Avoid email meltdown:
how segmentation can
improve response retention
and reputation
Steve Thomas, Purple Vision
@stevethomas393
@purple_vision

What is Segmentation?
• Classification of the population into
subgroups such that the subgroups
are:
– Distinguishable
– Identifiable
– Manageable
– Fit for purpose

Why Segment?
• Appreciation of motivations
– Communication
– Tone of voice
– Increased returns
• Facilitates Different Marketing Strategies
– Product segmentation
• Identification of ‘look alikes’
– Individual
– Area

One size doesn’t fit all

How to segment?
Frequency
Recency
Value

Frequency
Recency
Value
Creating segments

9
Creating segments
8
4
13
6
7
2
Frequency
Recency
Value

Profiling

Look alike logic
Universe
Your
Database
Your
Sector
Charity Supporters

Profile variables
• Income
• Housing Tenure
• Spending Power
• Education
• Occupation
• Social Grade
• Age
• Children
• Household Size
• Property Type
• Urbanicity
• Retail Accessibility

Example profiles - Age
TotalSketch Attributes
Supporters Regional Base Penetration Index
Counts % Counts % % 0 100 200
Age – Example 1
Rank 91-100 (High) 933 16.8% 23092 11.2% 4.04 150 █████
Rank 81-90 1012 18.2% 19816 9.6% 5.11 190 █████████
Rank 71-80 852 15.3% 20846 10.1% 4.09 152 █████
Rank 61-70 697 12.5% 20417 9.9% 3.41 127 ███
Rank 51-60 643 11.6% 23081 11.2% 2.79 104
Rank 41-50 459 8.3% 22491 10.9% 2.04 76 ██
Rank 31-40 316 5.7% 22152 10.7% 1.43 53 █████
Rank 21-30 202 3.6% 17995 8.7% 1.12 42 ██████
Rank 11-20 201 3.6% 19192 9.3% 1.05 39 ██████
Rank 1-10 (Low) 245 4.4% 17650 8.5% 1.39 52 █████
TOTAL 5560 206732 2.69
Age – Example 2
Rank 91-100 (High) 601 14.5% 23382 11.1% 2.57 130 ███
Rank 81-90 662 15.9% 21810 10.4% 3.04 154 █████
Rank 71-80 465 11.2% 18343 8.7% 2.54 128 ███
Rank 61-70 557 13.4% 23014 10.9% 2.42 123 ██
Rank 51-60 493 11.9% 22896 10.9% 2.15 109 █
Rank 41-50 375 9.0% 20015 9.5% 1.87 95 █
Rank 31-40 387 9.3% 22721 10.8% 1.70 86 █
Rank 21-30 270 6.5% 22811 10.8% 1.18 60 ████
Rank 11-20 171 4.1% 17574 8.4% 0.97 49 █████
Rank 1-10 (Low) 174 4.2% 17887 8.5% 0.97 49 █████
TOTAL 4155 210453 1.97
Age – Example 3
Rank 91-100 (High) 20 2.3% 10642 8.7% 0.19 27 ███████
Rank 81-90 14 1.6% 11145 9.1% 0.13 18 ████████
Rank 71-80 37 4.3% 10021 8.2% 0.37 53 █████
Rank 61-70 133 15.6% 12234 10.0% 1.09 156 ██████
Rank 51-60 144 16.9% 12409 10.1% 1.16 167 ███████
Rank 41-50 124 14.5% 11515 9.4% 1.08 155 █████
Rank 31-40 139 16.3% 14290 11.6% 0.97 140 ████
Rank 21-30 94 11.0% 14826 12.1% 0.63 91 █
Rank 11-20 69 8.1% 12608 10.3% 0.55 79 ██
Rank 1-10 (Low) 80 9.4% 13232 10.8% 0.60 87 █
TOTAL 854 122922 Sample

