The Total Economic Impact™ Of DeepL
Cost Savings And Business Benefits Enabled By DeepL For Multinational Organizations Operating Or Expanding Globally
A Forrester Total Economic Impact™ Study Commissioned By DeepL, March 2024
Accurate and relatable localization of content sets businesses apart from competitors. Forward-thinking companies understand that investing in quality localization services is not only about enhancing the customer experience, but also about improving the employee experience. Companies that have made investments in a secure, reliable, and comprehensive localization service that translates texts using artificial neural networks have witnessed notable returns. These returns include improved customer experience, productivity gains, operational cost savings from increased efficiencies, as well as enhanced ease and efficiency as the organization expanded into new markets.
DeepL offers a secure, AI-powered translation solution that allows firms to tackle the challenges around communication and language barriers. Supporting over 30 languages and accessible across multiple platforms, DeepL’s state-of-the-art encryption features and its own servers enable firms to use data in its platform in a secure and compliant manner.
DeepL commissioned Forrester Consulting to conduct a Total Economic Impact™ (TEI) study and examine the potential return on investment (ROI) enterprises may realize by deploying DeepL.1 The purpose of this study is to provide readers with a framework that allows them to evaluate the potential financial impact of DeepL on their organizations.
Return on investment (ROI)
345%
Net present value (NPV)
€2.31M
To better understand the benefits, costs, and risks associated with this investment, Forrester interviewed four representatives with experience using DeepL. For the purposes of this study, Forrester aggregated the interviewees’ experiences and combined the results into a single composite organization. The composite organization is a multinational organization headquartered in Europe with 10,000 employees and revenue of €2 billion per year and is looking to expand into further geographical regions.
Interviewees said that prior to using DeepL, their organizations used free, internet-based translation tools and native language experts to help with reviewing the results of translations for internal employee documents and communications. However, the lack of confidence in the security and quality of these tools left them with limitations that led to a lot of time spent on translation-related tasks, including cross-checking and reviews. Meanwhile, internal translation needs were a significant burden on their translation team. This impacted their customer experience offerings, as it affected the standard and quality of service that they provided to their end clients and consumers.
After the investment in DeepL, the interviewees had more confidence and trust in the compliance and quality of their translations. They saw less time spent on translation tasks and reviews. Bringing on DeepL created financial cost and efficiency savings and enabled teams to focus on their primary responsibility of creating translations for external audiences, while simultaneously improving communications and morale across the employee base.
Key Findings
Quantified benefits. Three-year, risk-adjusted present value (PV) quantified benefits for the composite organization include:
- Efficiency savings from reduction in internal document translation processing time of €2.8 million. DeepL enables users to translate text and documents in a timely manner. The composite organization’s widespread employees save up to 90% of their time from general translation-related tasks, quality checks, and reviews by using DeepL. Users have such a high confidence in the DeepL translations that they spend less time reviewing its results. This time savings equates to efficiency savings for the composite organization and allows its employees to focus on more productive and value-generating tasks.
- Reduced dependency on legacy translation service team represents cost savings of €189,000. The composite organization mainly uses DeepL for internal communication translations. Additionally, it uses a translation service team for their external legal-, business-, and client-related communications. Furthermore, the composite organization sees a 50% reduction in the number of translation submissions and overall workload for the translation services team while its usage of DeepL increases each year.
Unquantified benefits. Benefits that provide value for the composite organization but are not quantified for this study include:
- Improved service offerings and quality to end customers. DeepL also contributes to a positive experience for the composite organization’s end customers. The composite organization sees a reduction in complaints about preferences in communications and languages. The improvements in employee efficiency, quality in communication, and internal collaboration have a knock-on effect on the quality of service the composite organization provides, including reduced response time. This improves the end-customer experience.
- Ease of use and accessibility. Functionality, such as the glossary, API integration, document upload feature, and web and desktop access, allow users to obtain their translations in minimal time and effort. The quick and easy implementation of the DeepL solution means that the composite organization can start using the DeepL tools immediately with little to no IT resource effort needed to deploy the tool.
