. Table Stakes Language Multi-tenant, multi-shard architecture Cultures Formatting Currency I18N Time Global Hosting : 24x7, secure, resilient, performant 12
    13. 13. The first step
    14. 14. The first step leap 4 additional countries after 10 years !!
    15. 15. Localizing country by country Effort and Time Consuming
    16. 16. From “us” to “users” We were localizing country by country Users localize themselves (currency, formatting, taxes, reports, etc.)
    17. 17. Traditional Modularization Module UI Module APIs Module BL DB
    18. 18. Traditional Modularization Bank Routing# Tin# Module UI India Tax setup State, District VAT,S ales US IRAS Exempt Module APIs SG reports calc Australia Module BL DB
    19. 19. Functional Complexity and maintenance Complexity Curve Bad gets worse faster than Good gets better Geographies & variability
    20. 20. Modularization in Global world (for scale and variability) Tax Models – definition , forms structure, etc. Meta-Data/ Extension APIs Module UI Module APIs Module BL Meta-Data DB Enabling metadata DB Extension logic to w/ meta-data User (Extended) Data
    21. 21. Functional Complexity and maintenance Complexity curve - smoothened New way Upfront investments on technology lead to velocity gains Geographies & variability
    22. 22. Leap #2 150 countries in ~8 weeks!!
    23. 23. Pet Shop, Brazil, sets up for his business
    24. 24. Can be used by anyone, anywhere Finland
    25. 25. BUT …
    26. 26. Local(host) not found ? … missing delight …
    27. 27. Personalization is Delightful Speak my language Personali ze for me
    28. 28. Ok, so why not personalize for everyone ? WELL, WELL, WELL, … 28
    29. 29. 80+ staff years problem
    30. 30. 80+ ?? .. Not me, it’s my grandson .. 30
    31. 31. Eureka moment!!
    32. 32. Using power of many More Users Network Effects Virtuous Circle More Benefits
    33. 33. Using power of many Awakening: We already have peta-bytes of data !! Generate Use Data Product Users Personalize Derive Intelligence Machine Learning
    34. 34. Personalization Who is the user ? Where is s/he from ? 1 Personalized based on similar profiles 3 2 Crunching similar users’ data (anonymously)
    35. 35. Challenge
    36. 36. Machine Learning system view Near real time triggers Profile Classification (Who’s data) Bucketing (How is it similar to others) Implicit feedback (Watch and learn) User API Clustering (What is the data) Humanized Data (based on meta-data) Rank (A > B) Self-serviceable, Self-learning Variability Detector (profile attributes that are important)
    37. 37. (another) Pet Shop, Brazil – now personalized auto-magically
    38. 38. 1-800-ultra-personalization One product, automagically configured into 1000s while we sleep!! Cecilia, florist Alex, coffee house Craig, landscape & furnishing Liang, gardener Vijay, merchant
    39. 39. Leap #3 Personalized QB for every user worldwide while we sleep!! Hallo Hello Aloha P r Merhaba i C v i e a t Bon Jour o Hola Sayonara Hi
    40. 40. Demo 41
    41. 41. Summary 500M Delighted & active customers Benefit Higher conversion 150 countries in 8 weeks Network Effects, Power of many New Horizons Flexible, Open Architecture Table stakes (I18N, sharding, hosting) Technology Transformations (towards durability)
    42. 42. Future ? Personalized newspaper at your door steps based on contribution of others ..who knows !!
    43. 43. Q&A All the work was driven and done in India, E2E. Always hiring GREAT engineering talent.