The document describes 5 experiments conducted to better understand factors that influence app rankings in the Apple App Store and Google Play. The experiments analyzed ranking volatility, relationships between rankings and ratings, popularity metrics, and version freshness. Key findings include that Apple App Store rankings are more volatile, higher ratings correlate with more stable rankings, and apps with more ratings, installs and frequent updates tend to rank higher. The goal is to better understand app store algorithms and how to optimize apps for discovery.
The Ultimate App Store Optimization GuideMentorMate
How to Get Noticed in the Apple App and Google Play Stores.
To turn heads in the Apple App or Google Play Stores, apps must first face a rigorous test — public opinion. It must be downloaded. But, competition is fierce. According to Statistica.com, as of July 2015, there were 1,600,000 apps in the Google Play Store and 1,500,000 in the Apple App Store.
While there’s no magic solution, app store optimization marketing can help. In our App Store Optimization 2015 guide, we walk you step-by-step through the fundamentals and show you how to start reaping the benefits of this mobile app marketing tactic.
App Store Optimization (ASO) is the process of improving the visibility of a mobile app in an app store. ASO involves optimizing various factors like an app's title, description, keywords, screenshots, category, ratings and reviews to increase rankings and discoverability in app stores. Key aspects of ASO include app search optimization, deep linking, and optimizing the app's structure and internal navigation to improve the user experience.
Priori Data State of the (App) Union - July 2015Patrick Kane
From the Priori Data "How to Conquer the App Store" Meetup Event, held July 23rd, 2015 in Berlin.
Presentation contains detailed data and statistics around app store downloads, revenue, and Top Chart ranks.
Priori Data - App Market Trends & 2016 OutlookPatrick Kane
A presentation I gave at App Promotion Summit Berlin in December, 2015 recapping key trends from 2015 in the mobile app stores and providing an outlook on major shifts by Google and Apple that will change the structure of the app stores in 2016.
Distimo was pleased to be speaking at the Festival Of Games in Utrecht. At this event, we gave a presentation titled: "The Appstore Opportunity" . The presentation was aimed at Games developers and covers trends in monetization, changing impact of countries and stores. Moreover, more information about the top rankings is given and finally a demo of our new product, app.lk is showed.
App Store Optimization By MobileDevHQ (for SMX East)aoklein
This is the presentation our CEO, Ian Sefferman, gave at SMX East. It is an overview on ASO (App Store Optimization) and gives you the What, the Why and the How of ASO.
More than 6 million apps have been published on the app store and Play Store. Almost 2/3 of them do not generate more than 100 installs. The reality of the mobile app market can be pure chaos! And here comes… App Store Optimization! Join us and learn how to optimize your mobile app as to be searchable, improve your conversation rate of downloads and in-app purchases.
The Ultimate App Store Optimization GuideMentorMate
How to Get Noticed in the Apple App and Google Play Stores.
To turn heads in the Apple App or Google Play Stores, apps must first face a rigorous test — public opinion. It must be downloaded. But, competition is fierce. According to Statistica.com, as of July 2015, there were 1,600,000 apps in the Google Play Store and 1,500,000 in the Apple App Store.
While there’s no magic solution, app store optimization marketing can help. In our App Store Optimization 2015 guide, we walk you step-by-step through the fundamentals and show you how to start reaping the benefits of this mobile app marketing tactic.
App Store Optimization (ASO) is the process of improving the visibility of a mobile app in an app store. ASO involves optimizing various factors like an app's title, description, keywords, screenshots, category, ratings and reviews to increase rankings and discoverability in app stores. Key aspects of ASO include app search optimization, deep linking, and optimizing the app's structure and internal navigation to improve the user experience.
Priori Data State of the (App) Union - July 2015Patrick Kane
From the Priori Data "How to Conquer the App Store" Meetup Event, held July 23rd, 2015 in Berlin.
Presentation contains detailed data and statistics around app store downloads, revenue, and Top Chart ranks.
Priori Data - App Market Trends & 2016 OutlookPatrick Kane
A presentation I gave at App Promotion Summit Berlin in December, 2015 recapping key trends from 2015 in the mobile app stores and providing an outlook on major shifts by Google and Apple that will change the structure of the app stores in 2016.
Distimo was pleased to be speaking at the Festival Of Games in Utrecht. At this event, we gave a presentation titled: "The Appstore Opportunity" . The presentation was aimed at Games developers and covers trends in monetization, changing impact of countries and stores. Moreover, more information about the top rankings is given and finally a demo of our new product, app.lk is showed.
App Store Optimization By MobileDevHQ (for SMX East)aoklein
This is the presentation our CEO, Ian Sefferman, gave at SMX East. It is an overview on ASO (App Store Optimization) and gives you the What, the Why and the How of ASO.
More than 6 million apps have been published on the app store and Play Store. Almost 2/3 of them do not generate more than 100 installs. The reality of the mobile app market can be pure chaos! And here comes… App Store Optimization! Join us and learn how to optimize your mobile app as to be searchable, improve your conversation rate of downloads and in-app purchases.
This document presents a method for estimating app demand from publicly available data for Apple's iTunes App Store. The researchers developed an innovative technique to infer the relationship between an app's rank and downloads without access to private sales data. They found that the top paid app for iPhone generates 150 times more downloads than the 200th ranked app, and the top paid iPad app generates 120 times more downloads than the 200th ranked app. This framework could potentially be extended to other platforms like Android.
App promo-Best practices for App Store Optimization (ASO)Gary Yentin
The document discusses App Store Optimization (ASO), which is similar to SEO but for app stores. It provides insights and best practices for ASO, including optimizing app titles, descriptions, screenshots, keywords and metadata to improve search engine ranking and discoverability. Specific tactics covered include incorporating keywords, using bullet points in descriptions, including app portfolio links, and regularly updating text and metadata. The document also addresses challenges like limited data, trial and error testing across platforms and regions, and immature analytics.
