the science of social - an experiment in influence

8/13/2019 The Science of Social - An Experiment in Influence

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An Experiment in Influence

THESCIENCE OFSOCIAL


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Contents

Introduction ................................................................... 04

Hypothesis ..................................................................... 05

Key Findings .................................................................. 06

Approach ....................................................................... 07

Results............................................................................ 08

Conclusion ..................................................................... 14

Appendix:

Experimental Design.................................................... 17

Methodology ................................................................. 20

Real World Action ......................................................... 22

Contributors .................................................................. 23


4/2404

Introduction

Anyone whos been close to the marketing industry over

the last few years is fascinated with the evolving view of

social.

Once it was all about Facebook fans.

Then in 2009, as Facebook understandably wanted to

reflect more accurately the relationship that people have

with brands, fans became Likes.

And then in its collective wisdom, the UK marketing

industry declared that Its not all about Likes.

As an industry, we havent been great at articulating the

contribution that social media can and does make to a

business.

Social is changing marketing, and other areas of business,

at a pace that sometimes masks our ability to demonstrate

its true value. Theres an imperative to building the

business case, to evidence the impact social can have on

our clients business.

Whilst we agree that the opportunities presented by social

media go far beyond Likes, the question still remains -

what are they worth?

The problem, as we see it, is that there is a fundamental

disparity between the intellectual and financial investment

made in social media and the body of effectiveness to

support it.

Aegis Media has therefore conducted a research study to

at least begin to bridge this gap.

Unlike a lot of the research done in this area, which

focusses on the value of an individual fan to a business,

our study focusses on their indirect social value.

We are not yet in a position to share a definitive set of

results, but rather to post a substantiated hypothesis that

will warrant further investigation. So, we hope what we

are about to share is interesting, stimulating, thought-

provoking and, above all, practical.


5/2405

with existing customers, how can you make that show

up in an econometric study designed to track acquisition

performance?

Side-stepping the ROI question isnt good enough. Clients

should still expect a return on the not insubstantial

investment they put into their social platforms. We wanted

to move forward the thinking on creating robust and

measurable results for a businesses social media spend,

while considering the idea of how welcome brands are on

the social platform.

INFLUENCE OF GROUP BEHAVIOUR

Our insights into human behavior have become more

sophisticated in recent years. Their application to the

world of marketing is more prevalent, thanks to the

popularisation of theories developed by behavioural

scientists.

From this field has emerged the familiar notion of

social proof. This is a phenomenon whereby people

are unconsciously affected by the behaviour of others.

Sometimes these are referred to as social norms, or

herding effects.

Social networks can often feel like concentrated group

behavior in a way that echoes the herding effects we

observe in the real world. As peer-to-peer networks, they

can act as echo chambers that magnify ideas or behaviours

at scale, quickly, as people follow the majority behavior.

We wanted to test whether people are unconsciously

influenced by group behavior online, in a similar way to

how they are offline.

THE MEASUREMENT IMPERATIVE

Clients want to know that a fan is worth the money they

have paid to acquire them. Its equally natural that as

an industry we want to prove a fan will become a more

valuable customer.

Throughout Social Media Week, and at meetings held

between clients and agencies during almost any other

week, the value of a fan remains a hot topic. Often the

phrase ROI will be mentioned in the same breath. Return

On Investment is a financial metric that is concerned with

the transactional value an individual on a brands social

channel may represent. This is an established metric that

we are all familiar with and works in social the same way

as with any other marketing activity.

But social is not necessarily like any other marketing,

however, and not all definitions of value are transactional.

We wanted to test the value of social in a manner consistent

with the way people themselves use social.

PEER TO PEER

Social networks have never belonged to brands. They

belong to people. Users first thought or action is not

always to connect with brands, but to connect with each

other.

Understandably, brands want be involved. A good

marketing team will follow its customers and attempt to

add value to what their customers find important.

It has not always been easy to demonstrably deliver value

to business through social media. If social channels are

an opportunity to develop and maintain good relationships

Our Starting Hypothesis

The research outlined in this report, including experimental design

and data analysis, was planned and run in collaboration with Jon

Jachimowicsz and Joe Gladstone, academic researchers from the

University of Cambridge Judge Business School. There were 3 reasons

for commissioning this study.


6/2406

Summary of Key Findings

Our findings suggest that Likes generate an

unconscious and immediate effect, similar to

any number of cues in the real world. We can

conclude that human behaviour offline translates

into social networking behaviour online and

furthermore, that appropriate interventions on

brand pages can yield measurable effects.

