Big data in leading international company

Big data in Apple
fr.linkedin.com/in/paperon/ pierre.paperon@gmail.com

 These slides present a fraction of the work achieved for Apple few years ago. A business
process has been choosen to share knowledge in a structured way.
 It was part of a broader engagement aimed at a radical improvement of salesforce
performance (leading to iSales) and EMEA re-org due to iPhone weight
 Obviously, big data was available and datamining brought new perspectives on far more
than salesforce performance :
 Assessment of country potential as well as region or suburb
 Link between sales of different products
 Drivers and levers of POS (point of sales) growth
 Impact on sales of advertising campaigns
 Estimate of POS potential and identification of ideal mix of format to exploit area potential
 Purpose of this document is to show that
 datamining and big data are parts of a global innovation game plan for any company
 Each company have enough data to define and begin its own digital or bid data journey
Define a vision
or global move
path
Transform
findings in
actions
Exec summary
Kill
misleading
beliefs
Build models and
practice darwinism
between models
Generate first
findings and
expand
Define a path
to datamine
Big picture and
big questions

Big picture for data crunching + resource allocation : from HQ to markets
AppStore
iPhone/
Carriers
POS
APR
(Major
Accounts)
Retail
iTuneU
(media,
writers,
…)
OnLine
Store
iTune
Apple
Care
Apple
Retail
Store
Define a vision
or global move
path
Transform
findings in
actions
Kill misleading
beliefs
Install darwinism
between models
Generate first
findings and
expand
Define a path
to datamine
Big picture and
big questions

Always begin a Big Data work by few questions that may evolve during
the work (hypothesis driven approach/ McKinsey)
1. Can I model store sales and benchmark
store performance between them in a
fair way by taking into account
competitors and specific environments?
2. How can we forecast the number of
CPU in a town or geographical area
(close to potential) in order to allocate
efficiently resources among them?
3. What is the nature of the relationship
between CPU sales and iPhone sales at
a micro (zip code) or macro (country)
level?
4. Can I forecast PC market in a country by
using very simple set of variables? (that
is to say 2-3 max)
 What is the optimal channel mix
to capture potential of a specific
geographical area or maximize
my ROCE or ROI?
 What is optimal resource
allocation between
 existing POS,
 new town coverage or
 new emerging country?
“Begin Big data with big
picture and big questions”
Define a vision
or global move
path
Transform
findings in
actions
Kill misleading
beliefs
Install darwinism
between models
Generate first
findings and
expand
Define a path
to datamine
Big picture and
big questions

Find a way to structure data and define collectively one path to follow
 Whatever the kind of bid data you are doing or will do, find an elegant way to
structure information or data. More especially when you are facing
complexity brought by multi –
 Products or categories
 Channels
 Partners
 Geos
 Time frames and granulometries
 For the job done in Apple, a structure was looked for early on to
communicate, share insights and define a path to explore and classify
informations.
 The pyramid, ranking information from macro at the top down to micro at
the bottom, offered a neat image to combine sources and explore new paths
which had never been walked before
 Basement as been added for example to begin predictive modeling of
individual behavior or patterns
Define a vision
or global move
path
Transform
findings in
actions
Kill misleading
beliefs
Install darwinism
between models
Generate first
findings and
expand
Define a path
to datamine
Big picture and
big questions

Pyramid structure to classify predictive models and first R2 calculations
Registered PCs,
Online Sales, registered
mobiles, population,
GDP, income, kids ...
Training points, population in
X km radius around POS,
distance to competitors,
product registrations ...
Predictive
model of:
Correlation
rate (R2)
Key variables
PC Total
Market/country 91% GDP
Mobile
PC Available
market/country 89% Broad-band
access
iPhone pull effect
on PC sales per
week
50 to 90% Mobile sales
Units/POS
180 retail 77%
Mobile
Population
GDP
Training
Sales per
ZipCode 89%
Mobile
Population
GDP
GDP, Internet, literacy, mobile
subscriber, broadband penetration,
roads, >$35k income, life expectancy,
general physicians ...
Data available
EMEIA
France
(among 130
countries)
600 POS
(e.g. for France)
6 000 zip codes
(e.g. for France)
Online Sales, mobile registered,
PCs + mobile sales …
Define a vision
or global move
path
Transform
findings in
actions
Kill misleading
beliefs
Install darwinism
between models
Generate first
findings and
expand
Define a path
to datamine
Big picture and
big questions

