rhys jackson - audience segmentation strategy

W H Y M A R K E T E R S N E E D A G R E AT A . S . S .

R H Y S J A C K S O N

I ’ M R H Y S JAC K S O N

• Head of Data and Insight @ RocketMill

• Technical SEO• JavaScript Developer

@ R H Y S _ S E A R C H

I ’ M R H Y S


• Technical SEO• JavaScript Developer• Bassist


I ’ M R H Y S


• Technical SEO• JavaScript Developer• Bassist• Professional Cat enthusiast


W H AT I S A . S . S ?

A. S . S ?


No.

AUDIENCE SEGMENTATION

STRATEGY

“The process of dividing people into homogenous subgroups”


… B A S E D U P O N C R I T E R I O N S U C H A S :

• Product usage• Demographics• Psychographics• Communication

behaviours• Media use


WHAT’S THE BIG DEAL?

M A K E YO U R B U D G E T G O F U RT H E R

Spend more time targeting the right people, at the right time, with the right content.


Ready to buyResearchingDisengaged

W E C A N L E A R N F R O M E M A I L M A R K E T I N G

In 2015, MailChimp found that segmented campaigns achieved:• +14.25% Opens• + 59.05% Clicks• -7.41% Unsubscribes

http://mailchimp.com/resources/research/effects-of-list-segmentation-on-email-marketing-stats/


T H E M A R K E T E R ’ S G U I D E T O A U D I E N C E

S E G M E N TAT I O N

T I P # 1B E WA R E O F AV E RAG E S !

B E WA R E O F AV E RAG E S !

This does not describe your ‘average visit’:



Try Bounced vs. Non-Bounce Sessions instead:



Bounced vs. Non-Bounce Sessions:


Yes

No


Converting Users vs. Non-Converting Users


Low Value




Low ValueProspects




Low ValueProspectsValued Customers

T I P # 2T H E PA R E T O P R I N C I P L E

T H E PA R E T O P R I N C I P L E ( 8 0 - 2 0 R U L E )


80% of revenue comes from 20% of customersWhat if we were to grow the 20%?

0-0.2 0.2-0.4 0.4-0.6 0.6-0.8 0.8-1-10%

0%

10%

20%

30%

40%

50%

60%

5.3%9.8%

15.0%21.5%

48.3%

Distribution of Total Revenue by PercentileClient #1

Percentile

Prop

ortio

n of

Tot

al R

even

ueD O E S I T H O L D T R U E ?


0-0.2 0.2-0.4 0.4-0.6 0.6-0.8 0.8-1-10%

0%

10%

20%

30%

40%

50%

60%

5.3%9.8%

15.0%21.5%

48.3%


Percentile

Prop

ortio

n of

Tot

al R

even

ueD O E S I T H O L D T R U E ?


Almost 50% of total revenue generated by 20% of transactions

D O E S I T H O L D T R U E ?


0-0.2 0.2-0.4 0.4-0.6 0.6-0.8 0.8-1-10%

0%

10%

20%

30%

40%

50%

60%

5.5%12.5% 14.1%

21.0%

47.0%


Percentile

Prop

ortio

n of

Tot

al R

even

ue

D O E S I T H O L D T R U E ?


0-0.2 0.2-0.4 0.4-0.6 0.6-0.8 0.8-10%

10%

20%

30%

40%

50%

60%

3.0%7.8%

13.6%21.4%

54.2%


Percentile

Prop

ortio

n of

Tot

al R

even

ue

T H E 8 0 - 2 0 R U L E


20% of customers generate 80% of the

revenue

T H E 8 0 - 2 0 R U L E


20% of customers generate 80% of the

revenue50%

YO U R M I L E AG E M AY D I F F E R …

Try it out yourself!I’ve made a tool using Google Sheets and the Google Analytics API

• Easy to use• Easy to get

results


Download everything in this talk here: rocketmill.co.uk/brightonseo2016

https://www.rocketmill.co.uk/rocketmill_brightonseo_2016

VA LU E S E G M E N TAT I O N

Use the output of the tool to create value segments in Google AnalyticsCombine with demographics data = WIN.


Low Val Low/Med Val Med/High Val High Val

Age

Gender

T I P # 3FAC E BO O K A U D I E N C E S

FAC E BO O K A U D I E N C E S

Upload 1,000+ customer email addresses, and let Facebook do the hard work for you.


