Customer Acquisition & Modeling
Arthur Middleton Hughes
VP Solutions Architect
KnowledgeBase Marketing
Customer Acquisition
The tone of a good direct mail letter is as
direct and personal as the writer’s skill
can make it. Even though it may go to
millions of people, it never orates to a
crowd but rather murmurs into a single
ear. It’s a message from one letter writer
to one letter reader. – Harry B. Walsh
2
The best way to acquire customers
• Trick question– there is no one best way.
• TV, Radio, Print, Store Promotions, Direct Mail all work.
• Let’s begin with direct mail.
3
Direct Response
TV, Radio, Print or
Direct Mail Piece
Telesales
Database
Customer info put
into a database
Customer calls or
visits a store
Retail
These names are
Responders.
She RESPONDS
Web
4
Begin with a list
• There are 40,000 lists of consumers and businesses
available for rent.
• Use a list broker. Pay from $50 to $250 per thousand
names for a single use.
• Two types: response lists and compiled lists.
• Response lists always work better because half of US
households never buy anything by mail.
• For a large mailing of 2 million names, you may rent 200
different lists. You rent 3 million names because you will
find a lot of duplicates.
5
How a direct mail campaign is created
Marketers plan
their campaign
List Brokers
supply names
Service Bureau processes the
names
Typical Job: 300
Lists 3 million
names
Mail Shop Prints
the names on
letters and cards
Letters are
sent
6
Traditional Merge Purge
300 Lists 3 million
Reformatting
Duplicate
Consolidation
Suppression
3 Million
Same Format
CASS
Coding Accuracy Support
DPV
Delivery Point Validation
AEC
Address Element Correction
DSF2
Delivery Sequence File
Locatable Address Conversion
LACS
2 Million
Same Format
NCOALink
DCOA
Outgoing
Mail File
Dynamic Change of Address
Mail Shop
7
Processing saves money
Input Quantity:
2 Line Street Address Omits
Input To AEC Processing (Non-Zip+4 only):
Records Matched, Changed (Zip+4 appended)
Input To NCOA/DCOA/DSF Processing:
Nixie Omits
NCOA No Forwarding Address Omits
DCOA No Forwarding Address Omits
Unconfirmed or Invalid Zip code Omits
Quantity Percent
3,588,006 100.00%
88
0.00%
1.89%
67,659
13,189
0.37%
3,587,918 100.00%
48,397
1.35%
4,848
0.14%
26,036
0.73%
650
0.02%
NCOA Moved with Forwarding Addresses (not omitted)
79,979
2.23%
DCOA Moved with Forwarding Addresses (not omitted)
Post NCOA Processing:
APO/FPO Omits
South Pacific Omits
Prison Omits
Vulgar Omits
No Address Omits
DSF Vacant Address Omits
Input To Merge/Purge Processing:
Internal Duplicate Omits
Mail Preference File Omits
Deceased File Omit
Net Output from Merge/Purge:
Unmailable Addresses Removed
86430
3,507,987
1,076
397
294
636
88
10,994
3,494,502
52,458
107,953
65,747
3,268,344
319,662
2.41%
97.77%
0.03%
0.01%
0.01%
0.02%
0.00%
0.31%
97.39%
1.46%
3.01%
1.83%
91.09%
8.91%
8
Delivery Sequence File
DSF Improves Response Rate
9
Some Direct Response Rules
•
Choice kills: give them one offer – take it or leave it.
•
Always make every mailing a test so you learn from it.
•
Divide your list in half: send a test and a control.
•
The control is your best offer from the past.
•
Keep trying to beat your control. When you do, that offer becomes
your control.
10
What kind of response will you get?
•
2% is a good general rule. This means that 98% will throw your
mail piece in the trash.
•
Some companies make a profit with ¼ of 1% response.
•
The DMA sells a book listing response rates in various industries.
•
The Wall Street Journal has used the same letter successfully for
more than forty years…
11
THE WALL STREET JOURNAL
Dear Reader:
On a beautiful late spring afternoon, twenty-five years ago, two
young men graduated from the same college. They were very much alike,
these two young men. Both had been better than average students, both
were personable and both – as young college graduates are – were filled
with ambitious dreams for the future.
