List Sourcing Optimization - Merkle Inc. · Developed a Data Sourcing and Optimization Strategy...
32
List Sourcing Optimization Using Analytics to Own the Advantage
Transcript of List Sourcing Optimization - Merkle Inc. · Developed a Data Sourcing and Optimization Strategy...
List Sourcing Optimization
Using Analytics to Own the Advantage
2
For too long list owners and brokers have held the advantage in list acquisition
Now it’s your time to own the advantage!
3
Knowledge is Power
In this session you will learn how to use analytics to gain critical knowledge necessary to:
1. Compare lists unbiasedly
2. Dictate to list owners / brokers list value
3. Eliminate cost in your list acquisition
4
Typical Situation
• High volume mailer
• Large number of list sources
• Merge process eliminates duplicates
• Uncertain just how many sources are really needed
Opportunity
Reduce paying for duplicate names
Especially those that cost more!
5
List List
List List
List List
List
List
List
List
List
List
List
List
List
List List
List List
List
List
There is a point at which additional lists provide no additional value.
List List
List List
List
List
List
List
List
List
ROI
# of Lists
“N” lists
N lists
Your Existing List Sourcing Optimal List Sourcing
List
Approach
6
Compiled 28%
PDb 25%
Response 33%
Multi-Buyer 14%
Current
Compiled 20%
PDb 64%
Response 12%
Multi-Buyer 4%
Optimized
List Source Mix Outcome
Move from your current source mix to one that achieves the same or better performance at less cost.
7
List Source Optimization Evolution
List Sourcing Optimization Big 3
Unbiased Response Attribution Analysis
List Level Optimization
Individual Level Optimization
8
Response is assigned to each of the lists on which the individual exists
Response is randomly assigned to a single list, typically the list that got paid. Remaining lists do not get the credit hence resulting in incomplete attribution
UN-BIASED (APPEARED-ON) RESPONSE ATTRIBUTION
TRADTIONAL (CREDITED-ON) RESPONSE ATTRIBUTION List
1
List 2
List 3
List 4
List 3
List 1
List 2
List 3
List 4
List 1
List 3
List 4
List 2
Unbiased Response Attribution
Unbiased response attribution enables accurate list level performance analysis
9
List Efficiency Index
The final metric used to rank order lists
List Level Optimization
The List Efficiency Index is a normalized measure of a list’s ROI controlling for differences in average mailing depth across lists. This allows an “apples to apples” comparison that is not impacted by average decile mailed.
Average Decile Mailed
List A List B
5.0
2.5
10
• Norm RR Index = Normalized RR for the list / RR Overall • CPP Index = CPP for the list / CPP Overall
o Normalized RR = Appeared-on RR * List Decile Adjustment Factor o CPP = Total cost including list, package, creative / mail volume
List Efficiency Index
The final metric used to rank order lists
List Level Optimization (cont.)
List Norm Resp Index / List CPP Index
11
Normalized RR Appeared-on RR * Mailing Decile Adjustment Factor
List Level Optimization (cont.)
130
120
110
100
90
1 2 3 4 5 6 7 8 9 10
124
109
102
113
107 103
101 100 100 100 100 100
List A Avg Decile Mailed 2.5 Index of 109 All Lists Avg Decile Mailable 4.5
Index of 102
Response Model Decile
Cu
m. R
esp
on
se R
ate
Ind
ex
Response Model Used in Selection
Appeared-on RR List A = .85% List B = .80%
All Lists Avg. Model Index Mailable = 102
List Decile Adjustment Factor List A: 102/109 = .94 List B: 102/101 = 1.01
List A Normalized RR = .85% * .94 = .80%
List B Avg Decile Mailed 5.0 Index of 101
List B Normalized RR = .80% * 1.01 = .88%
12
Appeared-on RR = .85% Decile Adj Factor = .94 (102/109) Norm RR = .80% (.85% * .94) CPP = $.42 Norm RR Index = 99 (.80%/.81%) CPP Index = 93 ($.42/$.45) List Efficiency Index = 106 (99/93)
List A Efficiency Index 106
Appeared-on RR = .80% Decile Adj Factor = 1.01 (102/101) Norm RR = .88% (.80% * 1.01) CPP = $.48 Norm RR Index = 109 (.88%/.81%) CPP Index = 107 ($.48/$.45) List Efficiency Index = 102 (109/107)
List B Efficiency Index 102
Overall Average Appeared on RR = .81%
CPP = $.45
List Efficiency Index
List Level Optimization (cont.)
