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Mobile

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ALFA-ARKIV Mobile App Overview

ALFA-ARKIV is a digital novel masquerading as a game. Combining themes of surveillance culture, genetic modification, and Soviet history, it is as culturally relevant as it is technologically innovative.

Released in 2014, ALFA-ARKIV was featured as a "Best New Game" by Apple and lauded by media outlets such as CNET and the Canadian Broadcasting Corporation as one of the top mobile experiences of that year. More of a digital novel than a game, the core of the experience is a journal written by award-winning novelist Shani Boianjiu, author of "The People of Forever Are Not Afraid". The game requires players to explore the story through different platforms and features an extensive use of a Siri-like chatbot character who is preprogrammed with over 100,000 words of code.

Features

  • Features an AI chatbot who serves as your guide, hintbook, and salesperson.
  • Over 100,000 words of discoverable text.
  • Adaptive, context-sensitive soundtrack.
  • Unlocks content across mobile and the web, with geospatial data, geo-fencing, image recognition, AI chatbots, and various APIs and SDKs, including augmented reality.
  • Powered by a feature-rich backend, including a user engagement tracking system.

 

Developer: Hexagram

Release date: 24 July, 2014

Platforms: iPad

Price:Free, $3 to see all chapters

Post-launch

After the release of ALFA-ARKIV, we have continued to develop Hexagram's backend in partnership with entities such as Twentieth Century Fox, Intel/Vice, and several non-profits. 

History

ALFA-ARKIV's story began in 2009, when over a million people from around the world explored the online mystery of JUNKO JUNSUI. Shortly after the Junsui vanished, the creators of the original material began thinking about a new kind of text-based adventure. In 2012, they enlisted award-winning author Shani Boianjiu to lead the writing efforts. Creation involved over fifty individuals from nearly as many countries.

 

CNET: Alfa-Arkiv

Best Mobile Games of 2014

" Alfa-Arkiv is about as ambitious a multimedia project as we've ever seen. The core of it takes place in the iPad app where you, as a new operator at a mysterious organization, are reading through documents pertaining to the detention of a young woman named Rhea, a member of a resistance movement called the Liberation Army of Dagestan.

While it technically falls under the definition of an alternate reality game, Alfa-Arkiv isn't easy to categorize. It's sort of an interactive novel, but it's so much more: nearly 10 years in the making, it will send you crawling the web hunting for clues planted by the development team years before the app's release in July of this year.

It failed to get the attention it deserves, partially because it's not easy to categorize as either a novel or a game; partially because it asks things of the user that go beyond a single screen; and partially because it's so very realistic. It is, however, a spectacularly executed piece of work, and a magnificent experience."