Comparative profile - Education
TotalSketch Attributes Members Regional Base Penetration Index
Counts % Counts % % 0 100 200
Education – Group A
Rank 91-100 (High) 1574 28.3% 43382 21.0% 3.63 135 ███
Rank 81-90 1369 24.6% 43322 21.0% 3.16 117 ██
Rank 71-80 871 15.7% 30229 14.6% 2.88 107 █
Rank 61-70 550 9.9% 23148 11.2% 2.38 88 █
Rank 51-60 337 6.1% 17701 8.6% 1.90 71 ███
Rank 41-50 333 6.0% 15203 7.4% 2.19 81 ██
Rank 31-40 240 4.3% 14452 7.0% 1.66 62 ████
Rank 21-30 194 3.5% 13006 6.3% 1.49 55 ████
Rank 11-20 60 1.1% 4479 2.2% 1.34 50 █████
Rank 1-10 (Low) 32 0.6% 1810 0.9% 1.77 66 ███
TOTAL 5560
20673
2 2.69
Education – Group B
Rank 91-100 (High) 219 5.3% 24697 11.7% 0.89 45 ██████
Rank 81-90 805 19.4% 51462 24.5% 1.56 79 ██
Rank 71-80 778 18.7% 36515 17.4% 2.13 108 █
Rank 61-70 656 15.8% 25427 12.1% 2.58 131 ███
Rank 51-60 499 12.0% 19525 9.3% 2.56 129 ███
Rank 41-50 444 10.7% 18293 8.7% 2.43 123 ██
Rank 31-40 444 10.7% 17905 8.5% 2.48 126 ███
Rank 21-30 244 5.9% 10397 4.9% 2.35 119 ██
Rank 11-20 52 1.3% 4788 2.3% 1.09 55 ████
Rank 1-10 (Low) 14 0.3% 1444 0.7% 0.97 49 █████
TOTAL 4155
21045
3 1.97
Education – Group C
Rank 91-100 (High) 39 4.6% 6970 5.7% 0.56 81 ██
Rank 81-90 110 12.9% 21007 17.1% 0.52 75 ██
Rank 71-80 119 13.9% 17795 14.5% 0.67 96
Rank 61-70 120 14.1% 13430 10.9% 0.89 129 ███
Rank 51-60 94 11.0% 12150 9.9% 0.77 111 █
Rank 41-50 88 10.3% 10279 8.4% 0.86 123 ██
Rank 31-40 103 12.1% 12694 10.3% 0.81 117 ██
Rank 21-30 61 7.1% 9076 7.4% 0.67 97
Rank 11-20 65 7.6% 10093 8.2% 0.64 93 █
Rank 1-10 (Low) 55 6.4% 9428 7.7% 0.58 84 ██
TOTAL 854
12292
2 Sample

TotalSketch Model
Members Base Penetration Z-Score Index
Counts % Counts % % 0 100 200
Segm
ents
Segment 6 997 9.4 18688 1.8 5.3 5.33 510 ██████████ >200
Segment 11 1221 11.5 30654 3.0 4.0 3.98 381 ██████████ >200
Segment 2 420 3.9 13293 1.3 3.2 3.16 302 ██████████ >200
Segment 7 653 6.1 22626 2.2 2.9 2.89 276 ██████████ >200
Segment 15 903 8.5 35231 3.5 2.6 2.56 245 ██████████ >200
Segment 3 363 3.4 14471 1.4 2.5 2.51 240 ██████████ >200
Segment 14 784 7.4 33303 3.3 2.4 2.35 225 ██████████ >200
Segment 9 884 8.3 51180 5.0 1.7 1.73 165 ███████
Segment 10 185 1.7 12422 1.2 1.5 1.49 142 ████
Segment 1 616 5.8 42881 4.2 1.4 1.44 137 ████
Segment 13 291 2.7 23724 2.3 1.2 1.23 117 ██
Segment 4 729 6.8 64449 6.3 1.1 1.13 108 █
Segment 8 1051 9.9 111886 11.0 0.9 0.94 90 █
Segment 0 1273 12.0 266869 26.2 0.5 0.48 46 █████
Segment 5 262 2.5 94111 9.2 0.3 0.28 27 ███████
Segment 12 117 1.1 181701 17.9 0.1 0.06 6 █████████
Total 10643 1,017,489 1.05
Best fit model

• New areas may have a
different socio-
demographic profile to
the existing donor base
• Different motivations
require different
communication
strategies
• Missing all the towns!
Where are they?

Implications for marketing
– Support activity for the current profile
• Post Code initiatives
• More effective targeting
– Promote to new audiences
• Different ways to reach members
• May require new approaches & materials
– Message by prospect type
– Product development
– Regeneration

The case study shown during the
#CHASE15 event has been removed
from the presentation.
Please contact us if you would like
details from this case study.

Any Questions?
@stevethomas393
@purple-vision
www.purple-vision.com
steve.thomas@purple-vision.com