Transcript Optimal Chapter 3 - Cal State LA

Optimal Database Marketing Drozdenko & Drake, 2002
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Chapter 3
Defining Customer Data
Requirements
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Chapter Objectives
• Examine the types of data needed to achieve
marketing objectives.
• Review internal and external data sources.
• Examine the characteristics of various external
databases.
• Review case examples
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Database Development Steps
• Determine the data requirements needed to meet
the marketing objective
• Establish guidelines for database maintenance
and program coding
• Evaluate the database structure, including
hardware/software requirements
• Determine whether the database will be built and
maintained inside the organization or outsourced
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Data Needs Determination
• Database development begins by determining the
types of data attributes (fields) required to support
the marketing objectives.
• The amount of data residing on a database can vary
greatly from company to company depending on
needs and how the database will be used to meet the
marketing objectives.
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Types of Databases
• Fulfillment – for ordering, shipping and billing.
• Marketing – history of customer transactions and
customer characteristics.
• Prospecting – contains non-customers,
sometimes used for ‘cloning,’ once orders are
placed name moves to marketing and fulfillment
databases.
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Fulfillment Data
•Any direct marketer currently taking and fulfilling orders
of any kind must have, at the very least, a fulfillment
database or file. In all likelihood, this file is being
managed and maintained by an outside vendor.
•The sole purpose of this file is to maintain information on
customers regarding their order, product shipment and
billing information and status.
•Fulfillment files do not maintain historical data and
therefore cannot be used to conduct analyses of past
customer purchase behavior.
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Marketing Databases
• Marketing databases are structured for efficiency and
does maintain a history of all customer transactions over
time. These databases are derived from the customer
information sitting on a fulfillment database. How often
the fulfillment data feeds the marketing database depends
on the enterprise’s needs.
• Marketing databases allow direct marketers to more
easily obtain quick counts on active customers, select
names for future promotions across the various divisional
product lines and track customer performance over time.
In this course, we will focus solely on the use of a
marketing database.
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Prospecting Databases
• Prospecting databases - are a type of
marketing database but comprised solely of
non-customers. They are usually kept as a
separate file because they carry limited
information on the names residing on this
file. Once a prospect orders and becomes a
customer, their information will be
transferred from the prospecting database to
the marketing database.
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Sources of Information For
Marketing Databases
MARKETING
DATABASE
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Internal Data Sources
• After an organization has determined the need to
establish a database, the challenge often focuses on the
varied and unorganized sources of customer information
that currently exist.
• This information must be organized and structured in a
meaningful way in order to achieve the objective of
tracking customers.
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Internal Data Sources
Data collected by the organization
• Internal data can originate from different departments in
the organization such as accounting, marketing,
customer service, operations, research, etc.
• Even within those departments, sometimes several
different databases will exist.
• Internal data is also called house data and referred to as
the house file.
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Internal Data Sources
Data collected by the organization
• Previous Contact Information
• Purchase Records
• Product Returns Data
• Bad Debt Information
• Customer Service Data
• Surveys
• Voluntary Customer Registration Data
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Fulfillment Data
• Fulfillment data are basically raw or
transactional-level data and are
primarily used for billing and fulfilling
orders.
• However, a marketer also wants to use
such data. For example, the data field
“Last Bill Effort Sent” is used by a
marketer as an indicator of how quickly
a customer pays.
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Marketing Data
• Marketing data are any piece of customer
information used by marketers for the purpose
of increasing the effectiveness or efficiencies of
marketing activities.
• This includes data that can help marketers
promote customers, develop relationships with
customers, and establish marketing strategies
and programs.
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Marketing Data
Marketing Data can be classified into three type related
to the recency, frequency and monetary value of
customer transactions.
• Recency data are related to the recency of a
customer’s last promotion, order, payment, and
so on.
• Frequency data are related to a customer’s total
number of promotions, orders, payments, etc.
• Monetary data are related to a customer’s total
dollar value of orders placed, payments made,
etc.
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Customer Contact Data
• Customer contact data are the foundation of the
database. The marketer must have a means to
reach customers.
• Basic contact information includes name, address,
zip code, phone and fax numbers, and e-mail
address.
• Marketing efficiency and effectiveness is greatly
affected by the quality of the contact information.
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External Data
• External data can help supplement in-house
customer data and allow marketers to develop
more effective marketing programs.
• Several sources of external data allow
marketers to supplement and enhance their
databases.
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External Data Types
Based on their characteristics, external data
can be classified into four categories.
•Compiled List Data
•Census Data
•Modeled Data
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Compiled Data
•Compiled Data are lists of people or
organization gathered from telephone
directories, voter registration files, birth
records, housing purchase records, membership
rosters, etc.
•Companies such as Edith Roman, Polk,
USAinfo, Experian/Metromail compile lists.
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Compiled Data
The information in compiled list data can be
categorized in two ways:
• Demographic
• Psychographic
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Demographics
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Income
Age
Gender
Marital Status
Education level
National Origin, Ethnicity
Family Size
Occupation
Religion
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Psychographics
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Hobbies
Reading interests
Exercising habits
Music preferences (rock, country, etc.)
Movie preferences (action, drama, etc.)
Political Orientation
Social (opinions on the environment,
education, family, health care, etc.)
• Donor characteristics
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Census Data
www.census.gov
• Demographic and other data collected by the
US government on a periodic basis.