List Norm Resp Index / List CPP Index
List A
2.5
List B
5.0
13
List Level Optimization (cont.)
Utilize Excel Premium Solver to run list level optimization most efficiently when using multiple constraints.
14
Individual Norm Resp Index / List CPP Index
Individual Level Optimization
Individual Efficiency Index
The final metric used to assign an individual to a single list
• The Individual Efficiency Index is different for the same individual on each list they appear on and involves a number of adjustments to get to an ‘apples-to-apples’ comparison.
• Uses appeared-on list performance and expected individual and list performance based on the response model.
Required Adjustments Needed to Compare: o List average decile mailed (vs. average mailable) o Individual decile mailed (vs. average mailable)
Joe List A
25%
2.5
Joe List B
50%
5.0
15
Individual Model Percentile = 90
Resp Index = 110
Normalized RR List A: = .80% List B: = .88%
Individual Decile Adjustment Factor List A: 110/94 = 1.17 List B: 110/89 = 1.24
Joe’s List A Individual Adjusted RR = .80% * 1.17 = .94%
Individual Adjusted RR List Normalized RR * Individual Decile Adjustment Factor
150
140
130
120
110
10
0
99
9
8
97
9
6
95
9
4
93
9
2
91
9
0
89
8
8
87
8
6
85
8
4
83
8
2
81
8
0
79
7
8
77
7
6
75
7
4
73
7
2
71
7
0
69
6
8
67
6
6
65
6
4
63
6
2
61
6
0
59
5
8
57
5
6
55
5
4
153
100
136 130
125 122
116 114
106
98 96 93
90
92 91 90 89
110
89 Individual ‘Joe’ index List A or B
List B Individual Index at Avg Decile Mailed
Individual Performance Adjustment
Response Model Percentile
List A Individual Index at Avg Decile Mailed
94
Individual Level Optimization (cont.)
Joe’s List B Individual Adjusted RR = .88% * 1.24 = 1.09%
16
Joe List A
Joe List B
List Appeared-on RR = .85% List Decile Adj Factor = .94 (102/109) List Norm RR = .80% Indiv Adj Factor = 1.17 (110/94) Indiv Adj RR = .94% (.80% *1.17) Indiv Norm RR Index = 1.16 (.94%/.81%) List CPP = $.42 CPP Index = .93 ($.42/$.45) Individual Efficiency Index = 1.25 (1.16/.93)
List A Individual Efficiency Index 1.25
List Appeared-on RR = .80% List Decile Adj Factor = 1.01 (102/101) List Norm RR = .88% Indiv Adj Factor = 1.24 (110/89) Indiv Adj RR = 1.09% (.88% *1.24) Indiv Norm RR Index = 1.35 (1.09%/.81%) List CPP = $.48 CPP Index = 1.07 ($.48/$.45) Individual Optimization Score = 1.15 (1.35/1.07)
List B Individual Efficiency Index 1.26
Individual Norm Resp Index / List CPP
Individual Level Optimization (cont.)
50%
5.0
25%
2.5
Overall Average Appeared on RR = .81%
CPP = $.45
Individual Efficiency Index
17
Individual Level Optimization (cont.)
Why is it better to acquire Joe from list B?
