| Monica Merel |
Well, thank you so much, Erji, for joining me today. I really appreciate you taking the time to connect with us. Just a quick intro, so for the folks that are listening, Erji Wang is a Senior Globalization Program Manager at VMware. Erji has been working in product globalization for about eight years, and in the most recent years he’s used a lot of business data to build a roadmap around product globalization at VMware. He’s contributed to The Globalization Playbook along with 12 other industry leaders in the localization space and globalization space. |
| Monica Merel |
So I just want to learn a bit more about the book, how it came together, The Globalization Playbook, this process. This is a two part series, so this is the second part of the series. We’re going to be focusing just on the data portion. |
| Erji Wang |
Yes. Thanks, Monica Merel, for having me here join this session. I’m quite happy to join this group, to draft this playbook, especially for the data part. The journey for authoring this playbook is a good opportunity for us to learn from each other, and we are not only sharing the practice in our own company, but we also learn some good practice from other companies. Especially for this chapter for data analytics, and I know that different company, they have a different practice for data-driven decision making or to build a strategy. And so I do learn quite a lot from my partner in this chapter. Also, I have the opportunity to share what we have done at Vmware, so this is a very good practice for exchange. |
| Monica Merel |
So who were your partners, Erji? |
| Erji Wang |
Yes, it’s my pleasure to work with Liana from ServiceNow and also Jan Fran Sova from Adobe and Francesca from Pinterest, and also Christian from Siemens. So I work with four of them together to authoring this chapter. |
| Monica Merel |
Great, great. That seems like a really dynamic team. I can see it’s a lot of different industries and totally I’m sure different approaches to data and data collection. |
| Erji Wang |
And also, they have a lot of experience. All of these, my partners for this, they have extensive experience to build a strategy and also to utilize data and to justify the localization globalization strategy. |
| Monica Merel |
In reading the, in the description of the podcast we’ll include a copy of the globalization strategy playbook so folks can use that as a resource. But one of the things that I found really interesting when reading it just as an intro is why this is so important. Why this data is so important in the globalization and localization space. We’d love to hear directly from you why you think it’s so important. |
| Erji Wang |
You know that especially in, in a big company, I assume all the company, they are foster some of the culture for the data driven decision making, right? So globalization function is one of the way we also need to use the data to help us to make not only the big decision but also some of the daily operation, small decisions. Also, I think with the data we can justify or prove the value of localization or globalization. You know that in the company, not everyone they know the importance of globalization, especially in some of the new product team or some of the new acquired team so who just joined the big family. They always underestimate the value of globalization. So we need to use the data to have some sharing or education for them, to tell them that organization is helping you to enlarge your business opportunity in certain country. Also, data will also help us to measure this. Not only justify this, but also measure this from time to time. |
| Erji Wang |
Also, also another challenge we are facing right now is we have a limited resource, not only the budget, but also the human power. With the data, we can help us to fund the opportunity or fund the priority where we should put our resource on and to have the best ROI. So this is another thing. And always that data can tell us a lot of things we cannot easy to see in the daily operation. So this can help us to identify some of the new opportunity or some of the gap which we need to fill. So this is something we see the value for data in the globalization and the localization business. Thanks. |
| Monica Merel |
What do you see? So when you’re trying to convince, a new team has started and they’re looking into globalization, do you typically broach that conversation initially? Or is it more of a, they come to you interested about localization, then how do you broach the data conversation? |
| Erji Wang |
I think both. So you know that some of them are active. They want to approach to us to learn something about localization, globalization. But in some case, that we knock their door first because we see that they are … especially we see from the data. You know that in company we have some benchmark. For example, in our company, 40% of 50% revenue are from overseas markets, outside of US. And some of the new team, maybe their revenue from oversea markets are pretty low and we approach them saying that there is a good opportunity for us if you do the globalization. If you localize your product, you have a big opportunity to grab the revenue from overseas market, especially from some of the country localization play a big role, for example in Japan, in some of the APAC countries. |
| Monica Merel |
And then do you typically get any pushback or is it easy sell once you’re like, “You know, you can get more money. This is laddering up to your benchmarks.” What is your experience? |
| Erji Wang |
I think this is a very good question. To be honest, I think in most of case, we get some pushback that. So in this case, that data will play a big role. So we use a different data and we cook them together to educate our business partner in product team to let them know that data will tell you how much opportunity we can grab if we do the localization. Also, we are not cook the data by ourself, but we also work with different teams. For example, we will discuss this with region teams, collect some data from their hand because they are in the frontier of doing business in the region, in the country. So we get some of input from them. Also we have some historical data from the sales operation, and we can tell them that previously other product, after they’re doing the localization, what is the revenue jump they have? So we also have some successful story and with the data, and this also help us to convince the team. |
| Erji Wang |
Another thing is we also keep an eye on our competitors. So this is another thing we want to build in our data story to convince the team, saying that you have a competitor in the market and they are doing localization, so if you don’t do, from customer experience point of view, maybe this is not good. |
| Monica Merel |
What’s the data point that typically moves the needle to convince them? Is it the competitors? Is it the size of the prize? What is typically the ROI opportunity that they are “Oh, yeah. Let’s localize”? |