App Store - http://bit.ly/alfa-arkiv In the future, games will be used to conduct psychological warfare on a global scale. Or has that future already arrived? WikiLeaks - http://wikileaks.las-sgg.cl/alfa-cipher.html Movement - https://www.facebook.com/JunsuiMovement CNET - http://www.cnet.com/news/down-the-rabbithole-of-alfa-arkiv/ | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | MAKE NO MISTAKE - THIS IS JUST A GAME "Third Roman Intelligence Directorate" is just a shell, another FICTIONAL SUBSIDIARY created by LIARS who use ALIASES. | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | In this GAME there they have created there are THREE MAIN RULES: 1. YOU ARE A FREE AND INDEPENDENT INDIVIDUAL. 2. YOU MUST PURSUE WHATEVER MAKES YOU HAPPY. 3. SHOULD ANYTHING PREVENT (1) or (2), YOU MUST DESTROY IT. | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | It was made with the help of countless REAL PROFESSIONALS including: • INTERNATIONAL FINANCIERS • IT PATENT LAWYERS • AGENT PROVOCATEURS • PREDICTIVE INTELLIGENCE SPECIALISTS • PR TECHNOLOGISTS (CONSULTANTS) • SPONSORED POLITICIANS • VIP HOSPITALITY HOSTESSES • WELL-CONNECTED ARTISTS • CELEBRITY AUTHORS • NARCISSIST WHISTLEBLOWERS | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | Inside the ARKIV: WHO was JUNKO JUNSUI and WHAT really happened to THE SISTERHOOD of "BLACK WIDOWS"? WHAT is ROSTEC and WHY has ROSKOMNADZOR not yet built a GREAT FIREWALL of RUSSIA? WHY does SOCIAL MEDIA not reach its TRUE POTENTIAL until your CITY SQUARE is on FIRE? HOW can TECH STARTUPS that did not exist a few years ago now be VALUED IN THE BILLIONS? WHEN the NEW REALITY arrives, will your HAPLOGROUP or GENDER be needed? | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | "ИГРЫ" - ОРУЖИЕ БУДУЩЕГО. ТОЛЬКО СОСТРАДАНИЕ СПАСЕТ ОТ ТРЕТЬЕЙ МИРОВОЙ. Apple Аpp Store - bit.ly/alfa-arkiv WikiLeaks - http://wikileaks.las-sgg.cl/alfa-cipher-RU.html | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | МОЖЕТЕ НЕ СОМНЕВАТЬСЯ - ПЕРЕД ВАМИ ПРОСТО ИГРА "Третьеримское Разведывательное Управление" — плод воображения, очередная ВЫМЫШЛЕННАЯ ОРГАНИЗАЦИЯ, созданная ЛЖЕЦАМИ, скрывающимися под ПСЕВДОНИМАМИ. | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | В созданной ими ИГРЕ есть ТРИ ГЛАВНЫХ ПРАВИЛА: 1. ВЫ — СВОБОДНАЯ И НЕЗАВИСИМАЯ ЛИЧНОСТЬ. 2. ВЫ ДОЛЖНЫ СТРЕМИТЬСЯ К ТОМУ, ЧТО ДЕЛАЕТ ВАС СЧАСТЛИВЫМИ. 3. ЕСЛИ ЧТО-ТО МЕШАЕТ (1) ИЛИ (2), ВЫ ДОЛЖНЫ УНИЧТОЖИТЬ ЭТО | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | В создании участвовали бесчисленные НАСТОЯЩИЕ ПРОФЕССИОНАЛЫ, такие как: • МЕЖДУНАРОДНЫЕ ФИНАНСИСТЫ • ЮРИСТЫ ИЗ ОБЛАСТИ ВЫСОКОТЕХНОЛОГИЧНЫХ ПАТЕНТОВ • ПРОВОКАТОРЫ • СПЕЦИАЛИСТЫ ПО РАЗВЕДЫВАТЕЛЬНОМУ ПРОГНОЗИРОВАНИЮ • СПЕЦИАЛИСТЫ В ОБЛАСТИ ПИАР-ТЕХНОЛОГИЙ • МАТЕРИАЛЬНО ЗАИНТЕРЕСОВАННЫЕ ПОЛИТИКИ • СОТРУДНИЦЫ ВИП-ДОСУГА • ПРИБЛИЖЕННЫЕ ХУДОЖНИКИ • ИЗВЕСТНЫЕ АВТОРЫ • САМОВЛЮБЛЕННЫЕ РАЗОБЛАЧИТЕЛИ | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | В этой БЕТА-ВЕРСИИ взломанного АРХИВА: КЕМ были JUNKO JUNSUI и ЧТО случилось с СЕСТРИНСТВОМ «ЧЕРНЫХ ВДОВ»? ЧТО такое РОСТЕХ и ПОЧЕМУ РОСКОМНАДЗОР еще не ввел ПОЛНУЮ ЦЕНЗУРУ в РОССИИ? ПОЧЕМУ СОЦМЕДИА не раскроют СВОЙ ПОТЕНЦИАЛ, пока ВАШ РАЙОН не ВОСПЛАМЕНИТСЯ? КАК могут СТАРТАПЫ, которых не было несколько лет назад, СТОИТЬ МИЛЛИАРДЫ? КОГДА придет НОВАЯ РЕАЛЬНОСТЬ, понадобится ли ваша ГАПЛОГРУППА или ваш ПОЛ?

