ROI and the Value of Machine Translation Presented by Nicholas McMahon Moderated by Paula Shannon July 15, 2009
Aug 20, 2015
ROI and the Value of Machine Translation
Presented by Nicholas McMahon
Moderated by Paula ShannonJuly 15, 2009
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Introductions
Nicholas McMahon
VP, Global Technology Solutions, Lionbridge
Over 13 years experience working with some of the world’s most successful global organizations
Provided strategic and tactical global leadership as a foundational executive for two US divisions of European companies, as well as a VAR business
Paula Shannon
CSO, SVP and General Manager, Lionbridge
More than 23 years experience in the translation and localization industry
Responsible for sites in 26+ countries, driving new services and sustainable solutions, ensuring the continued delivery of innovation and execution excellence to a broad range of Global 1000 customers
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About Lionbridge
Global Scale
4,600 employees 26 countries• Global network of 25,000 translators
Market Leadership
Leader in $14B services industry
• Translation and adaptation of products
and content for international markets
Global Clients
Recurring relationships with 500+
global clients• 80% of revenue comes from recurring clients
• 12 of the Fortune 20 companies are client
Hosted Technology
Web-based language technology
platform• Enhances competitive advantage
• Drives efficiency
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Agenda
Business considerations and ROI Modeling
Top 3 Applications of MT
3 Basic Considerations for MT Evaluation
Enterprise Cost Considerations for MT
Live Q&A
Basic MT Introduction
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Three Types Of Machine Translation
Rules Based
iTranslator
Statistical
Hybrid
Systran Enterprise
Server 7
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Agenda
3 Basic Considerations for MT Evaluation
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MT performance in one language is NOT MT
performance in another language or even in another
language direction
Top Three Considerations for MT
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Top Three Considerations for MT
MT is not an
“every language solution” for any of the MT Engines
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MT at an Enterprise Level
is NOT free
Top Three Considerations for MT
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MT Chat Approaches
Human to human
instant dialogue
Human to semi auto
response based on
key word findings
Top Three Applications of MT:Chat
Evolution
Early adoption – Value focus, very little distinct ‘ROI’
modeling
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Top Three Applications of MT:Support Content
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FAQ MT Approaches
Raw MT output for un-translated content
Tiered Quality approach
Top Three Applications of MT:Support Content
Evolution
Volume Success and reasonable adoption
“Self serve” & “New content” increases user patience
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Top Three Applications of MT:User Guides
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Approaches
TM, MT + Post Editing of content
Considerations
Early Adoption – strong indicators of success
Goal is to MT to be ‘invisible’ in process
Top 3 Applications of MT
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Are you currently considering or using MT in any of these situations?
Considering MT for Chat
Considering MT for FAQs
Considering MT for Volume User Content
Using MT for Chat
Using MT for FAQs
Using MT for Volume User Content
POLLING QUESTION
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Agenda
Enterprise Cost Considerations for MT
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“Start up Costs” Costs
Data Identification/ Corpus creation $0k - $100k
Customization or training of content $5k - $250k
Licensing fees $0k - $200k
Integration fees $10k - $200k
Enterprise Cost Considerations
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“Post Editing” Costs
Training of resources for post editing $5k - $10k
Post editing rates 40% – 65% of word rate approx 10 cents – 16 cents per word
Ongoing customization/ corpus management annually $5k - $200k
Enterprise Cost considerations
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Agenda
Business considerations and ROI Modeling
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User 1 User 2
My Second figure is an animal with a tail and four feet
My role of a young handsome beau is a boy with a tail and 4 feet
What kind of meaning and nice boy?
What do you mean? A handsome boy?
My second figures looks like an animal
My second figure is like an animal
It has four feet and a tail It has four feet and a tail
Oh, I understand and am the 8th I got it, it is my 8th figure
Source: NTT Labs and Kyoto University,
Effects of Machine Translation on Collaborative Work, Dec 06
ROI modeling for MT within Instant communication
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Establishing the value of instant communications
Ability to resolve support requests
Cost per multilingual call handling
Ability to communicate better
Value of increased time to market
ROI modeling for MT within Instant communication
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MT in Chat Benefits Variables
Strong defined reference sets produce better results
Strong defined language use (specific to a domain or industry)
Patience and Flexibility of user (not for complaints!)