Correlation of 83% at a macro level (countries) between PC and mobiles…
Mobile sales
PC sales
Define a vision
or global move
path
Transform
findings in
actions
Kill misleading
beliefs
Install darwinism
between models
Generate first
findings and
expand
Define a path
to datamine
Big picture and
big questions

Correlation of 92% at a POS between PC and mobiles…
But let’s be cautious with correlation rate which is not causality. What’s the nature of the
relationship between the two variables and more specifically:
• what is the best predictor of PC (or CPU) sales ?
• what is the predictive model to forecast sales ?
Define a vision
or global move
path
Transform
findings in
actions
Kill misleading
beliefs
Build models and
practice darwinism
between models
Generate first
findings and
expand
Define a path
to datamine
Big picture and
big questions

Following initial findings came exhaustive gathering of informations
available for each of 6000 geographical units:
250 exogenous (external to Apple) variables, 50 endogenous variables
Define a vision
or global move
path
Transform
findings in
actions
Kill misleading
beliefs
Build models and
practice darwinism
between models
Generate first
findings and
expand
Define a path
to datamine
Big picture and
big questions

Mapping of data needed to do appropriate business intelligence
Define a vision
or global move
path
Transform
findings in
actions
Kill misleading
beliefs
Build models and
practice darwinism
between models
Generate first
findings and
expand
Define a path
to datamine
Big picture and
big questions

Define a vision
or global move
path
Transform
findings in
actions
Kill misleading
beliefs
Build models and
practice darwinism
between models
Generate first
findings and
expand
Define a path
to datamine
Big picture and
big questions

Define a vision
or global move
path
Transform
findings in
actions
Kill misleading
beliefs
Build models and
practice darwinism
between models
Generate first
findings and
expand
Define a path
to datamine
Big picture and
big questions
List of all statistical tools and maximum skills within team or company to
cover maximum of ground or push the envelop to its extreme.

What is the best predictor of CPU sales at micro level (zip code) ?
CPU info at
Zip Code
level
POS
sales
On Line
sales Total
Registere
d CPU 158 000 95 000 253 000
Non
registered
CPU
294 000
(2/3) 294 000
Tolal 452 000 95 000 547 000
R2 with
iPhone
registered
POS sales
On Line
sales
CPU 96% 94%
R2 with
population POS sales
On Line
sales
Registered
CPU 74% 72%
 iPhone and OnLine sales are
unbeatable predictors of sales
at a micro level = potential
 France ‘09 formula: 1 PC = 3
iPhone
 This allows to benchmark
existing coverage and shows
holes in coverage
Define a vision
or global move
path
Transform
findings in
actions
Kill misleading
beliefs
Build models and
practice darwinism
between models
Generate first
findings and
expand
Define a path
to datamine
Big picture and
big questions
Exemple of a formula for PC sales per zip code:
(linear simplification of genetic algorithm model for average value)
:
Example of POS sales based on other POS presence
(linear visualisation of a sigmoid function issued from neural network
modeling)

Kill misleading beliefs. Example : there is an halo effect for iPhone - 1
Number of POS per zip code
Define a vision
or global move
path
Transform
findings in
actions
Kill misleading
beliefs
Build models and
practice darwinism
between models
Generate first
findings and
expand
Define a path
to datamine
Big picture and
big questions
 It was a static vision of the impact
of iPhone on other products.
 Obviously something was going
on between the two products
 Some specific modeling led to key
findings
 Yes iPhone had an impact by
opening the market and building
brand equity and product value
 So, far more interestingly than
halo effect, it was a towing effect,
iPhone providing a tremendous
traction to all other products and
increasing sales potential

Kill misleading beliefs. Example : there is an halo effect for iPhone - 2
Define a vision
or global move
path
Transform
findings in
actions
Kill misleading
beliefs
Build models and
practice darwinism
between models
Generate first
findings and
expand
Define a path
to datamine
Big picture and
big questions

