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AWS Software Business Leader Forum

Building up a Big Data SaaS businessDr. Manuel Bähr

Where are we? How we got there? Next frontier

https://www.pexels.com/photo/lighthouse-shore-coast-coastline-9576/

Leading SaaS provider of predictive applications in retail.

3

Decisions beat insights, any time.

Prof. Dr. Michael Feindt - Founder of Blue Yonder

How many items should we offer?

At what price? Whom should we tell about it?

Replenishment Optimization

Pricing Optimization

TargetingOptimization

Our customers

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

0,30,7

-0,1

0,4

3,13,53,8

3,2

Revenue Profit

UK

2011 2012 2013 2014

-2,2

0,91,4

3,6

-0,6

0

1,7

3,3

US

2011 2012 2013 2014

3,03,5

3,1

4,3 4,5

3,0

4,3

6,3

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

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

Where are we? How we got there? Next frontier

https://www.pexels.com/photo/lighthouse-shore-coast-coastline-9576/

Succeeding with Big Data initiatives

Focus on the feedback loop between Data Science insights and business results.

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Magic happens if you do that

Amplify fusion of domain expertise and machine learning knowledge

Improve time to value

Enable effective hypothesis validation

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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

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SaaSCustomer Benefits

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

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Become data-driven

Acquire scarce skills on-demand

Catalyze your organizational changes

Scale as you move forward

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SaaSChallenges

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

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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

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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

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Where are we? How we got there? Next frontier

https://www.pexels.com/photo/lighthouse-shore-coast-coastline-9576/

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

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Blue Yonder GmbH @byanalytics_en

info@blue-yonder.comblue-yonder.com

Dr. Manuel Bähr @manuelbahr

Get ready for the future of retail!

© 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