Minimum Viable Localization Product (MVLP)

Before we talk about MVLP it is important to distinguish the difference between localized product and localization product. When we think of product localization, we think of the sum of things that makes the product feel and sound as if it was written for the target market. Localized product in itself contains linguistic localization process, cultural adaptation, local functionality adjustments, SEO, industry terminology polish. In other words, a localized product is the final client-facing version of content.

Localization product, on the other hand, is a concept that can be compared to a software product in many ways. It is helpful to think about it in terms of DevOps practices and how product is manipulated and iterated in sprints until it reaches its final form. Each version of localization product presents different added value that is defined by localization sprint objectives.

What is MLVP

MLVP or minimum viable localization product is a term that describes the initial stage of localization product. Apart from human workflow orchestration, MVLP contains no human input. It is a workflow that consists of a raw MT output, followed by a trained AI post-editing process.

Why Do We Need a Minimum Viable Localization Product

MVLP serves as the base working product of localization. Having been machine translated and AI post-edited, it is a fully functional placeholder content that, while not customer ready, can be used for gathering data on user behaviour and traffic analysis. In addition, it provides an option for product developers to perform A/B testing on features in local markets. That further allows for localization to no longer be a plug-in procedure at the end of the product development cycle, but rather start providing value much earlier in the pipeline.

Imagine the future where products are not just localized linguistically, but where localization is embedded into the fabric of the product itself, with different markets having different priority features based on hard, local-sourced data. This kind of product development has been done before, but historically it has been very expensive. MVLP allows these tests to be done basically for no added cost.

Similar Ideas or Notions

MVLP is a term that derives from the concept of Minimum Viable Product or MVP in DevOps methodology, due to its procedural similarities. DevOps focuses on creating products quickly and efficiently. It’s principles of constant collaboration, automation, continuous improvement, customer-centric action and creating with end in mind, all point to an infrastructure that is always evolving and always able to present a certain version of the product. MVP is often the first time a client sees a product coming to life. LangOps has played with these concepts for a good while, but I felt like it never had a starting point. MVLP seems to be naturally taking up this space.

How to Start Utilizing MLVP in Your Workflows

In terms of workflow, MVLP is not difficult to create. The creativity and value, however, lies in what kinds of data we can gather when putting MVLP to work. First thing that I think we should all thank my good friend Bruno Bitter for is his Process Innovation Award winning concept of introducing Google Analytics into a localization workflow. It essentially allows gathering of website traffic data on which sites or languages the MVLP has been applied to. It is an excellent and conscious way of deciding what to localize first based on which sites are visited the most. Create volume thresholds and stagger your spending to work smarter.

Second thing that comes to mind is assisting product design teams during A/B testing phases of product development. Localized A/B tests of product features add an extra layer of data that just was not there before. Users have already been used to beta versions not being perfect in English. Having localized betas can break down barriers and reservations of users, not only allowing you to gather local insights, but also enhancing the way your users interact with a product in a more personal way.

Finally, having instant, integrated localization at your fingertips for basically no cost allows for accelerated deployment of all supporting documentation that comes with the product (knowledge base, instructional videos, Wiki, etc.). There is no longer need for waiting for localization efforts to be done. Everything can be deployed at once and further iterated based on data the MLVP provides on every aspect.

Similarily to DevOps, LangOps is a methodology that drives thinking and process optimization. In the same manner, LangOps require a starting point and MVLP is a good contestant to be exactly that. An instantly available local content that can be put to use and iterated further. Localization sprints then have different objectives. They introduce human post-editing, SEO optimization, industry specific proofreading, further polishes until localization product becomes a localized product. The beauty of it all is the ability to present a product to your local audience at every stage, with its functionality adjusted based on data and actual need. More information on the topic can be found here.