The data logic behind the year-end growth of APP

The global app economy is in a period of rapid growth. According to App Annie, consumer spending in app stores will reach $120 billion by the end of 2019, which means that the growth rate of consumer spending in 2019 will be five times that of the global economy.

Recently, Liftoff released the "2019 Mobile App Trends Report". The sampling period of the report is from September 1, 2018 to August 31, 2019. It combines application market data and Liftoff internal data to analyze the trends and important indicators of user acquisition, interaction and retention rates of various application types and regions.

The following is Baijing Chuhai’s comment on “2019 Mobile Application’s Double Eleven” which just ended was even more lively than previous years. During the more than 20 days that the drama was staged, the APPs under the e-commerce giants formed groups in the form of a matrix, using all their tricks to promote themselves with dazzling and interlocking fancy gameplay.

The fierce competition in the "Double Eleven" is just the beginning. As data is gradually applied to APP promotion and operation, the "growth" theme of this year's APP year-end drama still revolves around keywords such as "precision" and "refinement".

APP growth is inseparable from the application of data, and the depth of application determines the effectiveness of growth. The basic application is to use data as an acceptance tool to measure and evaluate the effectiveness of each link of APP growth ; the advanced application is to use data as important information, conduct a comprehensive insight into feedback data, and find the real needs of users; the more in-depth application is to use data as an important resource for APP growth, dynamically manage and mine data, establish data models to develop the value behind the data, and provide data basis and verification for the optimization of each link of APP growth.

This article will analyze the entire "growth" case completed by Getui in cooperation with a shared bicycle APP, and interpret the role of data in the entire process of APP growth and how to play its role.

1. Crowd Insights: Scientific Verification and Prediction

Strategy is the cornerstone of APP growth, and crowd insight is the "digital navigator" that helps APP verify and clarify its target audience.

Before conducting crowd insights, the APP cannot act aimlessly, but must first have a preset target population. APP operators can combine industry understanding, experience summary and APP’s own data to conduct comprehensive analysis and formulate target groups. The shared bicycle APP in this case identified college students as the key target group for growth before conducting data insights.

After the APP has formulated the target population, the first thing it needs to do is to verify the development potential of the target population and whether it has "growth" value. Data insights can be verified through industry comparison from dimensions such as APP user portrait, user composition, and offline scenarios.

The case is compared and analyzed from two dimensions: the overall population portrait of the shared bicycle industry and the population portrait of the bicycle APP users. By comparing the two groups of user portraits, the following conclusions are drawn:

1. The 25-44-year-old group is the mainstream group of shared bicycle apps; the 18-24-year-old group accounts for 18.6%, which is a very competitive group outside the mainstream group;

2. The proportion of people aged 18-24 in the age structure of the bicycle APP population is 12.6%, which is slightly lower than the proportion of people aged 12-24 in the industry population age structure. This shows that there is still room for growth in the expansion of bicycle APP to the 18-24 age group;

In the user population structure analysis, the shared bicycle population was analyzed based on population tags, and the following conclusions were drawn:

1. College students are the second largest user group in the bicycle industry;

2. Among the users of this bicycle APP, college students account for only 7.3%, which is far lower than the industry share. The potential market space for college students is relatively large;

2. Offline scene insights break the spatial dimension wall

The dimension of APP’s user insights cannot be limited to online behavior, but also offline scenario factors must be considered. Because offline life scenarios will also have an impact on users' APP usage behavior. By adding offline scenarios to insights, the APP can break the spatial dimension wall, achieve organic linkage between online and offline, and help user growth. Just as e-commerce companies are pursuing the concept of "new retail", its core lies in connecting online and offline scenarios and promoting growth by solving offline needs through online services. This growth strategy requires strong offline scenario insights.

In this case, the "growth" strategy of the shared bicycle APP also requires a close integration of online and offline. Therefore, in addition to the comparison of online populations, offline scene insights are equally important. The case conducts a population heat map analysis of offline scenarios of shared bicycle users (sample locations: Shanghai and Chengdu), with a particular focus on areas with dense student populations such as university towns and entrepreneurial parks.

The heat map shows the density of the crowd through blocks of different shades of color. The redder the color, the denser the crowd. As shown in the above figure, whether in Shanghai or Chengdu, the locations of university towns are areas with highly concentrated regional populations (i.e. red blocks) and have expansion value.