- Trust and confidence in the system security and quality of results. The composite organization’s users have a high level of trust and confidence in the DeepL results due to the proven quality and accuracy of its translations. Users have the assurance of working efficiently with minimal meticulous reviews needed for DeepL’s translation results. DeepL’s secure levels of data encryption, non-storage of data, and secure network connection means the composite organization can meet its compliance requirements comfortably and reduce the potential risk of sensitive company content being breached by employees using the free online translation tools.
- Improved employee morale. DeepL improves the correspondence between different regional offices for the composite organization. The composite’s employees can communicate more effectively, which allows better collaboration and improves performance across the organization. DeepL also eliminates a lot of the mundane and heavy-duty manual tasks in reviewing employee translations and allows employees to focus on higher-value tasks. This reduces time spent on translations and reviews, which improves the overall employee experience.
Costs. Three-year, risk-adjusted PV costs for the composite organization include:
- Annual costs of DeepL of €617,000. DeepL offers various annual package licenses based on the number of monthly user licenses and usage amounts. The composite organization adopts DeepL Pro Advanced and purchases 600 licenses in Year 1, gradually expanding to 1,000 in Year 3.
- DeepL implementation costs of €43,000. The composite organization undertakes a six-month pilot which entails a very small level of assessment time and resources. Implementing the web and desktop access version of the DeepL Pro Advanced package requires minimal engineering time of two days.
- Ongoing management costs for DeepL of €10,000. The DeepL tool requires little day-to-day general management. The composite organization requires minimal resource time and effort to manage user accounts and DeepL licenses.
The representative interviews and financial analysis found that a composite organization experiences benefits of €2.98 million over three years versus costs of €669,000, adding up to a net present value (NPV) of €2.31 million and an ROI of 345%.
Key Statistics
-
Return on investment (ROI)
345%
-
Benefits PV
€2.98M
-
Net present value (NPV)
€2.31M
-
Payback
<6 months
Benefits (Three-Year)
Efficiency savings from reduction in internal document translation processing time Cost savings reduced dependency on legacy translation service teamTEI Framework And Methodology
From the information provided in the interviews, Forrester constructed a Total Economic Impact™ framework for those organizations considering an investment in DeepL.
The objective of the framework is to identify the cost, benefit, flexibility, and risk factors that affect the investment decision. Forrester took a multistep approach to evaluate the impact that DeepL can have on an organization.
-
Due Diligence
Interviewed DeepL stakeholders and Forrester analysts to gather data relative to DeepL.
-
Interviews
Interviewed four representatives at organizations using DeepL to obtain data about costs, benefits, and risks.
-
Composite Organization
Designed a composite organization based on characteristics of the interviewees’ organizations.
-
Financial Model Framework
Constructed a financial model representative of the interviews using the TEI methodology and risk-adjusted the financial model based on issues and concerns of the interviewees.
-
Case Study
Employed four fundamental elements of TEI in modeling the investment impact: benefits, costs, flexibility, and risks. Given the increasing sophistication of ROI analyses related to IT investments, Forrester’s TEI methodology provides a complete picture of the total economic impact of purchase decisions. Please see Appendix A for additional information on the TEI methodology.
Disclosures
Readers should be aware of the following:
This study is commissioned by DeepL and delivered by Forrester Consulting. It is not meant to be used as a competitive analysis.
Forrester makes no assumptions as to the potential ROI that other organizations will receive. Forrester strongly advises that readers use their own estimates within the framework provided in the study to determine the appropriateness of an investment in DeepL.
DeepL reviewed and provided feedback to Forrester, but Forrester maintains editorial control over the study and its findings and does not accept changes to the study that contradict Forrester’s findings or obscure the meaning of the study.
DeepL provided the customer names for the interviews but did not participate in the interviews.
Consulting Team:
Pooja Patel
Lara d’Armancourt
Drivers leading to the DeepL investment
Interviews
| Role | Industry | Region | Number Of Users |
|---|---|---|---|
| Lead for software applications | Energy | Global | 1,500 |
| Head of department for applications | Financial services | Global (Using DeepL in German region only) | 9,000 (API Pro) |
| Product manager | Legal services | Benelux | 780 |
| Director of cyber technologies | Pharmaceuticals | Global | 42 |
Key Challenges
Prior to implementing DeepL, the interviewees’ organization went through meticulous, time-consuming reviews of subpar translations. There was no paid solution for everyday use, leading employees to rely on free internet tools for internal communications. These tools posed security risks and delivered suboptimal translation accuracy and quality. For more critical business-related, client-facing, and professional communications, a specific translation team was employed; however, this led to delays and increased costs.
