Building up a Big Data SaaS business

Building up a Big Data SaaS business

Talk given at the AWS Software Business Leader Forum in Munich:


Manuel Bähr

June 15, 2016


  1. © 2016, Amazon Web Services, Inc. or its Affiliates. All

    rights reserved. AWS Software Business Leader Forum Building up a Big Data SaaS business Dr. Manuel Bähr
  2. Where are we? How we got there? Next frontier

  3. Leading SaaS provider of predictive applications in retail. 3

  4. Decisions beat insights, any time. Prof. Dr. Michael Feindt -

    Founder of Blue Yonder
  5. How many items should we offer? At what price? Whom

    should we tell about it?
  6. Replenishment Optimization Pricing Optimization Targeting

  7. Our customers

  8. Blue Yonder serves the hyper- competitive retail market Data based

    on Deloitte, Global Powers of Retailing 2013-2016 Germany % -3,00 -0,63 1,75 4,13 6,50 2011 2012 2013 2014 0,3 0,7 -0,1 0,4 3,1 3,5 3,8 3,2 Revenue Profit UK 2011 2012 2013 2014 -2,2 0,9 1,4 3,6 -0,6 0 1,7 3,3 US 2011 2012 2013 2014 3,0 3,5 3,1 4,3 4,5 3,0 4,3 6,3
  9. Automation leads to better results 0% 2% 4% 5% 7%

    Human decisions Automated decisions, same stock levels 5% average out-of-stock rate 1% average out-of-stock rate
  10. Our journey 2014 Warburg Pincus commits $75m Investment first go-live:

    Customer Targeting German Innovation Award 2008 company founded in Karlsruhe 2011 first go-live: Replenishment name change to “Blue Yonder” 2013 first go-live: Online Pricing office in London, UK opened 2015 first go-live: Brick&Mortar Pricing Technology Review: 50 most innovative companies Gartner: Cool Vendor in Data Science 2012 Retail Technology Award for best enterprise solution
  11. Where are we? How we got there? Next frontier

  12. Succeeding with Big Data initiatives Focus on the feedback loop

    between Data Science insights and business results.
  13. Magic happens if you do that Amplify fusion of domain

    expertise and machine learning knowledge Improve time to value Enable effective hypothesis validation
  14. Implementation Decision automation to close the loop SaaS to shorten

    it by frequent software updates Internal platform approach to enable 
 data scientists to ship into production
  15. SaaS Customer Benefits

  16. Time to value Be faster than your competitors Value refinances

    investments even in PoC Real-life timeline with very large customer:
 3 month to live A/B test, 12 month to full roll-out
  17. Become data-driven Acquire scarce skills on-demand Catalyze your organizational changes

    Scale as you move forward
  18. SaaS Challenges

  19. Handing out data requires trust Local data centers for german

    data protection laws Reference customers & discretion Add security layers via platform while still keeping high frequency of model updates
  20. Relying on SaaS for core processes requires trust PoC and

    A/B tests demonstrate reliability with a small blast radius Platform adds reliability features without overburdening data scientists
  21. Learnings German retail is a hard market for SaaS Many

    of our customers didn’t believe in SaaS at contract signature. Now they do. In retrospect never starting on-premise delivery was hard but essential to keep focus
  22. Where are we? How we got there? Next frontier

  23. Global expansion Keep data close to the place of jurisdiction

    Jump to the next level of speed and scale Do this with moderate staff growth to stay effective and agile
  24. Blue Yonder GmbH @byanalytics_en Dr. Manuel Bähr @manuelbahr

    Get ready for the future of retail!
  25. © 2016, Amazon Web Services, Inc. or its Affiliates. All

    rights reserved. Dr. Manuel Bähr Thank you! Use the opportunity to discuss openly with me during the break! Head of Platform | Blue Yonder GmbH