Download Lifetime Value In new format

CHAPTER 3
Research design , Data
sources
3-1 Research Design:
Delineating What Data to Collect and
How to Collect It
A research design is the basic plan that guides data
collection and analysis. It must specify:
 the type of information to be collected
(consistent with the project objectives)
 possible data sources
 the data collection procedure
(accurate, economical and timely)
3-1a Types of Research
1. exploratory research – to
improve research
2. conclusive research – to
help choose between courses
of action
3. performance-monitoring
research – feedback on
chosen course of action
Figure 3-1 Types of
research
3-1b Exploratory Research:
Determining the 'Space' of Possible
Marketing Actions
Exploratory research facilitates problem recognition and
definition. It is appropriate when the research objectives
include:
 identifying problems or opportunities
 gaining perspective on the nature of the problem
 gaining perspective on variables involved
 establishing priorities
 formulating possible courses of action
 identifying possible pitfalls in doing conclusive research
3-1c Conclusive Research: Narrowing
Down Strategic Alternatives
Conclusive research aims to narrow the field of strategic
alternatives down to one. Two types:
 Descriptive research characterizes marketing phenomena
without testing for cause-and-effect relationships. It is used
for:
 determining the frequency of certain marketing phenomena
 determining the degree of association between marketing
variables
 making predictions regarding marketing phenomena
 Causal research gathers evidence on cause-and-effect
relationships through experimentation.
3-1i Longitudinal Design and PanelBased Research
Consumer panels monitor performance continuously for
a fixed sample measured repeatedly over time
(longitudinally). Advantages of panels:
 reveal important aspects of consumer behavior that cannot be




gleaned from cross-sectional data
gather more accurate data than cross-sectional surveys
gather extensive background and geodemographic information
on participants
reduce bias through period-by-period recording of purchases
tend to cost less per data point than surveys
3-2 Data Sources for Marketing
Research Applications
Sources of marketing data:
1. respondents
 communication with respondents
 verbal response through focus group or in-depth interviews
 depends on self-reporting
 observation of respondents
 accurately records what people do and how
 omits reporting of underlying attitudes
2. analogous situations
 case histories
 simulations
3-2 Data Sources for Marketing
Research Applications (cont.)
Sources of marketing data (cont.):
3. experimentation to test cause-and-effect relationships
 direct manipulation of key independent variables and
measurement of their effects on dependent variables
 controlling other variables that might affect ability to make
valid causal inferences
4. secondary data
 data already collected for some other purpose
 internal or external
3-3 Secondary Data
 internal secondary data generated within the organization
 lower cost
 accurate
 more available
 external secondary data – generated by government or syndicated
sources






government publications
trade association data
books
bulletins
reports
periodicals
The Balancing Act with Secondary Data
*Inexpensive
*Can be Secured Quickly
*Unknown Accuracy
*Ill Fitting for the Problem
The Nature of Secondary Data
 Primary data
 Secondary data
 Internal Information
 Sales & Expense reports
 Salespeople’s reports
 Street News
 Executive Judgments
 Extended internal information
The Nature of Secondary Data (contd.,)
 Secondary data
 External Information
 Library sources




Books
Periodicals
Government documents
Computerized databases
 Nonlibrary sources





