the customer centric company

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

www.softcomputing.com Reproduction interdite sans l’accord écrit de Soft Computing 18/12/2012 2

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


Soft Computing : Id

Employee

400

Turnover 2011

35 M€

Innovation

R&D/CA

10 %

Quality

Solid

Leadership

Respon-sible

Equities

16 M€


Specialist : CRM, BI & Web

BI

CRM

Genetically Net

WEB

Customer differenciator

Decision booster


Services Continuum


Helping the best-in-class


Big data, they talk, we act

Who worked on 4 appliances un 2012 and

invested in R&D on Hadoop


Happy to share

Enseigner

Ecrire

Echanger

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SOMMAIRE SOMMAIRE

1. Introduction






7. What’s next


Being a consumer today

18/12/2012


Crisis + technology = change

Expectations Truth

Easiness Involvement

Usage Screen culture

Real time Information Anywhere -

anytime

Behaviours Bargaining

Contributing Grouping

Second hand Renting

Comparing


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).

http://www.init-marketing.fr/

http://journeedelafidelite.blogspot.com/


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


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


Technology shift : ubiquity


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


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


Lol

http://www.netbase.com/solutions


Answer #1 : be long-term


Answer #2 : Bring value


Answer #3 : Be close


Answer #4 : Act (truly) responsibly


Brand Survival Kit


SOMMAIRE SOMMAIRE

1. Introduction






7. What’s next


Big data : facts


Big data : explosion of connected things

Source : McKinsey


Big data : testimony

« Since the inception of ebay, not a single

data was deleted ! »


Big data : making it possible


Big data : big brother


Big data : survey & segmentation


Big data : sociodemo & marketing strategy

Age

Financial

capacities


Big data : purchase history & One2one customization


Big data : web navigation & retargetting


Big data : taking comments in account


Big data : geoloc for one2one mobile promo


Big data : social network & prescription marketing


Big data : device & mouse mouvements -> dynamic pricing

Source : wsj, June, 26, 2012

http://online.wsj.com/article_email/SB10001424052702304458604577488822667325882-lMyQjAxMTAyMDIwNTEyNDUyWj.html





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


Analytics : evolution


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


Analytics : the process


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


SOMMAIRE SOMMAIRE

1. Introduction






7. What’s next


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


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


CVM : not so easy

Spot value

NAV

Potential

Value

Future

Potential Value Retention

Cross-selling

Loyalty


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


SOMMAIRE SOMMAIRE

1. Introduction






7. What’s next


CVM : acquisition

Value (ie : 2+ eligible products ?


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


CVM : service

Measures : 7 steps, 12 channels, 1 Satisfaction survey Result : improved satisfaction / process re-engineering


CVM : retention

Value ?


SOMMAIRE SOMMAIRE

1. Introduction






7. What’s next


Not just for Internet companies


Not just for CMO


Not for everyone either


Not just CMO : A380 aisle quality @airbus

• Data : Process measures and output quality

• Analytics : identify the best production path


Not just CMO : Call center staffing @GDF

• Data : Call center logs

• Analytics : hourly call-center & emergency-team staffing


Not just CMO : shop layout @FNAC

• Basket analysis defines shop layout


Not just CMO shop location @pizza hut

Competition Potential Core market


Not just CMO : Predicting demand trends

• Data : classified ads, socio-demo, national stats

• Analytics : predicting real-estate demand


Not just CMO pricing a console @sony

• Data : survey, price & socio demo

• Analytics : optimum launch price


Not just CMO trafic regulation @atlanta

• Data : tweets

• Analytics : trafic congestion prevention


Not just CMO cancer risk calculator @novartis


SOMMAIRE SOMMAIRE

1. Introduction






7. What’s next


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

the customer centric company

Technology

soft computing1812201212

customer power

brandcustomer relationship

customercentric marketing

market listenseach customer

technology shift

consumer power

mobile users