The interviewees noted how their organizations struggled with common challenges, including:
- Time-consuming translation processes. interviewees noted that prior to implementing DeepL, the translation processes were time-consuming, encompassing both the actual execution of translations and the subsequent reviewing phase. Employees faced the challenge of using multiple tools to cross-reference the translation results or relied on one single tool, which often necessitated further review by a language expert to ensure a good translation quality. The lead for software applications at the energy business stated, “I had to put [the translation] through at least three translation tools just to make sure it was accurate because I didn’t want the translation to be misinterpreted.” This multistep approach contributed to a significant allocation of time. The nature of the process, involving coordination among tools and the need for expert oversight, underscored the inefficiencies inherent in the preexisting translation workflow.
- Lack of trust in the quality of translations. Interviewees noted that before implementing DeepL, there was a lack of trust in the quality of translations due to the limitation of the available tools. Previous alternatives — often utilized for daily internal communications — failed to instill confidence due to their inherent shortcomings, such as inaccuracies. This widespread doubt about accurate translations made users question the reliability of more important, client-facing, and professional communications.
- Compliance and security considerations. Interviewees faced compliance and security challenges when their employees handled translations. The reliance on free online tools for daily internal communications posed a security risk, such as leakage of confidential information in the translation process. This raised compliance concerns for the interviewees, especially given the sensitive nature of some of the communications (both internal and external). The interviewees highlighted the importance of addressing security vulnerabilities and compliance risks as an urgent priority. The lead for software applications from an energy company said, “DeepL is approved by our internal governance bodies because it satisfies our data protection policies.”
Investment Objectives
The interviewees’ organizations recognized the need to invest in a single, secure solution that would enable consistent, high-quality, and faster translations. They searched for a solution that could:
- Ensure global market penetration. The interviewees acknowledged the importance of investing in a solution that would support their organizations’ expansion plans and help them cater to a growing international client base with diverse locations.
- Offer a secure environment for translations. Interviewees sought to provide employees with a translation solution with robust data encryption, strong network security, and nonpermanent storage of proprietary data to minimize risks of data breach caused by using free online translation tools.
- Provide easy access and integration. Interviewees recognized the need to invest in a solution that could integrate seamlessly with their organizations’ existing infrastructures (e.g., content creation, product and app development, internal communication tools, project management). Another crucial factor driving their decision was the tool’s accessibility, which made usage effortless across the various applications already in use within their organizations.
- Provide a user-friendly interface. Interviewees noted that a key driver of their decision was DeepL’s user-friendly design, particularly the efficiency-enhancing document upload feature. The emphasis on ease of use was paramount for interviewees as they sought a solution that could be seamlessly integrated into their existing workflow with minimal learning curve. The user-friendly interface meant translations could be made available to a wider range of users within their organizations.
- Focus on speedy translations without compromising quality. The interviewees expressed a need for a solution that could meet their demands for both speed and quality in their translations. A crucial factor driving their decision was DeepL’s ability to provide swift translations without compromising the quality of the translated content. The emphasis on maintaining a high standard of accuracy and reliability, even in expedited translation processes, resonated strongly with all the interviewees.
Composite Organization
Based on the interviews, Forrester constructed a TEI framework, a composite company, and an ROI analysis that illustrates the areas financially affected. The composite organization is representative of the four interviewees, and it is used to present the aggregate financial analysis in the next section. The composite organization has the following characteristics:
Description of composite. The composite organization is a multinational enterprise organization headquartered in Germany and looking to expand into further geographical regions. It therefore has an increasing need for a compliant translation tool mainly to be used for internal communications. The organization has 10,000 employees and has an annual revenue of €2 billion. The composite organization has a professional translation service team that is used for client-facing, business-related communications.
Deployment characteristics. After a request for proposal (RFP) and business case process assessing the need for such solutions and evaluating multiple vendors, the interviewees’ organizations chose DeepL and began deployment.
- The composite organization started by deploying the free web version. Within Year 1, it upgrades to the Pro Advanced Package that includes up to 2,000 glossaries, secure single sign-on (SSO), and the integration of the computer-assisted translation (CAT) tool.