Recently these men returned to their college for their 25th
reunion.
They were still very much alike. Both were happily married.
Both had three children. And both, it turned out, had gone to work for the
same Midwestern service company after graduation and were still there.
But there was a difference. One of the men was manager of a
small department of that company. The other was its president.
12
Acquiring Retail Customers
•
Reverse phone append works.
•
The Sports Authority captured phone numbers in their stores
before Christmas.
•
They reverse appended the name and address.
•
They got 11% of those to whom they sent post cards to come
back once again before Christmas.
13
SPAM is out
•
Never acquire customers by sending emails to unknown people.
•
It is unethical. You will acquire the wrong kind of customer.
•
You may send emails to partners if the recipients have given
permission.
14
Principles behind modeling
•
Models permit you to predict how people will react to your
communications. By knowing this, you can send promotions to
those most likely to respond, and omit those less likely.
•
Modeling is also used for predicting who is most likely to defect.
•
Prospects and customers in many segments react in predictable
ways.
•
Clues to expected behavior can sometimes be discerned in
people’s previous behavior and demographics
•
Predictive models are developed from responses to previous
promotions.
15
How to begin a model
•
Do a promotion, or use an already completed promotion.
•
Keep both the respondents and those who did not respond.
•
To be valid, you should have at least 500 respondents.
•
Determine the size of a test by dividing the expected response
rate into 500:
– Response Rate 2%
Test Size = 500/.02 = 25,000
•
Assume you have 500 responses (buyers) and 24,500 who did
not buy. *
•
Append demographics to your entire file of 25,000
16
Divide your data into two parts
Promotion Results
24,500 Non Responders
Plus 500 Buyers
12,250 no
Plus 250 yes
Test Group
12,250 no
Plus 250 yes
Validation Group
Build Model with this
group
17
Discard the outliers
Build the model
•
There are always weird records – someone who bought 50 times
more than the average.
•
Discard these folks, as they will unbalance your model.
•
Use SAS or SPSS to build your model.
18
A model will determine the weight of
each variable.
Variable Description
Last Product = Other
Customer Email Flag = N
Sales Item Amount LTD $0-$100
Last Registration Recency 25-36
Sales Last Order Recency 0-6
Last Registration Recency 7-12
Sales Item Amount LTD $101-$250
Last Registration Method = Broadband
Customer Type = Unknown
Last Registration Method = Dial Up
Customer Type = Organization
Sales Last Order Recency 7-12
First Registration Method = Paper
Sales Last Order Recency 61+
Sales Last Pay Method = Credit Card
First Product = Other
Sales First Order Recency 0-6
Sales Item Amount LTD $251-$500
Last Registration Recency 0-6
Sales Item Amount LTD $1001+
Sales Last Item Amount $20.00-$39.99
First Registration Recency 25-36
First Registration Use = Business
Sales First Pay Method = Credit Card
Effect
+
+
+
+
+
+
+
+
+
+
+
+
+
-
Contribution % Coefficient
15.87%
-0.8988
14.22%
-0.6856
12.72%
-0.8494
7.90%
0.5511
5.13%
0.5330
5.01%
0.5953
4.87%
-0.5232
4.06%
-0.4365
3.66%
-4.8778
3.57%
-0.4432
3.35%
0.6922
2.95%
0.3867
2.63%
0.2668
2.35%
0.6308
2.32%
-0.2889
1.98%
-0.4011
1.46%
0.6904
1.22%
-0.2690
1.15%
0.2837
0.96%
0.2885
0.77%
0.1655
0.67%
0.2151
0.61%
0.3022
0.57%
-0.1337
100.00%
19
Develop an Algorithm
•
An algorithm is a mathematical routine that creates a score for
every computer record, based on the model.
Score = 1.05 +.04 * (Home ownership - 0 or 1)
+.05 * Total Orders
+.01 * Income in thousands
--.03 * Age
20
Score the file and divide into deciles
Index = Responses/Average Response times 100
Index of decile 2 = 640/366 * 100 = 175
21
Now, score the validation group
Promotion Results
24,500 Non Responders
Plus 500 Buyers
12,250 no
Plus 250 yes
Test Group
12,250 no
Plus 250 yes
Validation Group
22
If the model picks out the responders from
the validation group it is a good model
Validation
Test
These are almost the same. It is a good model.
