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How to Cut Costs in a Way that Drives Translators Mad

As translation rates are plummeting, translation agencies are looking for ways to cut costs. Even though translators, myself included, might be uncomfortable with some of the new ideas, it’s better to understand, and be prepared to deal with, them rather than be caught off guard. This post will help you understand internal fuzzy matches (IFMs) as one of such cost-cutting methods.


This method has been around for quite a while, but it’s not a mainstream thing. Normally, when you analyze a text against a TM, you get new words, fuzzy matches, and repetitions. What the traditional TM analysis doesn’t reveal is those segments that are very similar, but because the TM doesn’t have any similar segments, they don’t come up as fuzzy matches. If you enable the “Internal fuzzy matches” function (that’s what they’re called), your CAT tool will create a fake TM during the analysis, commit each segment to this TM as if it were already translated, and compare all next segments with it, thereby matching all segments to each other. Those similar segments will come up as fuzzy matches as a result.

We were asked to provide IFM discounts just a couple of times. I think some agencies don’t like to use this method, and some aren’t aware of it. For instance, Dave Grunwald of translation services company GTS just recently wrote about discovering this method for himself. He shared how he couldn’t figure out why analyzing two seemingly similar texts didn’t yield as much repetitions as he expected. In fact, those were internal fuzzy matches, and as a result of his search, he realized how he can use IFMs as a cost-cutting method.


The biggest problem about this approach is of course the negative impact it can have on translators’ bottom line. Translators charge the same rate for each word in a given text. Some words like those in IFMs are easy to translate. Others are challenging and can take a lot of time and effort. “Easy” words offset “challenging” words, making it possible to strike a balance in terms of the time it takes to translate a text on average. If “easy” words are gone due to IFM discounts, however, this balance will be broken.

Another problem is that many translators have difficulty translating internal fuzzy matches. Instead of reusing the previous translation, they often translate it from scratch. If they provide a discount on IFMs, they might actually start losing money because their performance with IFMs will be basically the same as with new words, but the rate will be lower.


IFMs help avoid breaking a project into batches. When an agency has, say, two similar manuals for translation, the natural desire is to have one manual translated, create a TM, and get fuzzy matches, i.e. discounts from a translator, for the second manual. Who loses if instead of this scenario, the agency sends both manuals at once with IFMs discounted? Nobody. In fact, both parties win. The price is the same because IFMs are discounted in one way or another. But both parties win from reduced management costs because it’s easier to handle a project as a single batch. And translator additionally wins since working on two manuals at once is easier than doing them one by one. This scenario is a win-win and might be seen as a win-lose (“lose” for translator) only compared to a scenario where a client sends two manuals at once without discounting IFMs, and translator has IFMs all to himself/herself.

In essence, IFMs are a form of a volume discount and can replace it. Many translators give volume discounts because they know it’s generally easier to translate the remaining 30% of a text after translating 70% of this text. What are most of those remaining 30% of words? Yes, they are IFMs.

What’s Next?

As a translator, am I ready to accept a discount for IFMs willingly? No, mainly because I don’t want to lose the balance between “easy” and “challenging” words. I’d prefer that this method didn’t exist in the same way that I’d prefer translators didn’t have to charge low rates and translate poorly as a result. But cost-cutting is what translation industry is all about now, and we might need to adjust to this new reality.

What’s your opinion about IFMs? Please share in the comments.


  • Thanks for the interesting article Roman,
    I stumbled across your post while researching different methods of calculating word counts (AnyCount, memoQ, etc.), fuzzy discounts, and tag weights (memoQ). It would seem that ‘internal fuzzy matches’ go by the name of ‘homogeneity’ in the latest versions of memoQ. I agree that it is very important for us translators to be aware of all of the latest developments in the world of calculating rates and prices. Particularly the larger agencies are very aware of how they count words and fuzzies, and so should we be.
    See e.g.:
    ‘Homogeneity: Analysis against the segments within the selected scope is called homogeneity analysis. This is one of memoQ’s power features. Check this check box to emulate building a translation memory during translation, and see the savings that will result from the internal similarities within the project. Using homogeneity, you are able to see the benefits of your future contribution – i.e. the contribution while you will be translating – to the translation memory. You are also able to give a much better estimation of your resources to be spent on translation than without homogeneity. If you use the analysis to give a quotation, always look for the aggregate results as they reflect the real productivity gain through using memoQ.’ (

  • Hello Michael,
    Yes, “Homogeneity” seems to be exactly the same thing as internal fuzzy matches. It’s definitely a good thing to see it in project statistics in order to be able to assess the volume of work better, but I am not sure if it is a valid basis for a discount. Thank you for your comment.
    Best regards,

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About the Author

Roman Mironov
Roman Mironov
CEO & Founder

As the founder of Velior, Roman has had the privilege of being able to turn his passion for languages into a business. He has over 15 years of experience in the translation industry. Roman has helped dozens of clients increase sales by making their products appealing for speakers of other languages.