Trade associations
Government Agencies
Media companies
Syndicated data
Internet sources
Creating an Internal Database
 An Internal Database is a collection of related information
developed from data already within the organization.
 Why is it important?
 Case of Capital One
 Lifetime Value
 Collective memory banks
 Created from qualitative data
 NUD*IST
How a modern database
system works
Mail, Email, Phone
Customer
Transactions
Marketing
Database
Inputs from Retail,
Phone, Web
Updated
several
times per
day
Data Access
And Analysis
Software
Appended
Data
Marketing
Staff
Access on
the web
Two Kinds of Database People
 Constructors
People who build databases
Merge/Purge, Hardware, Software
 Creators
People who understand strategy
Build loyalty and repeat sales
 You need both kinds!
Retention is the way to measure loyalty
90%
80%
70%
Percentage
Retained
from
Previous
Year
60%
50%
40%
30%
20%
10%
0%
1
2
3
4
Years as a customer
5
Retention pays better than acquisition
Annual Profit
$48
$60
$40
$20
$0
($20)
($40)
($60)
($80)
($62)
New Customer
3rd Year
Customer
Building Customer Value in four words...
Treat
different
customers
differently
What doesn’t work:
Treating all customers alike
This 28% lost 22% of the
bank’s profits!
79.67%
Profit %
80.00%
60.00%
24.82%
40.00%
15.83%
1.52%
20.00%
0.00%
-20.00%
-21.83%
Bank Customers by Profitability
-40.00%
5%
11%
28%
28%
28%
Compared with newcomers, Long term
customers:
 Buy more per year
 Buy higher priced options
 Buy more often
 Are less price sensitive
 Are less costly to serve
 Are more loyal
 Have a higher lifetime value
Key retention strategy: cross selling
90%
80%
70%
60%
Retention 50%
Rate
40%
30%
20%
10%
0%
1
2
3
4
Number of Products Owned
5
Why do businesses exist at all?
 Answer: Customers!
 Get more customers
 Keep them longer
 Grow them into bigger customers
Marketing to Customer Segments
Your Best Customers 80% of Revenue
Your Best Hope for New
Gold Customers
1% of Total
Revenue
GOLD
Move Up
These may be losers
Spend Service
Dollars Here
Spend Marketing
Dollars Here
Reactivate or
Archive
Examples of Profitable Strategies
 Newsletters
 Surveys and Responses
 Loyalty Programs
 Customer and Technical Services
 Friendly, interesting interactive web site
 Event Driven Communications
Lifetime Value
 Net profit you will receive from the transactions with a given
customer during the time that he/she continues to buy from
you.
 Lifetime value is “Good Will”
 To compute it, you must be able to track customers from
year to year
 Main use: To evaluate strategy
Long term customers buy more often
3.0
2.5
2.0
Number of
purchases 1.5
per yer
1.0
0.5
0.0
1
2
3
4
Years as a customer
5
Long term customers buy higher
priced items
$70
$60
$50
Average $40
Purchase
$30
Price
$20
$10
$0
1
2
3
4
Years as a customer
5
Retention rates go up over time
90%
80%
70%
Percentage
Retained
from
Previous
Year
60%
50%
40%
30%
20%
10%
0%
1
2
3
4
Years as a customer
5
Model Assumptions
 There is only one customer segment
 Acquisition of new customers only happens in year 1
 Lapsed customers
Revenue Side of the Equation
Year 1
Customers
Retention rate in %
Spending rate in $
Total Revenue
Year 2
20,000
40
150
3,000,000
Year 3
8,000
45
160
1,280,000
3,600
50
170
612,000
Cost Side of the Equation
Year 1
Year 2
Year 3
Variable costs in %
Variable costs $
Acquisition cost @ $40
60
1,800,000
800,000
50
640,000
0
45
275,400
0
Total costs
2,600,000
640,000
275,400
Profit Side of the Equation
 Gross Profit = Total Revenues – Total Costs
 Discount Rate = [1+(i * rf)] n
 where n = no of years to be discounted
 rf = risk factor
 Net Present Value (NPV) Profit = Gross Profit / Discount Rate
 Cumulative Profit = Sum of all NPV Profit till current year
 Lifetime Value = Cumulative Profit for the year / Total Number of
customers ‘N’
Profit Side of the Equation
Year 1
Gross profit
Discount rate
Net present value profit
Cumulative NPV profit
Lifetime Value
Year 2
Year 3
400,000
1
400,000
400,000
640,000
1
551,724
951,724
336,600
1
249,333
1,201,057
20.00
47.59
60.05
Scoring Customers – RFM Analysis
 Create a customer database. Include prospects.
 Use past customer behaviors to predict future behaviors.
Using RFM to find best customers