High Facebook users were more likely to be more

positive respondents overall. This demonstrates the

importance of social media as a realm for creating

positive brand perceptions and suggests that brands

are a welcome presence on the Social Media platform.

A high number of Likes do help improve brand

perceptions. There is a statistically significant

relationship between the number of Likes respondents

believed the brand to have, and how positively they

answered the questions relating to the brand.

However, while this positive trend is very steep asLikes increase from a Low to a Medium level (0-2000

Likes), it tails off significantly between Medium and

High (10,000+ Likes). Further Likes above the tens

of thousands do not seem to deliver a proportionate

level of increased positive perception. More study is

required to confirm the exact thresholds for consumer

herding effects and explore their meaning for brands.


7/2407

ASHWOOD FURNISHINGS

- beautifully designed affordable homeware

Established in 1864 in the UK, Ashwood Furnishings

is a family-owned business crafting a range of

beautifully designed products at affordable prices

to enhance your home. Our designs have beenfeatured in magazines such as Elle Decoration and

Living etc.

We are passionate about making quality British

designs affordable for many. Our teams of

designers are hard at work producing everything

from dinnerware and glasses to tables and sofas

and not a flat-pack in sight.

As we approach our 150th anniversary, were

looking to expand our business beyond our British

shores and bring our beautiful designs to the US

through your local stores and online.

Welcome to Ashwood.

Rather than running a field experiment including real brand

Facebook pages, we chose the boundaries of a controlled

lab experiment, run on Amazon Mechanical Turk. The

greater control offered by the experiment is traded off

against external validity (the extent to which the results

are applicable outside the experiment in the real world).

Despite this, we attempted to make the experiment as

realistic as possible, by creating a fictional premise that

we were conducting market research. This allowed us

to understand the cause and effect relationship between

Facebook Likes and a variety of brand perception variables.

The study created a new brand named Ashwood

Furnishings along with a storyline about their expansion

from the UK to the US on the occasion of their 150th

anniversary. Participants were asked to provide their views

on the brand based on its Facebook page, to help it make

the transition to the US market.

est

. 1 8 6 4Figure 1: Background Information and Logo of Ashwood

Furnishings provided to every participant

Approach


8/2408

Results Highlights

The experiment substantiated our hypothesis: It showed a statistically significant

relationship between the number of fans respondents believed the brand to

have, and how positively they answered the questions relating to the brand.

1. FACEBOOK LIKES IMPACT ON BRAND PERCEPTION

Facebook Likes influence consumers in their responses to the questions in

significant ways. If we split the conditions into small, medium and high numbers

of Likes, then we see clear patterns of higher Likes leading to higher positiveendorsements on the brands on many different characteristics.

3.70

3.80

4.00

4.10

3.90

4.20

4.30

1.00 2.00 3.00

MeanIwouldconsiderpurchasing

productsfromA

shFurn

Condition variable into small, medium and high

Figure 2: Chart showing overall correlation between number of Likes and positive perceptions of brand


9/2409

Results Highlights

2. PERCEIVED POPULARITY IMPROVES PERCEPTION

ON THE STATEMENTS THAT MATTER

The most significant relationships existed with six of

the statements that would perhaps matter the most to

marketing teams. These statements represent important

signals from customers. We can see them as sitting along

a spectrum of intensity towards the brand, suggesting that

a brands popularity on Facebook can impact on a users

intent towards a brand at quite a deep level.

Interest -I have a strong interest in this brand

Trust -I trust this brand

Consideration - I would consider purchasing this brand

Preference -This brand would be my first choice

Advocacy -I would encourage friends to buy this brand

Value - This brand could become very important to me

3.70

3.80

4.00

4.10

3.90

4.20

4.30

1.00 2.00 3.00

MeanIwouldconsiderpu

rchasing

productsfromA

shFurn


3.00

3.20

3.40

3.60

3.80

1.00 2.00 3.00

MeanLO1.

Icouldseemyse

lfbeingloyal


-0.05000

0.05000

0.10000

0.15000

0.20000

1.00 2.00 3.00

MeanZscore(mbe_

perceived_

quality)


0.00000

2.80

3.00

3.20

3.40

1.00 2.00 3.00

MeanAshFurncouldbecomeveryimportanttome


Figure 3: Charts showing correlation between number of Likes and specific positive brand perceptions


10/2410

Results Highlights

3. THE RELATIONSHIP BETWEEN FACEBOOK LIKES

AND BRAND PERCEPTION MAY BE SUBJECT TO

DIMINISHING RETURNS

With so many questions and quite a wide distribution of

data, we wanted to see if consolidating the data would

demonstrate a clearer trend at population level.