The above data insights show that college students are indeed a valuable target population. It is feasible for bike-sharing apps to target college students as their growth target users. However, the size of the college student population is limited, and they are not the mainstream user group in the shared bicycle industry. The APP's target growth group is only college students, which obviously does not cover enough. The APP still needs to find new growth target users to supplement.

According to previous crowd insights, office workers are the big users of bike-sharing apps, and bike-sharing solves the last-mile problem for commuters. Based on the understanding of the industry, the case shifts the direction of insight to commuters, and combines offline scenarios of transportation connections to conduct crowd insights at stations along the Shanghai and Chengdu subway lines. This insight combines users’ online behavior preferences with offline scenarios for verification, and the results show:

1. During the morning rush hour, there are more cyclists around subway stations;

2. During the evening peak hours, the number of cyclists around subway stations is more active;

3. Within a 1km radius of the subway station, potential bicycle users are concentrated;

Insights show that the 1km radius area around the location station is a new growth scenario for shared bicycle apps, which can cover more active people.

The case ends here, and the data insights for the growth strategy formulation period come to a temporary end. Through the above data insights, the shared bicycle APP not only verified that college students are a valuable target population, but also found that the target commuting population is relatively concentrated in the areas around subway stations and the APP activity is relatively high, which is an effective "growth" offline scenario. Next, the APP can optimize the growth strategy based on the results of data insights. For example, it can strengthen promotion in scenes with dense crowds of college students, such as university towns and entrepreneurial parks; during peak hours in the morning and evening, it can carry out bicycle dispatching operations and event operations along the subway lines.

3. Build a target population model to dynamically improve promotion accuracy

As the growth strategy is gradually implemented, the process of data insight will continue, and the stage of truly reflecting the compound value of data has just begun. We believe that good data insights should run through the entire cycle of APP growth and promotion, and be able to generate data models, accumulate and process data in real time during the growth process, continuously iterate and optimize, and guide APP growth strategies to execute in the optimal direction.

In the subsequent execution and promotion process, how the APP locates the target population and how to reach the target population more widely requires the use of a target population model.

If an APP wants to have a more accurate target population model, it needs to have more dimensional data features as a basis. In this case, the establishment of the target population model for the shared bicycle APP involved five major dimensions.

1. Bicycle industry users: The installation and usage behavior characteristics of bicycle apps can be used to distinguish between active users and silent users, and focus promotion on new app users and silent users who have the value of awakening.
2. College students: Based on the college student population label, add features that can improve differentiation, such as age group and permanent residence, to further target the student population;
3. White-collar workers: Based on the labels of white-collar workers, we add features that can improve the differentiation, such as working time period and professional scene, to further screen the white-collar workers;
4. Subway crowd: Use LBS geo-fencing technology to select subway station scenes to more accurately cover commuters;
5. High-density population: Find areas and populations with strong demand for bicycles by combining APP penetration rate and offline scenarios;

Among the above five dimensions, bicycle industry users and high-density populations cover users with demand who have not been reached by shared bicycle apps from a macro perspective; college students, white-collar workers, and subway people are relatively micro and play the role of locking in the target population. The target population model formed in this way is applied to APP growth promotion, which can not only accurately lock in the main target population, but also take into account the breadth of dissemination and influence more people.

The target population model is not static. It needs to be continuously iterated and optimized based on the data return from each delivery link so that its accuracy will continue to improve.

An effective promotion requires attention to every link in the entire conversion funnel. Each link requires collecting data, analyzing data, and carefully observing the problems reflected behind the data. The APP needs to conduct in-depth research on the characteristics of the converted population based on the actual delivery effect, better deepen the understanding of the target population, further optimize the population targeting, and prepare for the next delivery.

4. Set up the optimization effect attribution model for the natural traffic control group

The role of performance attribution in the growth promotion process, on the one hand, helps APP evaluate the effectiveness of the promotion combination, and on the other hand, helps APP solve the "real problem" of how to spend money to achieve growth.

The most important thing about performance attribution is to understand the various cross-channel interactions that led to conversions and the relative weight applied to each interaction. The more objective the data of effect attribution is, the greater its impact on the final delivery results. However, the current mainstream attribution logic still has the drawback of being not objective enough. For example, the attribution models provided by Facebook and Applovin cannot eliminate the interference of natural growth traffic on promotion effects.