- DeepL was deployed to all 10,000 regional employees in the company with 1,000 Pro Advanced user licenses at the end of three years.
Quantified benefit data as applied to the composite
Total Benefits
| Ref. | Benefit | Year 1 | Year 2 | Year 3 | Total | Present Value |
|---|---|---|---|---|---|---|
| Atr | Efficiency savings from reduction in internal document translation processing time | €874,800 | €1,137,240 | €1,399,680 | €3,411,720 | €2,786,741 |
| Btr | Cost savings from reduced dependency on legacy translation service team | €75,810 | €75,810 | €75,810 | €227,430 | €188,528 |
| Total benefits (risk-adjusted) | $950,610 | $1,213,050 | $1,475,490 | $3,639,150 | $2,975,269 | |
Efficiency Savings From Reduction In Internal Document Translation Processing Time
Evidence and data. Interviewees explained how employees used to spend a significant amount of their time on general translation tasks, such as copying and pasting internal document text, into multiple online translation tools for translation. The employees would then cross-reference and review the translations to ensure the content was not misconstrued and was as close to the native-language interpretation as possible. DeepL delivered a huge reduction in translation processing time for internal communications by eliminating some of these translation and review tasks. The time saved allowed the employees to focus their time and resources on more productive and value-generating tasks.
- The lead for software applications at an energy company said: “I used to spend 10% of my time on translation-related tasks and now probably only spend 1% of time on them. With DeepL, there is no need to search for translation tools and then use multiple tools to cross-reference the translations because there’s so much confidence and trust in DeepL; it’s just a quick skim and quality check (according to my own confidence and knowledge in the language). I’m happy with the translation and quality, especially now with the use of the DeepL shortcuts feature.”
- The product manager of a legal services firm described how they used to spend 2 to 3 hours each month translating a two-to-three-page document. The automation and document upload feature of DeepL meant that translating a two-to-three-page document could now be done in less than 15 minutes. Among the hundreds of employees who occasionally translated documents, this had an overall impact of reducing business-service-processing times by 60% to 70%.
- The head of department for applications at one of the financial services firms also saw time savings of up to 80% to 90%, which could be fully attributed to DeepL. They stated, “It [DeepL] has taken away the time to quality-review translations, because they trust the tool so much that they don’t even bother to review the results thoroughly.”
Modeling and assumptions. Based on the interviews, Forrester assumes the following about the composite organization:
- Before DeepL, an average general employee spends 108 hours over a 48 working week period translating and reviewing internal documents and memoranda.
- The composite organization achieves a 90% reduction in time spent on internal documents and memoranda translation and review tasks after DeepL is implemented.
- A 50% productivity recapture rate is applied to measure the resource productivity the composite reinvests into higher-value or similar-value tasks for knowledge workers.
- The average fully loaded base hourly salary of FTE users at the composite organization is €40. This includes junior to senior level positions.
- The number of FTEs in the composite organization that use DeepL increases each year from 500 in Year 1 to 800 in Year 3.
Risks. The potential risks that can impact the benefit can be both qualitative and quantitative. They can vary depending on:
- The initial time spent on overall translation tasks for processes and due diligence of the review of translations.
- The quantity of the documents required to be translated.
- The priority and importance of the content and documents required for translation.
- The trust and confidence in the translation results.
- The proficiency level of the employee translating and reviewing documents.
- The role and salary of the employee translating and reviewing documents.
- An organization’s growth rate and requirements for translating and reviewing content.
Results. To account for these risks, Forrester adjusted this benefit downward by 10%, yielding a three-year, risk-adjusted total PV (discounted at 10%) of €2.8 million.