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Immersive and Alternate Reality

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Project Overview: Queens of the Stone Age (Billboard #1 band): Vampyre of Time and Memory

Co-produced by The Creators Project from Vice & Intel

A synchronized video interactive experience, which drove traffic to the associated YouTube video and led to over 3 million views.

Consisting of multiple layers of streaming video in an explorable HTML5 world. Combined with a custom Google Chrome browser extension.

Artist: Queens of the Stone Age
Song: The Vampyre of Time and Memory
Interactive Experience Produced by Hexagram
Video Directed by Kii Arens and Jason Trucco.
Edited by Trailer Park

Selected articles: 

 

Full Project Overview

X-Men X-Tension Project Overview

20th Century Fox asked us to help them create an entirely new kind of Facebook page film promotion.

The result: a Google Chrome Extension which transforms the X-Men Facebook page into an immersive experience. 

http://www.x-menmovies.com/x-tension/

Project Partners:

20th Century Fox, Symantec, Grey Worldwide, and McBeard Media

Press

Fanlala: "With the X-Men: Days of Future Past Alternate Timeline X-Tension for Google Chrome, you can get a glimpse into the past where some major historical events were tweaked by some of the key mutants from the upcoming Marvel adventure."

MovieViral: "...the one-of-a-kind experience reveals historic moments like the progress of Trask Industries."

The Movie Network: "Check out the latest footage from the most highly anticipated film of the summer..." 

Fanboy Factor: "20th Century Fox invites you to explore this new immersive experience..."

We Are Movie Geeks: "Transform your Facebook into an interwoven history of the X-Men and their battle for mutant rights..."

Cosmic Book News: "...the Facebook page transforms into an alternate timeline with lots of images and information related to X-Men: Days Of Future Past."

With An Accent: "Have you ever wanted your Facebook to be more X-Men-y? Well, now it can be!"

Movie Web: "Check out the latest footage from director Bryan Singer's highly-anticipated superhero sequel."

Newsarama: "a way to transport yourself to an alternate timeline - as long as you're using Google's Chrome browser"

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Data-Driven Decisions

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Summary: Feed scoring system for comparison-shopping engine structured data feeds.

The Feed Scoring System gave merchants and the teams that supported them, more insight into the quality of a Yahoo! Shopping data feed. This data feed quality information could be used for a variety of activities including: feed reviews, optimization suggestions, and competitive benchmarking.

I initially created this scoring system with another developer as a hack day project, then as a minimum viable product, and finally as a fully functioning system with incremental updates on a standard two-week release cycle. Like many innovations, the feed scoring system was created on top of existing technology and within set parameters. The goals were manifold: to answer common questions, provide insights, and list prioritized optimization suggestions for merchants. The resulting technology was not available anywhere else within the company or by competing comparison-shopping engines and was highlighted by the sales and account management teams as a clear product differentiator.

Situation:

The feed scoring system improved data feeds by adjusting two levers: merchant insights and improved data quality.

Merchant Insights: Merchants participating in Yahoo! Shopping would submit data feeds filled with meta-data describing the products they sold. Using the Yahoo! Shopping front-end, consumers would find a merchant’s product, click on the product, and would be sent to a product page on a merchant’s site to complete the purchase. This transaction would be considered a lead. Merchants would be charged per lead on a cost per click (CPC) basis, according to a rate card with CPCs listed by product category.

The Yahoo! Shopping sales and account management teams would manually review data feeds, compare the feeds against a feed specification document and deliver feed optimization suggestions to merchants during in-person meeting. As part of supporting top-tier merchants (top 15% of revenue), account managers would select merchants as review candidates. Due to the lengthy review process, a limited number of merchants could receive feed reviews each quarter. In the past, these feed reviews would take up to four hours of work to complete.

