How to Improve SaaS Metrics with Localization

Localization is a global growth enabler for any software as a service (SaaS) business. It’s easy to see why. Customers strongly prefer to use software in their native language. Often, companies try to make a business case by calculating a theoretical return on investment (ROI) for localization. If you work at a SaaS company, I suggest you reframe the discussion. Don’t get lost in a conversation about ROI. Instead, focus on which SaaS metrics you can impact with localization.

Use SaaS Metrics to Drive Growth with Localization

Localization is a growth lever. It helps unlock global growth. So why use SaaS metrics to help inform decisions about localization? A few reasons:

  1. ROI alone won’t paint an accurate picture. SaaS companies and their metrics are complex. This is why so many localization ROI exercises fail. They are too simplistic. They usually look something like, “If we spend X to localize Y, how much new revenue (Z) can we obtain?” The answer to Z is not dependent just on whether you localize, but on many other factors, including sales, marketing, and logistics. And, investment at a SaaS company always comes at an opportunity cost. You can always be investing in other things to drive growth.
  2. Revenue isn’t the only thing that matters. Looking at revenue potential is fine, but in a SaaS business, that’s not the only metric that matters. The type of growth that is desirable in SaaS depends on many other metrics and how they relate to each other. New growth is great! But not at all costs. Retaining that revenue is incredibly important for a SaaS business. The customer acquisition cost (CAC), and their lifetime value (LTV), also matter hugely. And, how these metrics play alongside net new annual recurring revenue (NN ARR), install base (IB) growth, and other key SaaS metrics matters too.
  3. You’ll already have everyone’s buy-in. At a SaaS company, your core SaaS metrics and unit economics are what you live and breathe every day. No matter what else is going on amidst the flurry of growth at your business, these are the metrics you’ll always come back to. Your entire company and your operating system are built around these metrics. Your strategies and priorities will often be focused on improving these metrics. They are your anchor, your true North.

So, if you want to get attention for localization and use it to help your company go global, take the discussion beyond total addressable market (TAM). Look beyond just ROI. Center people’s attention on how localization can impact your core SaaS metrics instead.

How to Structure Your Data

If you want to drive focused growth at a SaaS company, you’ll need to clarify how localization can impact your top-line revenue. (This is true whether you’re a localization manager or someone driving international growth from within another function.)

To do this, I suggest using a data structure that covers six primary data cuts:

  • Global. All countries globally.
  • Domestic. Your HQ country.
  • International. All countries except your HQ country.
  • English. All countries whose primary language is English.
  • Non-English. All countries whose primary language isn’t English.
  • Localized. All countries whose languages you support other than English.

You can actually use these metrics at any global technology company, even if its not a SaaS company.

Standardize on Country to Prevent Data Problems Later

A common thread in all of the data cuts? They depend on country.

Country is the ultimate base component to use when you’re creating your data structure. All other cuts are roll-ups that hinge on the country base unit. Once you have this in place, you’ll be able to slice and dice your data in a number of different ways. As you scale, you cannot properly analyze your SaaS metrics for international purposes without doing so at the country level. Here’s why:

  • Country trends hide within geo-level data. A geo like “EMEA” is going to mask trends related to a single country in your data. Knowing what % of revenue comes from each country is critical, even in early days, so that you can fully understand what your data is saying and how to make improvements. Nearly every time I’ve seen an “unexpected trend” in a geo-level metric, it’s just one or two countries driving it. Sometimes, it’s just one customer in one country that can shift a metric for an entire geo. Knowing which country is driving a geo-level trend can help you pinpoint the problem.
  • Language data needs to map to country. Once you have country in place, you can assign a “primary language” field to country. (You can later do a “secondary language” field if you want to get fancy, to show there is some uplift from a market for which some of the population speaks a language).
  • Currency matters too. You’ll eventually need to map currencies to country fields, and you really cannot do this unless you have country as a primary field in all of your data. You might not be accepting many currencies today, but you’ll likely want to later as you grow and get bigger.

If there is one critical thing you need to do early on to enable smart decisions for international growth later on in a SaaS business, it’s this:

Make sure your company is using standard country fields across all your data sources. Doing so early will ensure international data integrity. This enables you to go faster later. Build in country-level reporting as early as you can.

What happens if you don’t get a standard country list in place early on? You’ll have to pay humans to either write code or manually de-dupe country fields, re-mapping them to geos and sub-geos, and fixing a problem that has grown a thousand-fold. Trust me, you don’t want to be the one trying to consolidate country fields that say “the United Kingdom,” “united kingdom,” “United Kingdom,” “UK,” “U.K.,” “Great Britain” and countless other variations, times 200 countries, many years from now.