State of The App Store & Role of Mobile Gaming, 2015 PRIORI DATA
Presentation on the State of the App Store from Pocket Gamer Connect, 2015.
- The relative size of the market
- Role of Games
- Growth in the App Store
- Some observations on what makes Publishers succeed
www.prioridata.com
This document discusses app store optimization (ASO) strategies for mobile apps. It explains that ASO, which involves optimizing app visibility in app stores, is important because over 60% of apps are discovered through app store searches. Key factors that influence app store search rankings include the app title, number of downloads, ratings and reviews, and frequency of app updates. The document also discusses how maintaining a balance of organic and paid user acquisition is important for success, and that the timing of campaigns can significantly impact install volumes. Always-on campaigns are recommended for brand awareness, lead generation, and re-engagement over the long run.
App Stores - Category Analysis (Apple App Store)PRIORI DATA
In this presentation, we go through various steps of increasing our understanding of an app store category: How big is it, how fast is it growing, how popular is it with developers, which markets are bigger and how difficult is it to reach a certain app store rank? Every view is from Priori Data PRO, a platform that is offered to developers free of charge when they partner with us at Priori Data https://prioridata.com/early_adopter_program.
If you have any question, please reach out. anders@prioridata.com
Google playstore, Android market study 2017Amrita Sarkar
The document analyzes 9,669 apps from the Google Play Store across several parameters. It finds that family, games, and tools are the most popular app categories. Arts/Design, Books/Reference, Education and Events have the highest ratings while Dating, Video Players, Tools and Lifestyle have the lowest. Most apps are free to download and $0.99 to $4.99 is the optimal price range for paid apps. Communication, Tools and Productivity apps have the highest downloads. Health/Fitness apps receive more positive user reviews than other categories like Games and Family.
Use App Store Optimisation to increase your mobile profitsLeadmill
Why is App Store Optimisation crucial for every business working with App marketing and what are the most crucial keys to higher rankings within Google Play and App Store?
The presentation was created as a part of our key note during App Day 2015
App Store Optimization - Metrics, Organic Discovery, & The Future | SMX Muni...Kahena Digital Marketing
Ari Nahmani, CEO / Founder of Kahena Digital Marketing presenting at SMX Munich on ASO. The presentation covers metrics & KPIs, Organic discovery, and predictions for the future including app indexing, paid search and keyword data in the app stores, and better data.
with our Monthly Focus we share a selection of our dataset with the global analyst community. Looking for more detailed information? www.prioridata.com
App-Promo Android Marketing TO PresentationGary Yentin
The document discusses marketing strategies for Android apps. It notes that the Android Market grew 127% in apps from Q4 2010 to Q2 2011, compared to 44% for the Apple App Store. It introduces App-Promo as an expert in custom marketing plans for apps, including PR, social media, and paid media tactics. Specific tactics covered include optimizing app store listings, building brands, leveraging reviews, distributing beyond app stores, working with bloggers, and testing different paid media approaches.
VERSION WITH PRESENTER NOTES: http://cl.ly/2z340V2m1N1d
How Apple and Google features apps and games is marred with rumors as well as a great mystery. Who is evaluating the games? How are they doing this? And what is the best way to approach them? I've not only talked to both Apple and Google representatives about these questions in detail but have also managed to get featured spots for my own or other client's games several times. It’s not rocket science but there are several rules and practices to increase your chances.
1) Reviews and ratings play a key role in app search optimization, discovery, and user acquisition. They significantly impact an app's ranking on platforms like Google Play and the App Store.
2) While reviews provide valuable user feedback, they also present challenges. Users tend to write reviews only when very satisfied or dissatisfied, and some reviews contradict the star ratings.
3) To address issues and maximize the benefits of reviews, developers should focus on listening to feedback, responding respectfully to reviews, and requesting reviews from happy users at opportune times through integrated and non-intrusive means.
Monthly Focus: Google Play_Japan_May/June_2014PRIORI DATA
With our Monthly Focus we share a selection of our dataset with the global analyst community. Looking for more detailed information? www.prioridata.com
This document provides an overview of app marketing strategies and techniques. It begins with introductions to Emilio Aviles, CEO of SlashMobility, and Oscar Escudero, co-founder of DressApp. The document then discusses the importance of having an app marketing strategy due to the large number of apps available. It covers techniques like ASO (App Store Optimization) and the importance of downloads, ratings, reviews and engagement for app rankings. The rest of the document outlines a 360 degree app marketing strategy framework covering areas like targeting, positioning, promotion, measurement and engagement. It emphasizes the need for retention beyond initial downloads. Overall, the document provides a comprehensive overview of factors and strategies important for effective app marketing.
Tug of Perspectives: Mobile App Users vs Developers (pp. 83-94)
Sandeep Kaur Kuttal (#1), Yiting Bai (#2), Ezequiel Scott (∗3), Rajesh Sharma (∗4),
(#) Tandy School of Computer Science, University of Tulsa, USA.
(∗) University of Tartu, Estonia.
Vol. 18 No. 6 JUNE 2020 International Journal of Computer Science and Information Security
https://sites.google.com/site/ijcsis/vol-18-no-6-jun-2020
This document is a report from We Are Social that provides global and regional statistics on digital trends as of January 2014. Some key findings include: there were over 2 billion internet users worldwide in 2014 representing 35% internet penetration globally; nearly 1.9 billion people actively used social media accounting for 26% of the world's population; and over 6.5 billion mobile subscriptions amounted to a 93% mobile penetration rate worldwide. The report also analyzes and compares regional data on internet, social media, and mobile connectivity.