• List B’s normalized RR (.88%) is much better than List A (.80%)
…but List B’s list efficiency index (102) is worse than List A’s (106)
• List B’s individual adjusted RR (1.09%) is much better than List A’s (.94%)
…and List B’s indiv efficiency Index (1.26) is just better than List A’s (125)
Joe’s expected ROI is slightly higher if purchased from List B
18
List A List B List C List D
Joe
Wendy
Susan
Brian
Alex
Actual 2 1 1 1
Recommended 3 2 0 0
Example List Assignment
Individual Level Optimization (cont.)
Credited-on Actual vs. Credited-on Recommended
19
Individual Level Optimization (cont.)
List Rollup
• All individuals are deduplicated by assigning them to their best list then they are rank ordered by their individual efficiency index.
• Individual selections are then made in order of individual efficiency index until the desired objective is reached (cost, mail volume, responses etc.)
• Selected individuals are then summed by list considering total individuals on each list and list costs are calculated.
20
Individual Level Optimization (cont.)
The full optimization process is run repeatedly with dynamic cost estimation until cost convergence is reached. This is when Effective CPP % Change is less than .0000001
Cost Convergence
Effective CPP = Total Cost / Net Mail Qty Effective CPP % Change = (Effective CPP current - Effective CPP previous) / Effective CPP previous
After each optimization run the list CPP is recalculated based on the recommendation and compared to the CPP from the previous run. If the difference is too great then the optimization runs again and a new CPP is calculated and compared to the previous. This keeps repeating. Convergence typically occurs after about 17 iterations at which point the recommendation is final.
• List order quantity for segments A and B could be reduced by 60% without impacting mailing quantity and campaign performance
21
List acquisition quantity in most segments could be reduced substantially
Segment
Actual Sourcing List Optimization/Recommendation List Reduction%
# Lists Order Qty Mail Qty # Lists Order Qty Mail Qty # Lists Order Qty
A 79 17,382,322 1,500,000 30 6,205,660 1,500,000 62% 64%
B 398 124,862,406 12,342,926 146 52,165,553 14,152,041 63% 58%
C 62 10,484,783 1,500,000 38 5,318,329 1,500,000 39% 49%
D 4 181,435 44,061 4 181,435 89,002 0% 0%
E 40 6,753,394 1,000,000 27 4,611,377 1,000,000 33% 32%
F 20 9,847,706 2,613,013 12 7,092,880 758,957 40% 28%
603 169,512,046 19,000,000 257 75,575,234 19,000,000 57% 55%
Individual Level Optimization (cont.)
Individual Optimization Results
22
• List and Individual optimization work together to lower costs without impact to performance.
Putting It All Together
23
Business Case Study
Large Non-Profit Mailer
24
– A nonprofit membership organization
– >50% of new members from direct mail program
– 200MM pieces of mail/year
– Monthly mailings
– Using hundreds of different list sources
Background
25
Background (cont.)
– Response rates decreasing
– Thus, cost per new member (CPNM) is increasing
** Only way to keep CPNM constant is to reduce costs**
26
Cost Per New Member Drivers
27
Solution
Developed a Data Sourcing and Optimization Strategy
– Began with a detailed analysis of the historical performance
– List Sourcing Optimization was applied to ensure the purchase of the optimal combination of lists that would yield the highest response and lowest costs
– Attributed value to data for selectivity, not for the name/address alone
28
0.2
30 35
20
11 0.3
60
31
20 14
0
20
40
60
House Compiled PDB Response Multibuyer
Inp
ut
Nam
es
(in
MM
)
2010 2011
Even though Compiled names doubled, cost was reduced significantly by licensing a secondary file (primary file is used for PDB)
Response list cost have gone down even though input quantity has stayed constant = buying smarter
Source Mix
29
Performance Improvement – 1.4% over Forecast
Savings: $2,651,082 – a 49% Expense Reduction
46% Reduction in List Cost Per Order
Improvement in nets of 8%
Impact
30
THANK YOU!
32
1. How do you manage your list costs today?
2. How does the LSO solution compare?
3. Have you ever performed a list level “appeared-on” response analysis across lists?
4. Did the LSO solution make sense to you?
5. What would prevent you from using an LSO solution in your organization?
Table Questions