Improved data quality: While the sales and account management teams were focused on answering merchants’ questions, the Yahoo! Shopping product management team was focused on improving the quality of the product catalog. This product catalog powers the front-end display and allows consumers who use Yahoo! Shopping to more easily find products and make more informed purchase decisions. Consumers find products on Yahoo! Shopping using three main functionalities: search, attribute narrowing, and comparison grids. The improved data quality in-turn improved the performance of each of these front-end functionalities.

Additionally, the product management team also kept track of the overall product catalog breadth and depth, and compared results against other competing shopping-comparison engines. If consumers couldn’t find a product on Yahoo! Shopping, they may try finding the product on a competing site. It’s possible that lack of product catalog breath and depth could result in not just losing one sale, but losing a customer for life.

Goals / Tasks:

To build the feed scoring system, I grouped the goals into two types: merchant insights and improved product catalog data quality.

Merchant insight goals - The merchant insight goals focused on answering the following merchant questions – Am I submitting enough data? What can I do to improve my feed? How does my feed compare against other merchants? Have changes to my feed improved my performance on Yahoo! Shopping? How should I prioritize feed improvement work with my development team?

Product catalog goals - The improved product catalog goals included: increased number of product attributes extracted from the data feed, increased number of matches to comparison grids, increased search terms extracted, increased merchant retention, and increased count of total product catalog.

Actions:

The feed scoring system was built in three phases: initial prototype, minimum viable product, and full release with incremental updates. In between each phase or release, I would gather feedback from external customers (Yahoo! Shopping merchants) and internal stakeholders (sales teams, account management teams, developers, business development teams and product management teams). Feedback from each group was incorporated to develop the prioritized feature roadmap. I then assembled a cross-functional team of developers, designers and product managers to build the required features. A selection of key features from each phase of development is listed below:

Prototype:

  • Initial field coverage data import

  • Prototype UI, UED plan and user stories

  • Basic System Architecture

  • Meta-data level weighting algorithm

 

Minimum Viable Product:

  • Integration of attribute extraction data

  • Scoring tool dashboard

  • Prioritized optimization suggestions

  • TSV report download functionality

 

Incremental improvements:

  • Algorithm weighting updates

  • Score comparisons

  • Score history snapshots

  • Score history graphs

  • Advanced UI improvements

  • Scores incorporated into other tools

  • Program-wide competitive benchmarking

Results:

The results of implementing the feed scoring system ranged from objective, quantifiable results to systematic and operational improvements. A selection of findings are listed below:

 

Merchant insights

  • Increased count of reviews available per quarter from XX to X,XXX

  • Reduced time for creating feed reviews from 4+ hours to seconds.

  • Increased average data quality scores by XXX%

  • Increased number of CPC leads by XX%

  • Provided data and insights for 5 key questions from merchants, that were previously unanswerable with existing toolset.

 

Product catalog

  • Increased depth of product catalog in vertical search by XX%

  • Increased number of extracted product attribute meta-data by XX%

  • Increased number of matches to comparison grids by XX%

  • Increased insight into program-wide data quality for current snapshots, historical trends, and seasonality.

  • Provided program-wide data and insights that were previously unavailable with existing toolset.

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Customer Success & Onboarding

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Example Support Launch Plan

Selected Product: Google Search

New Feature: Google Product Listing Ads (PLA) - Product Research Module

Core Use Case: Product research and purchase.

Selected category: 55” Smart LED TVs

Overview: The product team has created a new module to help consumers make more informed purchase decisions at the Zero Moment of Truth (ZMOT). The module appears as part of Google search for select product-related keywords and matches user reviews, trusted editorial sources (like  http://thewirecutter.com/) along with Google Product Listings Ads (PLA) placements. For this use case, the user (gender: female, age: 28, location: Seattle) is researching 55” Smart LED TVs with an initial intent to purchase before the soccer World Cup. 

