As IBM Ramps Up Its AI-Powered Advertising, Can Watson Crack the Code of Digital Marketing? | Ad Week

As IBM Ramps Up Its AI-Powered Advertising, Can Watson Crack the Code of Digital Marketing?; ; In Ad Week (Advertising Week); 2017-09-24.
Teaser: Acquisition of The Weather Company fuels a new division

tl;dr → Watson (a service bureau, AI-as-a-Service) is open for business.


The Weather Company

  • lines of business
    • location-based targeted audiences, delivered to the trade.
    • weather indica, delivered to consumers.
  • 2.2 billion locations/15 minutes
  • Dates
    • WHEN?, Acquisition by IBM
    • 2016-01, new business strategy,
      “AI” as a service (AIaaS)
  • Artificial Intelligence (AI)
  • Cloud Computing
  • Products
    • WeatherFx
    • JourneyFx
  • The Weather Company is a <quote>legacy business<quote> (deprecated).
  • AIaaS is a <quote>cutting-edge advertising powerhouse</quote> (house of power).

Watson Advertising

  • Cognitive Advertising
    • contra Computational Advertising, circa the ‘oughties (2005)
    • something about
      • <buzzzz>transform every aspect of marketing from </buzzz>
      • something about image and voice recognition to big data analysis and custom content.
  • What is it? (What is Watson-as-a-Service?)
    • Count: <quote>dozens</quote>
    • Interfaces
      • API
      • Projects <quote>studio-like</quote>
    • Pricing: <quote>millions of dollars</quote>
    • Structure: four (4) sub-units
  • “<snip/>It’s not been designed to target consumers the same way that Alexa or Siri have been,” attributed to Cameron Clayton.


The 4 pillars of Watson Advertising.
  1. Targeting, Audience construction & activation
  2. Optimization, Bidding & buying
  3. Advertising, Synthesis of copy and creative
  4. Planning, media planning, the buy plan, the execution plan

Audience Targeting

  • the flagship service
  • neural networks
  • scoring users, propensity scoring <quote>based on how likely they are to take an action</quote>
  • towards CPA or CCPV or CPVisit or <more!>
  • Performable on the Weather Company O&O
    • <quote>but on TV, print, radio and other platforms. <quote>
    • Partnerships
      • Cognitiv
      • Equals 3


Bidding Optimization
  • Is too boring for details early in the article.
  • Optimize against brand-specific KPIs.
  • Uses <buzzz>deep learning and neural networks</buzzz>
  • Optimize Cost Per Action (CPA).


  • Badged as Watson Ads and Watson Advertising
  • Services
    • content creation
    • content copywriting
  • Launched: 2016-06.
  • Is merely: nterest-Based Advertising (IBA)
    which in turn is a but regulatory term of art, that covers a wide range of in-trade practices.
  • Sectors, aspirational
    • <fancy>aviation</fancy> (airline ticket booking?)
    • insurance
    • energy
    • finance
  • Cognitive Media Council,
    • a focus group.
    • a user group, “friends & family” of the business.
    • a group of important customers representatives
      <quote>senior-level executives from agencies and brands</quote>
Reference Customers
  • Mirai
  • Prius Prime
  • Benefits
    Attributed to Eunice Kim, Toyota (TMNA), something about…

    • <buzzz>create a one-to-one conversational engagement</buzzz>
    • <buzzz>garner insights about the consumer thought process that could potentially inform our communication strategies elsewhere”</buzzz>
  • the Soup people
  • Something about creative synthesis
    themed as: recipe generation with flu symptoms with location
H&R Block
  • Something about creative synthesis
    themed as: automated robot tax expert, suggest tax deductions.
UM [You and Em]
  • An agency. Off shore? They have a “U.S. CEO” Maybe one of those English Invasion thingies.
  • Refused to name their client.
  • Something about auto dealerships.
  • <quote>meshing Watson data with client stats to analyze metrics across a large number of car dealerships in a way that optimizes ad spend while also checking local inventory to see whether or not it should personalize an ad to someone in that market.<quote>
  • <quote>combination of weather data, Google searches and pollen counts to trigger when media should be bought in various markets.</quote>


  • <quote>AI-powered planning</quote>


Something about a partnership for understanding marketing texts.
Jeremy Fain, CEO and co-founder
Equals 3
Lucy, a product-service-platform.
Something about <quote>to uncover extra insights and research.<quote>

Fairness & Balance


Ogilvy & Mather
  • Honorific <quote>longtime agency<quote> [fof record for IBM].
[Television] campaign, with Bob Dylan.
Synthesis of the trailier for Morgan (a move; genre: science fiction)
Performance, an “analysis” of the stylings of Antoni Gaudi, <quote>inspire an art installation </quote> (what does that mean?)
The “art installation” was exhibited at the Mobile World Congress in Barcelona.

…is quoted
the future is boosted.

  • “AI services”
  • “Big Data services”


  • The people are “afraid” of AI.
  • The people need to be groomed to accept AI.

Ensmoothen & enpitchen the Artificial Intelligence (AI) as…

  • humble
  • friendly
  • ”I’m here to help’ type personality”

Attributed to Lou Aversano, Ogilvy.


James Kisner, Jeffries

Via: James Kisner, A Report, Jeffries, 2017-07.
Jeffries is an opinion vendor in support of an M&A banking operation.
tl;dr → Watson is a failing product-service. <quote>IBM is being “outgunned” in the race…</quote> (yup, he mixed the metaphor).

  • as evidenced in measured job listings at
    Apple had more listings booked thereon than IBM.
  • Customers were interviewed.
    Watson’s performance/price ratio was low (the rate card is very high).
    2016-10, IBM reduced the rate card for API access <quote>by 70 percent</quote>
  • Lots of press
  • Not a lot of monetary results, as evidenced in the quarterly & annual reports.
Joe Stanhope, Gartner

Via: an interview, perhaps;
Gartner Group is an opinion vendor.

  • Too much hype, can be forgiven
  • Gartner runs the Hype Cycle brand
  • Claims: <quote>IBM does seem to be all-in with Watson.<quote> (be nice to hear that from IBM, not as a “hot-take” from a newshour pundit
DemandBase, Wakefield Research

A Report; attributed to “staff”; DemandBase and Wakefield Research

  • A survey,
    • “how do you feel?”
    • Do you “have plans-to …” in the next N months.
  • There are a lot of uncertainties


Training Data
  • Just isn’t there.
  • And … computers can only give answers, it can’t give [pose] questions.
Does it [even] Work?
  • No one knows.
  • Many are nervous.
  • No one wants to be first to fail
    (& be fired for outsourcing their job function to The AI).


  • Einstein, of Salesforce(.com)
  • Sensei, of Adobe
  • Buying operations, Xaxis of WPP
    the “AI” is a “co-pilot” to the trading desk operator; optimization recommendations towards CPM and viewability; North American operations only.
  • others?
    Surely everyone nowadays has some initiative that does “co-pilot”-level decision support to adops.
Research Efforts
  • Amazon
  • Facebook
  • Google
Venture Capital
  • Albert
  • Amenity Analytics
  • LiftIgniter
  • Persado
Amenity Analytics

An exemplar of the smaller-nimbler-smarter clones of the Watson genre.

  • A Watson-type experience, but cheaper
  • Does text mining of press releases
  • Reference customers:
  • A spin-out from some hedge fund, <quote>origins in the hedge fund world</quote>
  • Nathaniel Storch, CEO, Amenity Analytics.
  • <zing!>“Think of it as ‘moneyball’ for media companies,”<zing!>, attributed to Nathaniel Storch.


  • Siri, of Apple
  • Cortana, of Microsoft
  • Now, of Google


  • Lou Aversano, U.S. CEO, Ogilvy & Mather (Ogilvy, O&M).
  • Jordan Bitterman, CMO, Watson (Business Unit), IBM.
    attributed in quoted material aso “earlier this year” (2017?); c.f. Michael Mendenhall
  • Kasha Cacy, U.S. CEO, UM
    UM is an agency.
  • Cameron Clayton,
    • General Manager, Content and IoT Platform, Watson (Business Unit), IBM..
    • ex-CEO, The Weather Company
  • Jacob Colker, “entrepreneur in residence,” The Allen Institute
    …quoted for color, background & verisimilitude.The Allen Institute is a tank for thinkers.
  • Jeremy Fain, CEO and co-founder, Cognitiv.
  • Chris Jacob, director of product marketing, Marketing Cloud, Salesforce(.com).
  • Eunice Kim, media planner, Toyota Motor North America (TMNA).
    …quoted for color, background & verisimilitude.
  • James Kisner, staff, Jeffries.
    …quoted for color, background & verisimilitude.
    Jeffries is an advice shop, like Gartner, but different.
  • Francesco Marconi,
    …quoted for color, background & verisimilitude.

