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Big Data and Marketing : Trends and future Challenges

Presentation given at the conference "Big Data in the Platform Economy" on 13 May 2016 at Microsoft Innovation Center in Brussels

pierre-nicolas schwab

on 8 October 2016

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Transcript of Big Data and Marketing : Trends and future Challenges

Big Data : business Trends and Challenges
Big Data in a nutshell
Some industries more advanced than others
Retailers, banks, insurances, telecom are heavy data producers and users
Data often used for profits
2 types of (big) data usages : keep costs down vs. increase revenues
1. The future of retail
Big Data gives the impression to make any marketer's dream come true:
one-to-one personalized relationship
Dr. Pierre-Nicolas Schwab

Big Data manager RTBF
Founder IntoTheMinds
Researcher ULB

Keep costs down = control risks
Increase revenues = sell more
Mortgage risk evaluation
Selfscan control
Profile risk evaluation (insurances)
Cross- and upselling
Behavior-based pricing
Retail evolutions
Client A
Retailer 1
Client B
Retailer 2
in-store distribution
Consumption at home
Brand A
Brand B
Retailer 1
Retailer 2
Parallels between retail evolutions & Big Data
3 major evolutions in retail landscape
data capture of in-house consumption: replenishment and forecasting
Getting customers away from competitors
understanding total purchases to make consumer switch (coupons)
order automation to get rid of temptations (Nespresso)
bypassing retailers
Data inflation seems to be the only way to go
2. The future of insurance
Adaptive pricing ("Pay for your own risk")
insurance companies need more "user-centric" data
Example 1 (real) : car insurance
Example 2 (fictitious ?) : health insurance
Insurance companies will investigate new (intrusive) forms of data collection
A business perspective on Big Data trends
Big Data jeopardizes century-old models
(e.g. risk mutualization in insurance sector) : for the Greater Good or for profit seeking purposes ?
Happy to answer questions and welcome inquiries to speak about Big Data
+32 486 42 79 42
Churn prediction
Data collection has become easier and cheaper (IoT)
More data from the same sources is
not always meaningful
Real-time processing
is an important evolution made possible by mutualization (AWS, Azure, ...)
Firms will increasingly look to
enrich their data
with other businesses (e.g. banks + retailers, banks and social networks, ... )
New data sources
are key for better predictions but may be more intrusive
Sociological approaches to behaviors worryingly disappearing :
Big Data doesn't explain causation !
Qualitative approach still needed.
Algorithms are tools
like any other in the Human species' evolution : we need to harness it.
cowboys' behaviors
are common : not sustainable
"Black Box" algorithms are highly dangerous :
more transparency
(and Ethics) needed
The challenge of the coming years is not Big Data anymore ... it's
New variables
increasingly captured (time of consumption for instance) giving new possibilities (forecasting) but also increasing firms'
bargaining power
over consumers
New data collection methods
threaten established business models
Real-time processing
will enable new customer experiences (personalized coupons when you enter the store ... not after you have shopped)
In Belgium, smart boxes will not be adopted
Data currently collected does NOT improve significantly predictions
market structure impedes adoption (4m units in Italy vs. 30k in Belgium)
New variables need to be included in model (e.g. health-related with wearables)
Full transcript