 Recency, Frequency, Monetary (RFM) analysis can be used to
categorize customers.
 Best Customers are those who:
 Bought from you recently
 Buy from you frequently
 Spend a lot of money on your products and services.
Recency
 Recency is the time that has elapsed since the customer
made his most recent purchase.
 A customer who made his most recent purchase last month
will receive a higher recency score than a customer who
made his most recent purchase three years ago.
 Example of a Scoring system:
 1 = Customers who made a purchase more than 9 months
ago
2 = Customers who made a purchase more than 3 months
ago but fewer than 9 months ago
3 = Customers who made a purchase in the last 3 months
Frequency
 Frequency is the total number of purchases that a customer
has made within a designated period of time.
 A customer who made six purchases in the last three years
would receive a higher frequency score than a customer who
made one purchase in the last three years.
 Example of a Scoring system:
 1 = Customers who made a single purchase in the past 12
months
2 = Customers who made between two & 12 purchases in
the past year.
3 = Customers who made more than 12 purchases in the
past year.
Monetary
 Monetary is each customer's average purchase amount.
 A customer who averages a $100 purchase amount
would receive a higher monetary score than a customer
who averages a $20 purchase amount.
 Example of a Scoring system:
 1 = Customers with an average purchase amount up to
$15.
2 = Customers with an average purchase amount from
$15 to $50.
3 = Customers with an average purchase amount greater
than $50.
Calculating RFM
 Rank customers in your database based on time since last
purchase - Divide into 3 equal groups with 3 being the
33% of customers who bought most recently
 Do the same thing again for Frequency.
 Repeat the same exercise for Monetary or total dollars
spent.
 These three codes give us 27 different categories of
customers ranging from 333 – 111.
ANALYZE your Customers: Highest
Monetary Cells
113
213
313
123
223
323
133
233
333
ANALYZE your Customers: Lowest
Monetary Cells
111
211
311
121
221
321
131
231
331
Benefits of RFM Analysis
 RFM Analysis can provide answers to the following questions:
 Can I identify my best customers?
 Who do I e-mail offers to? When do I e-mail them? How often?
 Should I promote to some customers more often than others?
 How can I tell when I’m losing a customer?
 Can I refine my marketing mix variables?
 The next step after knowing and analyzing your
customers is CLONING your customers.
Advantages of Secondary Data
 Clarify or redefine the problem /opportunity
 May actually provide solutions
 May provide primary research method alternatives
 May divulge potential difficulties
 May provide necessary background information
Limitations of Secondary Data
 Lack of availability
 Lack of relevance
 Resources
Appraising Secondary Data









Who sponsored the research?
Who conducted the research?
Who provided the information?
Who reported the information?
What information was gathered?
Why was the information gathered?
When was the information gathered?
How was the information gathered?
Where was the information gathered?
A Decision Support System
 What is a DSS?
 An interactive, personalized mapping system designed to
be initiated and controlled by decision makers
 In Marketing, it is known as MKIS (Marketing
Information Systems)
 Some basic ideas about MKIS
 Complex systems
 Deal with a variety of data sources
 Cost-benefit considerations
Characteristics of an MKIS
 Interactive
 Flexible
 Discovery oriented
 Easy to learn and use
Advantages of an MKIS
 Cost savings
 Increased understanding of the decision environment
 Better decisions
 Improved value of the information
Data Mining
 What is Data Mining?
 the process of exploration and analysis, by automatic and
semiautomatic mean, of large quantities of data in order
to discover meaningful patterns and rules.
 The technology is "data mining." Extension of statistics.
Data Mining
 Primarily used by companies with a strong ‘customer’ focus
 Wal Mart
 NBA Advanced Scout
Data Mining
 Data Mining
 Customer Acquisition
 Customer retention or loyalty
 Customer abandonment
 Market-basket analysis