    • strategy manager and AI co-lead, Associated Press
    • visitor, MIT Media Lab
  • Michael Mendenhall, CMO, Watson (BU), IBM.
    announced as CMO in prior press [Ad Week, Marty Swant, 2017-07-07].
  • Sara Robertson, VP of Product Engineering, Xaxis of WPP.
  • Joe Stanhope, staff, Forrester
    …quoted for color, background & verisimilitude.
  • Nathaniel Storch, CEO, Amenity Analytics.
  • Marty Wetherall, director of innovation, FallonFallon is the agency that certain campaign booked on Watson for H&R Block


  • Antoni Gaudi, architect (per civil engineering), citizen of Spain.


In archaeological order, within Advertising Week

Previously filled.

Marketing Technology Landscape Supergraphic (2017): Martech 5000

(ChiefMarTec); Marketing Technology Landscape Supergraphic (2017): Martech 5000; In Their Blog; 2017-05-10.


Martech 5000
copy, original
copy, original
copy, original

Licentious licentiate: <quote ref=”cite“>Feel free to cut-and-paste this data and use it as a starting point for your own research.</quote>


  • Integration-Platform-as-a-Service(iPaaS, IPaaS)
    • are “distributed” platforms
    • perform <quote>[as] dynamically piping data between marketing applications and [a] data lake.</quote>
  • Content Management System (CMS)
    • are platforms, per se
    • are centralized
    • are repositories of data and services
    • Gartner staff renamed them digital marketing hub
  • Among: DMP, CDP, RTIM
    • is a subtle blending among them
    • The Spectrum
      • Data Management Platforms (DMP),
      • Customer Data Platform (CDP),
      • Real-Time Interaction Management (RTIM)


Content Management System (CMS)

  • Adobe
  • HubSpot
  • IBM
  • Marketo
  • Oracle
  • Salesforce
  • Sitecore

IPaaS, now with Microservices!

  • Boomi, a wholly-owned subsidiary of Dell, a.k.a. Dell Boomi
  • Informatica
  • Jitterbit
  • Mulesoft
  • Segment
  • Zapier

The Spectrum Among: DMP, CDP, RTIM

Customer Data Platform (CDP)
  • AgilOne
  • Lytics
  • RedPoint
  • Tealium
  • Treasure Data
  • Usermind
Data Management Platform (DMP)
As a feature, not even Line of Business
  • Adobe
  • Oracle
  • Salesforce
  • DataXu
  • MediaMath
  • Neustar
  • Rocketfuel, (sic) Rocket Fuel of Sizmek
Real-Time Importance Management (RTIM)
  • Experian
    but not Acxiom? EXPM contra ACXM …”the same, but different” aren’t they?
  • Infor
  • Pegasystems
  • SAS
  • Teradata




  • Content Management System (CMS)
  • Customer Data Platform (CDP)
  • Customer Relationship Management (CRM)
  • Data Lake, an inelegant metaphor,
    a body corpora of water data facts in a controlled-but-unstructured format.
  • Data Management Platform (DMP)
  • Digital Marketing Hub (DMH)
    Gartner ‘lingo for the MarTech genre.
  • Enabler
    Doesn’t actually do the work, but still sends a bill for allowing it to occur.
    Usage: <quote>iPaaS and microservice platform enablers.</quote>
  • Integration-Platform-as-a-Service (iPaaS)
  • Long Tail
  • Marketing Automation Platform (MAP)
  • Microservices
  • Platform-as-a-Service (iPaaS)
  • Real-Time Interaction Management (RTIM)
  • Service-as-a-Service (SaaS)
  • Success-as-a-Service, a scheme.
    e.g. 2&20.



In Their Blog


Licentiate: ibidem.

Pre-Conference AdTech Summarization | Gubbins

; Things you should know about AdTech, today; In His Blog, centrally hosted on LinkedIn; 2017-08-30; regwalled (you have to login to linkedin).


Boosterism in front of the trade shows
  • Exchange Wire #ATSL17
  • Dmexco
  • Programmatic IO


  • There be consolidation in the DSP category.
  • There will be more DSPs not less fewer.
  • Owned & Operated (O&O)
  • preferential deals
  • private equity companies
  • party data & a GDPR compliant screen agnostic ID
  • no “point solutions.”
  • Doubleclick Bid Manager (DBM), Google
  • Lara O’Reilly; Some Article; In Business Insider (maybe); WHEN?
    tl;dr → something about how Google DSP DBM guarantee “fraud-free” traffic.
  • Ads.txtAuthorized Digital Sellers, IAB Tech Lab
  • Claimed:
    comScore publishers are starting to adopt Ads.txt

Buy Side

Deal Flow
  • Sizmek acquired Rocket Fuel, (unverified) $145M.
  • Tremor sells its DSP to Taptica for $50M.
  • Singtel acquired Turn for $310M.
No flow, yet
  • Adform
  • MediaMath
  • DataXu
  • AppNexus

Sell Side

  • Header Bidding (HB)
    • Replaces the SSP category
    • <quote>effectively migrated the sell sides narrative & value prop of being a yield management partner to that of a feet on the street publisher re-seller.</quote>
  • QBR (Quarterly Business Result?)
  • Prebid.js
  • With server bidding, too.
  • Supply Path Optimization (SPO)
    • Brian O’Kelley (AppNexus); Article; In His Blog; WHEN?
      Brian O’Kelley, CEO, AppNexus.
    • Article; ; In ExchangeWire; WHEN?
  • Exchange Bidding in Dynamic Allocation (EBDA), Google
The Rubicon Project
a header tag, compatible with most wrappers, no proprietary wrapper, only Prebid.js
Index Exchange
a header tag, compatible with most wrappers, a proprietary wrapper
a header tag that, compatible with many (not ‘most’) wrappers, a proprietary wrapper
a header, compatible with many (not ‘most’) wrappers, a proprietary wrapper (that is better than OpenX’s which is not enterprise grade)
a header tag, compatible with many (not ‘most)’ wrappers, a proprietary wrapper.
  • TrustX
    • with
      • Digital Content Next
      • IPONWEB
      • ANA
    • Something about a transparent marketplace.
  • Something about another supply network
    • German
    • trade press in Digiday
  • No header bidding, yet.
  • Mobile equals Adware (“in app”)
    • but Apps don’t have “browsers.”
    • but App browsers don’t have “pages” with “headers.”
    • though Apps have SDKs (libraries).
  • RTL acquires SpotX
  • <quote>One could argue video is the perfect storm for header bidding, limited quality supply & maximum demand, the ideal conditions for a unified auction…</quote>
Talking Points
  • The industry is currently debating the pros & cons of running header bidding either client or server side (A lot boils down to latency V audience match rates)
  • Google offer their own version of header bidding, this is referred to as EBDA (Exchange Bidding in Dynamic Allocation) and is available to DFP customers.
  • Facebook recently entered header bidding by launching a header tag that enables publishers to capture FAN demand via header bidding on their mobile traffic.
  • Criteo entered header bidding by offering publishers their header tag (AKA Direct Bidder) that effectively delivers Criteos unique demand into the publisher’s header auction, at a 1st rather than cleared 2nd price.
  • Amazon have launched a server to server header bidding offering for publishers that delivers unique demand and the ability to manage other S2S demand partners for the publisher.
Extra Credit
  • <quote>senior AdTech big wigs</quote>
  • programmatic auction process
  • 1st v 2nd price
  • 2nd price was for waterfall
  • 1st price will be for unified (header bidding)

General Data Protection Regulation’ (GDPR)

  • 2018-05
  • Consent must be collected.
  • Will make 2nd party data marketplaces economical.
  • The salubrious effect.
  • Publishers have a Direct Relationship with consumers.
    this is argued as being “better.”
  • Industry choices
    • collect holistic consent
      <quote>one unified [process] of consumer [outreach] rather than one for every vendor</quote>
    • individual vendor consent
      <quote>for every cookie or device ID that flows through the OpenRTB pipes we have spent the last 10 years laying.</quote>

Viewability & Brand Safety

  • IAB
  • MRC

Talking Points

  • Moat was sold to Oracle for reported number of $800M.
  • PE Firm Providence Equity bought a % of Double Verify giving them a reported value of $300M.
  • Integral Ad Science remains independent, for now


  • Telcos have what everybody in AdTech wants:
    • accurate data
    • privacy compliant data
    • scaled data
    • 1st party data.
  • Telcos want what AdTech & publishing companies have:
    • programmatic sell and buy side tools
    • content creation functions
    • distribution at scale.
    • diversification of revenues

Talking Points

  • Verizon buys AOL & Yahoo to form Oath, a publisher, a DSP, a DMP.
  • Telenor buys TapAd, a cross-device DMP-type-thing
  • Altice buys Teads, a streaming video vendor)
  • Singtel buys Turn, a DSP
  • AT&T needs a line in this list; might want to buy Time Warner which is a movie studio, media holding copmany, a cable operator, an old owner of AOL.
Raised $18.75M, Series A. Why?
Raised $20M, through Series B, Why?