Favors Value of ‘currency’ of content versus quality
ROI Modeling for MT within Instant Communication
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Results Defined Data Sets
5 - 25% average success rate (resolution of issue)
User Satisfaction generally 30 –50%
ROI modeling for MT within Instant communication
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ROI Model – Statistical MT considered best match
10 Languages, 100,000 chat support requests
Alignment of data for Corpus Creation: $30k
Product Licenses $100k
Integration of tools $20k
Human system/ process training $10k
System tweaking : $20k
Total Cost $180k
ROI modeling for MT within Instant communication
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Averaged Results for Calculation
10% support cases resolved
Average cost of alternate solution: $15 (FAQ routing/ human Call)
Benefit
100,000 * 10% * $15 $150,000
Less Costs $180,000
Basic Financial ROI Year 1 -$30,000
ROI modeling for MT within Instant communication
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Based on the considered MT chat model do you think the suggested ROI is:
Very Low
Low
About Right
High
Very High
POLLING QUESTION
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Establishing the value of FAQs?
Ability of users to autonomously resolve support requests
Increased Customer Satisfaction
ROI modeling for MT applied to FAQs
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Benefits Variables
Strong defined reference sets produce better results
Quality of source content construction
Expectations of user –disclaimers on quality
Favors un-translated content
Rules based or Hybrid best solutions
Provision of English source links provide additional context
Cultural expectations
Volume of source data available
ROI modeling for MT applied to FAQs
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Results Defined Data Sets
20% - 35% average success rate (resolution of issue)
On average about 8% less than English
User Satisfaction generally 50 – 65%
ROI modeling for MT applied to FAQs
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ROI Model – Hybrid MT Engine considered
10 Languages, 100,000 FAQs docs 450 words per doc.
Alignment of data for Corpus Creation: $50k
Product Licenses $250k
Integration of tools/ solution $60k
Rating, feedback reporting $20k
Human system/ process training $10k
System tweaking : $50k
Total Cost $390k
ROI modeling for MT applied to FAQs
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Averaged Results for Calculation :
25% support cases resolved
Average cost of alternate solution: $35(Human Call Response)
Benefit
100,000* 25% * $35 = $875,000
Less Costs $390000
Basic Financial ROI Year 1 $485,000
ROI modeling for MT applied to FAQs
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Based on the considered MT FAQ model do you think the suggested ROI is:
Very Low
Low
About Right
High
Very High
POLLING QUESTION
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Establishing the value of MT within User Documentation
Improvement in output volume
Reduction in total attracted cost of translation
Increased user content available
ROI Modeling for MT applied to User Guides
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Benefits Variables
Strong defined reference sets produce better results
Quality of source content construction
Knowledge and skills of human resources
Systems and tools integration of MT and TM
Target Language complexity
ROI Modeling for MT applied to User Guides
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Results Defined Data Sets
-10% - +25% productivity improvement
Reduction in total attracted cost per word
ROI Modeling for MT applied to User Guides
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ROI Model – Rules Based MT considered
4 Languages, 2,000,000 words total
Alignment of data for Corpus Creation: $5k
Product Licenses $20k
Human system/ process training $20k
System tweaking : $5k
Total Cost $50k
ROI Modeling for MT applied to User Guides
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Averaged Results
15 % increased in productivity
Average fully loaded per word cost $0.25
(accounting for fuzzy matches)
Benefit
2,000,000 * 15%* 0.25 = $75,750
Less Costs $50,000
Basic Financial ROI Year one $25,750
(about 10% of expected costs)
ROI Modeling for MT applied to User Guides
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Early Adoption, but MT based projects are reporting
MT solutions are dramatically improving
MT source data is growing exponentially
MT ROI Take Away
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Questions?
Nicholas McMahonVP, Global Technology [email protected]
Lionbridgewww.lionbridge.com
http://localization2dot0.lionbridge.com
http://blog.lionbridge.com
http://twitter.com/Lionbridge