Efficiency Savings From Reduction In Internal Document Translation Processing Time
| Ref. | Metric | Source | Year 1 | Year 2 | Year 3 |
|---|---|---|---|---|---|
| A1 | Hours previously spent per FTE on translating and reviewing tasks per document | Interviews | 3 | 3 | 3 |
| A2 | Number of documents to be translated per FTE per month | Interviews | 3 | 3 | 3 |
| A3 | Time spent on translating and review tasks (hours) | A1*A2*12 | 108 | 108 | 108 |
| A4 | Reduction in time on translation tasks due to DeepL | Interviews | 90% | 90% | 90% |
| A5 | Time saving due to DeepL (hours) | A3*A4 | 97 | 97 | 97 |
| A6 | Productivity recapture rate | TEI standard | 50% | 50% | 50% |
| A7 | Fully loaded average hourly rate of FTE user (rounded) | TEI standard | €40 | €40 | €40 |
| A8 | Number of FTE users | Composite | 500 | 650 | 800 |
| At | Efficiency savings from reduction in internal document translation processing time | A5*A6*A7*A8 | €972,000 | €1,263,600 | €1,555,200 |
| Risk adjustment | ↓10% | ||||
| Atr | Efficiency savings from reduction in internal document translation processing time (risk-adjusted) | €874,800 | €1,137,240 | €1,399,680 | |
| Three-year total: €3,411,720 | Three-year present value: €2,786,741 | ||||
Cost Savings From Reduced Dependency On Legacy Translation Service Team
Evidence and data. Interviewees reported that labor costs for the professional translation team dropped, and turnaround times improved. They attributed the cost savings to employees using DeepL to translate less important, client-facing documents; official and business literature; and internal communications. With DeepL, the overall number of documents translated increased, revealing previously unmet demand. Employees were more satisfied because they did not have to wait on the service-level agreements (SLAs) and high costs of a provisioned translation team. Meanwhile, the professional translators could focus on higher-priority and higher-value translations. Using them for complex, nuanced, and culturally sensitive materials was a better use of their expertise and time.
In some instances, interviewees saw at least 50% reduction in the number of documents sent to the professional translation service team that they could attribute to the increased use of DeepL. The head of applications for the financial services firm saw a reduction of 50% in the number of documents sent to the translation team whereas the product manager for the legal services saw a cut of up to 70% in the number of documents sent to their in-house translation service team.
Modeling and assumptions. Based on the interviews, Forrester assumes the following about the composite organization:
- The composite organization’s professional translation service team sees a 50% reduction in the number of documents submitted to the translation team in Year 1, but a higher level of translations overall due to DeepL usage.
- The composite organization hires three FTEs as their professional translation service team with a fully burdened annual salary of €53,200 each.
Risks. The potential risks that can impact the benefit can vary depending on:
- The professional translation service team size and capacity.
- An organization’s purpose and requirements for a professional translation service team.
- The priority of translations.
- An organization’s demand and regional translation coverage.
- The cost of professional translation service teams.
Results. To account for these risks, Forrester adjusted this benefit downward by 5%, yielding a three-year, risk-adjusted total PV (discounted at 10%) of €189,000.
Cost Savings From Reduced Dependency On Legacy Translation Service Team
| Ref. | Metric | Source | Year 1 | Year 2 | Year 3 |
|---|---|---|---|---|---|
| B1 | Percentage of workload (documents submitted) saved for a translation specialist or manager | Interviews | 50% | 50% | 50% |
| B2 | Number of FTEs in dedicated translation team | Composite | 3 | 3 | 3 |
| B3 | Fully loaded average annual salary of translation team members | TEI standard | €53,200 | €53,200 | €53,200 |
| Bt | Cost savings from reduced dependency of legacy translation service team | B1*B2*B3 | €79,800 | €79,800 | €79,800 |
| Risk adjustment | ↓5% | ||||
| Btr | Cost savings from reduced dependency of legacy translation service team (risk-adjusted) | €75,810 | €75,810 | €75,810 | |
| Three-year total: €227,430 | Three-year present value: €188,528 | ||||
Unquantified Benefits
Interviewees mentioned the following additional benefits that their organizations experienced but were not able to quantify:
- Improved service offerings and quality to end customers. DeepL also contributed to a positive experience with the end customers. The lead for software applications at the energy firm noted their organization saw a reduction in customer complaints after they were able to communicate with customers in their preferred language. The increase in employee efficiency and quality of communication and internal collaboration had a knock-on effect on the quality of service that the interviewees’ organizations provided, as well as fewer response delays. This improved end-customer experiences.
- Ease of use and accessibility. Functionality, such as the glossary, API integration, the document upload feature, and web and desktop access, allowed users to obtain their translations with minimal time and effort. The interviewees also noted that the quick and easy implementation of DeepL also meant their organizations could start using DeepL tools immediately with little to no IT resource effort to deploy the tool.