Data Management Platform (DMP)

  • Not a pure-play business.
    • A division, not a business.
    • An interface, not a division.
  • Everyone wants to own one.
  • Should DMP’s also be in the media buying business?
  • What are DMP’s doing to stay relevant for a world without cookies?
  • Do DMP’s plan to build or buy device graph features / functions?
  • For platforms that process & model a lot of 1st, 2nd & 3rd party data, how will they be affected by the pending GDPR?
Talking Points
  • Adobe bought Tube Mogul, a video DSP, for $540M (based on information &amp belief).
  • Oracle bought Moat, a verification feature, for $800M
  • Oracle bought Crosswise, a cross-device database, for <unstated/>
  • Salesforce bought Krux, a DMP, FOR $700M

Lotame remains independent, for now

ID Consortium’s & Cross-Device Players

Probabilistic “won’t work”
<quote>The GDPR may make it very difficult for a number of probabilistic methods to be applied to digital ID management.</quote>
Walled Garden
They … <quote>are using their own proprietary cross-screen deterministic token / people based ID that in many cases only works within their O&O environments.</quote>
Universal ID
Is desired. <quote>CMO’s & agencies in the future will not be requesting a cleaner supply chain, but a universal ID (or ID clearing house) that will enable them to manage reach, frequency & attribution across all of the partners they buy from.</quote>
The DigiTrust
<quote>This technology solution creates an anonymous user token, which is propagated by and between its members in lieu of billions of proprietary pixels and trackers on Web pages.</quote>
Claim: “Many” leading AdTech companies are already working with the DigiTrust team. [Which?]
AppNexus ID Consortium
  • Scheme: people-based ID.
  • Launch: 2017-05
  • Trade Name: TBD
    • Index Exchange
    • LiveRamp
    • OpenX
    • Live Intent
    • Rocket Fuel
  • Adbrain
  • Screen6
  • Drawbridge



  • Blockchain is slow, too slow, way too slow
    Blockchain can handle 10 tps.
  • Does not work in OpenRGB
    • New York City
  • Some Q&A; In AdExchanger
    tl;dr → interview of Dr Boris WHO?, IPONWEB; self-styled “the smartest man in AdTech and he concurs”

Artificial Intelligence

  • Is bullshit.
  • c.f.(names dropped)
    • Deepmind
    • Boston Dynamics


  • DOOH
  • Audio
  • Programmatic TV
  • Over The Top (OTT)
  • MarTech != AdTech

Previously filled.

The Marketer’s Guide To Blockchain | AdExchanger

The Marketer’s Guide To Blockchain; ; In AdExchanger; 2017-07-06.


  • IBM
  • Comcast
  • MadHive
  • Rebel AI


AdLedger Consortium

  • IBM,, anchor
  • Integral Ad Science, trading
  • MadHive, a boutique
  • TEGNA, subsidiary of Premion, a DSP for OTT

Scope: unclear


  • Comcast
  • Altice USA
  • Cox
  • NBCUniversal
  • Disney

Scope: data share contracts, record transactions in blockchain.
Story: <quote>
a vetted, trusted media buyer could execute a campaign against segments provided by members of the Comcast consortium</quote>

Scope: sells guarantee contracts as futures; not live inventory.

  • TV ad buying
  • multiple stakeholders
  • multi-party contracts
Interactive Advertising Bureau (IAB)
“in an exploratory phase”, attributed to Allana Gompert.


All future tense. Very aspirational. Many qualifiers.

Qualifiers: Still forming, working group, will dictate policy, will dictate API specifications.
Qualifiers: Not until 2018 [e-o-2018] is “2019,” think: ~600 days
Qualifiers: is developing, proofs of concept, beta partners.


  • <quote>securely share their assets without exporting or handing them over to another stakeholder </quote>
  • <quote>And media owners can strike a blow against unauthorized sellers and domain spoofers. </quote>
  • <quote>[remove out unwanted supply chain intermediaries </quote>


  • Use [unique] blockchain keys instead of gimmicky [URL] names
  • Use blockchain <snip/> to log transactions, recording the use of “data”consumer dossiers.


  • <paraphrase>[As] Comcast and Cox, [I have] different inventory rates for specific content or audiences, or [as a] buyer [I] wants to blacklist certain supply sources, <snip> each company’s smart contract and dictates how others on the blockchain can access its data. <paraphrase>
  • <paraphrase>[As an] advertiser [I] could lock up inventory over the long term and publishers could score bigger upfront deals or offer different types of discounts. And in this instance, blockchain would serve as the ledger recording all of these transactions – and their value. </paraphrase>


  • Slow
  • Does. Not. Scale.
  • Ill-posed
    • The media business wants transparency.
    • The media business requires opacity.
  • AppNexus
  • DoubleClick Ad Exchange


  • Ken Brook, CEO, MetaX, a boutique
  • Peter Guglielmino, CTO, IBM’s Media & Entertainment Group.
  • Alanna Gombert, general manager of the IAB Tech Lab.
  • Adam Helfgott, founder, MadHive, a boutique
  • Will Luttrell, Curren-C, a boutique; co-founder, ex-former CTO, Integral Ad Science
  • Manny Puentes, CEO, Rebel AI, a boutique
  • Lou Severine, CEO, NYIAX CEO


  • government-backed legal tender
  • secured bank vaults,
  • bitcoin
  • blockchain ledger
  • guarantee security
  • full transparency
  • smart contracts


In Ad Exchanger


AdLedger Consortium, in data trading around OTT
Rebel AI
Something about “hoping to develop”, something about brand safety & ad fraud
On-(block-)chain and off-chain solutions. adChain, a protocol on Ethereum. with Direct Marketing Association Data & Marketing Association (DMA)
“like legacy data players” Acxiom and Experian; Blockchain Insights Platform; not before 2019.
Is NASDAQ’s proprietary blockchain; a futures recordation scheme, against The Upfronts. Among ex-AOL VP-levels, Bill Wise, founder and CEO, Mediaocean, is a board member
Bluemix, a services suite, cloud-blockchain frontrunner. Something about having deep pockets, being able to incur long periods of R&D costs, hoping to recoup on IBM services in other domains, e.g. healthcare and finance.
A boutique.

Previously filled.

Opera is acquired by a Chinese consortium (Kunlun, Qihoo 360, Golden Brick, Yonglian)

In archaeological order

Opera gets $1.2 billion buyout offer from mix of Chinese firms, board recommends deal; ; In ZDNet; 2016-02-10.
Teaser: There is “strong strategic and industrial logic to the acquisition,” according to the software maker’s CEO.

Original Sources


  • Price
    • $1.2B USD
    • 53% above Oslo close 2016-02-04.
  • Consortium
    • media
      • Kunlun
      • Qihoo 360
    • pure-play investment
      • Golden Brick
      • Yonglian
  • Who
    • Lars Boilesen, CEO, Opera
    • Sverre Munck, chairman of the board, Opera
    • Yahui Zhou, CEO, Kunlun,
  • Process
    • For sale since 2015-08.
    • Representors
      • Morgan Stanley International
      • ABG Sundal Collier

Qihoo 360-Led Chinese Consortium Makes $1.2 Billion Offer for Opera; Rick Carew (Hong Kong), Kjetil Malkenes Hovland (Oslo); In The Wall Street Journal (WSJ); 2016-02-10.
Teaser: Bid for Norwegian company adds to a busy start to 2016 for outbound Chinese acquisitions


  • Opera Software ASA, Norway
  • A consortium of Chinese companies
    • Operators
      • Qihoo 360 Technology Co.
      • Beijing Kunlun Tech Co.
    • Investors
      • Golden Brick
      • Silk Road Fund Management (Shenzhen) LLP
      • Yonglian (Yinchuan) Investment Co.
  • Bid (proposal)
    • Equivalently
      • $1.2B USD in cash
      • 71 Norwegian kroner ($8.27)/share
    • Factoid
      • a 46% premium over trading 2016-02-05
      • <quote>When trading resumed on Wednesday, the stock soared more than 40%, and closed up 33% at 65.10 kroner.</quote>
    • Support
      • Board of Directors, Opera Software ASA
      • 33% of the shares
  • Valuation
    • 2016: $690 million → $740 million (range)
    • 2015: $616 million.
  • Consortium
  • Competition
    sources via StatCounter

    • Android of Googleof Alphabet
      • Chrome → 36.8% market share
    • Microsoft
      • unstated products & market share.
    • Alibaba Group Holding Ltd.
      • UCWeb → ~20% market share
  • Market Share
    sources via StatCounter

    1. Something
    2. Something
    3. Safari
    4. Opera (Phone)→ 10.8%
    5. something
    6. Opera (All; Phone, Tablet, Laptop) → 5.7%
  • Background
    • Qihoo is
      • <quote><snip/>in the process of delisting from the New York Stock Exchange after agreeing in December to a buyout by a consortium including its chairman for $9 billion.</quote>
      • makes mobile and PC antivirus software,
      • operates a search engine
        • No. 2 search engine in China
        • Search engine behind Baidu Inc.
      • has a “secure” Web browser.
    • Kunlun
      • a 60% stake in gay-dating app Grindr LLC for $93 million 2016-01.
    • Other acquisitions by Chinese companies.
  • Who
    • Yu Ling, press relations, Qihoo
    • Havard Nilsson, staff, Carnegie ASA.


