This Excel template is designed to help translation managers calculate savings from moving parts of their translation budget to machine translation post-editing workflow. The table factors in the translation memory, repetitions in the text, and different levels of discount depending on the match rate. With some patience and persistence, you can adjust for differences in translation rates by language.

Example Calculation – Acme Engineering

Download the template here.

In our example calculation, the fictional company Acme pays $0.18 per word to a translation company. With a volume of 2 million source words per year and 5 target languages, Acme spends $1.3 million USD on translations, which they optimize in the course of this project.

They already have translation memory in place, and decided to keep using it for matches above 85%. Next, they invest 3% of their budget into machine translation and pass fuzzy matches through MT with human review. Based on the MT accuracy scores, they negotiate a 30% rate reduction with their supplier for MTed segments. Following the implementation, they start seeing savings, which come up to 27% of the budget. Acme sees an economic benefit of a million dollars over three years.

Calculating for Your Organization

Populate all fields marked in yellow with your information. Your savings will appear in the green fields below.

Translation memory match rates. Machine translation post-editing contracts reduce the cost of translating segments where translation memory is not used. The typical cut-off point is at 85%; anything above is propagated from the translation memory, and anything below uses machine translation. However, some organizations see the benefit in upping the ante and cutting off translation memory at 95% or even 99%.

Word rate. This is your negotiated per-word rate with your suppliers. It often varies per language because of the different costs of living in various countries. To estimate savings in a machine translation program with many variables, divide the costs into 3 tiers and calculate the savings individually per Tier. Example:

Tier 1: Japanese, Korean, Nordic – $0.20 per word

Tier 2: German, French (medium) – $0.15 per word

Tier 3: Spanish, Turkish, Portuguese – $0.09 per word

Share of volume. This is the split of your incoming translation work by discount level for the translation memory fuzzy matching. You can obtain the information on the split from your translation management system (TMS) by adding past analyzes or by tasking your translation company with compiling these numbers. If you don’t have any discounts in place yet, this will be a great starting point to negotiate them.

Current spend. This column represents your translation budget sliced by the translation memory match rate. It will be populated automatically based on a formula including your discounts and words sourced.

Leverage. Indicates which technology pre-translates incoming sentences, translation memory, or MT. As we stated earlier, a typical cut-off for MT is 85%, but if your MT is very accurate, go bolder, and target 95%.

Discount. MT discounts are a hot topic for debate; they are negotiated at various levels in real contracts. Great places to check discount levels are Proz and Smartcat marketplaces, where freelancers often indicate their pricing for translation and post-editing separately. 

Here is our guidance on translation discounts based on MT accuracy:

When negotiating MT discounts, it’s always great to have edit distance and machine translation scores on hand to make sure that the discussion is driven by data.
Spend after MT. The projected change in spending after MT discounts are implemented. Segments populated from the translation memory do not change, but the spending for MTed segments will go down.