- Trust and confidence in the system security and quality of results. Interviewees commented on the quality and accuracy of DeepL’s translations. Every interviewee praised the high level of confidence and trust DeepL earned. It was only with this assurance in the DeepL solution that users at the interviewees’ organizations allowed themselves to work efficiently with minimal review of the DeepL results. Interviewees reported that the implementation of DeepL meant their employees could move away from the free online translation tools that didn’t meet their company’s IT and data security and compliance criteria. DeepL’s secure levels of data encryption, non-storage of data, and secure network connection meant the interviewees’ organizations could meet their compliance requirements comfortably and reduce the potential risk of sensitive company content being breached by using the free online translation tools.
-
Improved employee morale. Interviewees noted that DeepL improved the correspondence between
different regional offices. Employees communicated more effectively, which
allowed better collaboration and improved performance across the
organization. DeepL also eliminated a lot of the mundane and heavy-duty
manual tasks in reviewing translations that employees used to do and allowed
employees to focus on higher-value tasks. The interviewees’ employee
feedback was very positive due to the results from DeepL and the reduction
in time spent on translations and reviews.
Flexibility
The value of flexibility is unique to each customer. There are multiple scenarios in which a customer might implement DeepL and later realize additional uses and business opportunities, including:
- Additional time and cost savings from using the glossary feature. The universal terminology stored in the glossary provided organizations with a standard and consistency that users could apply, which was relevant to their industry practice or region or was specific to the organization. This meant a further reduction in the dependency on professional industry-specific translation services.
- Faster time to market with improved translation capabilities. The global coverage of language pairs meant that the interviewees’ organizations could localize products and services with ease and operate efficiently as they entered new markets and expanded across new regions.
Flexibility would also be quantified when evaluated as part of a specific project (described in more detail in Appendix A).
Quantified cost data as applied to the composite
Total Costs
| Ref. | Cost | Initial | Year 1 | Year 2 | Year 3 | Total | Present Value |
|---|---|---|---|---|---|---|---|
| Ctr | Annual cost of DeepL | €0 | €188,924 | €251,899 | €314,874 | €755,698 | €616,500 |
| Dtr | DeepL implementation costs | €42,525 | €0 | €0 | €0 | €42,525 | €42,525 |
| Etr | Ongoing management cost for DeepL | €0 | €3,906 | €3,906 | €3,906 | €11,718 | €9,714 |
| Total costs (risk-adjusted) | $42,525 | $192,830 | $255,805 | $318,780 | $809,941 | $668,739 | |
Annual Cost Of DeepL
Evidence and data. Interviewees noted that DeepL has various packages to accommodate a company’s expanding usage and translation requirements as it grows. The annual cost of DeepL is influenced by the evolution in the type of package and the number of licenses an organization agrees to take on each year. Interviewees reported that as their organizations expanded, the demand for DeepL’s services also increased, prompting adjustments in the number of licenses that they required. It’s worth highlighting that all interviewees noted their organizations utilized up to 80% of their allocated licenses at any given time.
Modeling and assumptions. Based on the interviews, Forrester assumes the following about the composite organization:
- The monthly DeepL per-user license maintains a consistent annual cost of €24.99 over the three-year period.
- The average number of licenses increases from 600 in Year 1 to 1,000 in Year 3.
Risks. The potential risks that can impact the cost can vary depending on:
- The amount of translation workload of an organization, which affects the number of licenses required by a company.
- The organization’s regional evolution over time.
Results. To account for these risks, Forrester adjusted this cost upward by 5%, yielding a three-year, risk-adjusted total PV (discounted at 10%) of €617,000.
