International conference of data miming, Pisa 2009 - Marketing intelligence

1. © 2008 Exeura s.r.l. – Confidential and Proprietary
TM
Easy Analytics Everywhere
“Mining Value In Marketing Campaign Data”
(Domenico Greco – Aster Group President)
Pisa, 16th December 2008
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2. ASTER Group Profile
from Dealership to Leadership
Our company’s life time assets – 4 transformation phases
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3. Matching Business Needs
& Customer Value
Sustainability and
competiveness continuously
driven:
Gap analysis on B.U.:
what customers should
buy?
Cluster analysis on
customer “life time value”:
which customer contact,
when and how?
The results are merged
to determine the operating
budgets for One-to-One
marketing campaigns, thus
optimizing the allocation of
resources: time, budget,
information, and human
resources.
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4. The Challenge: to exchange experience
with analytics
> 1.000 Per year
Yearly
Customer
transactional
Data for
data on over
300,000
500M € Learning by Doing customers
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5. Business Analytics Needs
the collaboration with Exeura
1. What’s the value of our MKTG campaign? We need to define
an effective concept of value created by MKTG campaign. We
need to accurately forecast the results of each campaign;
2. Why MKTG campaigns generate value? We need to discover on a
short time the key variables that determine the success of a
marketing campaign;
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6. Business Analytics Solution
Marketing Effectiveness Index
How does a MKTG campaigns create value? What’s the real
effectiveness of a MKTG campaign?
Actual value – economic flow produced during the period of
time for the evaluation; EI (IE) = effectiveness index
Potential value – prospected economic flow generated after
evaluation period. PEI (IEP) = potential effectiveness index
General Effective Index: GEI (IET) = IE * IEP
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7. Step 1:
Campaign Assessment by GEI
We applied this model to our CRM DB: this was the first time we
were able to order our MKTG campaign by effectiveness
GEI per type
G Id u n
E istrib tio
1.600
10
60 1.400
10
40 1.200
10
20 1.000
10
00
GEI
800
GEI
80
0
600
60
0
400
40
0
200
20
0
-
0
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8. Step 2:
Campaign Value Prediction
why MKTG campaigns generate value ?
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9. Insights in Customer Behaviors
By analyzing the information we learned aspects of our customers
not well known or understood before:
The average life time of the relation with the customer;
The average of KM our customers make in life time relation;
The seasonality and cycles of our customer visits
The purchasing patterns (cars and services)
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10. Insights in Value Drivers
By Leveraging Multivariate regression model we have discovered:
The customers redemption is different according to the shop and
to the salesman related with the customer;
The automotive brands and models that are likely to produce
positive customer response to a marketing campaign;
The most profitable stages of a customer relationship.
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11. Integrating Analytics with
“Marketing Decision Making”
Customer DB 1
Optimum
2 Marketing Variables
MKTG Campaign
3
Attended
4 Value
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12. Our new Business Model powered
by Business Analytics
Gap Analysis Business analytiics Customer analysiis
K.P.I. MKTG Programs Value drivers
B.U. B.U. B.U.
A B C
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13. About Exeura & Rialto 2008
Exeura is 8 years old spin-off of University of Calabria with
offices in Chicago, Milan, Cosenza and soon Frankfurt
Leverages 20+ years of leading edge research and innovation
funded by government grants and tested in early market trials
Over 15 market trial applications in the areas of health care,
finance, insurance, government and intelligence
Rialto 2008: Easy Analytics Everywhere
Exeura Academic Program provides free access to instructors and
researchers as well as highly discounted licenses for University
Administrative Offices
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