This document presents a method for estimating app demand from publicly available data for Apple's iTunes App Store. The researchers developed an innovative technique to infer the relationship between an app's rank and downloads without access to private sales data. They found that the top paid app for iPhone generates 150 times more downloads than the 200th ranked app, and the top paid iPad app generates 120 times more downloads than the 200th ranked app. This framework could potentially be extended to other platforms like Android.
App promo-Best practices for App Store Optimization (ASO)Gary Yentin
The document discusses App Store Optimization (ASO), which is similar to SEO but for app stores. It provides insights and best practices for ASO, including optimizing app titles, descriptions, screenshots, keywords and metadata to improve search engine ranking and discoverability. Specific tactics covered include incorporating keywords, using bullet points in descriptions, including app portfolio links, and regularly updating text and metadata. The document also addresses challenges like limited data, trial and error testing across platforms and regions, and immature analytics.
State of The App Store & Role of Mobile Gaming, 2015 PRIORI DATA
Presentation on the State of the App Store from Pocket Gamer Connect, 2015.
- The relative size of the market
- Role of Games
- Growth in the App Store
- Some observations on what makes Publishers succeed
www.prioridata.com
This document discusses app store optimization (ASO) strategies for mobile apps. It explains that ASO, which involves optimizing app visibility in app stores, is important because over 60% of apps are discovered through app store searches. Key factors that influence app store search rankings include the app title, number of downloads, ratings and reviews, and frequency of app updates. The document also discusses how maintaining a balance of organic and paid user acquisition is important for success, and that the timing of campaigns can significantly impact install volumes. Always-on campaigns are recommended for brand awareness, lead generation, and re-engagement over the long run.
App Stores - Category Analysis (Apple App Store)PRIORI DATA
In this presentation, we go through various steps of increasing our understanding of an app store category: How big is it, how fast is it growing, how popular is it with developers, which markets are bigger and how difficult is it to reach a certain app store rank? Every view is from Priori Data PRO, a platform that is offered to developers free of charge when they partner with us at Priori Data https://prioridata.com/early_adopter_program.
If you have any question, please reach out. anders@prioridata.com
Google playstore, Android market study 2017Amrita Sarkar
The document analyzes 9,669 apps from the Google Play Store across several parameters. It finds that family, games, and tools are the most popular app categories. Arts/Design, Books/Reference, Education and Events have the highest ratings while Dating, Video Players, Tools and Lifestyle have the lowest. Most apps are free to download and $0.99 to $4.99 is the optimal price range for paid apps. Communication, Tools and Productivity apps have the highest downloads. Health/Fitness apps receive more positive user reviews than other categories like Games and Family.
Use App Store Optimisation to increase your mobile profitsLeadmill
Why is App Store Optimisation crucial for every business working with App marketing and what are the most crucial keys to higher rankings within Google Play and App Store?
The presentation was created as a part of our key note during App Day 2015
App Store Optimization - Metrics, Organic Discovery, & The Future | SMX Muni...Kahena Digital Marketing
Ari Nahmani, CEO / Founder of Kahena Digital Marketing presenting at SMX Munich on ASO. The presentation covers metrics & KPIs, Organic discovery, and predictions for the future including app indexing, paid search and keyword data in the app stores, and better data.
with our Monthly Focus we share a selection of our dataset with the global analyst community. Looking for more detailed information? www.prioridata.com
App-Promo Android Marketing TO PresentationGary Yentin
The document discusses marketing strategies for Android apps. It notes that the Android Market grew 127% in apps from Q4 2010 to Q2 2011, compared to 44% for the Apple App Store. It introduces App-Promo as an expert in custom marketing plans for apps, including PR, social media, and paid media tactics. Specific tactics covered include optimizing app store listings, building brands, leveraging reviews, distributing beyond app stores, working with bloggers, and testing different paid media approaches.
VERSION WITH PRESENTER NOTES: http://cl.ly/2z340V2m1N1d
How Apple and Google features apps and games is marred with rumors as well as a great mystery. Who is evaluating the games? How are they doing this? And what is the best way to approach them? I've not only talked to both Apple and Google representatives about these questions in detail but have also managed to get featured spots for my own or other client's games several times. It’s not rocket science but there are several rules and practices to increase your chances.
1) Reviews and ratings play a key role in app search optimization, discovery, and user acquisition. They significantly impact an app's ranking on platforms like Google Play and the App Store.
2) While reviews provide valuable user feedback, they also present challenges. Users tend to write reviews only when very satisfied or dissatisfied, and some reviews contradict the star ratings.
3) To address issues and maximize the benefits of reviews, developers should focus on listening to feedback, responding respectfully to reviews, and requesting reviews from happy users at opportune times through integrated and non-intrusive means.
Monthly Focus: Google Play_Japan_May/June_2014PRIORI DATA
With our Monthly Focus we share a selection of our dataset with the global analyst community. Looking for more detailed information? www.prioridata.com
This document provides an overview of app marketing strategies and techniques. It begins with introductions to Emilio Aviles, CEO of SlashMobility, and Oscar Escudero, co-founder of DressApp. The document then discusses the importance of having an app marketing strategy due to the large number of apps available. It covers techniques like ASO (App Store Optimization) and the importance of downloads, ratings, reviews and engagement for app rankings. The rest of the document outlines a 360 degree app marketing strategy framework covering areas like targeting, positioning, promotion, measurement and engagement. It emphasizes the need for retention beyond initial downloads. Overall, the document provides a comprehensive overview of factors and strategies important for effective app marketing.
Tug of Perspectives: Mobile App Users vs Developers (pp. 83-94)
Sandeep Kaur Kuttal (#1), Yiting Bai (#2), Ezequiel Scott (∗3), Rajesh Sharma (∗4),
(#) Tandy School of Computer Science, University of Tulsa, USA.
