The issue with Big data is not technical. It is the ability of turning data into actionable insights that can be clearly understood by the business to drive profitable decisions

1. K.I.S.S MY BIG DATA
CREATE A DATA-DRIVEN ORGANISATION
Pascal Moyon, Chief Digital Officer

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LASTMINUTE.COM AT A GLANCE
Household brand supported by 16 years of (irreverent) marketing investment

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• Invest in Big Data technology
• … improve your company
performance immediately
• … and grow happily ever after
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ONCE UPON A TIME …

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AMAZING SUCCESS STORIES WHICH INSPIRE ALL BUSINESSES
• Facebook,Google, Amazon, Trip Advisor: from limbo
to world companies in 10 years
• Role models for creating success how of data
• Focus on relevance and leveraging users/customers
input through continuous improvement
• Native data-driven culture

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HOW TO CREATE A DATA-DRIVEN ORGANISATION
• Bring transparency into
the business
• Identify, prioritise and
support the company
business needs
• Influence to improve
processes and indue
cultural changes
Analytics
Roadmap
Team
Communic
ation
Data
consistency
Tools
?

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ROADMAP
• Influence and consistency: top down!
– Start from management strategic indicators
– Understand the key strategic questions,
challenges and opportunities
• Accurate
– Unify company reports and create a common
set of definitions
– Identify the relevant source of data and
complement then if need be
• Actionable and quantifiable
– Focus on key drivers that the company can
actually influence
– Translate opportunity into monetary value
• Pragmatic
– Proximity with the business to ensure that the
insights can be easily consumed and
translated into action
– Educate: only provide the necessary data
Visits Conversion ABV Margin % Total
Product A -525,375 36,696 -614,926 -426,117 -1,529,722
Product B -1,687,958 994,686 34,206 -407,637 -1,066,703
Product C 416,548 -745,119 -134,449 -513,522 -976,542
Product D -155,545 208,143 24,703 -68,547 8,754
Product E 226,998 -29,667 -1,369 -191,050 4,912
Product F 316,003 -14,676 38,545 -66,948 272,924
Product G -183,104 171,408 4,747 -91,955 -98,905
Product H -1,271,169 21,932 88,041 -185,513 -1,346,709
Product I 600,467 -451,397 -4,471 -192,541 -47,942
Product J -67,827 -88,823 -37,218 -22,815 -216,683
Product M -118,160 -2,068 4,864 -12,824 -128,188
-2,955,083 531,700 -668,128 -2,306,060 -5,397,572
Align the business
on key issues

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• Identify key levers and opportunities
• Select relevant KPIs and data sources
• build self-improving processes based on robust data (quantitative and qualitative)
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IDENTIFY KEY PILLARS
• Speed and
stability
• Conversion
funnel
• Pinch points,
heatmaps
• Choosing the
right mix
• Executing
effectively (ROI)
• Performance
(conversion)
• Competition and
competitiveness
• Quality
• Profitability
• Experience
• Engagement and
feedback
• Segmentation
• Personalisation
Customer Product
UsabilityAcquisition

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• Common sense and hygiene still get a lot of mileage …!
– How much of Netflix’s success arise from its recommendation engine vs the size of
its inventory?
– The same for Amazon: customer focus, delivery promise and inventory size vs
clever recommendations?
• Complex algorithms need to be explained to get the business buy-in … and
they only work is they are understood, used and maintained
– Communication is key for acceptance, especially if engagement is required with
suppliers or customers
– Always weigh the benefit of increased performance versus the implementation and
maintenance risks
– Data scientists are in demand, with risks on turn-over and hand-over
• Conclusion:
– Walk before you can run
– Always prefer speed of implementation and test before perfection
– Start by a version 1 of an optimisation with the most likely segments, and make it better
=> will help a lot to bring the whole organisation up the learning curve
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BEING PRAGMATIC

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• One team!
– Significant risk otherwise to have different definitions, unnecessary
competition and confusion (let alone silos …)
• Complementary skills sets and styles
– Business: pragmatic and intuitive
– Reporting/Data: rigorous and process driven
– Data scientist: knowledge and technical
• Integration with the business
– Consider having business performance manager working alongside key
stakeholders (products and/or functions)
– And reporting and data science as shared services
– The team leader has to report as high in the hierarchy as possible: CEO
(ideally, CFO, CTO or CMO otherwise, with specific challenges)
• Invest on a robust team way above the tools
– Open-source tools such as Python and R are incredibly powerful
– Cloud computing is inexpensive
– Very few business needs require extensive computation capacity: create
a pilot first to test the concept and only industrialise after
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KEY ADVICE TO CREATE FOR A TEAM

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• The analytics paradox: more and more
data
• Less and less time bandwidth in the
organisation
• Finding ways to convey potentially
millions of data points into compelling
insights
– Speak the business language
– Present the relevant KPIs
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EFFECTIVE COMMUNICATION
Visits Conversion ABV Margin % Total YOY Margin, %
Product A 15,355 -40,095 -172,549 98,770 -98,519 -7%
Product B 257,481 12,048 -75,169 97,939 292,299 23%
Product C 814,432 28,695 -114,090 125,390 854,427 58%
Product D 5,796 120,761 -7,775 10,723 129,504 192%
Product E -98,297 339,071 -78,498 61,982 224,258 25%
Product F 84,988 -20,467 175 5,613 70,309 126%
Product G 5,102 23,971 -8,939 11,761 31,895 20%
Product H 32,893 9,568 -28,379 15,632 29,714 13%
Product I 107,411 130,001 -48,930 33,449 221,932 65%
Product J -64,050 -34,526 -5,616 4,940 -99,252 -64%
Product M -21,132 -9,166 -2,140 3,582 -28,856 -42%
1,900,599 -137,733 -621,285 483,833 1,625,415 26%

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• Link the C-suite and the digital execution to enable and improve the engagement
with the customers and grow the customer base effectively
• Create a consistent set of effective processes underpinned by a strong team
operating an architecture of cost-effective digital tools
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WHAT IS THE ROLE OF THE CDO?