In The Wall Street Journal (WSJ):

Factual’s approach to Location Data

Location Data Not Only Signals Where You Are, It Signals Who You Are; Cathy Boyle interviews Tyler Bell (Factual); In eMarketer; 2015-05-15; previously filled.
Tyler Bell is Vice President, Product at Factual


  • <quote>general patterns of behavior over time are usually indicative of age and gender, too.</quote>
  • <quote>Ethnicity and household income are hard to get from location. But by using that data we can make a guess as to which census block group a person is located in. The US Census aggregates information in a so-called block group level, which you can think of as one or more city blocks. That information is a pretty good metric for making assumptions about ethnicity in some circumstances, as well as household income.</quote>
  • <quote>Tell me where my customers go so I can better understand their behaviors in the real world?</quote>


  • Factual doesn’t own location data
    Publishers and their agents, the SSPs, do own the location data
  • Factual merely processes location data as a service provider to owners.
  • <quote>We create the audiences doing our location process, and then we give those audiences back to the partner who gave us the data in the first place.</quote>

Validating Mobile Ad Location Data at Factual; (Factual); In Their Blog; 2014-04-21.
Tyler Bell, GeoPulse Product Lead, Factual
Tom White, GeoPulse Engineering Lead, Factual

Location Validation Stack

Data via RTB and Apps is…
  • Unvalidated
  • Independent
  • Intermittent


… of errors.
  • Truncation
  • Invalid Coordinates; e.g. (0, 0), country, state, county, city centroid.
  • Matching Coordinates; i.e. (lat equals lon)
  • Out of Bounds
  • Blacklisted; e.g. artificial popularity.
  • Prevarication&Maliciousness; e.g. coordinate randomization, jittering, sharing.
  • Impossible Motion; i.e. devices move & jump between probable & valid locations.
  • Hardware; e.g. jitter.


… techniques & systems.
  • built on a statistical model that identifies blacklisted points via a hypothesis testing framework, that learns which points are over-represented based on all points
  • historical dossier of device behavior.
  • motion models


Swirl Networks (Swirl)



  • Indoor mobile marketing

Lines of Business

  • Swirl for Retailers
  • Swirl for Publishers
  • Swirl for Advertisers
    • Swirl Audience Network
    • Swirl Ad Exchange (SWx)


  • Boston, MA
  • 30 employees
  • Funding
    • Series C is $18M
    • Total: $32M
  • Investors
    • Twitter Ventures
    • Hearst Ventures
    • Softbank Capitals
    • Longworth Venture Partners
    • General Catalyst Partners
  • Business Model
    <quote cite=”ref“>Swirl makes money in three ways.

    1. It sells software subscriptions to retailers who want to use its technology to build an ad network in their stores.
    2. The startup also gets a percentage of any advertising buys that are made by outside marketers, and
    3. a percentage of the money that retailers might spend to get their store’s offers featured in third-party shopping apps that partner with Swirl.</quote>
  • SecureCast™ beacon protocol (a proprietary protocol)
  • Swirl App
    • Android, Manager & Configurator
    • iOS, SWIRL In-Store Explorer
  • Support
    • Beacon (Bluetooth Low Energy)
    • does not support NFC
    • Something vague about Apple Pay (or not) <quote>provides a seamless way for retailers and brands to measure the effectiveness of their in-store mobile marketing efforts by closing the loop between beacon-triggered messages/offers and actual consumer purchases in the store.”</quote> attributed to Rob Murphy.
  • Customers
    • Alex and Ani
    • Hudson’s Bay
    • Lord & Taylor
    • Marriott
    • Timberland.
    • Urban Outfitters
  • Relationships
    • Condé Nast, an app publisher
    • SnipSnap, a coupon app
    • Hearst, an app publisher, also an investor
    • Motorola Solutions (Zebra Technologies), indoor location system, MPact.
      a partnership to sell Swirl’s beacon+app+marketing cloud; standalone and bundled with Motorola’s MPact indoor location service+rigging.
  • Positioning
    • The tech is “done”
    • The money is for sales & marketing outreach.
    • Something about how the new investors (i.e. Twitter) will help.
  • Something about wearables.
  • Something about building an RTB interface.
  • Swirl Ad Exchange (SWx)
    <quote cite=”ref“>Swirl’s programmatic ad exchange for proximity-based in-store mobile marketing</quote>


  • Hilmi Ozguc, CEO, Founder
  • Rob Murphy, Vice President Marketing


  • Beacons monitor the consumers sojourn times in known locations
  • Intent & interest is imputed to location & sojourn time.
  • Push notifications are used to incite action out of the consumer on a target or retarget basis.
  • Perhaps Twitter DMs from a robot will notify you.

Use Cases

  • <quote>Estée Lauder and Michael Kors ran in-aisle campaigns in Lord & Taylor stores across the country during the past holiday shopping season. In both cases, the brands would send users who showed intent – signaled by their spending more than a minimum set amount of time in either the beauty department or the handbag department – push notifications to learn more about certain products on display.</quote>
  • <quote>Marriott uses Swirl to target people while they’re in specific places around the hotel. Guests lounging by the pool for more than 15 minutes, for example, might find themselves pinged with a message that says, “How about a free appetizer with your next drink order?”</quote>


  • SecureCastd™ beacons (a proprietary protocol)
  • Swirl Audience Network
  • Swirl Ad Exchange (SWx)
  • Ad Units
    creatives, permissioning, regulatory compliance; multi-page, interactivity, position-triggered.
  • Hardware
    • iBeacon mode
    • SecureCast™ mode
    • BYOB (supply your own gear)
  • Swirl Mobile Client SDK
    • Android
    • iOS
  • Advertising Console (a web site)
    • Programmatic access to (participating publisher) private marketplaces
    • Package Discovery (supply package, media bundles)
    • Campaign management
    • Swirl Creator™
      requires custom creatives to run on their rendering engine & delivery platform
    • Swirl Analytics
  • Publisher SDK
    • For App publishers
    • An SDK
      • Android
      • iOS
    • Compatibilities
      • iBeacon
      • SecureCast™
    • Beacon Signalling
      • detection
      • ranging
      • event logging
      • etc.
    • Consumer Messaging
      • Frequency Capping
      • Regulatory Compliance
  • Publisher Console
    • Inventory Management
    • Ad Operations (notifications, alerts, etc.)
    • Analytics
    • Participation in Swirl Audience Network
    • Partner Permissioning
  • Retail
    • Swirl Targeting Wizard™
      • a rules engine specifier
      • location
      • day part
      • profile management
    • Campaign Management
      • Something about bucket testing


Archaeological order, derivative works on top, older material below


Swift Beacon PlatformSwift Retailer PlatformSwift Publisher PlatformSwift Advertiser Platform

Via: backfill

Mobiad: Private and Scalable Mobile Advertising | Haddadi, Hui, Brown

Hamed Haddadi, Pan Hui, Ian Brown; Mobiad: Private and Scalable Mobile Advertising; In Proceedings of the Fifth ACM International Workshop on Mobility in the Evolving Internet Architecture (MobiArch ’10); 2010; pages 33–38; copy, paywall


We introduce MobiAd; a scalable, location-aware, personalised and private advertising system for mobile platforms. Advertising is the driving force behind many websites and service providers on the Internet. With the ever-increasing number of smart phones, there is a fertile market for personalised and localised advertising. They key benefit of using mobile phones is to take advantage of the vast amount of information on the phones and the locations of interest to the user in order to provide personalised ads. Preservation of user privacy is however essential for successful deployment of such a system. MobiAd would perform a range of data mining tasks in order to maintain an interest profile on the user’s phone, and use the infrastructure network to download and display relevant ads and reports the clicks via a Delay Tolerant Networking (DTN) protocol. In this paper we provide an overview into existing advertising systems and privacy concerns on mobile phones, in addition to the scalable local ad download and privacy-aware DTN-based click report dissemination methods that we propose for MobiAd.