Step #1 - Create custom audience

Step #2 - Analyse custom audience



Find the sweet spot where your audience is over-represented vs. the average (Facebook) population.

Vs.



Facebook has access to information you can’t find in Google Analytics.


Compile information into rich, detailed personas that genuinely inform marketing

strategy.

FAC E BO O K LO O KA L I K E A U D I E N C E S


Use your custom Facebook audience to identify similar users who are not already customers.

Facebook ads are a cost-effective method of seeding content to thousands of highly targeted individuals.

Don’t expect conversion – aim to get users engaged with your brand.

FAC E BO O K LO O KA L I K E A U D I E N C E S


Audience Lookalike Audience

T I P # 4DATA I S E V E RY W H E R E ,

U S E I T

O P E N DATA

Data is not limited to what you can record about your own usersA wealth of third-party data is available to use effectively and ethically to enhance your marketing


O P E N DATA E X A M P L E

A UCL study sponsored by the Office of National Statistics to cluster postcodes using publicly available national census data.

The study identified 72 distinct groups (audiences) within the UK population, each with their own defining characteristics.


The data, methodology, code and results are all open source and can be explored here: http://www.opengeodemographics.com/

http://www.opengeodemographics.com/

C LU S T E R 1 C 1 – “ E S TA B L I S H E D R U RA L”

• 1.5% of UK population• Median age 44 This Subgroup has a higher proportion of people aged 45 and over compared with the parent Group, it has a higher proportion of households with non-dependent children, and of persons providing unpaid care. Households are more likely to live in a caravan than with the parent Group. Part-time working is more prevalent. and those in employment are more likely to be employed in the mining and financial services related industries than with the parent Group.


C LU S T E R 1 C 1 – E S TA B L I S H E D R U RA L

• 1.5% of UK population• Median age 44 This Subgroup has a higher proportion of people aged 45 and over compared with the parent Group, it has a higher proportion of households with non-dependent children, and of persons providing unpaid care. Households are more likely to live in a caravan than with the parent Group. Part-time working is more prevalent. and those in employment are more likely to be employed in the mining and financial services related industries than with the parent Group.


O P E N DATA E X A M P L E


1a1

1a4

1b3

1c3

2a3

2c1

2d1

3a1

3b2

3c2

3d3

4a3

4c1

5a1

5b1

6a1

6a4

6b3

7a2

7b2

7c2

7d2

8a1

8b2

8c3

8d3

0%

1%

2%

3%

4%

5%

Distribution of Population by Cluster

A P P LY I N G T H E DATA


Customer Postcodes

W H AT I S R ?


• Open-source programming language for statistical computing

• Often used with R Studio, a graphical interface to the R programming language

• Huge community support• Used across multiple disciplines

Programming Language

Graphical Interface



1a1

1a4

1b3

1c3

2a3

2c1

2d1

3a1

3b2

3c2

3d3

4a3

4c1

5a1

5b1

6a1

6a4

6b3

7a2

7b2

7c2

7d2

8a1

8b2

8c3

8d3

0%

1%

2%

3%

4%

5%

6%

Distribution of Population by Cluster

National Distribution



1a1

1a4

1b3

1c3

2a3

2c1

2d1

3a1

3b2

3c2

3d3

4a3

4c1

5a1

5b1

6a1

6a4

6b3

7a2

7b2

7c2

7d2

8a1

8b2

8c3

8d3

0%

1%

2%

3%

4%

5%

6%5a2

6b2

Distribution of Population by ClusterCustomers have a high bias towards clusters 5a2 and 6b2

Client DistributionNational Distribution

F I N D I N G S


We identified two distinct demographics that would strongly identify with the client’s brand.

Plotted geographically, patterns emerge.

Cluster 5a2Cluster 6b2

S O W H AT ?

S O W H AT ?

Combine:• Behavioural data from Google Analytics• Social data from Facebook Audiences• Publicly available open data

To construct highly detailed personas…


…Use these highly detailed personas to...


Spend more time targeting the right people, at the right time, with the right content.


T H A N K YO U F O R L I S T E N I N G !


Download everything in this talk here: rocketmill.co.uk/brightonseo2016

http://www.rocketmill.co.uk/brightonseo2016

rhys jackson - audience segmentation strategy

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