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Big Data and Marketing : Trends and future Challenges
Transcript of Big Data and Marketing : Trends and future 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
Keep costs down = control risks
Increase revenues = sell more
Mortgage risk evaluation
Profile risk evaluation (insurances)
Cross- and upselling
Consumption at home
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)
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
Data collection has become easier and cheaper (IoT)
More data from the same sources is
not always meaningful
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.
are common : not sustainable
"Black Box" algorithms are highly dangerous :
(and Ethics) needed
The challenge of the coming years is not Big Data anymore ... it's
increasingly captured (time of consumption for instance) giving new possibilities (forecasting) but also increasing firms'
New data collection methods
threaten established business models
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)