• The census is updated every 10 years, updates
on specific data, such as population growth, are
done more frequently.
• Data can be broken down to specific
geographic areas in the country.
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Census Data
• Obtained from the U.S. Census Bureau at a geodemographic level (zip code, block group, census track,
etc.) It is not available at an individual level as compiled
list data.
• “Census tracts” are 50,000 subdivisions of counties.
• “Block groups” are 225,000 subdivisions of census tracts
formed by grouping blocks (streets).
• Being too fine of a split, some sensitive economic- and
personal data is not reported at the block group level;
however, it is reported at a census tract or zip code level.
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Examples of Census Data
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Average household size
Average home value
Average monthly mortgage
Percent ethnic breakdown
Percent married
Percent college educated
And even such measures as average
daily commuting time
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Census information for three very demographically distinct
census tracts are shown below. If you were to promote an
expensive children’s book series and could only market to
the names within one of the three census tracts below,
which would you choose and why?
Median
Age
Median
Monthly
Mortgage
Median
Household
Income
Percent with
Undergraduate
or Graduate
Degree
4.1
37.2
$2,000
$148,649
38.5%
Affton, MO 63123 (Block Group 1,
Census Tract 219800, FIPS Code
29189)
2.7
37.2
$661
$41,579
22.4%
West Frankfort, IL 62896 (Block
Group 3, Census Tract 040900, FIPS
Code 17055)
2.2
40.9
$490
$12,636
3.7%
Median
Household
Size
Chappaqua, NY 10514 (Block Group
2, Census Tract 013102, FIPS Code
36119)
Area
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Data Residing on the Marketing Database (Cont.)
• When lacking an abundant amount of individual level data
pertaining to customers, a direct marketer often relies on
census level data. The premise in using this type of data
for determining a product’s target market, when no other
data is available, is that all individuals living within a geodemographic region are similar to one another. While this
may be true in some cases, it certainly is not in others.
• Head-to-head, census level data will never be as powerful
as individual level data in predicting customer behavior.
However, as previously mentioned, when no other
information is available, it will and can play a fairly strong
role depending on your applications.
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Modeled Data
• Modeled Data are generated from statistical analysis
such as customer clustering.
• These data are often used to classify customers based on
purchase patterns, demographics and psychographics.
• For example, based on certain geographic data, income
or educational levels can be predicted.
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Lists Versus Data
• Names residing on lists can also be rented for
promotions or purchased and added to a
direct marketer’s prospecting database.
• Typically, contracts are set for a one-time use
or a one-year agreement with unlimited use.
Outside intermediaries such as list brokers or
managers often handle list rentals and
maintenance for an organization.
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INSOURCE A Database
Enhancement Service
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Delivers Data On …
a broad range of demographics
consumer interest and lifestyle
telephone numbers
mail order responsiveness
automotive ownership
real estate holding
census geodemographics
http://www.experian.com/yourmarket/tec/insource.html
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Response Lists
• Response lists are lists of people or
organizations that responded to offers such as
mail order catalogs or subscriptions.
• Organizations rent lists of people who
responded to their offers.
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Examples of Response List Data
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Subscription to a magazine
Purchase from a catalog
Donation to a charity
Request for information about a product
Some of these lists are further specified as to
most recent purchasers. These are called
“hot-line” lists.
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Response List Data
• House data can be enhanced with response list
data to determine if any of an organization’s
customers responded to offers of other direct
marketers.
• Some companies establish joint agreements
whereby they exchange certain pieces of
customer data. Examples include:
• Sharing of non-payer information
• Catalog buyers
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Compiled vs. Response Lists
• As mentioned, most compiled lists are
relatively simplistic lists of names and
addresses from sources such as public realestate records, and organization rosters.
• In contrast, response lists are people or
organizations who have previously responded
to some type of offer. Response lists can be
further defined by media, such as direct mail or
telemarketing responders.
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Compiled vs. Response Lists
• Response lists have a higher potential
response rate than compiled lists because
past behavior is a good predictor of
future behavior.
• Therefore, marketers seek response lists
composed of purchasers (or donors) in
the same or a similar category to their
own.
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Purposes of Data Enhancement
(Data Overlays or Supplements)
In addition to acquiring lists of potential new
customers, marketers overlay information
on an existing database…
• To learn more about individual customers
• To increase the effectiveness of customers
programs
• To predict responsiveness for prospective
marketing programs
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Applying and Using
Enhancement Data
If direct marketers wish to have enhancement data (census,
compiled, or modeled) overlaid on their customer file, they
often follow these steps:
1. The direct marketing company makes a copy of their
customer file (or whatever portion they wish to enhance)
and sends it to the to the enhancement service.
2. The service bureau matches their file to the direct
marketing company’s file using a name and addressmatching algorithm (Chapter 4).
3. Once matched, the service bureau appends the desired
information (e.g., age, income, lifestyle indicators) to the
copy of the file given to them.
4. The direct marketing company matches the file back to
their database (via a unique customer number) and
appends the enhancement data to their file for future use.
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Three case studies illustrating
the use of enhancement data.
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Review Questions
1. What are some of the sources of data that can be
included in the database?
2. Give some examples of demographic and
psychographic data elements.
3. How are compiled lists different from response lists?
4. Why are house files (internal databases) enhanced with
supplemental data?
5. Describe how marketers can use U.S. Census Bureau
data.
6. What steps are involved in using an outside service to
enhance a house file?
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