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.

Mentions

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.

Units

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

Optimization

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).

Advertising

  • 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
Toyota
  • 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>
Campbell’s
  • 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>

Planning

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

Partners

Cognitiv
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

Promotions

Ogilvy & Mather
  • Honorific <quote>longtime agency<quote> [fof record for IBM].
Stunts
2011
Jeopardy
2015
[Television] campaign, with Bob Dylan.
2016
Synthesis of the trailier for Morgan (a move; genre: science fiction)
2017-02
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.
Statista

…is quoted
the future is boosted.

Sectors
  • “AI services”
  • “Big Data services”

Themes

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

Ensmoothen & enpitchen the Artificial Intelligence (AI) as…

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

Attributed to Lou Aversano, Ogilvy.

Detractors

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 Monster.com
    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

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).

Competitors

  • Einstein, of Salesforce(.com)
  • Sensei, of Adobe
In-House
  • 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:
    Pepsi
  • 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.

Consumer

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

Who

  • 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

Pantheon

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

Previously

In archaeological order, within Advertising Week

Previously filled.

Is a Cambrian Explosion Coming for Robotics? I Gill Pratt

Gill A. Pratt (DARPA). 2015. Is a Cambrian Explosion Coming for Robotics? In Journal of Economic Perspectives, 29(3): 51-60. http://dx.doi.org/10.1257/jep.29.3.51. landing.

Gill A. Pratt will be stepping down [2015-09] from his position as a Program Manager of the Defense Advanced Research Projects Agency (DARPA), Arlington, Virginia, where he oversaw the DARPA Robotics Challenge and several other programs in robotics while on leave from the faculty of Franklin W. Olin College, Needham, Massachusetts.

Abstract

About half a billion years ago, life on earth experienced a short period of very rapid diversification called the “Cambrian Explosion.” Many theories have been proposed for the cause of the Cambrian Explosion, one of the most provocative being the evolution of vision, allowing animals to dramatically increase their ability to hunt and find mates. Today, technological developments on several fronts are fomenting a similar explosion in the diversification and applicability of robotics. Many of the base hardware technologies on which robots depend—particularly computing, data storage, and communications—have been improving at exponential growth rates. Two newly blossoming technologies—”Cloud Robotics” and “Deep Learning”—could leverage these base technologies in a virtuous cycle of explosive growth. I examine some key technologies contributing to the present excitement in the robotics field. As with other technological developments, there has been a significant uptick in concerns about the societal implication of robotics and artificial intelligence. Thus, I offer some thoughts about how robotics may affect the economy and some ways to address potential difficulties.

Introduction

<quote>

About half a billion years ago, life on earth experienced a short period of very rapid diversification called the “Cambrian Explosion.” Many theories have been proposed for the cause of the Cambrian Explosion, with one of the most provocative being the evolution of vision, which allowed animals to dramatically increase their ability to hunt and find mates (for discussion, see Parker 2003). Today, technological developments on several fronts are fomenting a similar explosion in the diversification and applicability of robotics. Many of the base hardware technologies on which robots depend—particularly computing, data storage, and communications—have been improving at exponential growth rates. Two newly blossoming technologies—“Cloud Robotics” and “Deep Learning”—could leverage these base technologies in a virtuous cycle of explosive growth. In Cloud Robotics — a term coined by James Kuffner (2010) — every robot learns from the experiences of all robots, which leads to rapid growth of robot competence, particularly as the number of robots grows. Deep Learning algorithms are a method for robots to learn and generalize their associations based on very large (and often cloud-based) “training sets” that typically include millions of examples. Interestingly, Li (2014) noted that one of the robotic capabilities recently enabled by these combined technologies is vision—the same capability that may have played a leading role in the Cambrian Explosion.

</quote>

Mentions

  • A model of technology adoption is presented, page 7.
    Technology creates more supply in some areas, creates new demand in other areas; rinse&repeat; goodness follows.
  • Human services cost more because they entail (human) time which cannot be repleased or scaled.  Hence the craft economy; the services economy.
  • When the robots arrive, do “we” use capitalism or communism to distribute the effusion of abundant bounty?
  • The Personal Preferences Information Economy
    <quote>Internet companies that had their start producing computer tools like search, email, maps and others have monetized the personal preferences about their users gathered by the tools themselves—which are typically given away “for free.” The gathered information is then sold to advertisers who use it to target individuals most likely to purchase specific goods. The business of these companies is fundamentally the arbitrage of personal preference information. Many people today don’t realize the value of their personal preferences, although the substantial profits of the companies that gather and sell such information makes clear its value.</quote>
  • What’s Holding Back Robots?, page 10
    not stated

    • only time will tell
    • the changes will be profound
  • (ahem) “they” don’t yet do enough to earn “their” keep..

Technology Drivers

  1. Moore’s Law
  2. 3D printing
  3. Energy Storage (batteries)
  4. Power management (power density, efficiency)
  5. Packet Radio (wireless, WiFi)
  6. Data Storage
  7. Computation (see #1, something about “embassingly parallel”)

Big Ideas

  1. Memory-based automomy
    delegate to memory in lieu of algorithms
  2. High-Speed experience sharing
    pubsub
  3. Learning from Imagination
    on-device simulation of the external world
  4. Learning from people
    something about social media, trawling photos & videos

Referenced

References

Via: backfill