My role in this exercise, is to advocate for the consumer throughout the development process, develop a support launch plan, measure and track consumer success, along with testing and optimizing new support features. The goal: to ensure that Google search consumers are getting the most out of the new Product Research Module, when they need it the most

Support Implementation Plan

 

Consumer Touch Points - Low Touch(scalable, self-serve)

  •  FAQs (accessible knowledge base)

  • Consumer support forums

  • Product support videos

  • Service status dashboards

  • Blog posts

  • Social media

  • Self-serve admin / user preferences

Google PLA Merchant Touch Points- High Touch (1:1 support)

  • Delayed Support: Email, contact forms

  • Real-time Support: Chat, phone, video

  • Support Staffing: Tiered escalations vs. Universal agent

Consumer Touch Points to Test- Experimental, scalable solutions to test. Focused on scalable, self-serve solutions.

  • AI chat support – Chatbot agent leveraging machine learning to access knowledge base
  • Hybrid support offering – similar to Project Fi hybrid support – offering multiple choices with estimated queue wait times.
  • User education – Support Academy (Similar to the optimizing AdSense online course).
  • Awards – Learning theory and gamification techniques: badges for successful usage, etc.

Stakeholders

 

Google Search Team

  • Search Product Team

  • Search Engineering Team

  • Search User Experience Design Team

  • Search Business Development Team

Google Product Listing Ads (PLA) Team

  • PLA Product Team

  • PLA Engineering Team

Google PLA Merchant Support Team

  •  PLA Account Management Team

  • PLA Sales Team

Google Consumer Support Team

  • Product Support Managers

  • Consumer Experience Specialists

  • Product Support Agents

External Support Vendors and Partners

  • Agent Support teams

  • 3rd Party product teams (for build, buy or partner decisions)

 

Consumer Support Considerations

 

1. Focus on core value proposition

  •  What are the core goals for consumers?  - Identify high value use cases.
  • How do they differ based on user demographics?
  •  How does the product offering vary based on device?  - Mobile, multi-screen vs. desktop

2. Establish consumer success dashboard

  •  Identify consumer goals and success funnels.
  • Prioritize quantitative data.
  • Provide qualitative insights to frame the conversation.
  • Select core success metrics.

3. Review all existing customer touch points – understanding the basics

  • User on-boarding and education
  • Low-touch (self-serve support) vs. high touch (1:1 support) 
  • Issue escalation, prioritization
  • Localization
  • Style Guides

4. Cross-Company Best Practices

  • How do we consistently deliver high quality service across all Google products?
  •  What are the company-wide considerations for managing expectations for paying customers and consumers using free versions of a product?

5. Competitive landscape review

  • What level of support are consumers expecting when they compare alternative or complimentary product offerings?
  • Do any support features in the marketplace solve existing pain-points for our consumers?

6. Implement innovations iteratively, test and optimize

  • Great ideas can come from anywhere – all new features must be evaluated, prioritized and ranked with the same set of metrics.
  •  New support features should be launched to a limited-set of users, tested, evaluated and optimized.

Selected Success Metrics

 

Support Costs

  • Cost per contact
  • Closed issue resolution cost
  • Staffing team costs
  • Self-Service product development costs

Customer Satisfaction / Customer Success

  •  CSat – Customer Satisfaction (7 tier surveys)
  • FCR – First contact resolution rates
  • Queue wait times
  • SLAs – Service level agreements
  • Customer loyalty, evangelism rates
  • Bad customer identification (repeated false issues)

External Sources of Customer Satisfaction

  • Net Promoter Score
  • Forrester Reports
  • Research surveys (such as North American Technographics)

Staffing (operational metrics)

  • % of call by agent
  • # of calls / agent / team
  • Average talk time
  • Closed vs. Escalated issue counts

Product Feedback loop

  • # of common, repeat issues
  • # of new issues highlighted
  • Identifying edge cases/  long-tail issues
  • Clarifying data with qualitative insights

Hybrid / Aggregated Metrics

  • Goal-based funnel metrics – multiple data points (similar to click-path)
  • Aggregated reports highlighting prioritized insights
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User sign-up flow | Using metrics to optimize user onboarding

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ALFA-ARKIV Mobile App Overview

ALFA-ARKIV is a digital novel masquerading as a game. Combining themes of surveillance culture, genetic modification, and Soviet history, it is as culturally relevant as it is technologically innovative.