23
What if it doesn’t work?
•
In many cases, a model does not work. It cannot accurately
predict responders. Why not?
•
Because the available data (behavior and demographics) cannot
predict which people will respond.
•
Example: predict which people will buy Windows XP if you send
them a promotion. It could be that nothing you can append to their
record will help you to predict this well enough to pay for the cost
of the model.
•
If it does not work, then you have to give up on modeling for this
situation.
•
Successful models are profitable because you avoid mailing
people who are unlikely to respond.
24
Getting better and better
Deciles
25
Who responds to mailings?
•
The highest deciles may be so enthusiastic about the product that
they buy without being mailed
•
The lowest deciles may not buy at all
•
In some cases, the promotion should be directed at the middle
deciles, since they are on the fence and need stimulus to buy.
•
In such cases we may be wasting money mailing to the top
deciles.
•
While all of this may be true, it is difficult to know for a fact.
•
Fleet Bank discovered it could not profitably send promotions to
its Gold customers. It concentrated on the Silver group.
26
Insurance Company Mailing
Total Mailed
Cost of Mailing
Number of Responses
Response Rate
Number of Sales
Sales Rate
Total Revenue
Revenue per Sale
Profit
Return on Promotion
Control
Optimized
%
Group
Group
Change # Change
1,264,571 1,264,571
0%
0
$547,559
$547,559
0%
0
13,366
16,090
20%
2,724
1.06%
1.27%
20%
0.22%
1,599
2,323
45%
724
12.00%
14.40%
21%
2.47%
$2,605,603 $3,158,151
21% $553,208
$1,630
$1,360
-17%
($270)
$95,896
$187,851
96% $91,955
18%
34%
96%
16.80%
27
A model raises the cost of a mailing
Quantity Price/M
Cost
Rented Names 1,000,000
$70 $70,000
Model
1 $25,000 $25,000
Appended Data 1,000,000
$20 $20,000
Scoring
1,000,000
$5
$5,000
Total Cost
$120,000
Names Used
600,000
Cost per name
$0.20
•
A typical response list will cost you about 12 cents per name.
•
In this case the model, though productive, will not be cost
effective
28
Using AmeriLINK will make this model
cost effective
Quantity Price/M
Rented Names
600,000
$70
Model
1
$0
Appended Data
600,000
$0
Scoring
600,000
$5
Total Cost
Names Used
600,000
Cost per name
Cost
$42,000
$0
$0
$3,000
$45,000
$0.075
•
AmeriLINK already has the data appended.
•
You can select the right names directly from AmeriLINK
29
KBM Case Study
•
Software manufacturer wanted to get previous customers to buy
upgrades.
•
The Low Probability customers were not worth mailing.
•
Using the model the response rate went from .85% to .95%, an
increase of 12%.
30
Modeling to predict churn permits a risk
revenue matrix
31
Modeling using CHAID
(Chi-square Automatic Interaction Detection)
32
CHAID gains chart
•
Gains Charts let you decide how deeply to go into a prospect file.
•
You can use these charts to create customer segments.
•
The top 3 segments are 28% of the file with average profit of
$1.75 per household.
33
KBM CHAID Case Study - Segments
CHAID
Level
1
2
3
4
5
6
7
8
9
10
Variable
Exact Age
AND Home Value
Exact Age
Exact Age
AND Percent Divorced
Exact Age
AND Home Value
Exact Age
AND Percent Divorced
Exact Age
AND Affluence Index
AND Donnelley Income
Exact Age
AND Home Value
AND Early Card Adopters Index
AND Nielson County Rank
Exact Age
AND Percent Divorced
AND Nielson County Rank
AND Percent Employed PTM
Exact Age
AND Home Value
Exact Age
AND Percent Divorced
AND Home Value
Values
Importance
19 to 20
>= 165
16 to 18
24 to 39
0 to 5
19 to 20
98 to 133
51 to 55
8 to 9
21
6 to 10
55 to 150
24 to 39
78 to 189
1, 2
2 to 4
40 to 50
9 to 14
B, C, D
26 to 99
22 to 23
>= 119
40 to 50
6 to 8
>= 73
187
174
149
149
131
130
130
126
123
122
34
Modeling to Reduce Churn
•
A phone company had a high defection rate. KBM was asked to
analyze who was leaving and why. They used modeling.
