emolument default oct 13

Introducing Emolument

Private & Confidential

What is Emolument and how does it work?

The first real-time salary & bonus database aimed at banking, finance, accounting,

IT and legal professionals

Users contribute their own data anonymously and in return receive detailed bonus &

salary statistics for their specific jobs for free

Entries are individually checked by Emolument

Emolument has already received over 13,000 individual contributions across:

2,500+ Institutions

60 countries

©2013 Copyright Emolument Limited


Transparent and accurate vs. Executive Search firms’ surveys

No disclosure of sample sizes per data point

Contributors have no incentive to provide accurate data

Typically use opaque data gathering and validation processes

Live and constantly updated vs. backward looking consultancy reports i.e. accurate salary data in real time

Intuitive to use:

Hundreds of customized reports only one click away

Search on your own terms

Efficient: No need to spend weeks compiling data: it is readily available the minute you sign up

Relevant: Highly detailed taxonomy allows for granular searches and more accurate results

Why is Emolument different from other data providers?

Press

Provide news outlets

with data for their

articles and features

Social Media

Publish weekly articles

which are relevant to

finance professionals

and spread through

social media channels

Targeted

Partnerships

Subscriber mailers,

banner exchanges,

white label surveys,

blog posts...

Twitter (1,300 followers)

LinkedIn Groups

Facebook

Google+

StructuredRetailProducts.com

• Existing contributors are also prompted to

refresh their data annually / when they change

jobs

• Number of validated entries has grown from 200

to 500 per week in recent months and is

constantly growing

Metalbulletin.com

Euromoney.com

Cityjobs.com

Finews.ch

Original Content and

Direct Marketing

Monthly Newsletters: key

Emolument numbers, original

content, competitions

Competitions to attract

contributors

Route-in.com

Original Career Advice

content: (top 10 mistakes to

avoid, Interview

techniques…)


Data gathering process

LinkedIn

Press: City AM,

EfinancialNews..

Advertising

Use specific channels

to target specific

professionals by

location/industry


Data validation process

Contributors enter their bonus & salary details to access their own job report:

• Incentive to provide accurate data or pollute their own report

• Entries are de-correlated from email addresses, therefore anonymous

Pre-emptive measures:

• Email address required for verification /login purposes

• Only 1 contribution per email address is allowed

• Entries can only be updated when contributors are logged in: abuses are easily spotted

Segregation of new data

• New entries are held in a holding pen awaiting processing

• They are not integrated to their contributors’ report

Rectifying obvious errors:

• Erroneous currencies

• Years of experience conflicting with titles

• Employer/industry spelling

• Removal of duplicates

• Matching employers to correct activity/sector

In-depth entry analysis

• Compare with existing data

• Coherence with more junior/senior entries

• Coherence of entry (titles/years of experience)

• Location and Employer Tier factor

Entries are validated only

when ALL aspects of

analysis have been satisfied

• Otherwise, they are terminated

• If in any doubt, the entry is tagged as ‘Quarantined’ to be reviewed by group head

Extra precautionary

measures:

• Consecutive entries are monitored to spot any abnormal patterns and capture any attempts to skew the data

• Development of IP address validation as additional safeguard

Automated system Team of trained experts


How many readings are needed for statistical accuracy?

Examples below are based on two data series from the Emolument database – at 25 readings, the 95% confidence upper and lower bound stabilize

Law of diminishing returns means that past a certain point, accuracy does not improve significantly.

The point is reached relatively quickly due to the highly granular nature of the job taxonomy


The formula below can be used to calculate the required sample size (n) to have a sample mean that is within a certain

proportion (α) of the standard deviation of the true mean with any specified statistical accuracy (Y)

𝑛 =1

(1−𝑌)α2

Statistical accuracy increases quickly as the number of entries rises

20 readings = 95% accuracy that the

sample mean is within 1 standard

deviation of the true mean


For our sample Series 1A with 34 readings (see table below) the sample mean is £50,853:

There is a 97% chance that this is within £6,368 of the true average

There is a 88% chance that this is within £3,184 of the true average

Due to the high granularity of our taxonomy, the standard deviations of our data sets

are low and so our confidence in the sample mean is high

Series 1A Series 2A Series 2B Series 3A

Data Points 34 50 21 44

Mean £50,853 £77,630 £84,298 £125,920

STD £6,368 £14,537 £16,794 £26,950

% of Mean 13% 19% 20% 21%

1/2 STD £3,184 £7,269 £8,397 £13,475

% of Mean 6% 9% 10% 11%

What this means for Emolument data

Subscribers use Emolument to…

Provide visibility on market rates during

recruitment and retention negotiations

Budget the cost of building a new team in a

specific location

Inform its gender equality compensation strategy


Determine local office compensation

Remunerate top performers accordingly while

monitoring the acceptable gap with their peers

Monitor and benchmark its employees’

compensation against peers on an on-going basis

The Management Team


Robert Benson, Chairman and CEO

Robert joined Emolument as Chairman in April 2013. He founded Arete Consulting Limited, a financial

information business with offices in London, New York and Hong Kong. The company was acquired

by Euromoney plc, with Robert staying on in a management and then consultancy role until February

2012. Prior to setting up Arete Consulting in 2001, he worked for 13 years at HSBC in London, where

he was responsible for Global foreign exchange derivatives and retail structured derivatives

businesses. Robert graduated with a BSc(Eng) in Mechanical Engineering from Queen Mary College

London in 1986, before completing an Msc in Management Science and a PhD in Finance from

Imperial College London.

Olivier Beau de Lomenie – Co-founder

Over the last 10 years, Olivier has been involved in a wide variety of new ventures in the online

space. He led the team that built Ocado award winning e-commerce platform, driving both the

product and technology roadmaps. He then co-founded and launched a groundbreaking loan

distribution service, Moneio, as well as Fashionette and MyShowcase, both online retailers. Olivier

started his career as a software engineer at Oracle in Silicon Valley after graduating from Stanford

University with a BS and a MS in Mechanical Engineering in 1995.

The Management Team


Thomas Drewry – Co-founder

Prior to Emolument, he founded and ran financial services executive search firm Veni

Partners. Previously, he worked for the corporate finance departments of Hambros Bank and ABN

Amro. Thomas graduated from the Institut d'Etudes Politiques de Paris (Sciences Po) with a first class

degree in Politics and Economics. He is a dual British and French citizen.

Alice Leguay – Business Development

Alice joined Emolument in March 2013. She began her career with Morgan Stanley High Yield &

Distressed Sales in 2001, based both in Paris and London. In 2008, Alice moved to Cognis Capital

Partners, a credit special situations fund, as Head of Marketing & Investor Relations. More recently,

following an interest in online business development, Alice co-launched the French subsidiary of a

large Rocket Internet GmbH online design store. Alice holds an MA in Modern History & Italian from

Wadham College, Oxford.

Thank you
