 Hello. Thank you for coming to this webinar. This webinar will focus on how to make subscription products successful, which is related to the revenue of companies like Netflix and Disney Plus and most of the utility apps. I hope I can help you have a better understanding of what subscription is all about and how to make it successful in terms of revenue. So before we start, I would like to give a quick intro for myself. I'm Joseph Chen. I was working at Canva, the design platform, Beauty Plus, the camera and photo editor. And you can make up the virtual makeup photo editor. I was working as Prada Rose in all of them. And I got my master's degree in computer science from Tsinghua University in Taiwan. Then I pursued my career in product management ever since. So in the coming session for subscription, let's shorten it as sub for our convenience and easier pronunciation. So regarding today's agenda, I would like to split it into four parts. What is sub? What are the factors to sub success? How to make sub successful? What else to do to boost sub? I believe everyone will have encountered sub dialogues on mobile apps in daily life, more or less. Let's have a definition of sub first. So with sub, users purchase the time to use premium features. And sub is a common monetization model for premium products. For example, Spotify allows users to listen to music for free. But to enjoy an ad for experience, users have to subscribe. In terms of plans, there could be but not limited to monthly, quarterly, semi-annual and annual plans. There is no right or wrong in having only one or a few plans for a product. Now, here are two key concepts when it comes to the sub. Let's try to understand the definition first. One is prepaid, you know, with which users purchase a plan for a certain time without auto renew. The other is recurring, with which user purchase a plan for a certain period with auto renew. So recurring plans usually go with free trials and discounts because you may have a higher subscriber retention rate with the auto renew mechanism. Prepaid usually accompanies a higher trial rate, but lower retention rate because users have to manually subscribe again. So in terms of LTV, these two might vary in different products. But usually recurring users might have a higher LTV, but it really depends. For every product, experiments have to be run. So what are the factors to sub's success? Developers adopt sub to make revenue, which we usually track as AR. AR is like annualized recurring revenue, or MR, which stands for a monthly recurring revenue. LTV, lifetime value, RPU, average revenue per user, etc. Let's talk about three main breakdown metrics. Trial rate, trial to pay rate, subscriber retention rate. To help us understand better, we can form a pay user funnel, and here are three major conversions. Free users converting into trialing users, trialing users converting into paying users, and paying users converting into retaining paying users. So let's talk about some definitions again. So trial rate refers to the probability of free users converting to trialing users upon seeing sub dialog. Generally speaking, this rate help us track whether users are willing to give you a try. So this is like a very important metric. Trial to pay rate refers to the probability of trialing users converting to paying users by the end of the trial. And the third one is subscriber retention rate, which refers to the probability of paying users continuously subscribing to a plan. So with this matrix, we can have like a starting point or like a benchmark to identify issues and make assumptions. Last but not least, let's talk about how to make sub products successful. One disclaimer here is that to make a paid product successful, it is essential to make the free product experience stand out as well, or like at least improved or refined. Otherwise, these free products or like free users may just churn without paying. I will talk about some like methodologies, like a bit after this part. So before going into specifics, I would like to mention that like the core of success is all about running experiments. There's actually no secret. It's rather important to do, you know, competitor analysis to understand the markets better. However, it's also important to make sure that like when you shift features to your audiences, this shift, you know, this shift features should be what they really need or what they really want. So it is highly recommended that you identify issues through data from user research in numbers and make assumptions prioritize and run experiments to verify your assumptions. And decide whether to roll out certain features. Let's talk about what potential directions might better your sub products. Usually, we can think based on, you know, conversions. The first one is about trial rate. There are two moments for trial rate. One is discoverability. More like sub discoverability, which is when and where in the product users see the sob dialogue. The other is what users see in the sob dialogue. So the first thing is that you can always experiment on the entry points of sob dialogue. For example, will it improve, you know, revenue by putting the entry point at a home button? Or is revenue earned the same if the entry point is put under the settings page only different, you know, different products may have different results. It's always worth experimenting. Another frequent question is, is it necessarily bad to display sob dialogue when a new user opens your app? Or is it necessarily good to display sob dialogue after a new user activates? Run the experiment to see which leads to better revenue and decide your product strategy. There is no absolute answer to this question. So in addition, if you can get user behavior data, you can segment them into different clusters with certain characteristics defined by you and do a targeted marketing. For example, you can do an app push notifications or email marketing to users who ever went trial and but turned with a targeted copy to see if they convert into paying users. Last but not least, regarding what to display on the dialogue, first thing is that you must confirm or like check the terms and policy of the app stores you're publishing your apps to like Apple and Google Play. Meaning these terms and policy is key for your product to be approved before publishing. Now back to our topics. So when users see your dialogue, here are several things to consider. Discounts. Copy. So, you know, copies are something that sounds like very easy, but here's an example. How can you make sure, like, how can you make users more comfortable by having the word in like enjoy three day free trial, cancel any time? Run experiments to see which one will lead to better conversions. And also you have to consider the pricing and payment localization. For example, if your product is launched in the States and launched in Singapore, the payment methods should be localized because you know in Singapore grab pay as a very trending way for like electronic payment. But you know what applies in the States might not be working in Singapore. And also you have to consider like the discoverability of the city at bottom city refers to call to