Google’s Red Guide to the Android App Store

 

As they approach the one million apps mark, smartphone and tablet app stores leave users stranded in thick, uncharted forests. What are Google and Apple waiting?

Last week, Google made the following announcement:

Mountain View, February 24th, 2013 — As part of an industry that owes so much to Steve Jobs, we remember him on this day, the 58th anniversary of his birth, with great sadness but also with gratitude. Of Steve’s many achievements, we particularly want to celebrate the Apple App Store, the venerable purveyor of iPhone software. 

Introduced in 2008, the App Store was an obvious and natural descendant of iTunes. What wasn’t obvious or foreseen was that the App Store would act as a catalyst for an entire market segment, that it would metamorphose the iPhone from mere smartphone to app phone. This metamorphosis provided an enormous boost to the mobile industry worldwide, a boost that has benefitted us all and Google more than most.

But despite the success of the app phone there’s no question that today’s mobile application stores, our own Google Play included, are poorly curated. No one seems to be in charge, there’s no responsibility for reviewing and grading apps, there’s no explanation of the criteria that goes into the “Editors’ Picks”, app categorization is skin deep and chaotic.

Today, we want to correct this fault and, at the same time, pay homage to Steve’s elegant idea by announcing a new service: The Google Play Red Guide. Powered by Google’s human and computer resources, the Red Guide will help customers identify the trees as they wander through the forest of Android apps. The Red Guide will provide a new level of usefulness and fun for users — and will increase the revenue opportunities for application developers.

With the Google Play Red Guide, we’ll bring an end to the era of the uncharted, undocumented, and poorly policed mobile app store.

The Red Guide takes its name from another great high-tech company, Michelin. At the turn of the 20th century, Michelin saw it needed to promote automotive travel in order to stimulate tire sales. It researched, designed and published great maps, something we can all relate to. To further encourage travel, Michelin published Le Guide Rouge, a compendium of hotels and restaurant. A hundred years later, the Michelin Red Guide is still considered the world’s standard; its inspectors are anonymous and thus incorruptible, their opinions taken seriously. Even a single star award (out of three) can put an otherwise unknown restaurant on the map — literally.

Our Red Guide will comprise the following:

- “Hello, World”, a list of indispensable apps for the first time Android customer (or iPhone apostate), with tips, How-To guides, and FAQs.
– “Hot and Not”. Reviews of new apps and upgrades — and the occasional downgrade.
– “In Our Opinion”. This is the heart of the Guide, a catalogue of reviews written by a select group of Google Play staff who have hot line access to Google’s huge population of in-house subject matter experts. The reviews will be grouped into sections: Productivity, e-Learning, Games, Arts & Creativity, Communication, Food & Beverage, Healthcare, Spirituality, Travel, Entertainment, Civics & Philanthropy, Google Glass, with subcategories for each.

Our own involvement in reviewing Android apps is a novel — perhaps even a controversial — approach, but it’s much needed. We could have taken the easy path: Let users and third-parties provide the reviews. But third party motives are sometimes questionable, their resources quickly exhausted. And with the Android Store inventory rapidly approaching a million titles, our users deserve a trustworthy guide, a consistent voice to lead them to the app that fits.

We created the Red Guide because we care about our Android users, we want them to “play safe” and be productive, and we feel there’s no better judge of whether an application will degrade your phone’s performance or do what it claims than the people who created and maintain the Android framework. For developers, we’re now in a position to move from a jungle to a well-tended garden where the best work will be recognized, and the not-so-great creations will be encouraged to raise their game.

We spent a great deal of time at Google identifying exactly the right person to oversee this delicate proposition…and now we can reveal the real reason why Google’s Motorola division hired noted Macintosh evangelist, auteur, and investor Guy Kawasaki as an advisor: Guy will act as the Editor in Chief of the Google Play Red Guide.

With Guy at the helm, you can expect the same monkish dedication and unlimited resources we deployed when we created Google Maps.

