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什么是影響日活/月活的因素?先天決定論vs.后天培養論

意想不到的是,要想判斷你的產(chǎn)品是否做到了與市場(chǎng)匹配是一件很困難的事情,主要原因之一就是要想得到有可比性的數字非常困難,甚至可遇而不可求。你不得不去比較類(lèi)別相近或相同的產(chǎn)品來(lái)看一看,然而有時(shí),這些比較難以開(kāi)展。

先天決定論 VS 后天培養論

考慮這個(gè)問(wèn)題的一種思路,產(chǎn)品具備某種先天或后天的要素,決定了它們的指標。有些產(chǎn)品類(lèi)別,比如聊天或電子郵件,生來(lái)屬于高頻使用產(chǎn)品。你老是會(huì )用它們。而其他一些產(chǎn)品,比如稅務(wù)軟件,可能會(huì )給你帶來(lái)價(jià)值,但你每年才只使用它一次。有許多電商產(chǎn)品在兩者之間,你可能會(huì )每?jì)芍苜徺I(mǎi)一次日用品,而不是每天購買(mǎi)。如果只是因為人們每年只使用你的產(chǎn)品一次,并不意味著(zhù)你的產(chǎn)品與市場(chǎng)不匹配,因為你做的是一款稅務(wù)產(chǎn)品,而不是聊天工具。

這里有一篇很棒的文章,對一大堆手機應用類(lèi)別的留存&使用頻率進(jìn)行了分解:http://blog.flurry.com/bid/26376/Mobile-Apps-Models-Money-and-Loyalty

September 25, 2009 | Peter Farago

This article comes from the Flurry Smartphone Industry Pulse, August 2009.

The data in this report is computed from a sample size of over 2,000 live applications and over 200 million user sessions tracked each month across Apple (iPhone and iPod Touch), Google Android, Blackberry, JavaME platforms.

Understanding Mobile App Retention: They Use or You Lose

With more than 75,000 applications in the App Store, consumers have a vast choice of alternatives to the applications they have already downloaded. And while discovery of new applications is a challenge for consumers, retaining users can be equally difficult for developers. To shed light on the kinds of applications that tend to be used over a longer period of time, Flurry studied user retention across 19 categories over a 90-day period. We monitored if consumers returned to use a downloaded application within 30, 60 and 90-day periods, as well as how frequently consumers used applications over those time periods. Flurry measures user retention by the number of users who downloaded an application, at any time in the past, and used that app within the last seven days.

Reviewing the chart on the previous page, Quadrant I is comprised of the most frequently used apps over the longest period of time; categories like News and Reference (e.g., Dictionaries, Thesauruses, Recipes, etc.). Thinking about News apps, this makes sense given that news content is constantly being refreshed, providing consumers nearly infinite value over time. This logic continues to hold up when we consider that news apps get re-used more than once per day, at a rate of 11 times per week.

On the other end of the spectrum, in Quadrant III, we find the Entertainment category, which could better be described as a collection of “gimmick” apps (think Lighter, Fart, IQ Test and Ringtone apps). Once downloaded, these apps are typically used only a few times and then abandoned.

In Quadrant II, we find categories like Books and Games, among the two largest app categories in both the App Store and Android Market. These application categories are characterized, on average, by intense usage over a finite period of time. Because games and books offer content that typically is consumed only once, the user usually moves on after reading a book or finishing a game.

Finally, Quadrant IV contains Productivity (e.g., List, Drawing, Wi-Fi Finder apps), Navigation and Medical apps. These kinds of apps remain on a consumer’s handset for a long period of time, but get used only occasionally. Unlike “gimmick” apps, they are perceived as having sustainable value and therefore consistently revisited over time.

Mapping categories by usage frequency and retention also provides insights into pricing models. Quadrants I and IV (the right-hand side) are better suited, on average, to subscription (if supported by the respective app storefront) and advertising-supported models. The main reason is that these apps have perceived enduring value by consumers over a long period of time, and therefore more successfully retain their user bases. For ad-supported apps, this high repeat usage translates into more ad impressions served. Categories on the left-hand side, Quadrants II and III, are better suited for one-time download fees. Those apps may provide higher immediate satisfaction to users but their content, once consumed, rapidly loses their value.

