the customer centric company
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Soft Computing – 55, quai de Grenelle – 75015 Paris – tél. +33 (0)1 73 00 55 00 – www.softcomputing.com
The customer centric company
Big data, analytics and customer insight as strategic assets to
leverage customer value
Gilles Venturi – Soft Computing
15/10/2012
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SOMMAIRE SOMMAIRE
1. Introduction
2. C2B : consumer power
3. Customer Insight, analytics, Big data : overview
4. Customer value : various definition for various usage
5. Customer-Centric Marketing : leveraging acquisition, sales & loyalty
6. Customer Insight driven company
7. What’s next
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Soft Computing : Id
Employee
400
Turnover 2011
35 M€
Innovation
R&D/CA
10 %
Quality
Solid
Leadership
Respon-sible
Equities
16 M€
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Specialist : CRM, BI & Web
BI
CRM
Genetically Net
WEB
Customer differenciator
Decision booster
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Services Continuum
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Helping the best-in-class
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Big data, they talk, we act
Who worked on 4 appliances un 2012 and
invested in R&D on Hadoop
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Happy to share
Enseigner
Ecrire
Echanger
http://blog.softcomputing.com/
https://twitter.com/#!/SoftComputing
http://www.facebook.com/softcomputing
http://www.softcomputing.com/fr/news/
https://plus.google.com/u/0/?tab=wX#118151087212566428287/posts
Discourir
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Want more ?
Blog Facebook Twitter Flux rss
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SOMMAIRE SOMMAIRE
1. Introduction
2. C2B : consumer power
3. Customer Insight, analytics, Big data : overview
4. Customer value : various definition for various usage
5. Customer-Centric Marketing : leveraging acquisition, sales & loyalty
6. Customer Insight driven company
7. What’s next
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Being a consumer today
18/12/2012
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Crisis + technology = change
Expectations Truth
Easiness Involvement
Usage Screen culture
Real time Information Anywhere -
anytime
Behaviours Bargaining
Contributing Grouping
Second hand Renting
Comparing
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Brand-Customer relationship
23%
One quarter of the people who had a bad experience published a negative comment
86%
9 out of 10 french consumer read others comments on product on the Internet
15 persons After a bad experieince, 64% declare they talk about it with « about 15 persons ».
3 times more Friends & family recommandations are 3 times more trusted thant advertising
15% Only 15% declare they are « loyal to brands »
HE DOES NOT TRUST BRANDS ANYMORE
HE CAN HAVE A NEGATIVE INFLUENCE
HE ZAPPS
(Source INIT pour la journée de la fidélité).
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Technology shift : social
. 500 millions users each day
. 1h/day
. 60% Internet Users
. 100 000 tweets / minute
. 4% Internet Users
. 1,65 million French active users
Source : ebg.fr / Nov 2011
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Technology shift : mobile
(source : Observatoire nouvelle tendance 2011)
. 18 M mobile users (27% vs 55% in 2015) . 9 M connected at least once a week . 25% bought online using a mobile . 3,4 connections / day
Europe
(source : Ipsos google 2011)
. 57% looking for infos before purchasing . 29% looking for inspiration . 23% looking for the best price
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Technology shift : ubiquity
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Technology shift : changing expectations
58% brand beaking news
73 % POS geoloc
69% promotions
55% brand avant-premières
41% custom advices
79% : reduction coupons 39% : Mobile eCommerce
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Customer power
Brand had a monopoly on
message transmission
The « Mad men » way
Brand talks, market listens
The y generation way
Each customer can talk and dialog
Customer takes the power
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Lol
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Answer #1 : be long-term
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Answer #2 : Bring value
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Answer #3 : Be close
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Answer #4 : Act (truly) responsibly
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Brand Survival Kit
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SOMMAIRE SOMMAIRE
1. Introduction
2. C2B : consumer power
3. Customer Insight, analytics, Big data : overview
4. Customer value : various definition for various usage
5. Customer-Centric Marketing : leveraging acquisition, sales & loyalty
6. Customer Insight driven company
7. What’s next
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Big data : facts
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Big data : explosion of connected things
Source : McKinsey
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Big data : testimony
« Since the inception of ebay, not a single
data was deleted ! »
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Big data : making it possible
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Big data : big brother
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Big data : survey & segmentation
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Big data : sociodemo & marketing strategy
Age
Financial
capacities
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Big data : purchase history & One2one customization
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Big data : web navigation & retargetting
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Big data : taking comments in account
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Big data : geoloc for one2one mobile promo
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Big data : social network & prescription marketing
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Big data : device & mouse mouvements -> dynamic pricing
Source : wsj, June, 26, 2012
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Analytics : tentative definition
"The extraction of original useful information out of data " (Frawley & Piateski-Shapiro),
"The discovery of new correlations, trend and models through the filtering of a large volume of data" (John Page),
"Torture the available information until it talks " (Dimitris Chorafas)
a decision support process that extracts
knowledge out of databases
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Analytics : evolution
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Analytics : techniques
Pre
dic
tion P
erf
orm
ance
Low
H
igh
Results "readability" Low High
Neural Networks
Genetic Algorithm
Bayesian Engines
Scores Regression Cluster
Association Analysis
Decision Trees
Textmining
Skills Specialized Generalized
Expert Systems
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Analytics : the process
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Analytics : time accelerates
-
1
2
3
4
5
6
7
8
9
10
- 20 40 60 80 100 120 140 160 180
Targ
e v
olu
me
Analytical process time
Every x month
Unit : months
Event management
Unit : weeks
Just after
Unit : days
During
Unit : seconds
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SOMMAIRE SOMMAIRE
1. Introduction
2. C2B : consumer power
3. Customer Insight, analytics, Big data : overview
4. Customer value : various definition for various usage
5. Customer-Centric Marketing : leveraging acquisition, sales & loyalty
6. Customer Insight driven company
7. What’s next
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CVM : why bother ?