Notes on Base Language

I’m assuming most readers of this blog will have a base language of English. However, if your base language isn’t English, but rather a language of another large market, like say, German, simply swap “English” for “German” and “Non-English” for “Non-German,” for example.

If you’re in a smaller market where English is not your HQ country language but you’re primarily targeting English-speaking markets (for example, you’re a SaaS company in Finland), I suggest you still orient the data around English, since that is likely where most of your revenue will come from.

Applying the Cuts to SaaS Metrics

Once you’ve built out this data structure, you can apply the cuts to any SaaS metrics you choose. Here’s an example of what this would look like in the context of one of the most commonly used SaaS metrics, monthly recurring revenue (MRR):

Let’s explain the example in the chart and why these cuts matter in the MRR context above a little further:

  • International + domestic = global. Your international/domestic split is a two-way split of your global MRR. This helps you spot whether your international MRR is having an outsized impact on global MRR compared to domestic MRR.
  • English + non-English = global. Your English/non-English MRR is also a two-way split of your global revenue. Your English MRR includes your domestic MRR in this example. This helps you spot whether there are any trends between non-English markets and English-speaking markets.
  • Localized revenue = a % of your non-English and Global revenue. Non-English revenue is, in turn, a % of your global revenue. This can help you understand how the revenue coming from markets for which you have localized are driving improvements on the non-English revenue and thus, the international and global totals. I also sometimes refer to this as “supported language revenue,” because the depth of localization can vary from one language to another.

Note: If you’re new to this topic and want more details on SaaS metrics in general, I highly recommend reading SaaS Metrics 2.0 from Matrix Partners.

Other Data Cuts to Consider

There are a few other cuts you can consider, especially as revenue grows. Once you get into more markets and are more advanced, evaluate whether you need the following:

  • Developed + Emerging = Global. I’ve also used splits for Developed/Non-English vs. Developed/English, and Emerging/Non-English vs. Emerging/English as two other cuts that are useful in some cases. Whether or not you need that cut really depends on several factors. First, it depends on how many markets you are targeting simultaneously. Second, it depends on their share of your IB. Third, it depends on whether you actually have any differentiated strategies (pricing, packaging, go-to-market) for Emerging versus Developed markets.
  • International/English + International/Non-English = International. This will basically allow you to take English out of the equation to see how trends in these two different types of markets might be affecting your international revenue, no matter the sub-geo.
  • Domestic/English + Domestic/Non-English = Domestic. This matters more if your HQ is in a market that covers multiple languages. For example, let’s say you’re a Canadian company with your product in French and English. Or, a US-based company selling to Spanish and English speakers. You might want to consider a domestic language split like this one.
  • Localized + Non-localized = Non-English. You can also divide up the revenue from non-English markets into the ones for which you localize as compared to the ones for which you don’t. Sometimes, this can help you spot opportunities where localization might be a deciding factor between two similar markets that can help influence a given metric.
  • EMEA + APAC + LatAm + NAM = Global. This is the top-level “Geo” structure most SaaS companies use to create sales territories to begin with. These are your traditional top-level international business metrics. You’ll want to look at these too! But, while these are helpful, don’t make the mistake of relying exclusively on these for analysis of your SaaS metrics. If you do, you can’t identify opportunities for localization to impact your metrics as easily. Also, you’ll quickly find that you’re masking important trends across languages (English vs. non-English).

One note on how to treat Canada, one of my pet “low-hanging fruit” markets that many US-based companies overlook. Canada usually falls into “NAM” (North America) or gets lumped under “Americas.” However, for financial reporting purposes alone, you’ll need to bucket Canadian revenue as International if you’re based in the US. But this is important for other reasons too. Remember, data from Canada should always be targeted and analyzed separately from the US market. Don’t treat it like a 51st state, or you will most certainly overlook its potential for growth and impact.

How Localization Fits Into the SaaS Metrics Picture

Now, let’s take a look at how you can use these standard data cuts for international and localization, as applied to some other SaaS metrics. This will give you a sense of the types of trends that these different data cuts enable you to see.