For more data on retention by category, as well as frequency of use, we provide the chart below:

這里面的兩個(gè)極端非常有趣:

  • 醫藥類(lèi)應用:它們可能具有高留存率,因為如果你患有慢性疾病,你就可能持續地使用某款與你的情況相關(guān)的應用,但可能不會(huì )每天都用。
  • 圖書(shū)/游戲:你可能會(huì )不停地讀上幾天、甚至一兩周,一旦讀完其中的內容,你就再也不會(huì )回來(lái)。

我注意到的一點(diǎn)是:特定的產(chǎn)品類(lèi)別本質(zhì),決定了產(chǎn)品的日活/月活、+1天留存和+1周留存指標的自然范圍。這是產(chǎn)品類(lèi)別的“先天”部分。無(wú)論稅務(wù)軟件做的多么好,你也做不到讓人們每天都來(lái)使用。

不過(guò),通過(guò)你的產(chǎn)品執行,你能夠使指標在自然范圍內達到最大。一款真心不錯的新聞類(lèi)產(chǎn)品,比如Flipboard,能夠將日活/月活提升50%以上,這是極好噠。

有些產(chǎn)品類(lèi)別無(wú)法獲得高日活/月活

對此的一個(gè)關(guān)鍵性結論是:除非你的產(chǎn)品與Twitter或Facebook屬于相同類(lèi)別,否則試圖與它們的日活/月活的50%進(jìn)行比較是毫無(wú)意義的。許多社交類(lèi)游戲以Facebook日活/月活的30%為目標,但我們從Flurry的圖表中也可以看到,社交類(lèi)游戲也在日活/月活最高的產(chǎn)品類(lèi)別之列。

也就是說(shuō),如果屬于相同的類(lèi)別,那么這些競爭對手的產(chǎn)品才能真正讓你辨別出你的產(chǎn)品指標多好才是真的好,并且還要在你正確執行它們的前提下。

總之,別和你的產(chǎn)品的本質(zhì)過(guò)不去:)

更新:來(lái)自Flurry的新圖表

在本文寫(xiě)完之后不就,Flurry發(fā)不了一張新版本的圖表,就在下面。原文點(diǎn)這里。有意思的是,你可以看看哪些類(lèi)別發(fā)生了一點(diǎn)點(diǎn)變化,我猜這些變化是因為每一類(lèi)產(chǎn)品中新應用的數量發(fā)生了不少變化。

October 22, 2012 | Peter Farago

Regardless of a company’s earlier success, thriving in the new mobile app economy depends on engagement and retention. After acquiring users, the real battle to keep and ultimately monetize consumers begins.  In the brave new world of “mobile first,” engagement is the new battleground.

This research is a redux to one of Flurry’s most popular reports, entitled Mobile Apps: Money, Models and Loyalty. Released three years ago, the initial report organized app category usage into a loyalty matrix. We do the same again now, while also acknowledging that a lot has changed in the app economy since then. To start, there is an order of magnitude more available apps in the App Store, now brimming with over 700,000 app choices for consumers. We are three generations beyond the then-new iPhone 3GS. We have since met the iPad, and perhaps tomorrow will meet the iPad Mini.

Combined, smart devices – iOS and Android smartphones and tablets – are the fastest adopted technology in history; adopted faster than electricity, televisions, microwaves, personal computers, cell phones, the Internet, dishwashers, stoves, and a whole lot more. Last month, Mark Zuckerberg, CEO of Facebook – the number two most visited website on the web – declared “we are now a mobile company” explaining that “you just could do so much better by doing native [application] work” versus using languages like HTML5 on top of browsers.  Each month, approximately 600 million of Facebook’s 1 billion monthly active users already accesses Facebook via mobile.

Each app category has different user engagement and loyalty characteristics. Understanding a given app audience based on the category to which it belongs can inform a company’s app acquisition, retention and monetization strategies. For this analysis, we use a sample of apps used more than 1.7 billion times each week. In total, more than 80,000 companies use Flurry Analytics across more than 230,000 apps to understand consumer behavior and improve their apps.