# Customer
Profits-100
-50
0
50
100
150
200
250
Niv 2Niv 1
Niv 0Niv -1
Niv -2
5 % of the customers 250 % of the profits
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CVM : (one) academic view
R : recruiting costs
n : relationship duration
M : margin
C : management costs
PM : marekting costs
F : separation costs
))((1
FPPPMCMVANR iii
n
i
i
Composante Exemple
R : coûts de recrutement VPC : coût global d'une campagne de recrutementrapporté au nombre de recrutés (300 à 600 F)
VAN : valeur actualisée par le rendement descapitaux visés par l ’entreprise
Sociétés cotées : cashflow/capitaux propres (10 à40% selon les secteurs)
n : nombre d'années de la relation Télécom mobile : 11 mois environ
M : marge brute générée par la relation Banque : environ 600 F/an pour un produitd'épargne
C : coûts de gestion
PM : pression marketing sur le client Programme de fidélité : somme des coûts decommunication direct sur le porteur
PP : pression promotionnelle sur le client Grande distribution : somme des allocations depromotions effectivement utilisées par le client
F : coûts de fin de la relation. Télécom mobile : valeur nette comptable duterminal
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CVM : not so easy
Spot value
NAV
Potential
Value
Future
Potential Value Retention
Cross-selling
Loyalty
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CVM : call for action
) ) ( ( 1
F PP PM C M VAN R i i i
n
i i
Churn anticipation
Best customer scoring and extrapolation on prospects
Cross-sell score
Channel preference monitoring and tuning
A/B testing and response optimization
Promotion elasticity measure
Segmentation and resource allocation
CVM prediction and resource allocation
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SOMMAIRE SOMMAIRE
1. Introduction
2. C2B : consumer power
3. Customer Insight, analytics, Big data : overview
4. Customer value : various definition for various usage
5. Customer-Centric Marketing : leveraging acquisition, sales & loyalty
6. Customer Insight driven company
7. What’s next
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CVM : acquisition
Value (ie : 2+ eligible products ?
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CVM : customization
Animation customized based on CV segmentation 1. Lower marketing costs 2. Increase average baskets by 3
to 9 % depending on segments 3. Winback of 2 to 5 % of inactive
Customers
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CVM : service
Measures : 7 steps, 12 channels, 1 Satisfaction survey Result : improved satisfaction / process re-engineering
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CVM : retention
Value ?
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SOMMAIRE SOMMAIRE
1. Introduction
2. C2B : consumer power
3. Customer Insight, analytics, Big data : overview
4. Customer value : various definition for various usage
5. Customer-Centric Marketing : leveraging acquisition, sales & loyalty
6. Customer Insight driven company
7. What’s next
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Not just for Internet companies
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Not just for CMO
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Not for everyone either
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Not just CMO : A380 aisle quality @airbus
• Data : Process measures and output quality
• Analytics : identify the best production path
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Not just CMO : Call center staffing @GDF
• Data : Call center logs
• Analytics : hourly call-center & emergency-team staffing
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Not just CMO : shop layout @FNAC
• Basket analysis defines shop layout
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Not just CMO shop location @pizza hut
Competition Potential Core market
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Not just CMO : Predicting demand trends
• Data : classified ads, socio-demo, national stats
• Analytics : predicting real-estate demand
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Not just CMO pricing a console @sony
• Data : survey, price & socio demo
• Analytics : optimum launch price
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Not just CMO trafic regulation @atlanta
• Data : tweets
• Analytics : trafic congestion prevention
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Not just CMO cancer risk calculator @novartis
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SOMMAIRE SOMMAIRE
1. Introduction
2. C2B : consumer power
3. Customer Insight, analytics, Big data : overview
4. Customer value : various definition for various usage
5. Customer-Centric Marketing : leveraging acquisition, sales & loyalty
6. Customer Insight driven company
7. What’s next
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Not so obvious
• What to fear :
– Public data policy
– Privacy laws
– Property laws
– Liability laws
– Skill shortage
• What to expect :
– Transparency
– Performance
– Comfort
– Automated decision
– Disruptive business
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