(∗) University of Tartu, Estonia.
Vol. 18 No. 6 JUNE 2020 International Journal of Computer Science and Information Security
https://sites.google.com/site/ijcsis/vol-18-no-6-jun-2020
This document is a report from We Are Social that provides global and regional statistics on digital trends as of January 2014. Some key findings include: there were over 2 billion internet users worldwide in 2014 representing 35% internet penetration globally; nearly 1.9 billion people actively used social media accounting for 26% of the world's population; and over 6.5 billion mobile subscriptions amounted to a 93% mobile penetration rate worldwide. The report also analyzes and compares regional data on internet, social media, and mobile connectivity.
This document provides an overview and statistics on digital trends in the Asia-Pacific region in 2014. It includes statistics on population sizes, internet penetration rates, social media usage, and mobile phone adoption for various countries in the region. The data is from sources like the UN, World Bank, and social media companies and is presented visually through charts and graphs.
This document discusses a study analyzing factors that affect the probability of mobile apps crossing into the top 25 rankings on the Apple App Store. The study used logistic regression to examine how app characteristics like app size, name length, inclusion of Chinese language, and OS compatibility relate to the odds of an app crossing the top 25 threshold. The results found that app size and name length are significant factors, and that including a Chinese version has a positive interaction effect for certain app categories. The findings provide insights that could help app developers in planning which factors to prioritize for increasing their chances of rising in the App Store rankings.
Discovery of ranking fraud for mobile appsBoopathi Kumar
1. The document proposes a ranking fraud detection system for mobile apps. It aims to detect fraudulent activities aimed at artificially boosting an app's popularity ranking.
2. The system first identifies periods of high activity or "leading sessions" for each app. It then analyzes ranking, rating, and review data to extract evidence of anomalies during these sessions using statistical tests.
3. An optimization-based method is used to aggregate all evidence to evaluate the credibility of leading sessions and detect ranking fraud. The system is evaluated on real app data and shown to effectively detect fraud while scaling to the large number of apps.
The document discusses 8 tips for improving app store optimization and rankings. It outlines important on-page and off-page factors like app title, description, keywords, click-through rate, usage, reviews, and social signals. It recommends focusing on natural keyword incorporation, conversion optimization, keeping the app top of mind through social media, offering reviews, using PR, preventing bouncing, open graph integration, and creating a high quality app.
IEEE PROJECTS 2015
1 crore projects is a leading Guide for ieee Projects and real time projects Works Provider.
It has been provided Lot of Guidance for Thousands of Students & made them more beneficial in all Technology Training.
Dot Net
DOTNET Project Domain list 2015
1. IEEE based on datamining and knowledge engineering
2. IEEE based on mobile computing
3. IEEE based on networking
4. IEEE based on Image processing
5. IEEE based on Multimedia
6. IEEE based on Network security
7. IEEE based on parallel and distributed systems
Java Project Domain list 2015
1. IEEE based on datamining and knowledge engineering
2. IEEE based on mobile computing
3. IEEE based on networking
4. IEEE based on Image processing
5. IEEE based on Multimedia
6. IEEE based on Network security
7. IEEE based on parallel and distributed systems
ECE IEEE Projects 2015
1. Matlab project
2. Ns2 project
3. Embedded project
4. Robotics project
Eligibility
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2. BCA/B.E(C.S)
3. B.Tech IT
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5. MSc (C.S)
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7. MCA
8. MS (IT)
9. ME(ALL)
10. BE(ECE)(EEE)(E&I)
TECHNOLOGY USED AND FOR TRAINING IN
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2. C sharp
3. ASP
4. VB
5. SQL SERVER
6. JAVA
7. J2EE
8. STRINGS
9. ORACLE
10. VB dotNET
11. EMBEDDED
12. MAT LAB
13. LAB VIEW
14. Multi Sim
CONTACT US
1 CRORE PROJECTS
Door No: 214/215,2nd Floor,
No. 172, Raahat Plaza, (Shopping Mall) ,Arcot Road, Vadapalani, Chennai,
Tamin Nadu, INDIA - 600 026
Email id: 1croreprojects@gmail.com
website:1croreprojects.com
Phone : +91 97518 00789 / +91 72999 51536
IEEE PROJECTS 2015
1 crore projects is a leading Guide for ieee Projects and real time projects Works Provider.
It has been provided Lot of Guidance for Thousands of Students & made them more beneficial in all Technology Training.
Dot Net
DOTNET Project Domain list 2015
1. IEEE based on datamining and knowledge engineering
2. IEEE based on mobile computing
3. IEEE based on networking
4. IEEE based on Image processing
5. IEEE based on Multimedia
6. IEEE based on Network security
7. IEEE based on parallel and distributed systems
Java Project Domain list 2015
1. IEEE based on datamining and knowledge engineering
2. IEEE based on mobile computing
3. IEEE based on networking
4. IEEE based on Image processing
5. IEEE based on Multimedia
6. IEEE based on Network security
7. IEEE based on parallel and distributed systems
ECE IEEE Projects 2015
1. Matlab project
2. Ns2 project
3. Embedded project
4. Robotics project
Eligibility
Final Year students of
1. BSc (C.S)
2. BCA/B.E(C.S)
3. B.Tech IT
4. BE (C.S)
5. MSc (C.S)
6. MSc (IT)
7. MCA
8. MS (IT)
9. ME(ALL)
10. BE(ECE)(EEE)(E&I)
TECHNOLOGY USED AND FOR TRAINING IN
1. DOT NET
2. C sharp
3. ASP
4. VB
5. SQL SERVER
6. JAVA
7. J2EE
8. STRINGS
9. ORACLE
10. VB dotNET
11. EMBEDDED
12. MAT LAB
13. LAB VIEW
14. Multi Sim
CONTACT US
1 CRORE PROJECTS
Door No: 214/215,2nd Floor,
No. 172, Raahat Plaza, (Shopping Mall) ,Arcot Road, Vadapalani, Chennai,
Tamin Nadu, INDIA - 600 026
Email id: 1croreprojects@gmail.com
website:1croreprojects.com
Phone : +91 97518 00789 / +91 72999 51536
This document discusses strategies for optimizing apps on the App Store to improve visibility and increase downloads and sales. It recommends focusing on three key factors: having a trustworthy storefront with quality graphics, descriptions, and policies; promoting apps purposefully through updates, social media, reviews, and giveaways; and allowing room to experiment with different monetization and marketing approaches over time as the App Store changes. It provides examples of developers who optimized these factors and achieved success on the App Store over several app releases and years.