•
Developed 68 models in all
•
Rated the models based on performance versus a control group
•
The key findings of the neural network model were:
•
Two-thirds of all defections occurred within 15 months
•
Approximately 4 out of 10 defections were preventable
•
53% of preventable defections occurred before the seventh month
35
Quadrant Analysis
36
Strategies based on the models
•
Identify key customer segments – Focus on Group A.
•
Allocate marketing investment based on revenue and profit
•
Provide different treatment for each segment within the loyalty
program
•
Provide super services to the best customers
•
Provide individual loyalty rewards based on a customer’s life
stage, needs, and value
•
Use models to trigger proactive communications to customers
with high attrition risk
•
Establish a system to detect problems and resolve them before
the customer headed for the door.
37
Success from using the strategies
•
The program generated a return on investment of $2.09 for every
$1 invested.
•
Attrition of those customers receiving the rewards
communications was 1.27 points lower than those in a control
group.
•
Average revenue ($1,412) in the rewards test group was 5%
higher than in the control group ($1,358).
•
There was an increase of $19.6 million dollars in annual sales to
those 13,881 customers who were retained by the loyalty program
(compared to a control group).
38
Profiling
39
Education Profile
40
How to use profiles
•
Profiles tell you who to contact, when you do not have the
information necessary to build a model
•
Profiles help you in designing your promotion copy: if they are all
PhD’s your text would be different than if none had finished high
school.
41
Prospect Databases
Why you can’t afford to append data to build
models with monthly rented names
Mail These
$$$
300 response lists
5,000,000 names
every month
$$$
Append Data
Merge
Purge
De-dupe
Mailing Universe
$$$
Score records
High Scores
Low Scores
$$$
Throw away
43
Prospect Database saves money,
increases response rate.
Model Scoring
Hot Line and
Other Names
Prospect Database Mail Top Deciles
250,000,000
names
•No wasted names
Compiled
Names
Used to supply
names and to
append attributes
•Lower monthly
processing
Promotion
History
•Selection based on
demographics and history
•Output sent weeks earlier
44
Mail selection process – done fast
Prospect Database
Selected Records
Select based on
Model scores
Suppress existing
customers and
others
Marketing Staff
Merge Purge
Process,
Segmentation &
Final Suppression
Hot Line Names
To mail shop
45
Suppression boosts response.
•
Suppress previous responders
•
Suppress deceased, prison, nursing homes.
•
Suppress DMA and other lists
•
Mail only good names
Good Names
Unlikely
Deceased Responders
46
Example: How a prospect database
can save list rental costs
Current System
Monthly Rent
House File
Compiled Names
Response Names
Total
Prospect Database
Annual Rent
House File
Compiled Names
Response Names
Data Append
Total
Savings
Numbers
600,000
4,000,000
9,000,000
13,600,000
Cost Per
Thousand
0
$78.80
$134.61
$108.71
Total
List Cost
Numbers
600,000
10,000,000
3,000,000
3,600,000
13,600,000
Cost Per
Thousand
0
$50.00
$130.00
$20.00
$70.74
Total
List Cost
0
$315,214
$1,163,309
$1,478,523
0
$500,000
$390,000
$72,000
$962,000
$516,523
47
Advantages of a Prospect Database
• Reduce list rental costs
• Increase response rates to initial mailing
 Target by behavior and demographics rather than by
list and age
 Target based on previous promotion history
 Cut up to four weeks off mailing prep time – more rapid
access to hot lines
• Increase percent of long term loyal customers
 Target mailing to high retention segments
• Read results right after responses arrive. Use
them to plan the next campaign.
• One Annual fee, not CPM. You can plan ahead.
48
Summary: Prospect Database
•
Compiled and vertical names rented for a year, stored in a
database and scored with many attributes
•
Promotion and response history stored in prospect database
•
Monthly models will permit selecting high responding, high
converting, high retention loyal customers
•
Models permit use of compiled names at lower cost
•
Result: higher response, conversion, retention
•
Significant increases in net revenue and reduction in costs
49
Thank You