Big Data challenges for
business
Email: Giles.Pavey@dunnhumby.com
Twitter: @GilesPavey
© dunnhumby 2013 | confidential
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dunnhumby is a world wide big data company
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3,000
660 MILLION
$500 BILLION
EXPERTS
CUSTOMERS GLOBALLY
RETAIL SPEND
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dunnhumby’s blueprint for business success
through customer centricity c.2005
DM
responses
Customer
KPI's
Prospecting
models
Customer
journey
Capture changes in customer behaviour via
data and begin the journey again
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© dunnhumby.com 2013 | Confidential
Product
development
Pricing
Strategy
ATL
Advertising
Retention
programmes
Retail
strategy
Changes in
Customer
behaviour
Call centre
data
Propensity
models
Action
Research
results
Customer
segmentation
link
Demographics
link
Data
Transaction
history
Insight
Acquisition
DM
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RISE OF THE MACHINES
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© dunnhumby 2014
2013 | Confidential
Sensory overload?
Accelerometer
GPS
Gyroscope
Wi-Fi
Magnetometer
Bluetooth
Barometer
Microphone
Proximity
NFC: Near Field
Light sensor
Camera (front)
Touch screen
Camera (back)
14 Sensors!
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© dunnhumby.com 2013 | Confidential
dunnhumby’s blueprint for business success
through customer centricity TODAY
DM
responses
Click Stream
GPS
Social Media
Mobile Apps
+ many more
Customer
KPI's
Prospecting
models
Customer
journey
Machine Learning
Graph Theory
Neuroscience
Agent Based Models
+ many others
Capture changes in customer behaviour via
data and begin the journey again
8
© dunnhumby.com 2013 | Confidential
Product
development
Pricing
Strategy
ATL
Advertising
Retention
programmes
Changes in
Customer
behaviour
Call centre
data
Propensity
models
Action
Research
results
Customer
segmentation
link
Demographics
link
Data
Transaction
history
Insight
Acquisition
DM
Retail
strategy
Recommendation
Personalisation
Mobile offers
Multi-channel
+ many others
Customer
advocacy
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Complementing Customer Data Insight with
Customer Data Science
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● Historic Performance
● Prediction
● Batch Processes
● Real Time
● Structured data
● Polystructured data
● Reports & segments
● Data Products
● Classical Statistics
● Machine Learning
● DIY
● Crowd-sourced
● Known unknowns
● Unknown unknowns
© dunnhumby.com 2013 | Confidential
Big data opportunities
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Sampling:
● Rigorous random sampling is increasingly
too hard and expensive to do.
● Large biased samples are increasingly
plentiful – e.g. followers on Twitter; Friends
on Facebook.
● How can the bias be accounted for?
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Forecasting:
● Tens of thousands of new products and variants
are launched in the UK every year.
● Fast diagnosis of a potential hit or miss can make
or save £1,000,000’s.
● Big Data provides unprecedented potential for
exploration and prediction. Performance of similar
products; customer sentiment; repeat behaviour
● Can we quickly and reliably predict future
success?
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Recommendation:
● We can build predictive models of customer
behaviour by looking at both their individual history
and those of customer “like them”.
● Within the history will be some time dependent
predictive purchases.
– e.g. iPad and iPad cover; charcoal and BBQ burgers
● These models can be further improved by
combining in additional “real time” contextual data.
● How can these factors be combined to build the
best recommendation?
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Simulation:
● dunnhumby have partnered with Sandtable and are
developing Agent Based models of human behaviour
● The models have 3 elements:
– Environment
– Agent characteristics
– Agent behavioural rules
● The models can become incredibly complex, incredibly fast
● How can we most efficiently compute the simulations?
● How can we best validate the models?
● For a validated model, how can we best optimise the
environmental factors to give the best future results e.g.
Marketing Mix Optimisation?
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Online anomaly detection:
● Not only is Big Data large it is also often streaming
and instantly accessible
● There is potential for building models that detect
anomalies in these streams of data. Therefore
enabling us to intervene and rectify an unwanted
situation
● One example, how can we use real time sales
data to identify when certain products within a
store have sold out?
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● Or, can we identify the signs that a customer’s
behaviour in the store or online indicates that they
are likely to stop shopping with us?
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Network flow:
● Customers are increasingly well digitally
connected to each other.
– e.g. Facebook, Mumsnet
● There is a great opportunity to use these networks
to increase retail efficiency by better matching
customers with relevant products.
● However, there is a risk of offers going “too viral”
● How can we predict the final reach of an offer
across a network from an initial communication?
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Questions?
© dunnhumby 2013 | confidential