In this case, the target population is divided into a promotion sample set and a control sample set in a 9:1 ratio. Promote to the population in the sample set and conduct statistics according to the mainstream attribution logic. The population in the control sample set will not be promoted, and their natural growth will be counted. In the final data attribution stage, the interference of natural traffic in the mainstream attribution logic is reduced by removing the natural traffic growth rate collected from the control sample set, so as to explore better promotion channels. Of course, the natural installation rate of the control sample set is calculated through sampling, and there is an error compared to the actual natural installation rate. This requires the APP to make some optimization adjustments based on the actual situation.

5. Optimize APP promotion execution details through multi-data insights

During the growth of an app, there are still many details that can be solved through data insights. For example, identifying and protecting against black traffic, finding the optimal bidding range, the correlation between exposure times and promotion effects, and optimizing offline promotion scenarios.

In this case, data insights into exposure times and promotion effects helped the shared bicycle APP achieve the best promotion effect at the optimal cost.

Insights show that when the exposure times are less than 5 times, the number of exposures is positively correlated with the promotion effect, and the CPA cost can be controlled at around 6.24 yuan; when the exposure times are 5 times or more, the number of exposures has no direct relationship with the promotion effect, and the CPA price increases exponentially. On the other hand, the marginal CPA analysis shows that the promotion effect brought about by each additional exposure has not changed much.

From this we can conclude that high cost does not necessarily result in good results. APP does not need too much unnecessary exposure. The solution of controlling the number of exposures to less than 5 times is the most cost-effective and can achieve the best results at the lowest cost.

In general, with the gradual implementation of data intelligence in the Internet industry, big data will become the new generation of growth "black technology". Large companies have made arrangements in this area very early and have accumulated a certain amount of models and data. Other APP developers can also quickly implement data applications through tools and services provided by third-party data service providers. With both hardware and software adequately prepared, APP still needs a little more patience with data intelligence. After all, the iteration and optimization of data models requires slow and careful work. Interpretation of the key contents of the "Trend Report". Click here to get the full report.

1. A large number of tool apps were launched on the iOS platform, which is the third category of new additions

1. The total number of new Android apps is three times that of iOS

In terms of the number of new applications, from September 2018 to August 2019, the total number of new Android applications was 1.45 million, and the total number of new iOS applications was 489,000. The total number of new Android applications is almost three times that of iOS.

Looking at the areas that developers are focusing on, the top three application types in terms of the number of new applications in Google Play are games (222,903), entertainment (160,069), and music and audio (154,456). The top three application types in terms of the number of new applications in the iOS App Store are Business (59,299), Games (55,572), and Tools (54,303).

An interesting point is that the tools category ranks third in the number of new iOS apps. I don’t know if it is related to Google Play’s crackdown on tool advertising monetization, or the category division in this report.

In terms of app downloads, although Android app downloads reached 143 billion, Android's lead over iOS has gradually narrowed to 2:1. Compared to other app types, gaming apps lead the pack in terms of total downloads.

2. iOS in-app revenue is 1.5 times that of Android

Despite Android's strong market share, its download growth has been slowing. In comparison, iOS is more profitable. From September 2018 to August 2019, the total in-app revenue of iOS was US$32.6 billion, about 1.5 times the total in-app revenue of Android.

Obviously, applications with leisure and entertainment attributes have the highest revenue, including games, entertainment, social, music and audio. Among these application types, the iOS platform accounts for the majority of revenue.

Among all app categories, gaming apps generate the most revenue. Game revenue reached $41.5 billion, a year-on-year increase of 4.27%. This is mainly due to the advertising model, which generates revenue by attracting the audience's attention.

It is worth noting that the in-app revenue of entertainment and social applications is second only to games, but the difference between them and game revenue is huge, at 2.7 billion and 2.4 billion respectively.

At the same time, some app categories showed signs of weak revenue. In-app revenue for travel apps was approximately $79.1 million, a decrease of 46.3%. In-app revenue for financial apps was approximately $133.9 million, down 28.8% year-on-year.