  • Directive 95/46/ec of the european parliament and of the council of 1995-10-24 on the protection of individuals with regard to the processing of personal data and on the free movement of such data. OJ L 281 pp.31-50, 1995-11.
  • Google investor relations, financial tables, 2008.
  • Admob mobile metrics report, 2010.
  • Multimedia broadcast/multicast service (MBMS); stage 1, 3GPP specification detail, 2010.
  • S. Burleigh, A. Hooke, L. Torgerson, K. Fall, V. Cerf, B. Durst, K. Scott, H. Weiss. Delay-tolerant networking: an approach to interplanetary internet. In IEEE Communications Magazine, 41(6):128–136, 2003.
  • R. Dingledine, N. Mathewson, and P. Syverson. Tor: The second-generation onion router. In Proceedings of the 13th USENIX Security Symposium, pages 303–320, 2004.
  • K. Fall. A delay-tolerant network architecture for challenged internets. In Proceedings of the 2003 Conference on Applications, Technologies, Architectures, and Protocols for Computer Communications (SIGCOMM ’03), pages 27–34, New York, NY, USA, 2003. ACM.
  • B. Greenstein, D. McCoy, J. Pang, T. Kohno, S. Seshan, D. Wetherall. Improving wireless privacy with an identifier-free link layer protocol. In Proceeding of the 6th International Conference on Mobile Systems, Applications, and Services (MobiSys ’08), pages 40–53, New York, NY, USA, 2008. ACM.
  • S. Guha, A. Reznichenko, K. Tang, H. Haddadi, P. Francis. Serving Ads From Localhost For Performance, Privacy, and Profit. In Proceedings of the Eighth ACM Workshop on Hot Topics in Networks (HotNets-VIII), New York City, NY, 2009.
  • H. Haddadi. Fighting Online Click-Fraud Using Bluff Ads. In ACM Computer Communication Review, 40(2), 2010.
  • P. Hui, J. Crowcroft, E. Yoneki. Bubble Rap: Social-Based Forwarding In Delay Tolerant Networks. In Proceedings of the 9th ACM International Symposium on Mobile Ad Hoc Networking & Computing (MobiHoc ’08), 2008-05.
  • X. Lu, P. Hui, D. Towsley, J. Pu, Z. Xiong. Anti-Localization Anonymous Routing for Delay Tolerant Networks. In Elsevier Computer Network, 2010.
  • S. Milgram. The small world problem. In Psychology Today, (2):60–67, 1967.
  • C. Song, Z. Qu, N. Blumm, A.-L. Barabsi. Limits of Predictability in Human Mobility. In Science, 327(5968):1018–1021, 2010.
  • T. Spyropoulos, S. Member, K. Psounis, C. Raghavendra. Single-Copy Routing in Intermittently Connected Mobile Networks.In Proceedings of IEEE International Conference on Sensing, Communication and Networking (SECON), 2004.
  • F. Stajano, R. J. Anderson. The Cocaine Auction Protocol: On the Power of Anonymous Broadcast. In Information Hiding, pages 434–447, 1999.
  • V. Toubiana, A. Narayanan, D. Boneh, H. Nissenbaum, S. Barocas. Adnostic: Privacy Preserving Targeted Advertising. In Proceedings of the Network and Distributed System Security Symposium (NDSS). 2010, San Diego, California, USA.

Via: backfill

Some impressions on Internet advertiser security | Citizen Lab (U. Toronto)

Andrew Hilts (Citizen Lab); Some impressions on Internet advertiser security; In Their Blog; 2015-03-30.
Andrew Hilts, Executive Director of Open Effect and Research Fellow, Citizen Lab.

Promotional Cross-Posts


<quote>We found a significant disparity between the level of HTTPS support in the ad industry referred to on the IAB’s blog and what we measured with our tests. We furthermore found that more than half of the ad trackers found on popular news websites that use cookie-based tracking mechanisms have no security measures in place to stop bad actors from collecting and correlating these unique identifiers with other browsing data. An important area of future work will be to repeat these tests in six months, and again in a year’s time to determine the relative success of the IAB’s call to security.</quote>


  • Cookie-based tracking
  • NSA uses Google cookies to pinpoint targest for hacking ; Ashkan Soltani, Andrea Peterson, Barton Gellman; In The Washington Post; 2013-12-10.

    • Google’s cookie PREFID
  • How Advertisers Use Internet Cookies To Track You; Christina Tsuei; In Wall Street Journal Video; a tutorial; 2010-07-30; 7:04.
  • Brendan Riordan-Butterworth (IAB); Adopting Encryption: The Need for HTTPS. In Their Blog; 2015-03-25.

  • TrackerSSL
  • Disconnect
  • HTTPS Everywhere
  • Surveys
    • Alexa 100 News Sites
      • <quote>Overall the results show that news websites are slightly beyond the midway point of getting their third party dependencies secured before they themselves can reliably implement HTTPS.</quote>
    • Digital Advertising Alliance (DAA)
      • <quote> 38% of the 123 advertisers in the Digital Advertising Alliance’s own database support HTTPS, less than half of the 80% figure referred to by [the IAB]</quote>
    • Disconnect Tracker Inventory
      • <quote>[Under] 11% of ad trackers in this list supported HTTPS in practice <snip/> Another 3.8% did support HTTPS but used server configurations to actively redirect users away from a secure to an insecure connection. The remaining 85.7% of advertising trackers did not support HTTPS at all</quote>


Alexa 100 News using HTTPS
DAA Ad Choices, use of SSL
Disconnect Census of Trackers' use of HTTPS

Via: backfill

Evolution of TV | Google

  • Rany Ng, Anish Kattukaran (Google); Evolution of TV: The Promise of Programmatic TV; In The Blog entitled Think with Google; 2015-03; 11 slides; landing.
    Rany Ng is Director of Product Management, Video, Google
    Anish Kattukaran is Product Marketing, Video & Brand Measurement, Google
    tl;dr => TV is big, there are skeptics; promotes the previous #thinkpieces.
  • Greg Philpott, Anish Kakkukaran (Google); Evolution of TV: 7 Dynamics Transforming TV; In The Blog entitled Think with Google; 2014-12; 19 slides; landing.

    1. Delivery: Reaching Across Screens
    2. Delivery: Internet TV Streaming
    3. Delivery: TV Distribution and the Cloud
    4. Advertising: Measurement
    5. Advertising: Programmatic Ad Technology
    6. Advertising: Addressable Advertising
    7. User: Viewer Engagement
  • Greg Philpott, Anish Kakkukaran (Google); Evolution of TV: Reaching Audiences Across Screens; In The Blog entitled Think with Google; 2015-02; 9 slides; landing.
  • Marco Bertozzi (VivaKi); Does Ad Tech Stack Up?  How It’s Working at VivaKi; In The Blog entitled Think with Google; 4 slides; landing.
    Marco Bertozzi is Executive Managing Director, VivaKi.
    tl;dr => they like it.


  • Definitions
    • Live TV
    • TV Distributor
    • Smart TV
    • Linear TV
    • Over-the-Top (OTT)
    • On-Demand TV
    • TV Programmer
    • TV Everywhere
  • Promise of Programmatic
    • Automated Buying
    • Data-Driven Targeting
    • Cross-Screen Measurement
    • Unified Campaign Management
    • Real-Time Optimization
  • Recitation
    • Advertiser demand is high (for TV supply);l supply is low [fixed].
    • Addressable inventory in linear or cross-screen TV is low & undersold.
    • Waste exists because TV buying is on a GRP basis, not a targeted audience basis.
    • Inventory prices are dropping in a race to the bottom; programmatic will fix this.
    • Something about how programmatic mitigates business risk.  Probably the ability to set delivery rules.
    • A single buying platform to rule them all.
  • Vision
    1. Premium buying
    2. Streaming & Video-on-Demand (VOD) buying
    3. Better integration



Firefox Tiles







  • 290×180
  • 142×70 =

This preference can be set to anything that returns JSON, setting this to an empty JSON object will disable Tiles from showing and fetching new Tiles. With the change below a new user would only see empty Tiles and Firefox could no longer fetch new Tiles. =   data:application/json,{} =

This is the tile reporting interface back to the Mozilla mother ship. Changing or disabling this pref maywill prevent Firefox from being able to report metrics on Tiles. Setting this to nothing will disable the ping.