We aggregated the six statements that showed the

most significant correlations with Facebook Likes, and

we grouped the 12 different factors into three clusters

of respondents who were shown Low, Medium and High

numbers of Likes.

This helps us see a clear overall trend. Facebook Likes do

help improve brand perceptions. While this trend is very

steep as Likes increase from a Low to a Medium level, it

tails off significantly between Medium and High.

At a population level, the study showed that more Likes

didnt deliver a proportionate level of increased positive

perception.

A key aim of the experiment was to find out the level of

diminishing returns to Facebook Likes (to decide when

investing is worthwhile, and when it is not). Although clear

differences exist between Low Likes and High Likes (see

Figure 4) the data does not provide a clear picture for

all the conditions in-between, meaning answering this

question is difficult.

This difficulty arose because participants responses tothe questions were highly variable, meaning participants

generally gave very different responses to the questions,

regardless of how many Likes they were exposed to. It

is apparent that consumers have subjective and diverse

opinions on brands based on Facebook Likes. This

variability is to be somewhat expected in a between-

subjects design.

Running a multi-varient analysis of varience with all

dependant variables (i.e. questions) and condition

as IV (i.e. the 12 different categories of number of

Likes) showed significant difference for condition at

p


11/2411

Results Highlights

MeanAshFurncouldbecome

veryimportanttome

4.00

3.60

3.40

3.20

3.00

1 2 1 54 8 73 1 70 0 1 3 00 0 2 20 00 7 80 00 89 00 0 2 . 4m 2 .8 m 8 .6 m 9 .2 m

Condition

Error Bars: 95% CI

In this example, there is a clear difference between lower

numbers of Likes (left) and higher (right).

However, in-between these two is a lot of variability and

few decisive patterns.

Due to the messiness of the data from individual questions,

we decided to run a factor analysis (reducing the amount

of data into fewer super-categories). A factor analysis is

a statistical technique used to compress a wide range

of data points into fewer data points with as little loss in

information as possible. In a factor analysis, dependent

variables (i.e. what was measured) that share variability.This means that we tried to group those questions together

that changed similarly as the number of Facebook Likes

changed. Factor analysis is especially useful in a dataset

that is overall rather messy, especially as it can show

linkages between questions as well as disassociations

between one set of questions and another. Moreover, the

question-categories created usually have a lower overall

variability, which facilitates follow-on statistical analysis .

The first factor, which included the participants responses

about Loyalty, Preference for Ashwood Furnishings and

Purchasing Intention, explained over 30% of the overall

variance, and so is a good proxy for the overall results.

This factor overall shows a significant difference across

the condition p=0.036. So, even when we reduce the

variability considerably, Facebook Likes are still important.

When we graphically represent Factor 1, across low,

medium and high Facebook Likes, we see that there is a

big jump from small (thousands or less) to medium (tens ofthousands). After that there is a much smaller difference

between tens of thousands and millions.

EXAMPLE ASHWOOD FURNISHINGS COULD

BECOME VERY IMPORTANT TO ME

These graphs show the general trend at a detailed level

of how the largest jump in consumers responses to the

brand are usually at the beginning of the Likes distribution(0-2000), and dont consistently jump thereafter.

Figure 5: Graph showing perceptions of Loyalty to

brand according to number of page Likes


12/2412

Results Highlights

MeanAW

2.Icanquickly

recallthe

symbolorlogo

3.501.00


3.55

3.65

3.70

2.00 3.00

3.60

3.75

MeanIwou

ldconsiderpurchasing

produ

ctsfromAshFurn

5.00

4.50

4.00

3.50

3.001 2 1 54 8 73 1 70 0 1 3 00 0 22 00 0 78 00 0 89 00 0 2 . 4m 2 .8 m 8 .6 m 9 .2 m

Condition

Error Bars: 95% CI

Mean

ItrushAshFurn

3.60

3.90

3.30

3.00

2.70

2.40

2.10

1 2 1 54 8 73 1 70 0 1 30 00 22 00 0 78 00 0 89 00 0 2 . 4m 2 .8 m 8 .6 m 9 .2 m

Condition

Error Bars: 95% CI

4.20

MeanAshFurnisanhonestbrand

4.00

3.80

3.60

3.40

3.20

3.001 2 1 54 8 73 1 70 0 1 3 00 0 22 00 0 78 00 0 89 00 0 2 . 4m 2 .8 m 8 .6 m 9 .2 m