Understanding the Relationship Between Paid and Organic Installs FinalMohamed Mahdy
For every paid install of an app, the app can expect an additional 1.5 organic installs on average across all categories and platforms. However, the relationship between paid and organic installs varies by category and operating system. For example, an iOS game can expect 5.4 organic installs for each paid install, while an Android tools app can expect only 0.3 organic installs for each paid install. Marketers can use these organic multiplier metrics to inform their marketing strategies and balance of paid versus organic install efforts.
App Store Optimization - SMX Munich - Emily GrossmanMobileMoxie
This document discusses app store optimization strategies for ranking highly in app store search results. It recommends optimizing app metadata like titles, categories, and keywords to include important search terms. It also recommends strategies to improve app performance metrics like download volume and velocity, star ratings, and review count which account for 65% of an app's ranking. Additionally, it notes the importance of frequent app updates to maintain freshness. The document suggests these optimization strategies will become even more important as app search evolves to work more like web search with apps indexed and deep linked on platforms like Google.
This document analyzes Google Play Store apps that were installed by users. It examines the correlation between various variables like rating, reviews, content type, and cost that may impact installations. The analysis was conducted using SPSS and Tableau on a dataset of 10,500 Google Play Store apps. Key findings include: 1) Installations had a positive correlation with app size and reviews but negative correlation with rating, price and content rating. 2) Games were the most installed category while medical apps had the highest prices. 3) Education apps had the highest ratings while games had the most reviews. The analysis aims to help app developers understand factors that influence user installations.
How to get 30k+ App Store reviews every monthAppFollow
This document discusses the importance of app ratings and reviews for conversion rates. It notes that 79% of users check reviews before downloading an app and that apps with ratings of 1-3 stars can lose half of potential downloads. The document recommends regularly monitoring reviews and using in-app rating requests to significantly increase ratings. It provides tips on the best times to prompt users to rate the app to receive more positive reviews and higher download numbers.
MOBILE APP BENCHMARK: TOP 10 MOBILE SHOPPING APPS IN THE USAT Internet
Discover who's leading the shopping mobile app market in the US. This free benchmark report from AT Internet outlines the top 10 shopping apps in the US market.
For more benchmarks and studies from AT Internet, visit: http://www.atinternet.com/en/resources/surveys/
Five Reasons why your game is not in the TOP of search in the store. Anastasia Keyapp.top
Hi everyone! My name is Olena and I am Sales&Support Team Lead at Keyapp, service for mobile apps keyword promotion. What will we talk about today?
- 5 reason why your game is not in top for keywords
- what factors influence the top positions in Google Play and App Store
- how to increase the app organic traffic from search
- what is keyword promotion and how it influences the amount of traffic the app gets
Get more downloads for your app now https://bit.ly/3S7N3lC
Entering new markets in mobile: how to gather insights and succeedAppFollow
Presentation from the webinar "Entering new markets: strategy, pitfalls, and tips" hosted together with Social Peta.
Watch the video here https://appfollow.io/webinar/webinar-entering-new-markets-strategy-pitfalls-and-tips
The document discusses a proposed system for detecting ranking fraud for mobile apps. It begins by describing existing ranking fraud and some current detection systems. It then outlines the proposed system which first identifies "leading sessions" in an app's historical ranking data that indicate periods of popularity. It then detects fraud by analyzing ranking, rating, and review behaviors during these sessions using statistical tests. Finally, the proposed system aggregates all the evidence to evaluate sessions for fraud and was tested on real app store data.
Discovery of ranking fraud for mobile appsnexgentech15
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NEXGEN TECHNOLOGY as an efficient Software Training Center located at Pondicherry with IT Training on IEEE Projects in Android,IEEE IT B.Tech Student Projects, Android Projects Training with Placements Pondicherry, IEEE projects in pondicherry, final IEEE Projects in Pondicherry , MCA, BTech, BCA Projects in Pondicherry, Bulk IEEE PROJECTS IN Pondicherry.So far we have reached almost all engineering colleges located in Pondicherry and around 90km
Nexgen Technology Address:
Nexgen Technology
No :66,4th cross,Venkata nagar,
Near SBI ATM,
Puducherry.
Email Id: praveen@nexgenproject.com.
www.nexgenproject.com
Mobile: 9751442511,9791938249
Telephone: 0413-2211159.
NEXGEN TECHNOLOGY as an efficient Software Training Center located at Pondicherry with IT Training on IEEE Projects in Android,IEEE IT B.Tech Student Projects, Android Projects Training with Placements Pondicherry, IEEE projects in pondicherry, final IEEE Projects in Pondicherry , MCA, BTech, BCA Projects in Pondicherry, Bulk IEEE PROJECTS IN Pondicherry.So far we have reached almost all engineering colleges located in Pondicherry and around 90km
DISCOVERY OF RANKING FRAUD FOR MOBILE APPS - IEEE PROJECTS IN PONDICHERRY,BUL...Nexgen Technology
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Near SBI ATM,
Puducherry.