Other Preferences

about:config for the newtab cluster

Preference Name Status Type Value
browser.newtab.preload default boolean true
browser.newtab.url default string about:newtab
browser.newtabpage.blocked user set string …JSON blob…
browser.newtabpage.columns default integer 3
browser.newtabpage.enabled default boolean true
browser.newtabpage.pinned user set string …JSON blob…
browser.newtabpage.rows default integer 3
browser.newtabpage.storageVersion default integer 1



$ curl --location --verbose
* About to connect() to port 443 (#0)
*   Trying
* Connected to ( port 443 (#0)
* Initializing NSS with certpath: sql:/etc/pki/nssdb
*   CAfile: /etc/pki/tls/certs/ca-bundle.crt
  CApath: none
* SSL connection using TLS_DHE_RSA_WITH_AES_128_CBC_SHA
* Server certificate:
* 	subject: CN=*,O=Mozilla Foundation,L=Mountain View,ST=CA,C=US
* 	start date: Apr 08 00:00:00 2014 GMT
* 	expire date: Oct 26 12:00:00 2016 GMT
* 	common name: *
* 	issuer: CN=DigiCert SHA2 Secure Server CA,O=DigiCert Inc,C=US
> GET /v2/links/fetch/en-US HTTP/1.1
> User-Agent: curl/7.29.0
> Host:
> Accept: */*
&lt HTTP/1.1 303 SEE OTHER
< Content-Type: text/html; charset=utf-8
< Date: Thu, 26 Mar 2015 14:02:03 GMT
< Location:
< Content-Length: 405
< Connection: keep-alive
* Ignoring the response-body
* Connection #0 to host left intact
* Issue another request to this URL: ''
* About to connect() to port 443 (#1)
*   Trying
* Connected to ( port 443 (#1)
*   CAfile: /etc/pki/tls/certs/ca-bundle.crt
  CApath: none
* SSL connection using TLS_RSA_WITH_AES_256_CBC_SHA>
* Server certificate:
* 	subject: CN=*,O=", Inc.",L=Seattle,ST=Washington,C=US
* 	start date: Feb 19 00:00:00 2015 GMT
* 	expire date: Oct 19 23:59:59 2015 GMT
* 	common name: *
* 	issuer: CN=VeriSign Class 3 Secure Server CA - G3,OU=Terms of use at (c)10,OU=VeriSign Trust Network,O="VeriSign, Inc.",C=US
> GET /desktop/US/en-US.eb4cb64172c72f108cbb2301b958ecf3c9895373.json HTTP/1.1
> User-Agent: curl/7.29.0
> Host:
> Accept: */*
< HTTP/1.1 200 OK
< Content-Type: application/json
< Content-Length: 3909
< Connection: keep-alive
< Date: Tue, 24 Mar 2015 17:43:48 GMT
< Content-Disposition: inline
< Cache-Control: public, max-age=31536000
< Last-Modified: Tue, 24 Mar 2015 00:30:12 GMT
< ETag: "a90166163cf89dd1e2d6c2591b18a988"
< Accept-Ranges: bytes
< Server: AmazonS3
< Age: 159496
< X-Cache: Hit from cloudfront
< Via: 1.1 (CloudFront)
< X-Amz-Cf-Id: ZjFMeI8aQEwExP2f9Xp4LFPW09Gqo87vJBW3BSue79xeYOHbTgi_nw==
{"en-US": [{"bgColor": "", "directoryId": 498, "enhancedImageURI": "", "imageURI": "", "title": "Mozilla Community", "type": "affiliate", "url": ""}, {"bgColor": "#ffffff", "directoryId": 499, "enhancedImageURI": "", "imageURI": "", "title": "Firefox for Android", "type": "affiliate", "url": ""}, {"bgColor": "", "directoryId": 701, "enhancedImageURI": "", "imageURI": "", "title": "TurboTax", "type": "sponsored", "url": ""}, {"bgColor": "", "directoryId": 500, "enhancedImageURI": "", "imageURI": "", "title": "Mozilla Manifesto", "type": "affiliate", "url": ""}, {"bgColor": "", "directoryId": 502, "enhancedImageURI": "", "imageURI": "", "title": "Customize Firefox", "type": "affiliate", "url": ""}, {"bgColor": "#fff", "directoryId": 690, "imageURI": "", "title": "Mozilla Developer Network", "type": "affiliate", "url": ""}, {"bgColor": "", "directoryId": 504, "enhancedImageURI": "", "imageURI": "", "title": "Firefox Marketplace", "type": "affiliate", "url": ""}, {"bgColor": "#3fb58e", "directoryId": 505, "enhancedImageURI": "", "imageURI": "", "title": "Mozilla Webmaker", "type": "affiliate", "url": ""}, {"bgColor": "", "directoryId": 506, "enhancedImageURI": "", "imageURI": "", "title": "Firefox Sync", "type": "affiliate", "url": ""}, {"bgColor": "", "directoryId": 507, "enhancedImageURI": "", "imageURI": "", "title": "Privacy Principles", "type": "affiliate", "url": ""}]}
 * Connection #1 to host left intact


 [{"bgColor": "",
   "directoryId": 498,
   "enhancedImageURI": "",
   "imageURI": "",
   "title": "Mozilla Community",
   "type": "affiliate",
   "url": ""},
  {"bgColor": "#ffffff",
   "directoryId": 499,
   "enhancedImageURI": "",
   "imageURI": "",
   "title": "Firefox for Android",
   "type": "affiliate",
   "url": ""},
  {"bgColor": "",
   "directoryId": 701,
   "enhancedImageURI": "",
   "imageURI": "",
   "title": "TurboTax",
   "type": "sponsored",
   "url": ""},
  {"bgColor": "",
   "directoryId": 500,
   "enhancedImageURI": "",
   "imageURI": "",
   "title": "Mozilla Manifesto",
   "type": "affiliate",
   "url": ""},
  {"bgColor": "",
   "directoryId": 502,
   "enhancedImageURI": "",
   "imageURI": "",
   "title": "Customize Firefox",
   "type": "affiliate",
   "url": ""},
  {"bgColor": "#fff",
   "directoryId": 690,
   "imageURI": "",
   "title": "Mozilla Developer Network",
   "type": "affiliate",
   "url": ""},
  {"bgColor": "",
   "directoryId": 504,
   "enhancedImageURI": "",
   "imageURI": "",
   "title": "Firefox Marketplace",
   "type": "affiliate",
   "url": ""},
  {"bgColor": "#3fb58e",
   "directoryId": 505,
   "enhancedImageURI": "",
   "imageURI": "",
   "title": "Mozilla Webmaker",
   "type": "affiliate",
   "url": ""},
  {"bgColor": "",
   "directoryId": 506,
   "enhancedImageURI": "",
   "imageURI": "",
   "title": "Firefox Sync",
   "type": "affiliate",
   "url": ""},
  {"bgColor": "",
   "directoryId": 507,
   "enhancedImageURI": "",
   "imageURI": "",
   "title": "Privacy Principles",
   "type": "affiliate",
   "url": ""}]}


bgColor directoryId title type url enhancedImageURI imageURI
498 Mozilla Community affiliate
#ffffff 499 Firefox for Android affiliate
701 TurboTax sponsored
500 Mozilla Manifesto affiliate
502 Customize Firefox affiliate
#fff 690 Mozilla Developer Network affiliate (empty)
504 Firefox Marketplace affiliate
#3fb58e 505 Mozilla Webmaker affiliate
506 Firefox Sync affiliate
507 Privacy Principles affiliate


Indeed there is an advertisement in there., It’s a native advertisement, perhaps you can spot it?

enhancedImageURI imageURI
enhancedImageURI imageURI
enhancedImageURI imageURI
enhancedImageURI imageURI
enhancedImageURI imageURI
(empty) imageURI
enhancedImageURI imageURI
enhancedImageURI imageURI
enhancedImageURI imageURI
enhancedImageURI imageURI


Gimbal, Inc.
11010 Roselle St, Ste 150
San Diego CA 92121-1226
United States

Gimbal Store

Gimbal Proximity BeaconsGimbal Proximity Beacon Specification Sheet

Gimbal Proximity SDK Deployment ConceptMonetization

  • This is a “for rent” scheme
    You don’t own these devices or control the service, not really. PAYGO-in-the-Cloud.
  • Use of Gimbal beacons requires a Gimbal Developer Account
  • Fees follow the count of user count
    the number of users whose devices use the Gimbal SDK and contact the Gimbal backend servers.

References: Daniel Eagle


  • Accuracy: maybe.
  • Cost: seems cheap enough
  • Proprietary Lockin: mild but definitely present; c.f. that library jarfile SDK.
  • Platform Lockin: iOS is definitly the primary
    Android seems a very very distant second thought.
  • Organizational Reliabliity: it’s a PAYGO-in-the-Cloud service model
    This seems to be the centerpiece risk issue.
    <quote>Also, in order for the SDK to work it has to call home to the Gimbal backend service once every 24 hours.</quote>

The devices, SDK & service are closed and single-supplier.  Your use is dependent upon the kindness & SLAs of Gimbal’s financial health.  Fear this and plan for this as you design. Envision that you’ll wake up some morning to read that they are shutting down the service (they can’t run it even at cost, they’ve sold it, they’ve found a better business model with someone else, they need to move offshore to get access to cheaper labor, etc.).