Annual Cost Of DeepL
| Ref. | Metric | Source | Initial | Year 1 | Year 2 | Year 3 |
|---|---|---|---|---|---|---|
| C1 | Cost of DeepL Pro Advanced License (monthly per user) | Interviews | €24.99 | €24.99 | €24.99 | |
| C2 | Average number of licenses | Composite | 600 | 800 | 1,000 | |
| Ct | Annual cost of DeepL | C1*C2*12 | €179,928 | €239,904 | €299,880 | |
| Risk adjustment | ↑5% | |||||
| Ctr | Annual cost of DeepL (risk-adjusted) | €0 | €188,924 | €251,899 | €314,874 | |
| Three-year total: $755,698 | Three-year present value: $616,500 | |||||
DeepL Implementation Costs
Evidence and data. The approach to DeepL’s deployment varied for each interviewees’ organization, contingent upon their chosen solution version. Interviewees reported that the planning and preparation for tenders and approvals could take up to one year. The head of department for applications at the financial services firm stated, “It was a year-long fight to get [DeepL] on board but it was important for us to have a tool that protects our confidential data.” All interviewees undertook a six-month proof of concept (POC) to help them assess the value and justify the business case to their senior executives. Legal, IT, and security teams along with technical document evaluations, which involve assessing the accuracy, completeness, and usability of technical materials to ensure they meet standards and facilitate successful implementation and operation of the technology, took two months to complete, with 15% of participants contributing their time. Board approval added two weeks to the timeline. The subsequent implementation afterward, which included single sign-on (SSO) and online setup, required only two days from one engineer with an extra day for communication efforts.
The director of cyber technologies at a pharmaceutical business described the whole implementation process as seamless, stating, “I think that [the seamless implementation process] speaks highly of DeepL, being able to work with our development team so easily.”
Modeling and assumptions. Based on the interviews, Forrester assumes the following about the composite organization:
- The composite organization undergoes a six-month POC to help it assess the DeepL tool.
- The monthly cost of the implementation team, which comprises of six members from various departments, each spending approximately 15% of assessing the DeepL solution over the project period.
- The composite organization requires one engineer at a fully-burdened day rate of €300 to implement DeepL over two days.
Risks. The potential risks that can impact the cost can vary depending on:
- Variation in implementation teams’ and IT engineers’ salary.
- An organization’s implementation requirements and complexity of the system landscape.
- The choice of DeepL package undertaken (API implementation may have longer timelines).
- An organization’s IT and developer capacity and skill set.
- An organization’s business and budget assessment processes.
Results. To account for these risks, Forrester adjusted this cost upward by 5%, yielding a three-year, risk-adjusted total PV (discounted at 10%) of €43,000.
DeepL Implementation Costs
| Ref. | Metric | Source | Initial | Year 1 | Year 2 | Year 3 |
|---|---|---|---|---|---|---|
| D1 | Months for POC assessment | Interviews | 6 | |||
| D2 | Monthly cost of team for POC assessment | Composite | €6,650 | |||
| D3 | Implementation time (days) | Interviews | 2 | |||
| D4 | Fully loaded average daily rate of engineer (rounded) | TEI standard | € 300 | |||
| Dt | DeepL implementation costs | (D1*D2)+(D3*D4) | €40,500 | €0 | €0 | €0 |
| Risk adjustment | ↑5% | |||||
| Dtr | DeepL implementation costs (risk-adjusted) | €42,525 | €0 | €0 | €0 | |
| Three-year total: €42,525 | Three-year present value: €42,525 | |||||
Ongoing Management Cost For DeepL
Evidence and data. Interviewees explained that the ongoing management of the DeepL solution was minimal after all components were live and in a stable state. The product manager of a legal services firm said: “At most, the day-to-day management is no more than 15 minutes a day, every day, and that is because of rehires. Every month, accounts need to be deleted and reassigned. That’s the main task.”
Modeling and assumptions. Based on the interviews, Forrester assumes the following about the composite organization:
- On average, 30 minutes per day over 48 working weeks are spent on general management and user-account management for DeepL licenses.
- The administration and application manager performing these tasks has an average base annual salary of €45,000, plus a 1.33X salary modifier to account for additional costs that the employer incurs (e.g., benefits, insurance).
Risks. The potential risks that can impact the cost can vary depending on:
- The variation in hourly rates for an organization’s applications and admin manager.
- The consideration of differences in administration time, strategy, and procedures of an organization.
- Assessment of the reassignments of the users, accounts, and teams requiring DeepL access.
Results. To account for these risks, Forrester adjusted this cost upward by 5%, yielding a three-year, risk-adjusted total PV (discounted at 10%) of €10,000.