Email Id: praveen@nexgenproject.com.
www.nexgenproject.com
Mobile: 9751442511,9791938249
Telephone: 0413-2211159.
NEXGEN TECHNOLOGY as an efficient Software Training Center located at Pondicherry with IT Training on IEEE Projects in Android,IEEE IT B.Tech Student Projects, Android Projects Training with Placements Pondicherry, IEEE projects in pondicherry, final IEEE Projects in Pondicherry , MCA, BTech, BCA Projects in Pondicherry, Bulk IEEE PROJECTS IN Pondicherry.So far we have reached almost all engineering colleges located in Pondicherry and around 90km
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Deconstructing the app store rankings formula
1. Deconstructing the App Store Rankings Formula with 5 Mad
Science Experiments
After seeing Rand's "Mad Science Experiments in SEO" presented at last year's MozCon, I was
inspired to put on the lab coat and goggles and do a few experiments of my own--not in SEO, but in
SEO's up-and-coming younger sister, ASO (app store optimization).
Working with Apptentive to guide enterprise apps and small startup apps alike to increase their
discoverability in the app stores, I've learned a thing or two about app store optimization and what
goes into an app's ranking. It's been my personal goal for some time now to pull back the curtains on
Google and Apple. Yet, the deeper into the rabbit hole I go, the more untested assumptions I leave in
my way.
Hence, I thought it was due time to put some longstanding hypotheses through the gauntlet.
As SEOs, we know how much of an impact a single ranking can mean on a SERP. One tiny rank up or
down can make all the difference when it comes to your website's traffic--and revenue.
In the world of apps, ranking is just as important when it comes to standing out in a sea of more
than 1.3 million apps. Apptentive's recent mobile consumer survey shed a little more light this claim,
revealing that nearly half of all mobile app users identified browsing the app store charts and search
results (the placement on either of which depends on rankings) as a preferred method for finding
new apps in the app stores. Simply put, better rankings mean more downloads and easier discovery.
Like Google and Bing, the two leading app stores (the Apple App Store and Google Play) have a
complex and highly guarded algorithms for determining rankings for both keyword-based app store
searches and composite top charts.
Unlike SEO, however, very little research and theory has been conducted around what goes into
these rankings.
Until now, that is.
Over the course of five experiments analyzing various publicly available data points for a cross-
section of the top 500 iOS (U.S. Apple App Store) and the top 500 Android (U.S. Google Play) apps,
I'll attempt to set the record straight with a little myth-busting around ASO. In the process, I hope to
assess and quantify any perceived correlations between app store ranks, ranking volatility, and a few
of the factors commonly thought of as influential to an app's ranking.
But first, a little context
2. Image credit: Josh Tuininga, Apptentive
Both the Apple App Store and Google Play have roughly 1.3 million apps each, and both stores
feature a similar breakdown by app category. Apps ranking in the two stores should, theoretically,
be on a fairly level playing field in terms of search volume and competition.
Of these apps, nearly two-thirds have not received a single rating and 99% are considered
unprofitable. These experiments, therefore, single out the rare exceptions to the rule--the top 500
ranked apps in each store.
While neither Apple nor Google have revealed specifics about how they calculate search rankings, it
is generally accepted that both app store algorithms factor in:
Average app store rating
Rating/review volume
Download and install counts
Uninstalls (what retention and churn look like for the app)
App usage statistics (how engaged an app's users are and how frequently they launch the app)
Growth trends weighted toward recency (how daily download counts changed over time and how
today's ratings compare to last week's)
Keyword density of the app's landing page (Ian did a great job covering this factor in a previous Moz
post)
I've simplified this formula to a function highlighting the four elements with sufficient data (or at
least proxy data) for our experimentation:
3. Ranking = fn(Rating, Rating Count, Installs, Trends)
Of course, right now, this generalized function doesn't say much. Over the next five experiments,
however, we'll revisit this function before ultimately attempting to compare the weights of each of
these four variables on app store rankings.
(For the purpose of brevity, I'll stop here with the assumptions, but I've gone into far greater depth
into how I've reached these conclusions in a 55-page report on app store rankings.)
Now, for the Mad Science.
Experiment #1: App-les to app-les app store ranking volatility
The first, and most straight forward of the five experiments involves tracking daily movement in app
store rankings across iOS and Android versions of the same apps to determine any trends of
differences between ranking volatility in the two stores.
I went with a small sample of five apps for this experiment, the only criteria for which were that:
They were all apps I actively use (a criterion for coming up with the five apps but not one that
influences rank in the U.S. app stores)
They were ranked in the top 500 (but not the top 25, as I assumed app store rankings would be
stickier at the top--an assumption I'll test in experiment #2)
They had an almost identical version of the app in both Google Play and the App Store, meaning they
should (theoretically) rank similarly
They covered a spectrum of app categories
The apps I ultimately chose were Lyft, Venmo, Duolingo, Chase Mobile, and LinkedIn. These five
apps represent the travel, finance, education banking, and social networking categories.
Hypothesis
Going into this experiment, I predicted slightly more volatility in Apple App Store rankings, based on
two statistics:
Both of these assumptions will be tested in later experiments.
Results
4. Among these five apps, Google Play rankings were, indeed, significantly less volatile than App Store
rankings. Among the 35 data points recorded, rankings within Google Play moved by as much as 23
positions/ranks per day while App Store rankings moved up to 89 positions/ranks. The standard
deviation of ranking volatility in the App Store was, furthermore, 4.45 times greater than that of
Google Play.
Of course, the same apps varied fairly dramatically in their rankings in the two app stores, so I then
standardized the ranking volatility in terms of percent change to control for the effect of numeric
rank on volatility. When cast in this light, App Store rankings changed by as much as 72% within a
24-hour period while Google Play rankings changed by no more than 9%.