2. User acquisition: Game users are more cost-effective, and financial and e-commerce apps have high conversion rates.

1. The conversion cost of in-app purchases in gaming apps is still the highest

All apps have a high user acquisition cost, but games have the highest cost of converting in-app purchases, at $86.61. The report shows that the purchase conversion of tools reached 85.32 US dollars, which is quite strange and may be due to the classification of App categories. In comparison, the conversion costs of e-commerce and financial applications are not that high.

But in reality, game users get the best value for money, and that’s because in-app purchases aren’t the only way for gaming apps to make money. The development of in-game advertising models and incentive videos provides developers with more options for monetization.

2. E-commerce and financial applications: low acquisition cost and high user activity

Looking at the interaction rates of different types of applications, financial apps have the highest interaction rate, reaching 77.8%; dating apps rank second with an interaction rate of 61.8%; and e-commerce apps rank third with an interaction rate of 34.3%.

By comparing with the cost of in-app purchases, we can find that e-commerce and financial apps can achieve higher user activity with lower investment costs.

3. Financial and dating apps have the fastest conversion rates

There is a strong correlation between demand and speed, and app types with clear download goals convert the fastest.

The conversion rate of financial apps is the fastest among all categories of apps. It only takes 1 hour and 10 minutes for users to install financial apps and make purchases. This is because consumers usually have a strong purpose when downloading financial applications, such as paying bills, checking credit scores, investing, etc.

Social interaction is a basic human need, and dating apps rank second in conversion rate, with users taking approximately 14 hours and 28 minutes from installation to purchase.

E-commerce and gaming apps take longer to complete purchase conversions, with the time from installation to purchase exceeding one day.

3. User Retention: iOS has a higher retention rate

1. Use re-engagement from an early stage to extend the user lifecycle

Retention represents the stickiness of the app. If you can’t retain users, no matter how many installations you have, it will be in vain. User retention rate also reflects the usage rate and frequency of application. Only by understanding the app value and user behavior can you set the right app user retention goals.

AppsFlyer’s retention data shows that 25.2% of users continue to engage with the app on the first day. On the third day, only 13.1% of users interact with the app. This is a critical period for interactive activities and related information delivery, and marketers need to seize the opportunity. By day 7, 65.9% of users had churned, a significant drop. Therefore, marketers need to use re-engagement from an early stage to extend the user life cycle.

2. iOS has a higher retention rate over time

Although the user retention rates for Android and iOS platforms are the same on the first day, the user retention rate for iOS platform gradually exceeds that of Android over time.

The report shows that the user retention rate on the first day of Android and iOS platforms is the same, which means that users on both platforms are very clear about their purpose when downloading the app. On the 3rd day, the user retention rate of iOS and Android differed by only 0.6%. On the 7th day, the gap widened to 0.8%. By the 30th day, the difference in retention rates between iOS and Android users dropped to 0.5%.

4. Regional analysis: Russia and Brazil are still in the customer acquisition and conversion window period

Mature markets have huge application demands and are developing at a very fast pace, but the cost and difficulty of acquiring users are also very high. Japan is the most expensive at $5.15, followed by Australia. The United States ranks third, but its costs are only two-thirds of Japan's.

The average cost to acquire a user in the EMEA region is $2.55. Brazil has the lowest CPI at just $0.5.

1. Brazil has the lowest registration cost

In terms of registration costs, Japan ranked first at $8.73, ahead of the United States and Australia.

The registration cost in Russia is US$1.25, and in Brazil it is US$0.73, both of which are particularly cost-effective.

2. In-app purchases: Russia and Brazil have the lowest conversion costs

In the United States, the conversion cost of an in-app purchase for a user is $112.76, which is higher than Japan ($111.64). Canada and Australia follow closely behind, with conversion costs almost identical.

Compared with Germany, Italy, a rapidly developing market, has a very high cost-effectiveness ratio, with user conversion costs 35.8% lower than in Germany. Overall, Russia and Brazil have the lowest user conversion costs.

3. Retention rate: North American users are more loyal

Overall, as time goes by, the retention trends of mobile app users in Asia Pacific, Europe, Africa and the Middle East, Latin America, and North America are the same, all gradually declining, and the retention rate drops the most on the third day compared to the first day.

But in comparison, the retention rate of North American users is higher than that of users in other regions, indicating that North American users are more loyal to their apps.

Author: Personal recommendation

Source: Personal recommendation