People want a seamless peer-to-peer experience. They also want to have instant answers to questions about projects such as progress, costs, quality and results. We don’t want layers of manual procedures and complexity to keep us from what we need.
If a content creator has a question for their English to Swedish proofreader, then they should be able to contact them directly. And it should be as straightforward as writing a message on WhatsApp.
If you want to know your language spend for the month, then you should be able to see that at the click of a button. What’s more, you should be able to quickly get a thorough breakdown of your spend to help forecast future spend.
Is there a tight deadline on a more complex project involving multiple processes? Say you’re translating a white paper into eight languages and the deadline is approaching. You want to be able to see exactly at what stage each of those translations is. Is a translation stuck in the validation stage on the desk of a colleague? Is there a bottleneck in DTP? Is Swedish taking a bit longer than the other languages? I want that information to be easily accessible.
Shorter delivery times, higher quality and lower costs
Finally, buyers of language services want better content, with shorter delivery times and at a lower cost.
Businesses realise the difficulty here. They know that quality language services require experienced translators, proofreaders, copywriters, DTP engineers and voice-over artists. They also understand that the more you work with someone, the better they get, but the costs will increase by utilising them. Should they use less experienced people who might be cheaper but have longer delivery times and lower quality?
The competing demands of speed, quality and cost are something LSPs can only solve with technology.
We need to eliminate manual tasks, give customers more control, build connected platforms and rethink pricing models to optimise how we deliver value to brands. This change, by the way, is very similar to the CMS example provided at the beginning of this blog.
Fortunately, we are well on the way to delivering on these goals.
As mentioned, NMT is helping us to translate more than was previously possible. Post-editing of custom-trained NMT also reduces the cost of translating content, while maintaining quality. NMT is a massive innovation for our industry and will soon be incorporated into all language workflows. The custom-trained aspect, where the machine learning algorithm uses existing translations to understand how a particular brand speaks, has delivered a step change in machine translation quality.
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Another vital improvement is the integration of systems so that users can order translation and localisation from their digital platform, for example. The integration of translation with CMS, DAM and PIM platforms dramatically reduces the complexity and time needed for creating multilingual content. If you’re still sending product information in Excel sheets for translation via email, then you desperately need to look into integration options for your PIM as the ROI is considerable.
We also need to get better at handing over the automated tools we have developed to businesses. Global brands want to bring the management of language workflows in-house, and using global content platforms, such as LanguageWire, are how they can do just that.