Condition

Error Bars: 95% CI

Figure 6: Graphs showing perceptions of other positive qualities of brand according to number of page Likes

4. FACEBOOK LIKES ACT AS UNCONSCIOUS CUES

FOR USERS

We know from existing behavioural science studies that

people are influenced by thousands of unconscious

cues every day. Our brains use shortcuts also known

as heuristics that make manageable the numerous

decisions we constantly make.

Our experiment was designed to test whether Facebook

Likes might act as one of these cues.

Since respondents knew nothing of the brand other

than the short biography they had been shown, Likes

are the only variable that might be affecting the results.

(SeeFigure 9, in Appendix)

We also surveyed respondents immediately after showing

them the test stimulus.

This suggests that Likes generate an unconscious and

immediate effect, similar to any number of cues in the

real world. We can conclude that offline human behaviour

translates into online social networking behaviour in direct

ways that, through appropriate interventions, can be

manipulated to yield measurable effects.


13/2413

Results Highlights

5. DIFFERENT TYPES OF USER SHOWED DIFFERENT

EFFECTS

One of the most clear variances from the overall population

level trend was seen when we compared people who use

Facebook frequently and for long periods (High users) with

people who are less active on the platform (Low users).

High Facebook users were more likely to be more positive

respondents overall. Interestingly, they were also more

likely to be more positively affected by high numbers of

Likes than Low users.

This graph summarises the relationship. High numbers of

Likes influence high-users of Facebook more. Low users

are not any more influenced once you get to Medium

number of Likes on a page.

High users are less likely to be subject to the diminishing

returns relationship we showed in finding 3.

3.30

Low

Facebook Likes

3.40

3.60

3.70

Medium High

3.50

3.80

Facebook Use

Low

High

Figure 7: Graph showing level of Facebook usage

against overall positive perceptions of brand


14/2414

INFLUENCING OFFLINE BEHAVIOURS

We can conclude from this lab-style experiment that Facebook Likes matter. The

results demonstrate that the phenomenon of herding, described in behavioural

economics for offline behaviour, also applies in the case of Facebook Likes to

online behaviour. This raises the question of what other facets of behaviour

change expertise may be applied to the online realm. What markers may be

found for these and how they might form part of a new system for online

marketing that has a robust methodology for measuring the effect of marketing

interventions.

A next phase to this research will identify the influence on actual behaviours,

such as signing up to an email list and actual purchasing of goods, rather than

only the users perceptions of a given brand after exposure to their Facebook

page. Ideally, it would not be a lab-style experiment but a test in the public

realm and even using a known brand. This will rule out the problems with

the lab conditions themselves affecting the results, and have more exciting

implications for the use of behavioural thinking in the world of social media.

THE BUSINESS CASE

Now that we know Facebook Likes contribute in a measurable, significant way to

the herding effects that create perceptions around a brand, it is time to further

interrogate this behavioural journey.

We always guessed that a Low number of Likes will create poorer brand

perceptions compared to a High number, but we now know that there are

diminishing returns over all. We have also discovered that it is a different picture

for High versus Low Facebook users. For example, for Low users, it seems that

there is a really interesting moment when the positive effect of Facebook Likes

levels off after a certain number of Likes higher than medium is reached. That

magic number - the minimum number of Likes needed to influence Low social

media users to think more positively about your brand - is a potentially exciting

and useful new marketing product. The process by which this magic number is

arrived at will require further research that takes into account sector, brand and

what we want people to do after they have been exposed to the page.

On a business level, we can conclude that it is worthwhile for brands to spend

money on promoting social media pages early in their marketing plan and, once

a certain threshold of Likes has been reached, not continuing to invest heavily.

We consider this study to be a step towards being able to formally advise brands

on what the business case is for allocating and apportioning their social media

spend; what they stand to get from it and where there is space for robust return

versus innovation in how they market their products.

Conclusion


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FUTURE RESEARCH

We wish to take a broader view of social media use and other factors that

affect unconscious behaviour while browsing online to flesh out this enquiry.