Also of note, daily rankings tended to move in the same direction across the two app stores
approximately two-thirds of the time, suggesting that the two stores, and their customers, may have
more in common than we think.
Experiment #2: App store ranking volatility across the top charts
5. Testing the assumption implicit in standardizing the data in experiment No. 1, this experiment was
designed to see if app store ranking volatility is correlated with an app's current rank. The sample
for this experiment consisted of the top 500 ranked apps in both Google Play and the App Store, with
special attention given to those on both ends of the spectrum (ranks 1-100 and 401-500).
Hypothesis
I anticipated rankings to be more volatile the higher an app is ranked--meaning an app ranked No.
450 should be able to move more ranks in any given day than an app ranked No. 50. This hypothesis
is based on the assumption that higher ranked apps have more installs, active users, and ratings,
and that it would take a large margin to produce a noticeable shift in any of these factors.
Results
One look at the chart above shows that apps in both stores have increasingly more volatile rankings
(based on how many ranks they moved in the last 24 hours) the lower on the list they're ranked.
This is particularly true when comparing either end of the spectrum--with a seemingly straight
volatility line among Google Play's Top 100 apps and very few blips within the App Store's Top 100.
Compare this section to the lower end, ranks 401-)500, where both stores experience much more
turbulence in their rankings. Across the gamut, I found a 24% correlation between rank and ranking
volatility in the Play Store and 28% correlation in the App Store.
To put this into perspective, the average app in Google Play's 401-)500 ranks moved 12.1 ranks in
the last 24 hours while the average app in the Top 100 moved a mere 1.4 ranks. For the App Store,
these numbers were 64.28 and 11.26, making slightly lower-ranked apps more than five times as
volatile as the highest ranked apps. (I say slightly as these "lower-ranked" apps are still ranked
higher than 99.96% of all apps.)
6. The relationship between rank and volatility is pretty consistent across the App Store charts, while
rank has a much greater impact on volatility at the lower end of Google Play charts (ranks 1-100
have a 35% correlation) than it does at the upper end (ranks 401-500 have a 1% correlation).
Experiment #3: App store rankings across the stars
The next experiment looks at the relationship between rank and star ratings to determine any trends
that set the top chart apps apart from the rest and explore any ties to app store ranking volatility.
Hypothesis
Ranking = fn(Rating, Rating Count, Installs, Trends)
As discussed in the introduction, this experiment relates directly to one of the factors commonly
accepted as influential to app store rankings: average rating.
Going into the experiment, I hypothesized that higher ranks generally correspond to higher ratings,
cementing the role of star ratings in the ranking algorithm.
As far as volatility goes, I did not anticipate average rating to play a role in app store ranking
volatility, as I saw no reason for higher rated apps to be less volatile than lower rated apps, or vice
versa. Instead, I believed volatility to be tied to rating volume (as we'll explore in our last
experiment).
Results
7. The chart above plots the top 100 ranked apps in either store with their average rating (both historic
and current, for App Store apps). If it looks a little chaotic, it's just one indicator of the complexity of
ranking algorithm in Google Play and the App Store.
If our hypothesis was correct, we'd see a downward trend in ratings. We'd expect to see the No. 1
ranked app with a significantly higher rating than the No. 100 ranked app. Yet, in neither store is
this the case. Instead, we get a seemingly random plot with no obvious trends that jump off the
chart.
A closer examination, in tandem with what we already know about the app stores, reveals two other
interesting points:
The average star rating of the top 100 apps is significantly higher than that of the average app.
Across the top charts, the average rating of a top 100 Android app was 4.319 and the average top
iOS app was 3.935. These ratings are 0.32 and 0.27 points, respectively, above the average rating of
all rated apps in either store. The averages across apps in the 401-)500 ranks approximately split the
difference between the ratings of the top ranked apps and the ratings of the average app.
The rating distribution of top apps in Google Play was considerably more compact than the
distribution of top iOS apps. The standard deviation of ratings in the Apple App Store top chart was
over 2.5 times greater than that of the Google Play top chart, likely meaning that ratings are more
heavily weighted in Google Play's algorithm.
8. Looking next at the relationship between ratings and app store ranking volatility reveals a -15%
correlation that is consistent across both app stores; meaning the higher an app is rated, the less its
rank it likely to move in a 24-hour period. The exception to this rule is the Apple App Store's
calculation of an app's current rating, for which I did not find a statistically significant correlation.
Experiment #4: App store rankings across versions
This next experiment looks at the relationship between the age of an app's current version, its rank
and its ranking volatility.
Hypothesis
Ranking = fn(Rating, Rating Count, Installs, Trends)
In alteration of the above function, I'm using the age of a current app's version as a proxy (albeit not
a very good one) for trends in app store ratings and app quality over time.
Making the assumptions that (a) apps that are updated more frequently are of higher quality and (b)
each new update inspires a new wave of installs and ratings, I'm hypothesizing that the older the age
of an app's current version, the lower it will be ranked and the less volatile its rank will be.
Results
9. The first and possibly most important finding of this experiment is that apps across the top charts in
both Google Play and the App Store are updated remarkably often as compared to the average app.
At the time of conducting the experiment, the current version of the average iOS app on the top
chart was only 28 days old; the current version of the average Android app was 38 days old.
As hypothesized, the age of the current version is negatively correlated with the app's rank, with a
13% correlation in Google Play and a 10% correlation in the App Store.
10. The next part of the experiment maps the age of the current app version to its app store ranking
volatility, finding that recently updated Android apps have less volatile rankings (correlation: 8.7%)
while recently updated iOS apps have more volatile rankings (correlation: -3%).