action. For example, you can experiment with having discounts on recurring plans and see if the overall revenue increases. The second conversion is trial to pay rate. To think of the potential directions, you must lay users experience the value. For example, a net drive product may lay users experience the super fast downloading time upon traveling. So users know what it is like when they are paying for the products. And you can experiment on, you know, notifying users how much they have saved in certain area in settings every time they use premium features. The core concept is to let them understand the value of the premium plans. But in the long term, it is necessary to always listen to paying users feedback. My experience is that you can create certain user groups or chats on whatever social platforms or instant messengers to keep in touch with this paying users and try to identify what to improve for the existing paying features and what could be like the innovation. But you always have to remember that you also have to track the final numbers to make rationale and like a rationale decisions. Last but not least, if a user is trying to turn, you can experiment on having certain prompt and let users know the benefits of this premium plans and see if the revenue actually improves. Now let's talk about a conversion from subscribers to return subscribers. So for this state to drive more revenue, not only do we have to keep improving paying features, but we may have some strategies, you know, on loyal users. Let's put it this way. If a user keeps purchasing prepaid plan and your product's recurring plans have better ARPU, it may be worth it to experiment with offering a discount to convert prepaid users to recurring users. So they might be more committed to your product. The same logic can apply to the experiment of converting monthly subscribers to annual subscribers. It might be great to see if this experiment could actually improve your revenue. You know, in Asia, some apps will mention how much a subscriber has saved from using premium features under settings page. And when he tries to turn, a prompt mentions how much he has saved throughout the, you know, other sub time to let him know the value more. It could be another space to experiment on. Lastly, I would like to mention that for most products, the majority of users may turn upon trailing. And that's common and normal. But usually, these users are unlikely to convert into paying users ever since. What if they're given a second chance of trial after certain days, like a month, a season, 90 days? Some of them may still be active users and they're still using your product. What if they're given a discount to be, you know, paying users? Will that increase the overall revenue? This subscription strategy could be another space to dive into as well. So now I would like to talk about some non-product things that could improve your sub-products. First is that it is essential to track the art pool of users coming from page channels. So you may identify certain issues. For example, you may identify issues on the landing pages you use for performance marketing. Second is that it is crucial to work with data analysts to know which user clusters may have higher chances to convert into paying users. Like, you know, they did certain behavior, they fired certain events, things like that. And you can work with the marketing team to do targeted marketing, like with the push notifications, with the email marketing, etc. Third is that you must have, like, custom obsession. It is encouraged to urge paying users to join certain groups or chats you can keep in touch with. And listen to whatever issues they have encountered when using your paid products. And you try to serve them better by running some experiments. So the revenue earned is just about product itself. It is also about how users interact with your products. Generally speaking, it is crucial to track revenue conversion performance by organic and non-organic channels and see what could be improved. Like I just mentioned, the landing pages for performance marketing may have issues to be discovered. Or the assets like the creative and copy for performance marketing and the product are not matching. So the revenue conversions are not performing well from these channels. That's also likely. So you have to track every conversion from the top funnel. Now I'd like to share one basic methodology that I organized myself. It can be utilized for both free and paid products. Let's say we are designing the strategy for Netflix. So step one, the key metrics could be ARR, analyzed recurring revenue. Step two, the key actions are trailing and subscribe. Step three, the key fundamentals are trial rate, trial to pay rate, subscriber retention rate. So let's continue. So for quantitative and qualitative data, I would do user analysis through user research, interviews to identify issues. Or through the data analytics of funnel, user profile, etc. And could be based on comparator and market analysis. Then I would define the problem like entry point, discoverability issues, or pricing issues, blah blah blah, and make assumptions, prioritize them, and try to design MVP experiments to see if they will actually improve the revenue. And if they work, I would go further. Usually for prioritization, I will use eyes or rise frameworks. As I said, it is rather important to improve your free product. So users will not turn before converting into paying users. So I would like to talk about it for a bit. For most of the apps, it is essential to track malware and ARR. ML, which stands for monthly active users, is composed of new and return users. You need to have strategies for new users and return users. Specifically, how you acquire users through organic and non-organic channels. And you must track their performances and keep improving to see how to drive better revenue for these two. For ARR, as I mentioned earlier, you break it down into trial rate, trial to pay rate, and subscriber retention rate. Then try to identify issues and design strategies. I hope this visualized chart can help you have a very basic picture of how to improve free and paid products. And this chart is basically for demonstrating how I usually design a strategy presentation to senior executives. To make your sub-products successful, it is really important to get buy-ins from your senior management team as well. I would suggest visualizing your strategy as much as possible and be super clear about directions, goals, and success metrics to help a senior management team assess whether it's worth it to invest in resources. So before the end of this session, I'd like to provide you all a summary. One is that prepay and recurring sub is a common monetization model that enables users to use paid features. Two is that there are three metrics, trial rate, trial to pay rate, and subscriber retention rate. Three is that you always have to run experiments to verify your assumptions based on issues identified from qualitative and quantitative data. And here is my LinkedIn. Feel free to search Joseph Chen on LinkedIn and add me as a connection if needed. That's all about my presentation today. Thank you.