Likes are just part of the picture and there is a question mark over authenticity

here; Facebook Likes can be purchased in bulk and manipulated by marketers

with ease if they are willing to spend that money. What about the quality of

Facebook Likes? What is the effect when a brand is recommended by a friend

or you can see that a number of your Facebook friends already Like a given

brand? The role of messenger is likely to be powerful and will have a different

resonance for better known and less known brands. For which of these will

there a stronger positive effect on brand perceptions? We can easily guess the

outcome here, but how can we measure when in the brands journey the pace

of change diminishes? What is the story here for different types of user?

The effects of marketing interventions on High users of social media are likely

to be very different to Low users and what types of social media audience are

most effectively targeted with different styles of marketing intervention is a

worthwhile area for future research.

We look forward to partnering with curious marketers in exploring and expanding

this new learning, and invite further discussion with a purpose to conduct a

series of real world brand specific trials over the forthcoming months. For

anyone interested in exploring this with us, please contact us via the details at

the back of this paper.

Conclusion


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Appendix: Experimental Design

The study employed 600 participants in the United States.

Each participant was given the exact same information: the

brand, logo, story, and Facebook page were all identical.

The only difference was in the number of Likes they were

presented with, 12 categories ranging from 12 to 9.2

million Likes. This approach allowed us to ensure any

differences found between groups were purely because

on the number of Likes they saw, and not anything else.

Participants were then asked a series of questions

related to the brand, based on what they had seen from

the Facebook page, including questions such as I would

consider purchasing products from Ashwood Furnishings

and I would say positive things about Ashwood Furnishings

to other people.

Figure 9: Examples of Facebook pages with alteration in the number of Likes according to group condition

12 154 873 1700 13000 22000 78000 89000 2.4m 2.8m 8.6m 9.2m

Figure 8: Number of Likes presented to participants per condition


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REAL LIFE PURCHASE BEHAVIOUR

The study attempted to move beyond just asking about

peoples intentions to buy and attitudes towards the

brand, but to also see if we could find differences in their

real life behaviour following the study.

Participants were asked to enter their email address

in order to receive a voucher code for a discount off

Ashwood Furnishings when it arrived in the US market.

The number of people entering their email addresses was

recorded. Specially designed emails were then sent out

to participants 48 hours later, containing unique links

which could be tracked via Google Analytics, to measure

differences in the number of people who clicked through

to the link.

To our valued future customers!

Many thanks for recently signing up to receive

voucher codes from Ashwood Furnishings.

This is such an important time for our company -

having begun building furniture as a family business

all the way back in 1864, we are so excited at the

chance to bring our unique style along with our

affordable prices to the US market, and we are

sure our range of home ware and furnishings will

be as popular here as they are in the UK.

Please click on the link below for your unique

voucher code for money off your first order with us!

Its our way of saying thank you for your interest.

We very much hope to see you in our stores or

online soon.

Kind Regards,

The Ashwood Furnishings Team!

DEBRIEF

Thank you for your interest in Ashwood

Furnishings. We are sorry to let you know thatAshwood Furnishings is not a real company. It

was set up as part of an academic study into the

impact of Facebook Likes on consumer decision

making. We greatly appreciate you taking part in

our study on m-turk, and this email was a follow

up to that study. We used the offer of a voucher

code to assess how interested participants were

in the company Facebook page you viewed. If

you have any concerns or questions regarding

the study, please email****************** at*******.********@googlemail.com.


19/2419


RIGOROUS SELECTION PROCESS

Every effort was made to ensure that the participants we were using on

Amazon Mechanical Turk were reliable and relevant for our study. This selection

process was done in two stages.

Firstly, various criteria were set before people could sign up to the study. They

had to be: based in the US, have a full or part-time job, and be experienced with

tasks on Amazon Mechanical Turk. Moreover, we preferred English to be their

first language (587/600 met this condition).

Secondly, we had to make sure that participants were completing the

study properly, and giving it proper care and consideration. Therefore, we

had stringent checks in place to filter out those who did not engage fully.

Participants were asked three questions:

How many comments were on the Facebook page?

What was the anniversary date being celebrated by Ashwood Furnishings?

How many Likes were there on the previous page?

The criteria for using a participants responses was that they had to answer 2out of 3 questions correct (the possibility of answering these two by chance

is less than 5%). Following this process of filtering, we were left with 383

responses to analyse. The attrition rate was similar across conditions (no

systematic differences), with participants per condition ranged from 25 to 43.


20/2420

Appendix: Methodology

HOW DID WE SELECT THE QUESTIONS FOR THE STUDY?