| Erji Wang |
I think that, to be honest, there’s two things which is, to me, set the needle more. One is the revenue. We always borrow the number from region team to have some of the projection of the revenue. If we do the localization, what is new business case the team can grab? So this is one more. Another thing is customer impact. So especially we need to let them know that not every customer they want to use the product in English. Especially we use some data of so-called English tolerance. For example, in Japan, the tolerance is pretty low. You cannot assume that all Japanese customer they want to use English. Maybe some European country, the tolerance is okay, but for sure in some of APAC countries, yeah. These kind of data will help us to convince our friends in the product team. |
| Monica Merel |
What is the best resources or what’s the best data points that you think you can use for a business case for different regions? For example, like if I’m running my business and I want to expand into there’s so many markets, and like you mentioned before there’s limited resources. How do you recommend deciding what are the key businesses and key markets to dive into for localization? |
| Erji Wang |
You know that we also consume a lot of data actually from the third party. We got some data from IDG and/or Gartner. They have a lot of market analysis or insights. So they have for different business, for different products, for different business, they have some of analysis. They have a so called total addressable market size. It’s called TAM. Based on this we can let the marketing team or product team know what is the total market share there. And so, this is one thing can tell us how big is the total cake, total size of the cake. Also, we can compare our current revenue data in that country and see what is our position. In some case, that localization will help the team to do the market penetration in that country. |
| Monica Merel |
So it’s kind of a combination of the size of the prize and then what the internal data shows? |
| Erji Wang |
Yes. In this chapter, we have built a very interesting content we call that is data inventory. And so, like my friends who are working on this chapter, we do some brainstorming and we list all the datas together. Also, from this data inventory, we have another element called the decision making matrix. We link all the datas to different scenario. To handle or to make different decision, what kind of data you need to refer. So this is interesting things in this chapter. I do recommend people to check these two section in this chapter. |
| Monica Merel |
Yeah, it was very interesting. Also, I found very interesting, the hidden and available, like what is hidden versus available as in the chapter. Can you expand on that a little bit for the listeners? |
| Erji Wang |
Yes. I think this is another thing I like in this chapter. You know that in different companies, it’s like the globalization team or localization team are always will be essential function in the organization. And so, actually we are sitting in a gold mine for different datas. Actually, we have access to more data than we know. If we keep exploring or we keep partner with the different teams, we can have a access to more datas. |
| Erji Wang |
But there also some data which is missing. I like the quote from my friends, Jan Fran Sova and Liana, they mentioned you should never fight for the perfect data. For most of scenario, we are working with limited data. But there is some way we can handle that nicely. For example, we can apply some of the reasonable assumption if we see some data is missing. With the assumption, we share this honestly with our partners saying that this is the logic we are using and we have this assumption, even we don’t have that data. So this is okay. |
| Erji Wang |
Another thing is that, like I mentioned, we keep exploring and we add the data incrementally. Maybe currently in this culture, we don’t have that data, but we build a channel. We partner with different teams. We collecting those data. In the next cadence, we will have those data. So this is another strategy. With this, I think even some data are missing, but we still can build something which give us more meaningful insights. So this is something about the working with the missing data. |
| Monica Merel |
I’d love for you to expand on that. Do you have any examples of when you had a challenge of missing data or did anyone share any stories and then how you guys went over that challenge? |
| Erji Wang |
Yes, I can share one thing which is happening at VMware. When we want to deploy the low machine translation to drive the cost, we lack of some data to show how people receive this low machine translation’s quality. We know that we are missing this. We carefully start with some of the safe language. At that time, we want to introduce a widget in our doc center. And for people who using this low machine translated content, we ask them to have a just some feedback to us. Gradually, after several quarters, we save a lot of data. Then we have enough data to show how people are like this, or the quality of low machine translation that we can briefly to enlarge the scope for low machine translation. So in this cases is we try that first and also we see some data missing and we add that to the roadmap, then we utilize the data to help us to enlarge our scope. |
| Monica Merel |
That’s a really fascinating example because it ties into my next question about how do you define the OKRs? You have your goals for your business, there’s overall business goals, then there’s the localization goals, and then you need your data points. So when you see those hidden or those opportunities, how do you broach that? How do you tackle that opportunity for your overall goals? |
| Erji Wang |
I think data actually is kind of things that support all our OKR, especially for the measurement. And also, like I mentioned, data is also one thing can help us to identify what is missing in our OKR or in our objective. So this is something. We collect the data and also we see the opportunity, see the gap, and we take action. So this is a circle we are building. And typically for OKR, we need to have some of KPI, right? We need to have some measurement. So also data play a big role there. After we achieve something, data will also tell us what can help us to finding what else missing, and this leads to another objective or another key result we need to achieve. |
| Monica Merel |
So what are some examples of like tools that you guys would use? You’ve mentioned the widget. What other examples would you guys use for localization data? |
| Erji Wang |
This is another, a good question. So actually, if we talk about data, so we need to rely on different tools. Right now in the market, there’s a lot of tools can help us to collect the data and also to do the data analytics. Especially right now, like in the web portal or in the SaaS product, software as a services product, actually you have access more data, especially for SaaS business. You host all the service in your different data center so that’s why you collect more data from a customer and you can see the whole customer journey. |