Findings in actions: Identification of Zip Codes with highest untapped potential
 Use of X iPhone for 1 CPU ratio to define
highest potential zip codes in France
 4500 zip codes plotted
 The asymptote is 1.5 and is reached in many zip
codes and more often with POS>4
 French potential for CPU is x millions but it
moves with iPhone penetration growth
 Utility function of cost of coverage has to be
plugged in to find cut-off points
 iPhone POS and sales per zipcode is critical to
balance resource allocation
iPhone sold/
CPU sold off-line
per zip code
Number of POS per zip code
Angers 2.6
OnLine = 15%
Strasbourg1.5
OnLine=13%
Toulouse1.9
OnLine=13%
Montpellier1.7
OnLine=6%
Geneviliers5.2
OnLine=14%
Pau 5.3
OnLine = 40%
Concarneau 6
OnLine = 40%
Lannion 9
OnLine = 55%
Paris172.3
OnLine=16%
Nancy2
OnLine=13%
asymptote
Define a vision
or global move
path
Transform
findings in
actions
Kill misleading
beliefs
Build models and
practice darwinism
between models
Generate first
findings and
expand
Define a path
to datamine
Big picture and
big questions

Ranking of most attractive zip codes for CPU and extra coverage opportunities
*red bar means no POS
Define a vision
or global move
path
Transform
findings in
actions
Kill misleading
beliefs
Build models and
practice darwinism
between models
Generate first
findings and
expand
Define a path
to datamine
Big picture and
big questions
 Use of best models
provides neat insight and
tool for country managers
and sales account
managers
 Opportunities of better
coverage or untapped
potential are just “popping
up” when 1000 towns are
listed as on beside chart
 This model combined with
the one of point of sales
provides the “Coke
formula” for optimal POS
format choice per zone.

How big data is connected with salesforce performance ?
How to improve
Account Manager
performance ?
Use few IT systems
leveraging Corp. tool
and support
Assess account and
geos potentials for
better coverage and
allocation of resource
Be world-class in
Account
Management for
clients (new+current)
Get better forecasts
weekly, M, Q, H and
Y wise to free up time
Client 3.0
initiative +
incentives
Predictive
analytics
Definition and making
of single SalesForce
automation tool for
the whole company
New predictive
tools available
Define a vision
or global move
path
Transform
findings in
actions
Kill misleading
beliefs
Build models and
practice darwinism
between models
Generate first
findings and
expand
Define a path
to datamine
Big picture and
big questions

Which dynamic path to extract the most actionable and profitable levers from data ?
 Data collection
and warehousing
 Cleaning of data
 Security
management
 Choice of
updating
frequency
Data
Analysis and
visualization
Static modeling
Predictive
modeling
Global and real time
modeling
 2-3 dimensional
cuts on charts
 Geo mapping
 Benchmarking
amongst
formats or geos
 Broaden set of
users
 Simple statistics
with correlation
rates to identify 4-5
key variables
 Simple linear
regression models
 Portfolio cinematic
view
 Field testing with
relevant managers
 Advanced stats with non-
linear modeling
 Models in competition
with each others
 Broad business
perspective: town against
country against levers
 Integration in Account
management tools
 Real time improvement of
models
 Generation of new models
 Direct link with EDW
 Global/WW business
perspective
 Apply to all services (e.g. HR
for 6 month ahead needs)
Time
Collective
datamining
skill
Keyactivities
Current EMEIA position
Outputs:
Main business levers
Link between products
Potential estimates
POS performance
SAMI impact
Training needs
Outputs:
New coverage needs
Optimal mix of POS
Forecast weekly/quart.
Efficiency of allocations
KPI and reco in SFA
“What if” functions
Individual buyer model
Cross-selling directions
Outputs:
Resource optimization
between Bus
Identification of lead
countries
Collective sharing of
findings and best practices
Cross-selling opportunities
Better accuracy due to
meta-models
Live and new models
Define a vision
or global move
path
Transform
findings in
actions
Kill misleading
beliefs
Build models and
practice darwinism
between models
Generate first
findings and
expand
Define a path
to datamine
Big picture and
big questions

Big data in leading international company

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