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Information Systems:
Customer Relationship Management
Source Régis Meissonier
1
Economics of customer retention
“Winning back a lost customer can cost up to 50-100 times as much as keeping a
current one satisfied.”
Rob Yanker, Partner, McKinsey & Company
Understanding your customer is key to retention…..
2
CRM: towards the « one to one »
Marketing One to One
Offer
Personalization
level
Offer segmentation
Mass market
XXth century
XXIth century
3
CRM level
Collaborative level :
CRM spread to the suppliers,
CRM
Development
level
distributors, etc.
Operational level :
Interactions with the customer by all the
fitting media to this effect
Analytical level :
Collect, archiving of the data customers in
order to establish a segmentation,
typologies, informers, etc.
Relational developed level
4
The CRM market
Leaders: Siebel, Peoplesoft, SAP, Oracle
Sales in France (Source 01Net.com, Professional internet)
2001 = 1,115 B€
2002 = 1,224 B€
2005 = 1,990 Billion Euros
Development costs = from hundreds thousands to
several hundreds million of Euros.
Duration = from some months to several years
The main question: which return on investment?
5
CRM architecture
Customer
Data collection
web, telephone, meeting, mail, etc.
Data aggregation
datawarehouse or SGBD
Customer’s knowledge
Relationship
management
segmentation,
predictibility of the
models, datamining
recommendation,
campaign
management
6
Data collection
CTI (Coupling
Telephony Computer):
call centers
forms, logs and stats
files, clikstream,
received messages,
etc.
7
Data aggregation
Supply Chain
Stocks management
outflow of the products
storage cost
Supplying
management
Income cost
Historic of the estimates,
orders,
Anticipations of the sales
Costs of
purchases
Datawarehouse
Evaluation of the risk customer Allocation
of the indirect loads
Check management
Customer
Worked hours for the
customer
Human ressources
8
Customer knowledge: datamining
Prediction level
Neurones networks
Genetic algorithms
Bayésians networks
Results
legibility level
Scores
Regression
Cluster
Decision shaft
Association analyze
Reasoning on cases
9
Data Mining
The non-trivial extraction of novel, implicit, and
actionable knowledge from large database
Extremely large datasets
Discovery of the non-obvious
Useful knowledge that can improve processes
Can not be done manually
Technology to enable data exploration, data analysis, and
data visualization of very large databases at a high level
of abstraction, without a specific hypothesis in mind.
Sophisticated data search capability that uses statistical
algorithms to discover patterns and correlations in data.
Source : Seyyed Jamaleddin Pishvayi
10
Customer knowledge : segmentation
Reconquest
policy
Fidelisation
policy
Customer value
Relationship intensity
Abandonment
policy
Rationalisation
policy
11
Data Mining in CRM (cont.)
Data Warehouse
Customer Profile
Data Mining
Customer Life Cycle Info.
Marketing Campaign
Source : Seyyed Jamaleddin Pishvayi
12
An other way to predict customer purchases:
the recommendation technique
Information given back to the customer according
to his profile
Description user centered
Customer photofit picture
Collaborative Filtrage
13
In the facts…
65-69% of corporations
questioned consider that
CRM involved no
improvement
(Deloitte & Touches ; Meta Groups)
Firms tend to integrate
functions that do not
correspond to business
objectives
Information system
oversized compared to the
effective needs
Source : Mc Kinsey, 2002, How to rescue CRM?
14
Conclusion
Define clear objectives: what wants to do the company?
To increase its customer portfolio?
To increase its incomes with the current customers?
To increase the number of customers representing large value?
To reduce the number of customers representing small value?
Etc.
Which strategy to use?
To improve the quality of the distribution chanels?
To improve customer satisfaction ?
To increase crossed sales?
Etc.
15