As we welcome everyone to the Google Play Red Guide, we again thank Steve Jobs for his leadership and inspiration. Our algorithms tell us he would have approved.

The Red Guide is an open product and will be published on the Web at AppStoreRedguide.com as well as in e-book formats (iBookstore and Kindle formats pending approval) for open multi-platform enjoyment.
——– 

No need to belabor the obvious, you’ve already figured out that this is all a fiction. Google is no better than Apple when it comes to their mobile application store. Both companies let users and developers fend for themselves, lost in a thick forest of apps.

That neither company seems to care about their online stores’ customers makes no sense: Smartphone users download more apps than songs and videos combined, and the trend isn’t slowing. According to MobiThinking:

IDC predicts that global downloads will reach 76.9 billion in 2014 and will be worth US$35 billion.

Unfortunately, Apple appears to be resting on its laurels, basking in its great App Store numbers: 40 billion served, $8B paid to developers. Perhaps the reasoning goes like this: iTunes served the iPod well; the App Store can do the same for the iPhone. It ain’t broke; no fix needed.

But serving up music and movies — satisfying the user’s established taste with self-contained morsels of entertainment — is considerably different from leading the user to the right tool for a job that may be only vaguely defined.

Apple’s App Store numbers are impressive… but how would these numbers look like if someone else, Google for example, showed the kind of curation leadership Apple fails to assert?

JLG@mondaynote.com

Google News: The Secret Sauce

 

A closer look at Google’s patent for its news retrieval algorithm reveals a greater than expected emphasis on quality over quantity. Can this bias stay reliable over time?

Ten years after its launch, Google News’ raw numbers are staggering: 50,000 sources scanned, 72 editions in 30 languages. Google’s crippled communication machine, plagued by bureaucracy and paranoia, has never been able to come up with tangible facts about its benefits for the news media it feeds on. It’s official blog merely mentions “6 billion visits per month” sent to news sites and Google News claims to connect “1 billion unique users a week to news content” (to put things in perspective, the NYT.com or the Huffington Post are cruising at about 40 million UVs per month). Assuming the clicks are sent to a relatively fresh news page bearing higher value advertising, the six billion visits can translate into about $400 million per year in ad revenue. (This is based on a $5 to $6 revenue per 1,000 pages, i.e. a few dollars in CPM per single ad, depending on format, type of selling, etc.) That’s a very rough estimate. Again: Google should settle the matter and come up with accurate figures for its largest markets. (On the same subject, see a previous Monday Note: The press, Google, its algorithm, their scale.)

But how exactly does Google News work? What kind of media does its algorithm favor most? Last week, the search giant updated its patent filing with a new document detailing the thirteen metrics it uses to retrieve and rank articles and sources for its news service. (Computerworld unearthed the filing, it’s here).

What follows is a summary of those metrics, listed in the order shown in the patent filing, along with a subjective appreciation of their reliability, vulnerability to cheating, relevancy, etc.

#1. Volume of production from a news source:

A first metric in determining the quality of a news source may include the number of articles produced by the news source during a given time period [week or month]. [This metric] may be determined by counting the number of non-duplicate articles produced by the news source over the time period [or] counting the number of original sentences produced by the news source.

This metric clearly favors production capacity. It benefits big media companies deploying large staffs. But the system can also be cheated by content farms (Google already addressed these questions); new automated content creation systems are gaining traction, many of them could now easily pass the Turing Test.

#2. Length of articles. Plain and simple: the longer the story (on average), the higher the source ranks. This is bad news for aggregators whose digital serfs cut, paste, compile and mangle abstracts of news stories that real media outlets produce at great expense.

#3. “The importance of coverage by the news source”. To put it another way, this matches the volume of coverage by the news source against the general volume of text generated by a topic. Again, it rewards large resource allocation to a given event. (In New York Times parlance, such effort is called called “flooding the zone”.)