The above matrix plots application categories by how often they’re used compared to how long consumers continue to use them over time.  Specifically, we plot the 90-day retention rate of app categories on the x-axis against the frequency of use per week on the y-axis. We lay the “scatterplot” out in a Cartesian coordinate system with four quadrants. For our categories, we started by taking the application categories defined by Apple in the App Store. In cases where a cluster of applications within a parent category showed meaningful usage differences, we created a sub-category. For example, Flurry divides games into Social Games and Single Player Gamesgiven how differently consumers use these sub-categories.

Quadrant I includes apps that are used intensively and to which consumers are loyal over time. News and Communication apps are the two categories that appear in this category. On average, because these apps tend to have stable, growing audiences, they are best positioned to generate advertising revenue or charge a subscription. Consumers perceive these apps to deliver enduring value over time.

Quadrant II is comprised of apps that are used intensively, but for finite periods of time. They are perceived by consumers to deliver value in bursts. Streaming Music, Dating and Social Games best typify this quadrant. Consider for a moment why Dating is a category that appears in this quadrant. For most people, we can assume that finding a long-term “significant other” is the ultimate goal of dating. As a result, the app maker should expect customer churn. While usage may be high during the time when a consumer looks for a suitable partner, once that person is found, usage stops. An implication could be that to maintain a growing audience, apps in this category require heavy, constant acquisition to find consumers who are “in the market” for dating. Ironically, the better the app is at match making, the more churn it should expect.

Quadrant III contains apps that are used infrequently and have high churn. They contain the most “one-and-dones.” Personalization is an example that makes sense for this quadrant, since a consumer uses this app to change her screen saver or select a theme for her operating system. Once this set-up is complete, it’s unlikely that the user will need to re-use this application. Since the app’s value is diminished almost immediately, applications with this kind of usage pattern are best served with premium pricing models; that is, charging the consumer before providing access to the content.

Quadrant IV is made up of apps that are used infrequently but deliver very high value when used. Even though they’re used only occasionally, these apps can remain on a consumer’s handset almost indefinitely. For example, consider how useful an airline, hotel or rental car-booking app is to a business traveler. While the app remains unused between business trips, its value spikes as soon as the next business trip needs to be scheduled.

The quadrant an app falls into can help the content creator decide what business model is best. On average, Quadrants I and IV (the right-hand side) are better suited to subscription and advertising-supported models. The main reason is that these apps have perceived enduring value by consumers over a long period of time, and therefore more successfully retain their user bases. For ad-supported apps, high repeat usage translates into more ad impressions served. Categories on the left-hand side, Quadrants II and III, are better suited for one-time download fees. Additionally, quadrants II and IV (top left and bottom right) are likely best for in-app purchase models. For Quadrant II, the intense usage means that consumers find very high value during a short window. This creates the opportunity to offer new content or functionality during “binge” usage. Adroit social game makers are masters at driving in-app purchases during a consumer’s greatest moment of engagement. For Quadrant IV, because the user will return again and again, there also exists the possibility to find new ways of increasing value, which includes offering add-on functionality or content for a fee.

For more data, the table below provides 30, 60 and 90-day retention rates as well as weekly frequency of use numbers.  Note that some of the categories included in the table below are not included in the matrix chart above.

 

Compared to Flurry’s 2009 analysis, 90-day retention rates have increased from 25% to 35%. Additionally, frequency of use has decreased from 6.7 in 2009 to an average of 3.7 now. We attribute increased retention rates to increased quality in the market, driven by more competition. With tens of thousands of more companies building apps and hundreds of thousands of more available apps, the quality of apps has risen dramatically. Simply put, app makers are getting better at holding a consumer’s attention longer. Additionally, we believe usage rates are lower because consumers have more choice than ever and are splitting their time across more applications. While Flurry included 19 categories in its 2009 report, we now include 30 distinct categories as the industry has matured and more distinct verticals have appeared.

With more than a billion smartphones and tablets now in use, as well as the eventual move of apps into the living room through connected TV efforts by the likes of Apple and Google, digital distribution is changing the way the world does business. No matter what category your app belongs to, understanding and improving user engagement is the new currency of doing business in the new digital world.

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