Released in 2014, ALFA-ARKIV was featured as a "Best New Game" by Apple and lauded by media outlets such as CNET and the Canadian Broadcasting Corporation as one of the top mobile experiences of that year. More of a digital novel than a game, the core of the experience is a journal written by award-winning novelist Shani Boianjiu, author of "The People of Forever Are Not Afraid". The game requires players to explore the story through different platforms and features an extensive use of a Siri-like chatbot character who is preprogrammed with over 100,000 words of code.

Features

  • Features an AI chatbot who serves as your guide, hintbook, and salesperson.

  • Over 100,000 words of discoverable text.

  • Adaptive, context-sensitive soundtrack.

  • Unlocks content across mobile and the web, with geospatial data, geo-fencing, image recognition, AI chatbots, and various APIs and SDKs, including augmented reality.

  • Powered by a feature-rich backend, including a user engagement tracking system.

Situation: We noticed a high drop-off rate before the paywall on our iOS app. Was the user attrition caused by the AI chatbot implementation or the face / image recognition SDK?

Task: Normalize and marry two sets of metrics to identify cause of drop-off.

Action: I looked at timestamp metrics between events, from download through to paywall. I then split the usage stats into quartiles, by session time for each event.

Result: The analysis identified the image recognition SDK as the source of the problem. Based on this finding, our developers adjusted the acceptance threshold for the SDK. The analysis also highlighted the high conversion rate of chatbot for further investigation. 

Description: The attached diagram tracks user flow through an AI chatbot-gated paywall on our iOS app using the Hexagram user-state tracking system.

Problem: During initial user tests, we noticed a sharp drop-off between the initial download and the paywall. We suspected either a flaw in the AI chatbot design or the augmented reality face recognition feature as the source of user attrition. However, we had little insight into this flow because we had not yet built a fully integrated and comprehensive reporting system. The only indication of the user attrition issue was the reporting from apple indicating the number of downloads and the number of purchases at the pay wall.

Solution: The attached diagram helped us to identify the camera feature as a major friction point and to identify the AI chatbot as a solid conversion gate with ~5% overall conversion (against industry standard or 1-3%). Even better, later tests with larger, statistically significant user counts have shown up to 20% conversion rates at the AI-gated paywall! I pulled reports from the Apple store and married them with aggregated timestamps of user state changes in our tracking system to get the data used in this diagram. This ad-hoc report could also be turned into an automated reporting tool to identify similar friction points across all inputs for other partners on the Hexagram platform. In the end, we adjusted the threshold settings for this camera feature to allow users to easily progress to the next chapter of the game.

Note: in an effort to preserve confidentiality, some of the data provided in this example has been redacted, abstracted or altered. 

CNET: Alfa-Arkiv

Best Mobile Games of 2014

" Alfa-Arkiv is about as ambitious a multimedia project as we've ever seen. The core of it takes place in the iPad app where you, as a new operator at a mysterious organization, are reading through documents pertaining to the detention of a young woman named Rhea, a member of a resistance movement called the Liberation Army of Dagestan.

While it technically falls under the definition of an alternate reality game, Alfa-Arkiv isn't easy to categorize. It's sort of an interactive novel, but it's so much more: nearly 10 years in the making, it will send you crawling the web hunting for clues planted by the development team years before the app's release in July of this year.

It failed to get the attention it deserves, partially because it's not easy to categorize as either a novel or a game; partially because it asks things of the user that go beyond a single screen; and partially because it's so very realistic. It is, however, a spectacularly executed piece of work, and a magnificent experience."