Banish Email Overload – how segmentation can improve response, retention and reputation

Steve Thomas7th October 2014

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

67

2

Frequency

Recency

Value

Profiling

Look alike logic

Universe

Your Database

Your Sector

Membership

Profile variables

• Income• Housing Tenure• Spending Power• Education• Occupation• Social Grade

• Age• Children• Household Size• Property Type• Urbanicity• Retail Accessibility

Example profiles - AgeTotalSketch 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 - EducationTotalSketch 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 556020673

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 415521045

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  

Segments    

  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-dem. profile to the existing donorbase

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

Segmentation by analysing behaviour

The King’s Fund

• Charity established in 1897 with the aim of helping London’s voluntary hospitals.

Today its mission has developed to:• ‘…the promotion of health and the

alleviation of sickness for the benefit of the public by working with and for healthcare organisations…’

What do they do?

• Provide guidance and information :– Produce reports – Online resource including extensive library– Events– Developing

• Influencing policy makers– Parliamentary and stakeholder engagement

• Generate additional income– Venue hire

Background

• C. 30k active records held on Integra• Email is the key direct media to

audience• Key information:

– Job title– Organisation– Most purchase behaviour– Email preferences

Marketing issues

• Perception that email is ‘free’ and easy

• Assuming what people initially signed up to is what they really want

• Lack of confidence as to understanding the engagement level of audience

The Challenge

• The targeting had been intuitive to an extent• There was no way to assess performance of

customers• Dependent on email but were we getting the

targeting right?• Potential that some people getting too

much, others too little or very inconsistent communications

• Unsubscribing and blacklisting

The solution

Categorisation of behaviour into 3 dimensions:• Recency• Engagement• Quality

The solution – segmentation criteria

• Recency – how long since latest activity?– 1 month; 3 months; 6months

• Engagement – ‘richness’ of activity– Email opens; clicks; subscriptions; events;

• Quality – ‘ladder’ of known indicators– job title; key event attendance; influence

The solution – 8 segments

Zeros Segment 1

Segment 2Potentials

The solution – a customer journey

first bitersSegment 3

activists Segment 4

keen but stuckSegment 5

7 on sabbaticalSegment 6

7 on holidaySegment 7

super close

66492213

3111

39763301

35118845

7250

Segment 0

The solution – understanding shifting

7 0.1 0.6 1.1 2.1 3.3 9.4 11.7 89.9

6 80 6.7

5 86.1 5.9 3.4

4 0 96.7 4.5 2.4

3 0.4 0.4 2.3 93.6

2 0.1 2.4 90.8 2.1

1 96.6 5.7 2

0 99.4 0.2

0 1 2 3 4 5 6 7

Probabilities of being present in each segment next month depending on presence this month

Zeros Segment 1

Segment 2Potentials

The solution – moves and blocks

first bitersSegment 3

activists Segment 4

keen but stuckSegment 5

7 on sabbaticalSegment 6

7 on holidaySegment 7

super close

66492213

3111

39763301

35118845

7250

Segment 0

The Benefits

Targeting:• Make targeting more appropriate to audience• Avoid scattergun• Protect against unnecessary unsubscribe• Makes internal expectations realistic

Reporting• Health checks –

– Really know theaudience– are the amount of ‘good’ people growing– Gaining warning signs of decline in quality– Have confidence when audience ‘healthy

Implications for marketing strategy

1. Reaching out to new, lapsing and previously undeveloped contacts

2. Making better use of communications spend and achieving better relationships

3. New ways to achieve better results4. Enriching relationships with key

influencers

Banish Email Overload – how segmentation can improve response, retention and reputation

Any Questions?

www.purple-vision.comsteve.thomas@purple-vision.com