Here is an example of a fictitious SaaS company with $150M in ARR globally.

saas-metrics

In this example, a few things stand out:

  • LTV:CAC ratio is best in International markets, and ones for which localization has already taken place are the highest globally. Perhaps your CAC was high in the early days when you made most of your localization investments, and now costs have gone down considerably, so you’re getting more leverage for localization from shared global resources.
  • Revenue retention also looks highest in these same markets, driving up the global average. This might be a sign that your past investments in localization are paying off, and that you should consider following a similar approach for other non-English markets.
  • Domestic market LTV:CAC isn’t as high. Perhaps the cost to acquire companies is higher in the company’s domestic market, or there is more competition than in international markets. This might be a sign that more investment should go into international markets where metrics are promising, while you work to fix the problems in your domestic market.
  • Retention is troublesome in non-English markets. Why? Perhaps the lack of proper support for these markets means that the NN ARR is high, but the retention is poor. Also, the CAC might be high, because perhaps the salespeople addressing these markets are sitting primarily in an expensive office location. Maybe the LTV is poor because people are churning out due to a local competitor who offers their solution in-language, while you have not yet localized for that market.

Using Country and Language-Level SaaS Metrics

Now, this is where things get interesting. 😉

Let’s look at an example to see how these cuts of the data can be useful, even if you’re not yet localizing into any other languages yet.

In the example above, I’m showing just a couple of key SaaS metrics along with various cuts from the set listed above, as applied to an analysis for just one country. At a glance, this enables some quick takeaways. We can see that the UK is out-performing other English-speaking markets, other International markets, other EMEA markets, and is likely lifting the averages for all of these, along with the global average (assuming the % of revenue from the UK is materially significant).

Applying the Metrics to Non-English Markets

Now, to see how this all creates a nice picture to help us understand localization’s role and potential for impact, let’s look at a non-English market. I’ve used France for this example, and let’s assume that French is a language that is offered with localization at this SaaS company. Again, all the data is fictitious.

So, from this example, we can see that France is performing higher than the international average on both metrics. Also, it appears that revenue retention for France is higher than it is for the overall average for French speakers globally. Perhaps more attention needs to be paid to French-speaking customers in Canada, Belgium, Switzerland, or various countries in Africa, for example.

But, while the numbers for France are good, they are still not as great as they are for the grouping of countries for which localization is provided. This might speak to the depth of localization. Perhaps localization efforts today are primarily targeting France only, but at the detriment of some of the other French-speaking markets where the LTV:CAC is worse, as is retention. Yet, perhaps those same countries are driving solid NN ARR growth.

Seven Key Takeaways for International SaaS Leaders

There are many ways to slice and dice your international data. Here are some key takeaways:

  1. Implement a standard country list in your data reporting early
  2. Assign a primary language to every country
  3. Track your SaaS metrics for countries whose languages you support with localization
  4. Track the metrics for unsupported languages separately
  5. Compare the metrics for “localized” countries with other cuts of your data
  6. Always analyze at the country level first, and use other groupings to put that country’s data into context and understand its role within the broader cuts
  7. Assign someone who can “own” these metrics and report on them regularly

On this last point, I want to emphasize that the sooner you can ingrain the idea of using these global data cuts and building them into your BI systems, the easier it is to make it part of your operating system.

Localization-Friendly Data Cuts Now Provide Critical Insights Later

If you enable these data cuts today, as you grow and scale, people at your company will increasingly want to see these cuts and will start to ask questions like:

  • How did our monetization rate improve after we launched that new UI language?
  • Can we do an A/B test with a localized form to see if it impacts sign-up rates?
  • What is our cross-product adoption rate for localized vs. non-localized markets?
  • Did the pricing update cause the same behavior in English vs. non-English markets?
  • What does our NPS look like for countries where we are localized versus not?
  • Did we see any uplift from adding that language on WAUs and WATs?
  • How does our helpability score on localized support articles change from language to language?
  • Is our support ticket volume lower for languages in which our knowledge base is also localized?

These are just a few examples of the things you can track down later, if you create the right data structure for localization-specific reporting within your SaaS metrics today.

I hope this post offers you a set of simple and practical data cuts that are easily put in place to help your SaaS business grow across borders. This will help you ensure that localization isn’t viewed just as a one-time thing with a simplistic ROI attached. Instead, view localization as a driver of growth, a central part of your go-to-market strategy, and a key part of the operating system that enables a SaaS business to achieve its true global growth potential.

Nataly Kelly

Nataly leads localization at HubSpot and has previously held diverse roles in marketing, international operations and strategy, research, product development, and localization. She writes for Harvard Business Review on topics of international marketing and business. Nataly grew up in rural Illinois, lived in Ecuador, and resides in Boston (for now).

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