Experiment #5: App store rankings across monthly active users
In the final experiment, I wanted to examine the role of an app's popularity on its ranking. In an
ideal world, popularity would be measured by an app's monthly active users (MAUs), but since few
mobile app developers have released this information, I've settled for two publicly available proxies:
Rating Count and Installs.
Hypothesis
Ranking = fn(Rating, Rating Count, Installs, Trends)
For the same reasons indicated in the second experiment, I anticipated that more popular apps (e.g.,
apps with more ratings and more installs) would be higher ranked and less volatile in rank. This,
again, takes into consideration that it takes more of a shift to produce a noticeable impact in average
rating or any of the other commonly accepted influencers of an app's ranking.
Results
The first finding leaps straight off of the chart above: Android apps have been rated more times than
iOS apps, 15.8x more, in fact.
11. The average app in Google Play's Top 100 had a whopping 3.1 million ratings while the average app
in the Apple App Store's Top 100 had 196,000 ratings. In contrast, apps in the 401-)500 ranks (still
tremendously successful apps in the 99.96 percentile of all apps) tended to have between one-tenth
(Android) and one-fifth (iOS) of the ratings count as that of those apps in the top 100 ranks.
Considering that almost two-thirds of apps don't have a single rating, reaching rating counts this
high is a huge feat, and a very strong indicator of the influence of rating count in the app store
ranking algorithms.
To even out the playing field a bit and help us visualize any correlation between ratings and rankings
(and to give more credit to the still-staggering 196k ratings for the average top ranked iOS app), I've
applied a logarithmic scale to the chart above:
From this chart, we can see a correlation between ratings and rankings, such that apps with more
ratings tend to rank higher. This equates to a 29% correlation in the App Store and a 40%
correlation in Google Play.
12. Next up, I looked at how ratings count influenced app store ranking volatility, finding that apps with
more ratings had less volatile rankings in the Apple App Store (correlation: 17%). No conclusive
evidence was found within the Top 100 Google Play apps.
And last but not least, I looked at install counts as an additional proxy for MAUs. (Sadly, this is a
statistic only listed in Google Play. so any resulting conclusions are applicable only to Android apps.)
Among the top 100 Android apps, this last experiment found that installs were heavily correlated
13. with ranks (correlation: -35.5%), meaning that apps with more installs are likely to rank higher in
Google Play. Android apps with more installs also tended to have less volatile app store rankings,
with a correlation of -16.5%.
Unfortunately, these numbers are slightly skewed as Google Play only provides install counts in
broad ranges (e.g., 500k-)1M). For each app, I took the low end of the range, meaning we can likely
expect the correlation to be a little stronger since the low end was further away from the midpoint
for apps with more installs.
Summary
To make a long post ever so slightly shorter, here are the nuts and bolts unearthed in these five mad
science experiments in app store optimization:
Across the top charts, Apple App Store rankings are 4.45x more volatile than those of Google Play
Rankings become increasingly volatile the lower an app is ranked. This is particularly true across the
Apple App Store's top charts.
In both stores, higher ranked apps tend to have an app store ratings count that far exceeds that of
the average app
Ratings appear to matter more to the Google Play algorithm, especially as the Apple App Store top
charts experience a much wider ratings distribution than that of Google Play's top charts
The higher an app is rated, the less volatile its rankings are
The 100 highest ranked apps in either store are updated much more frequently than the average
app, and apps with older current versions are correlated with lower ratings
An app's update frequency is negatively correlated with Google Play's ranking volatility but
positively correlated with ranking volatility in the App Store. This likely due to how Apple weighs an
app's most recent ratings and reviews.
The highest ranked Google Play apps receive, on average, 15.8x more ratings than the highest
ranked App Store apps
In both stores, apps that fall under the 401-500 ranks receive, on average, 10-20% of the rating
volume seen by apps in the top 100
Rating volume and, by extension, installs or MAUs, is perhaps the best indicator of ranks, with a 29-
40% correlation between the two
Revisiting our first (albeit oversimplified) guess at the app stores' ranking algorithm gives us this
loosely defined function:
Ranking = fn(Rating, Rating Count, Installs, Trends)
I'd now re-write the function into a formula by weighing each of these four factors, where a, b, c, d
are unknown multipliers, or weights:
14. Ranking = (Rating * a) + (Rating Count * b) + (Installs * c) + (Trends * d)
These five experiments on ASO shed a little more light on these multipliers, showing Rating Count to
have the strongest correlation with rank, followed closely by Installs, in either app store.
It's with the other two factors--rating and trends--that the two stores show the greatest discrepancy.
I'd hazard a guess to say that the App Store prioritizes growth trends over ratings, given the
importance it places on an app's current version and the wide distribution of ratings across the top
charts. Google Play, on the other hand, seems to favor ratings, with an unwritten rule that apps just
about have to have at least four stars to make the top 100 ranks.
Thus, we conclude our mad science with this final glimpse into what it takes to make the top charts
in either store:
Weight of factors in the Apple App Store ranking algorithm
Rating Count Installs Trends Rating
Weight of factors in the Google Play ranking algorithm
Rating Count Installs Rating Trends
Again, we're oversimplifying for the sake of keeping this post to a mere 3,000 words, but additional
factors including keyword density and in-app engagement statistics continue to be strong indicators
of ranks. They simply lie outside the scope of these experiments.
I hope you found this deep-dive both helpful and interesting. Moving forward, I also hope to see
ASOs conducting the same experiments that have brought SEO to the center stage, and encourage
you to enhance or refute these findings with your own ASO mad science experiments.
Please share your thoughts in the comments below, and let's deconstruct the ranking formula
together, one experiment at a time.
https://moz.com/ugc/app-store-rankings-formula-deconstructed-in-5-mad-science-experiments