Ongoing Management Cost For DeepL
| Ref. | Metric | Source | Initial | Year 1 | Year 2 | Year 3 |
|---|---|---|---|---|---|---|
| E1 | Ongoing management time involving DeepL (hours) | Interviews | 120 | 120 | 120 | |
| E2 | Fully loaded average hourly rate of FTE to manage DeepL (rounded) | TEI standard | € 31 | € 31 | € 31 | |
| Et | Ongoing management cost for DeepL | E1*E2 | €3,720 | €3,720 | €3,720 | |
| Risk adjustment | ↑5% | |||||
| Etr | Ongoing management cost for DeepL (risk-adjusted) | €0 | €3,906 | €3,906 | €3,906 | |
| Three-year total: €11,718 | Three-year present value: €9,714 | |||||
Consolidated Three-Year, Risk-Adjusted Metrics
Cash Flow Chart (Risk-Adjusted)
Total costs Total benefits Cumulative net benefits-
The financial results calculated in the Benefits and Costs sections can be used to determine the ROI, NPV, and payback period for the composite organization’s investment. Forrester assumes a yearly discount rate of 10% for this analysis.
These risk-adjusted ROI, NPV, and payback period values are determined by applying risk-adjustment factors to the unadjusted results in each Benefit and Cost section.
Cash Flow Analysis (Risk-Adjusted)
| Initial | Year 1 | Year 2 | Year 3 | Total | Present Value | |
|---|---|---|---|---|---|---|
| Total costs | (€42,525) | (€192,830) | (€255,805) | (€318,780) | (€809,941) | (€668,739) |
| Total benefits | €0 | €950,610 | €1,213,050 | €1,475,490 | €3,639,150 | €2,975,269 |
| Net benefits | (€42,525) | €757,780 | €957,245 | €1,156,710 | €2,829,209 | €2,306,530 |
| ROI | 345% | |||||
| Payback | <6 months | |||||
Appendix A: Total Economic Impact
Total Economic Impact is a methodology developed by Forrester Research that enhances a company’s technology decision-making processes and assists vendors in communicating the value proposition of their products and services to clients. The TEI methodology helps companies demonstrate, justify, and realize the tangible value of IT initiatives to both senior management and other key business stakeholders.
Total Economic Impact Approach
-
Benefits represent the value delivered to the business by the product. The TEI methodology places equal weight on the measure of benefits and the measure of costs, allowing for a full examination of the effect of the technology on the entire organization.
-
Costs consider all expenses necessary to deliver the proposed value, or benefits, of the product. The cost category within TEI captures incremental costs over the existing environment for ongoing costs associated with the solution.
-
Flexibility represents the strategic value that can be obtained for some future additional investment building on top of the initial investment already made. Having the ability to capture that benefit has a PV that can be estimated.
-
Risks measure the uncertainty of benefit and cost estimates given: 1) the likelihood that estimates will meet original projections and 2) the likelihood that estimates will be tracked over time. TEI risk factors are based on “triangular distribution.”
The initial investment column contains costs incurred at “time 0” or at the beginning of Year 1 that are not discounted. All other cash flows are discounted using the discount rate at the end of the year. PV calculations are calculated for each total cost and benefit estimate. NPV calculations in the summary tables are the sum of the initial investment and the discounted cash flows in each year. Sums and present value calculations of the Total Benefits, Total Costs, and Cash Flow tables may not exactly add up, as some rounding may occur.
Appendix B: Supplemental Material
The Future Of B2B Content Localization, Forrester Research, Inc., January 20, 2023.
Make Digital Employee Experience The Centerpiece Of Your Digital Workplace Strategy, Forrester Research, Inc., November 15, 2022.
The Six-Step Roadmap To Scalable B2B Content Localization, Forrester Research, Inc., December 1, 2023.
Kathleen Pierce, The Future Of Localization Is Here: Are You Ready?, Forrester Blogs.
Introducing The B2B Localization Prioritization Tool, Forrester Research, Inc., December 1, 2023.
Appendix C: Endnotes
1 Total Economic Impact is a methodology developed by Forrester Research that enhances a company’s technology decision-making processes and assists vendors in communicating the value proposition of their products and services to clients. The TEI methodology helps companies demonstrate, justify, and realize the tangible value of IT initiatives to both senior management and other key business stakeholders.
PDF