  • Supports
    • Apple-native
    • Android
      • Client deployment is new & rough; beta.
      • Manager app is not available.
  • Line
    • Series 10
    • Series 20
    • Series 21
  • Gimbal Beacon Manager App (Apple only)
  • Beacon Proximity Beacon Series, specification; 1 page (all 3 products)
    • Bluetooth® Smart (aka Bluetooth 4.0 Low Energy)
    • Channels at 2.4GHz for non-connectable advertisements.
      • Channel 37 (2402MHz)
      • Channel 38 (2426 MHz)
      • Channel 39 (2480 MHz)
    • Compatible with iBeacon.
    • Configurable via Gimbal Manager/Gimbal Beacon Manager App
    • Modes
      • foreground
      • foreground/background
    • Levels
      • Low => -23dBm
      • Full => 0dBm, expect 164 ft (50m)
  • Gimbal Manager Servers




Via: backfill.


  • Eventbase
    • The app lets you meet people who are “nearby”
    • Concepts
      • indoor location
      • indoor geo-location
      • “hyper-local networking.”
    • Promotional Partners
      • USA Networks
      • something about experiential marketing at the conference
  • Jeff Sinclair, co-founder, Eventbase
  • Ben West,, co-founder, Eventbase.
  • Gimbal
    • spun out of Qualcomm
    • Transmitter units are $3-$50.


(the Conference App)

Eventbase South by Southwest mobile app

Outside Voices: The Good, the Bad and the Ugly of Digital Advertising | Terence Kawaja (LUMA Partners)

The Good, the Bad and the Ugly of Digital Advertising; Terence Kawaja; On YouTube; 2015-03-17; 3:47; landing.


Terence Kawaja (LUMA Partners); Outside Voices: The Good, the Bad and the Ugly of Digital Advertising; In The Wall Street Journal (WSJ); 2015-03-17.
Terence Kawaja is Founder and CEO of LUMA Partners,

tl;dr => oped promotes the larger presentation


  • Problems
    • Fraud
    • Viewability
    • Privacy
  • Growth
    • The Ad Market is growing at 35% CAGR.
    • Programmatic growing at 300% CAGR.
  • Something about SaaS (helping).
  • Terms
    • AdTech
    • MarTech
  • AdTech is being bought by
    • Customer Relationship Management (CRM)
    • Data (?)
    • Consumer Internet
    • eCommerce
    • Telecom


Exit Candidates, LUMA Partners

Open URL Redirect Vulnerability On Revive Adserver

Open URL Redirect Vulnerability On Revive Adserver; some dude using the self-asserted identity token xian; In Revive Adserver Forum; 2015-02-27.

tl;dr => oadest is a redirect parameter on …/delivery/ck.php


  • Matteo Beccati, 2015-02-25
  • It’s always been there
  • They know about it
  • They want the unguarded redirect behavior
  • They don’t know how to fix it


There are two known solutions to “fix” open redirects

  • Signatures => Time- and range-bounding the redirect URL target, validated with a publisher-supplied signature.
  • White lists => A white list of valid redirect target (URLs or domains) managed at the ad server, globally or per supply source.

Via: backfill

Ad Servers must be Open Source



Revive Ad Server


  • LAMP
  • Apache, mod_php, FastCGI
  • PHP v5.3 and up
  • zlip, pcre, xml, mysql, curl, openssl, gd, various cache accelerators
  • MySQL v4.1
  • PostgreSQL v8.1





  • Active in 2014 & 2015.


mAdserve is no longer supported

  • mAdserve
  • Becomes MobFox
  • <quote>The mAdserve project has evolved into the MobFox Publisher Platform, a hosted ad serving solution that allows you to manage direct sold campaigns, mediate ad networks, and monetize through RTB. The Open Source Version of mAdserve is no longer being developed/supported.</quote>
  • Source code is download only, no github, not even a stale one..


  • LAMP
  • MySQL
  • PHP v5.3 and up
    • cURL or fsocket
    • mbstring
  • iOS
  • Android
  • Web



  • GPL v2


  • Matomy


  • Orphaned since 2012-08?


Orbit Open Ad Server


  • WordPress
  • Drupal


  • LAMP
  • MySQL v4.1
  • PHP v5.2.6+
    • mbstring
    • SimpleXML
    • JSON
    • PDO-MySQL
    • allow_url_fopen



  • GPL v2



  • Ben Bowler?


  • Source is moribund since 2012-07, last commit was 2012-07-11.
  • Forum has administrator’s updates dated 2014-11-28; chatter through 2013-09.


OASIS Ad Server

  • Ad Delivery Solutions (ADS)
  • <quote>OASIS can also be provided by Ad Delivery Solutions (ADS) as a hosted or dedicated turn-key ad serving product for banner, text, rich media and video advertising. ADS provides full service OASIS support, management and installation services as well as outsourced OASIS ad trafficking.</quote>
  • Claims 130 q/s (as 500,000 q/hr)
  • Moribund since 2007-11.




  • GPL v2



  • Not yet known


  • OASIS v2.3 was released 2007-07-13.
  • Orphaned since 2007-11.

Geo-Social Targeting for Privacy-friendly Mobile Advertising | Dalessandro,Hook, Martens, Murray, Provost, Zhang


  • S. Aral, L. Muchnik, and A. Sundararajan. Distinguishing influence-based contagion from homophily-driven diffusion in dynamic networks. In Proceedings of the National Academy of Sciences; 106(51):21544–21549; 2009.
  • C. Cortes, D. Pregibon, and C. T. Volinsky. Communities of interest. pages 105–114, 2001.
  • D. J. Crandall, L. Backstrom, D. Cosley, S. Suri, D. Huttenlocher, and J. Kleinberg. Inferring social ties from geographic coincidences. In Proceedings of the National Academy of Science; 107(52):22436–22441, 2010-12.
  • S. Hill, D. K. Agarwal, R. Bell, and C. Volinsky. Building an effective representation for dynamic networks. In Journal of Computational & Graphical Statistics; 15(3):584–608; 2006.
  • S. Hill and F. Provost. The myth of the double-blind review? Author identification using only citations. In ACM SIGKDD Explorations; 5(2):179–184; 2003.
  • S. Hill, F. Provost, and C. Volinsky. Network-based marketing: Identifying likely adopters via consumer networks. In Statistical Science; 22(2):256–276; 2006.
  • S. Hill, F. Provost, and C. Volinsky. Learning and Inference in Massive Social Networks. In Proceedings of the 5th International Workshop on Mining and Learning with Graphs; 2007.
  • G. Kossinets and D. J. Watts. Origins of homophily in an evolving social network. In AJS, 115(2):405–450, 2009-09.
  • S. A. Macskassy and F. Provost. Classification in networked data: A toolkit and a univariate case study. In Journal of Machine Learning Research, 8:935–983; 2007.
  • M. McPherson, L. Smith-Lovin, and J. M. Cook. Birds of a feather: Homophily in social networks. In Annual Review of Sociology, 27:415–444, 2001.
  • J. Neville and F. Provost. Predictive modeling with social networks. Tutorial at the ACM SIGKDD, 2008.
  • F. Provost, B. Dalessandro, R. Hook, X. Zhang, and A. Murray. Audience selection for on-line brand advertising: privacy-friendly social network targeting. In Proceedings of the 15th ACM SIGKDD International Conference On Knowledge Discovery And Data Mining (KDD); pages 707–716; 2009; separate updated version 2011-05-25; separately filed.


  • S. Aral, L. Muchnik, and A. Sundararajan. Distinguishing influence-based contagion from homophily-driven diffusion in dynamic networks. Proceedings of the National Academy of Sciences, 106(51):21544–21549, 2009.
  • C. Cortes, D. Pregibon, and C. T. Volinsky. Communities of interest. pages 105–114, 2001.
  • D. J. Crandall, L. Backstrom, D. Cosley, S. Suri, D. Huttenlocher, and J. Kleinberg. Inferring social ties from geographic coincidences. Proceedings of the National Academy of Sciences PNAS, 107(52):22436–22441, December 2010.
  • T. Fawcett. An introduction to ROC analysis. Pattern Recognition Letters, 27(8):861–874, 2006.
  • S. Hill, D. K. Agarwal, R. Bell, and C. Volinsky. Building an effective representation for dynamic networks. Journal of Computational & Graphical Statistics, 15(3):584–608, 2006.
  • S. Hill and F. Provost. The myth of the double-blind review? Author identification using only citations. ACM SIGKDD Explorations, 5(2):179–184, 2003.
  • S. Hill, F. Provost, and C. Volinsky. Network-based marketing: Identifying likely adopters via consumer networks. Statistical Science, 22(2):256–276, 2006.
  • S. Hill, F. Provost, and C. Volinsky. Learning and Inference in Massive Social Networks. In Proceedings of the 5th International Workshop on Mining and Learning with Graphs, 2007.
  • A. Hotho, A. Nürnberger, and G. Paass. A brief survey of text mining. LDV Forum, 20(1):19–62, 2005.
  • G. Kossinets and D. J. Watts. Origins of homophily in an evolving social network. American Journal of Sociology (AJS), 115(2):405–450, September 2009.
  • S. A. Macskassy and F. Provost. Classification in networked data: A toolkit and a univariate case study. Journal of Machine Learning Research, 8:935–983, 2007.
  • M. McPherson, L. Smith-Lovin, and J. M. Cook. Birds of a feather: Homophily in social networks. Annual Review of Sociology, 27:415–444, 2001.
  • J. Neville and F. Provost. Predictive modeling with social networks. Tutorial at the ACM SIGKDD, 2008.
  • F. Provost. Geo-social targeting for privacy-friendly mobile advertising: Position paper. Working paper CeDER-11-06a, NYU/Stern School of Business, 2011.
  • F. Provost, B. Dalessandro, R. Hook, X. Zhang, and A. Murray. Audience selection for on-line brand advertising: privacy-friendly social network targeting. In Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data mining, pages 707–716, New York, NY, USA, 2009. ACM.
  • O. Stitelman, B. Dalessandro, C. Perlich, and F. Provost. Estimating the effect of online display advertising on browser conversion. In Proceeding of the Conference on Data Mining and Audience Intelligence for Advertising (ADKDD 2011), page 8, 2011.

