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Basis of reporting

The information presented in this report is collected from various online and offline, internal and external resources. In many cases, interviews with partners and employees took place in order to write the text. For the data, a variety of systems were used, including but not limited to our SAP systems and specific project data.

This information should be read in conjunction with the basis of preparation, describing the scope, materiality, boundaries, reliability & completeness and reporting process (Annex 2).

Strategic KPIs

In calculating the value of our strategic KPIs, we have applied the following data definitions:

Regulatory quality: % of regulatory reviews (reviews issued by PCAOB, AFM, NBA, ADR, and Inspectie OCW), of which the results were communicated in the reporting year that are satisfactory as a percentage of all regulatory reviews issued in the reporting year.

Carbon emission reduction: the % by which our mobility and housing related carbon emissions were increased or decreased compared to comparable emissions in the base year 2018/2019 (32,662 tonnes). This year was chosen as reference year by DTTL for the global WorldClimate programme.

NPS at C-level among strategic clients: the net promotor score as determined during the client service assessment conversations , in which we regard a score of 9 or higher (on a 1-10 scale) as active promotors minus detractors ( a 6 or lower). Where clients indicate to be an active promotor and is considering a score between 8 and 9, the independent interviewer will seek confirmation with the client. When confirmed, these clients are also categorised as active promotors.

Client satisfaction (engagements): the average satisfaction score received from clients on post-engagement questionnaires sent out by the businesses during the financial year.

# employer of choice in relevant ranking: ranking in the benchmark study performed by Universum in the Netherlands for the universities: University of Amsterdam, VU University Amsterdam, University of Groningen, Erasmus University Rotterdam, University of Tilburg, Technical University Eindhoven, Delft University of Technology for Business / Commercial studies and for STEM profiles.

Talent data

Unless otherwise indicated, our Talent data excludes interns as inclusion would distort insights provided by the indicators used (e.g. on important areas such as % of employees receiving regular performance & career development reviews, and employee turnover). Also, only active employees on the payroll (excluding contractors/externals/WIA/WAO, among others) are included in the talent data. Lastly, average FTE and headcount is used when calculating talent indicators.

Female positions in leadership roles: # women in Supervisory Board, Executive Board and Executive Committee divided by total membership of Supervisory Board, Executive Board and Executive Committee as per the end of our fiscal year (May 31).

Sickness leave: total number of sick days divided by the total number of scheduled days.

Female partners as % of total partners: # of female partners divided by total # of partners (headcount on May 31).

Gender pay equity: The ratio of basic salary (fixed, monthly average paid to an employee) and remuneration of women to men per job level as per December 1.

Percentage of employees receiving regular performance and career development reviews: eligible employees for snapshot divided by the number of employees that have at least one requested and completed snapshot excluding partners and Interns. Our interns (e.g. thesis intern or internships) are not part of our regular performance cycle, as the main purpose for these functions are education and learning, rather than performance.

Average hours of training per year per employee: The average hours of internal and external training that our employee has undertaken during the reporting period, broken down by gender, jobgrade and per business. External trainings are training followed for example at external universities. Internal trainings are training followed through the learning management system SABA. Examples of internal trainings are anti-corruption trainings and independence policy awareness trainings.

Talent engagement score: average weighted score for talent engagement as measured by the Deloitte Talent Survey that is performed throughout the year. The Deloitte Talent Survey is sent out to all employees (excluding contractors) every quarter, provided that the employee has been employed for at least 180 days (otherwise the employees will receive a New Joiners Survey) and no departure date is known.

Ecological footprint

Our Ecological footprint includes the emissions related to our mobility, housing and purchased goods and services. For the allocation into the different scopes, we follow the Greenhouse Gas Protocol Corporate Accounting and Reporting Standard, using the operational control approach.

For buildings, we are tennants of our offices, meaning that we are for a large extend dependent on our landlord for the energy contracts. Where we do our own purchasing, GHG emissions resulting from energy usage are reported in our scopes 1 and 2, whereas those where we cannot influence the energy sources we report emissions in scope 3.

For the conversion of natural gas consumption to MJ, we used the conversion factor from the GasUnie: caloric value per m3 is 35,17 MJ.

For lease case, the data included are obtained from our fleet supplier. As we do not separately monitor business trips, commuting and privat use of lease cars, our data includes all these elements. Our lease emissions are reported under Scope 1 and 2, seeing that we do our own purchasing of fuels and electricity.

For conversion of these fuels to CO emissions we used the most up to date emission factors based on :

  • Petrol: 1 litre equals 2.821 kilogrammes CO2

  • Diesel: 1 litre equals 3.256 kilogrammes CO2

  • LPG: 1 litre equals 1.802 kilogrammes CO2

  • Electricity: 1 kWh equals 0.337 kilogrammes CO2

Total kilometres travelled by plane are obtained from our travel agents. It is standing policy that we use the most recent conversion factors. Hence, for the calculation of the related CO2 emissions, we have used the 2022 conversion factors as provided by DEFRA ( using a classification that distinguishes economy, premium economy, business class and first class and categorises air travel in domestic, short-haul international and long-haul international flights. For the various subgroups, the following CO2 conversions are used:

  • Domestic average: 0.24587 kg CO /kilometre per passenger

  • Short-haul international average: 0.15353 kg CO /kilometre per passenger

  • Short-haul international economy class: 0.15102 kg CO /kilometre per passenger

  • Short-haul international business class: 0.22652 kg CO /kilometre per passenger

  • Long-haul international average: 0.19309 kg CO /kilometre per passenger

  • Long-haul international economy class: 0.14787 kg CO /kilometre per passenger

  • Long-haul international premium economy class: 0.23659 CO /kilometre per passenger

  • Long-haul international business class: 0.42882 kg CO /kilometre per passenger

  • Long-haul-international first class: 0.59147 kg CO /kilometre per passenger

The total kilometres travelled by train are obtained from our supplier Nederlandse Spoorwegen. For thecalculation of related CO2 emissions for national rail, we used a conversion factor of 0.003 kg CO2 / kilometre per passenger as published by Nederlandse Spoorwegen. 

For international rail, the total kilometres travelled are provided by our travel agency. For the calculation of related CO2 emissions for international rail, we used a conversion factor of 0.0046 kg CO2 / kilometre per passenger for intracontinental rail travel between different countries and of 0.0346 kg CO2 / kilometre per passenger for intracontinental rail travel within the same country (taking an international train for a domestic journey).

To calculate the carbon emissions caused by hotel stays by Deloitte partners and employees, we have multiplied the total number of hotel nights with 29.7 kg CO2. This conversion factor has been developed by DTTL on the basis of the Cornell University Hotel Benchmarking tool.


Incident reporting includes the number of reported occurrences as registered in NAVEX, broken down into individual incident categories. Validation and follow-up on incidents reported lies with our ethics team and leader.

Number of hours spent on DIF projects: the KPI represents hours spent by Deloitte during the period June 1, 2022 to May 31, 2023 on pro bono work under the Deloitte Impact Foundation. These hours result from projects we perform at a large variety of societal initiatives in order to give back to society. Projects are evaluated based on their impact on society, which can either be initiated bottom-up (employees proposing a societal impact project) or top-down, including initiatives from Deloitte Global such as under our WorldClass programme or supporting Financial Health.