We consulted the following study as it presents a reasonable

proxy for what we are interested in investigating:

Developing and validating a multidimensional consumer-

based brand equity scale.

Boonghee Yooa and Naveen Donthub (2001). Journal of

Business Research.

http://people.hofstra.edu/Boonghee_Yoo/papers/2001_

JBR_Brand_Equity_Scale.pdf

This is an influential paper, cited over 780 times. The

scope of the study was as follows:

A multi-step study to develop and validate a

multidimensional consumer-based brand equity scale (MBE)

drawn from Aakers and Kellers conceptualizations of

brand equity. A total of 1530 American, Korean American,

and Korean participants evaluated 12 brands from three

product categories (athletic shoes, film for cameras,

and color television sets). Multi-step psychometric tests

demonstrate that the new brand equity scale is reliable,

valid, parsimonious, and generalizable across several

cultures and product categories.

The fact the questions have already been validated (i.e.

shown to work) in different countries is helpful to us,

as we are hoping to demonstrate that our study has

meaning outside the US where our study was conducted.

The present research went above and beyond Yooa &

Donthub (2001), who used these questions in various

sub-categories, and tailored their questionnaire for our

particular research agenda.

Figure 10: Effects Sizes and individual significance tests


21/2421

Appendix: Methodology

Other questions were also asked but which did not show significant results.

EFFECT SIZES PARTIAL ETA SQUARED

Were the effects sizes we found large or small?

Small: >0.01; Medium: >0.06; Large: >0.14

In Cohens terminology, a small effect size is one in which there is a real effect

-- i.e., something is really happening in the world -- but which you can only seethrough careful study. A large effect size is an effect which is big enough, and/

or consistent enough, that you may be able to see it with the naked eye, one

which is very substantial.

Several of the significant questions were around medium effect sizes. This is

likely to have occurred as our study was a lab study. In real life, consumers

would spend longer on Facebook pages, take them more seriously because

theyre real, and wed expect the effects therefore to be larger in field trials.

WHAT ABOUT THE QUESTIONS THAT WERE NOT SIGNIFICANT?

The reasoning behind the non-significant results:

Our choice of master Amazon workers who had already completed at least

1000 HITs. Hence, we didnt get many nave participants

The tiny difference in things we changed. Out of an entire page array and text

presentation, we changed just the number of Likes, a tiny detail

The huge variability in participants, due to prior dispositions to Facebook,

preferences for furniture sales etc.


22/2422

Appendix: Real World Action

Our call to action described under Approach, where we sent voucher codes

to all participants and asked them to click through to a link, was not successful.

Ideally, we would have seen more numbers of click throughs in conditions with

higher Facebook Likes.

The problem was that too few participants clicked through to our link (e.g. 5

in 47 in Condition 2). With so few people clicking through, it is not possible to

make meaningful comparisons between the groups.

One explanation for this may have been that participants knew they were taking

part in a lab experiment, despite our efforts to mask it as market research, and

so provided fake email addresses to us, or ignored emails related to the task.

Another explanation is that our emails could have been filtered into peoples

junk folders (though in pre-testing we made efforts to make sure this wouldnt

happen). However, perhaps no such explanations are needed when we consider

that many direct marketing campaigns have response rates below 5%.

It is always risky to try to bridge real life with a laboratory experiment. In

future experiments, we should make sure the test uses real consumers, either

through a partnering website, or directly through social media.


23/2423

Contributors

With special thanks to

Joe Gladstone PhD Researcher in Behavioural Economics,

University of Cambridge Judge Business School

Jon Jachimowicsz PhD Researcher in Behavioural Economics,

University of Cambridge Judge Business School

Mark Cross Senior Partner, Equal

Stephen Donajgrodzki Senior Partner, Equal

Ed Hartigan Head of Social, iProspect

James Caig Head of Social Strategy, Isobar

Nick Siantonas Behavioural Strategist, Isobar

Ian Edwards Managing Partner - Head of Strategy, Vizeum

Richard Morris Managing Director, Vizeum


24/24

Connections that Count

vizeum.co.uk | @vizeumUK

Bringing people and brands

together, like never before.

isobar.com | @isobar

Driving Digital Performance

iprospect.co.uk | @iprospectUK

Experts in behaviour change

equaluk.com

CONTACT

Mandy Rayment |Head of Communications

T:+44 (0) 20 7550 3253

E:[email protected]

the science of social - an experiment in influence

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