Finding Similar Users with a Privacy-Friendly Geo-Social Design | Provost, Martens, Murray

Foster Provost, David Martens, Alan Murray (Every Screen Media); Finding Similar Users with a Privacy-Friendly Geo-Social Design; working paper; undated; 26 pages; original.


This design-science paper investigates “geo-social” similarity and proposes a new design based on using consumer location data from mobile devices (smart phones, smart pads, laptops, etc.) to build a “geo-social network” of similar users. The geo-social network (GSN) combines the advantages of social and local targeting in a mobile setting. The GSN could be used for a variety of analytics-driven applications, such as targeting advertisements in a manner that is both effective and privacy friendly, and to improve online interactions by selecting similar users. This paper focuses on mobile advertising as the motivating application. The basic idea is that two devices are similar, and thereby connected in the geo-social network, when they share at least one visited location. They are more similar as they visit more shared locations. This paper first introduces the main ideas and ties them to theory and related work. Next we introduce a specific design for selecting entities with similar location distributions. The paper then presents the results of an empirical study using real mobile location data. We focus on two high-level questions: (1) does geo-social similarity allow us to select different entities corresponding to the same individual, for example on different devices? And (2) do entities linked by similarities in local mobile behavior show similar interests, as measured by visits to particular websites? The results show positive results for both. Specifically, for (1) even with the data sample’s limited observability, 40-50% of the time the same individual is connected to herself in the GSN. For (2), the GSN neighbors of visitors to a wide variety of websites are substantially more likely also to visit those same websites. Highly similar GSN neighbors show very substantial lift. Interestingly, the highest lift corresponds to social-networking websites, providing a suggestion that the geo-social network indeed embeds a true social network.


  • Ritu Agarwal, Anil K. Gupta, Robert E. Kraut. 2008. Editorial overview – the interplay between digital and social networks. Information Systems Research 19(3) 243–252.
  • Sinan Aral, Lev Muchnik, Arun Sundararajan. 2009. Distinguishing influence-based contagion from homophily-driven diffusion in dynamic networks. Proceedings of the National Academy of Sciences (PNAS). 106(51) 21544–21549.
  • Mauro Bampo, Michael T. Ewing, Dineli R. Mather, David Stewart, Mark Wallace. 2008. The effects of the social structure of digital networks on viral marketing performance. Information Systems Research 19(3) 273–290.
  • Corinna Cortes, Daryl Pregibon, Chris T. Volinsky. 2001. Communities of interest. 105–114.
  • David J. Crandall, Lars Backstrom, Dan Cosley, Siddharth Suri, Daniel Huttenlocher, Jon Kleinberg. 2010. Inferring social ties from geographic coincidences. Proceedings of the National Academy of Sciences (PNAS). 107(52) 22436–22441.
  • Brian Dalessandro, Foster Provost, Claudia Perlich. 2012. Evaluating & optimizing online display advertising. Technical Report.
  • Tom Fawcett. 2006. An introduction to ROC analysis. Pattern Recognition Letters 27(8) 861–874.
  • Andrew T.Fiore, Judith S. Donath. 2005. Homophily in online dating: when do you like someone like yourself? Extended Abstracts on Human Factors in Computing Systems (CHI EA), ACM, New York, NY, USA, 1371–1374.
  • Helen Fisher. 1992. Anatomy of Love: A Natural History of Mating, Marriage, and Why We Stray. Fawcett-Columbine, New York.
  • Mark Hachman. 2012. Facebook is getting killed – by itself. PC Magazine.
  • Jungpil Hahn, Jae Yun Moon, Chen Zhang. 2008. Emergence of new project teams from open source software developer networks: Impact of prior collaboration ties. Information Systems Research 19(3) 369–391.
  • Julia Heidemann, Mathias Klier, Florian Probst. 2010. Identifying key users in online social networks: A pagerank based approach. Information Systems Journal 4801(December) 157–160.
  • Shawndra Hill., F. Provost. 2003. The myth of the double-blind review? Author identification using only citations. ACM SIGKDD Explorations 5(2) 179–184.
  • Shawndra Hill., F. Provost, C. Volinsky. 2007. Learning and Inference in Massive Social Networks. Proceedings of the 5th International Workshop on Mining and Learning with Graphs.
  • Shawndra Hill, Deepak K. Agarwal, Robert Bell, Chris Volinsky. 2006a. Building an effective representation for dynamic networks. Journal of Computational & Graphical Statistics 15(3) 584–608.
  • Shawndra Hill, Foster Provost, Chris Volinsky. 2006b. Network-based marketing: Identifying likely adopters via consumer networks. Statistical Science 22(2) 256–276.
  • Andreas Hotho, Andreas Nürnberger, Gerhard Paass. 2005. A brief survey of text mining. LDV Forum 20(1) 19–62.
  • Steve Kerho. 2012. Mobile marketing – a new analytics framework, what we have & what we need. Presented at Marketing on The Move Conference at The Wharton School, Philadelphia.
  • Gueorgi Kossinets, Duncan J. Watts. 2009. Origins of homophily in an evolving social network. American Journal of Sociology (AJS) 115(2) 405–450.
  • Sofus A. Macskassy, Foster Provost. 2007. Classification in networked data: A toolkit and a univariate case study. Journal of Machine Learning Research 8 935–983.
  • Martens, D., F. Provost. 2011. Pseudo-social network targeting from consumer transaction data. Previously filled.
  • Miller McPherson, Lynn Smith-Lovin, James M. Cook. 2001. Birds of a feather: Homophily in social networks. Annual Review of Sociology 27 415–444.
  • Jennifer Neville, Foster Provost. 2008. Predictive modeling with social networks. Tutorial at the ACM SIGKDD.
  • Roozbeh Nia, Christian Bird, Premkumar T. Devanbu, Vladimir Filkov. 2010. Validity of network analyses in open source projects. Jim Whitehead, Thomas Zimmermann, eds., Proceedings of the Working Conference on Mining Software Repositories (MSR). IEEE, 201–209.
  • Harri Oinas-Kukkonen, Kalle Lyytinen, Youngjin Yoo. 2010. Social networks and information systems: Ongoing and future research streams. Journal of the Association for Information Systems 11(2) 61–68.
  • Wei Pan, Nadav Aharony, Alex Pentland. 2011. Composite social network for predicting mobile apps installation. Wolfram Burgard, Dan Roth, eds., Association for the Advancement of Artificial Intelligence (AAAI). AAAI Press.
  • Claudia Perlich, Foster Provost, Jeffrey S. Simonoff. 2003. Tree induction vs. logistic regression: a learningcurve analysis. Journal of Machine Learning Research 4 211–255.
  • Foster Provost. 2010. Workshop Machine on Machine Learning for Display Advertising (MLOAD-2010). Keynote.
  • Foster Provost, Brian Dalessandro, Rod Hook, Xiaohan Zhang, Alan Murray. 2009. Audience selection for online brand advertising: privacy-friendly social network targeting. Proceedings of the 15th ACM SIGKDD international conference on Knowledge Discovery and Data Mining (KDD). ACM, New York, NY, USA, 707–716.
  • Pubmatic. 2010. Understanding real-time bidding (RTB) from the publisher’s perspective. Technical Reprt, Pubmatic.
  • G. Shmueli, O.R. Koppius. 2011. Predictive analytics in information systems research. Management Information Systems Quarterly 35(3) 553–572.
  • O. Stitelman, B. Dalessandro, C. Perlich, F. Provost. 2011. Estimating the effect of online display advertising on browser conversion. Data Mining and Audience Intelligence for Advertising (ADKDD 2011) 8.

Via: backfill.