#4. The “Breaking News Score”:   

This metric may measure the ability of the news source to publish a story soon after an important event has occurred. This metric may average the “breaking score” of each non-duplicate article from the news source, where, for example, the breaking score is a number that is a high value if the article was published soon after the news event happened and a low value if the article was published after much time had elapsed since the news story broke.

Beware slow moving newsrooms: On this metric, you’ll be competing against more agile, maybe less scrupulous staffs that “publish first, verify later”. This requires a smart arbitrage by the news producers. Once the first headline has been pushed, they’ll have to decide what’s best: Immediately filing a follow-up or waiting a bit and moving a longer, more value-added story that will rank better in metrics #2 and #3? It depends on elements such as the size of the “cluster” (the number of stories pertaining to a given event).

#5. Usage Patterns:

Links going from the news search engine’s web page to individual articles may be monitored for usage (e.g., clicks). News sources that are selected often are detected and a value proportional to observed usage is assigned. Well known sites, such as CNN, tend to be preferred to less popular sites (…). The traffic measured may be normalized by the number of opportunities readers had of visiting the link to avoid biasing the measure due to the ranking preferences of the news search engine.

This metric is at the core of Google’s business: assessing the popularity of a website thanks to the various PageRank components, including the number of links that point to it.

#6. The “Human opinion of the news source”:

Users in general may be polled to identify the newspapers (or magazines) that the users enjoy reading (or have visited). Alternatively or in addition, users of the news search engine may be polled to determine the news web sites that the users enjoy visiting. 

Here, things get interesting. Google clearly states it will use third party surveys to detect the public’s preference among various medias — not only their website, but also their “historic” media assets. According to the patent filing, the evaluation could also include the number of Pulitzer Prizes the organization collected and the age of the publication. That’s for the known part. What lies behind the notion of “Human opinion” is a true “quality index” for news sources that is not necessarily correlated to their digital presence. Such factors clearly favors legacy media.

# 7. Audience and traffic. Not surprisingly Google relies on stats coming from Nielsen Netratings and the like.

#8. Staff size. The bigger a newsroom is (as detected in bylines) the higher the value will be. This metric has the merit of rewarding large investments in news gathering. But it might become more imprecise as “large” digital newsrooms tend now to be staffed with news repackagers bearing little added value.

#9. Numbers of news bureaus. It’s another way to favor large organizations — even though their footprint tends to shrink both nationally and abroad.

#10. Number of “original named entities”. That’s one of the most interesting metric. A “named entity is the name of a person, place or organization”. It’s the primary tool for semantic analysis.

If a news source generates a news story that contains a named entity that other articles within the same cluster (hence on the same topic) do not contain, this may be an indication that the news source is capable of original reporting.

Of course, some cheaters insert misspelled entities to create “false” original entities and fool the system (Google took care of it). But this metric is a good way to reward original source-finding.

#11. The “breadth” of the news source. It pertains to the ability of a news organizations to cover a wide range of topics.

#12. The global reach of the news sources. Again, it favors large media who are viewed, linked, quoted, “liked”, tweeted from abroad.

This metric may measure the number of countries from which the news site receives network traffic. In one implementation consistent with the principles of the invention, this metric may be measured by considering the countries from which known visitors to the news web site are coming (e.g., based at least in part on the Internet Protocol (IP) addresses of those users that click on the links from the search site to articles by the news source being measured). The corresponding IP addresses may be mapped to the originating countries based on a table of known IP block to country mappings.

#13. Writing style. In the Google world, this means statistical analysis of contents against a huge language model to assess “spelling correctness, grammar and reading levels”.

What conclusions can we draw? This enumeration clearly shows Google intends to favor legacy media (print or broadcast news) over pure players, aggregators or digital native organizations. All the features recently added, such as Editor’s pick, reinforce this bias. The reason might be that legacy media are less prone to tricking the algorithm. For once, a know technological weakness becomes an advantage.

frederic.filloux@mondaynote.com