| Erji Wang |
And so, I know that due to some of the regulation like GDPR, maybe you cannot see all the data you want to see, but you still can see some of the rough journey or collect the past for different customers. And you have good opportunity to build some of the customer story based on those datas. For example, in our text data center, sorry, in our doc center, we can see how customer they spend the time in each page and in which scenario they want to change the locale. And if they don’t find the target language in the language select, what is the next step they will do. All of this tell us some of story that how localization or globalization will help customer. |
| Monica Merel |
I didn’t even think about measuring from a website, like how people are spending time on the page and then comparing the two different, in English versus another language in the experience. In the article I mentioned is the sentiment analysis analytics. I was wondering if you could expand on that a little bit. |
| Erji Wang |
Sentiment analysis actually is a very useful tool which is applied for study the customer feedback. This is broadly used in English content. You know that a lot of tool actually provide to you the sentiment analysis function, for example in Power BI or in other tool they have this function. But for localized content, for example, for customer tool who can give you feedback in French or in Japanese, you can also apply this. With this, you can generate to see what is it they’re feeling about your localization or your content, and to show what is the percentage of people they like your content and which percentage that they don’t like. |
| Monica Merel |
That’s so interest- So, if a team has … okay, what would you give advice to someone who has no localization budget and they’re just starting from the ground up, and then also in limited amount. They’ve got C-suite support. They have a ton of money. What do you think would be advice for the low end and then the high end as well? |
| Erji Wang |
You know that we are all facing the challenge of a limited resource or limited budget. Especially in some of the startup company. They may not have this because they have a lot of other priorities they need to work. Maybe in the beginning that localization, globalization is not their priority. But when the business grow to a certain level, that they need to consider this. In this case that the business leader or the localization or globalization leader, they need to use that data. They need to carefully to build up their data strategy to collect the data and build the data. Also, more important is that they need to partner with different teams. You know that it’s not about your closed door and cook the data by yourself, then you share this to others. During the old journey in the data analysis, you need to work with the different teams and build the trust, build the partnership with the different team. Then you will see the power of data. |
| Monica Merel |
If you have no money and you’re just kind of from the ground up, starting a localization team or wanting to explore localization, what do you recommend are some data points with limited budgets to leverage? |
| Erji Wang |
Like I mentioned, there’s one thing is you need to check some third party market analytics, and this is telling you what is the market you want to enter. Also in that market, what is the people’s language preference. And also, you need to check with competitors’ status. What is their language offering in the market? And also, maybe you also can have some of the customer survey to collect some input from the end users. Also, you need to talk with the region team who is doing the business in that region and to collect some data. For example, one of the example in our chapter is localization leader talk with the region business team, saying that in past several quarter certain amount of business case that lost due to lack of localization, lack of the translation. So this is all good data we can cook together to build some of the justification to ask for money, ask for budget to investment. |
| Erji Wang |
And also that maybe you don’t need to build this very big from beginning. You can build this gradually from like a localized certain amount of content first, and then we can see the reaction in the market, and then we add content gradually. Then we, for example, we build localization for documentation first, and then we add UI, and then we add this to all content, and we start from one language first and gradually we add different locales. So this is our all possible to have no budget and gradually to get more budget. |
| Monica Merel |
So that’s the starter pack kind of tips for starting a localization strategy. What about for the company that’s established? How do you keep that momentum going in your localization strategy leveraging the data? |
| Erji Wang |
For the established company that also that we always have a lot of room to build optimization. So we have a lot of, we spend a lot of budget and we also need to justify the budget, which is really helping the business. And so, we’ll set up a different dashboard which showing the trend that even that we are established, we have a lot of locales, but we also can see which locales perform the best and which locale we see that may not have that good ROI so we do some optimization. For example, for some of the low performance locale, maybe we need to consider machine translation or low machine translation so we can save the money to summon more high return locales, or we add some of these, the budget, to some new locales we never localized before. |
| Monica Merel |
So a little risk, a little continuing, and seeing how to optimize? We’re towards the end of the conversation. I’d love to know any kind of tips or recommendations you have for anyone listening on their strategy. |
| Erji Wang |
I think this book or this playbook or this chapter will tell you how the data is function as a crystal ball to you. You can use the data to do a lot of prediction or forecast of the business, and also can help you to make a more meaningful dialogue with the partner or the friends in your business unit. Also, it can help you to convince them to support your localization initiatives. Actually, data is not that easy to approach, but it’s just something in your data operation. You just collect them and build them, use them every day. Gradually you will build your very nice data strategy and to help you to grow your business. |
| Monica Merel |
Thank you so much. I’m excited to see what other exciting data points come out. Hopefully maybe in the next year or two, we can hear back from you like, “Oh, this is some new insights, some new data strategy.” Thank you so much, Erji. I really appreciate it. |
| Erji Wang |
Thanks Monica Merel for having me here. |
