Octavianworld: Cesar Brea's Weblog, continued.

@AMABoston #Marketing #Attribution 9/17 Panel Discussion Guide - Suggestions Welcome!

On September 17, I'm moderating a panel on marketing attribution analysis at a meeting of the American Marketing Association's Boston Chapter.

Here are some questions we've put together to guide the discussion.  What's on your mind?

1. Let’s start with a plain English question: How do you decide where to spend your marketing dollars?

2. What assumptions do you make about the relative effectiveness and efficiency of different options?

3. What are these assumptions based on?

4. So, for me, attribution is a fancy word for giving different marketing investment options -- channels, partners within those channels, creative alternatives -- proper, or at least more proper credit for their impact on a metric of interest, like a lead or a sale.  How would you define it, or improve on this?

5. Earlier you told us about how you do it today, but I’m also interested in your attribution journey.  Where was the organization’s level of attribution sophistication when you got into your latest role?

6. What attribution adjustments did you make first, and next?

7. What was the logic for starting there? How much was it the potential value -- the magnitude of the spend, for example -- and how much was it about feasibility - you could reasonably expect to get the data?

8. How much of the adjustment was based on “top down” statistical modeling of overall spend and impression data, and how much of it was bottom up based on user-level proxies like cookie data?

9. How long did it take to get up and to an interesting insight you could put to work?

10. What worked, and didn’t?

11. What surprised you?

12. How did you balance building the capability and getting results?

13. What kind of results have you gotten so far?

14. What’s next on your roadmap?

15. How far do you think you can push attribution analysis before you hit diminishing or negative ROI?

16. What’s the limiter? Is it data quality? Integration challenges like tracking a user across channels? Is it your ability to put insights to work?

17. Let’s take each of those:

1. We hear a lot about how cookies are dead, and then the death of the cookie has been greatly exaggerated -- how far can you trust cookie based data for attribution purposes?

2. Now that the big platforms -- Facebook, Google -- can track us everywhere, and use that to do cross-channel advertising programs, do you try to beat them, or join them? If you try to beat them, how far can you take cross-channel tracking? When and how does privacy come into play -- what are your bright lines for this?

3. What parts of your attribution insights are then integrated with your marketing automation platforms? What’s still manual?

18. What does managing based on  more sophisticated attribution look like? Who does it? What kind of interfaces and reports does this person or team use?  How often do they review and act on the results?

19. What kind of people and organizational challenges have more sophisticated attribution approaches created? How have you solved them?

20. What advice would you offer to someone with a marketing budget of $1M? $10M? $100M? 

21. What’s the practical technical frontier of what people are doing?

22. How do you keep up?  What resources would you suggest for someone interested in learning more and staying up to date?

Mary Meeker's @KPCB #InternetTrends Report: Critiquing The "Share of Time, Share of Money" Analysis

Mary Meeker's annual Internet Trends report is out.  It's a very helpful survey and synthesis of what's going on, as ever, all 164 pages of it. But for the past few years it's contained a bit of analysis that's bugged me.

Page 15 of the report (embedded below) is titled "Remain Optimistic About Mobile Ad Spend Growth... Print Remains Way Over-Indexed."  The main chart on the page compares the percentage of time people spend in different media with the percentage of advertising budgets that are spent in those media.  The assumption is that percentage of time and percentage of budget should roughly be equal for each medium.  Thus Meeker concludes that if -- as is the case for mobile -- the percentage of user time spent is greater than budget going there, then more ad dollars (as a percent of total) will flow to that medium, and vice versa (hence her point about print).

I can think of  demand-side, supply-side, and market maturity reasons that this equivalency thesis would break down, which also suggest directions for improving the analysis.  

On the demand side, different media may have different mixes of people, with different demographic characteristics.  For financial services advertisers, print users skew older -- and thus have more money, on average, making the potential value to advertisers of each minute of time spent by the average user there more valuable.  Different media may also have different advertising engagement power.  For example, in mobile, in either highly task-focused use cases or in distracted, skimming/ snacking ones, ads may be either invisible or intrusive, diminishing their relative impact (either in terms of direct interaction or view-through stimulation). By contrast, deeper lean-back-style engagement with TV, with more room for an ad to maneuver, might if the ad is good make a bigger impression. I wonder if there's also a reach premium at work.  Advertisers like to find the most efficient medium, but they also need to reach a large enough number of folks to execute campaigns effectively.  TV and print are more reach-oriented media, in general.

On the supply side, different media have different power distributions of the content they can offer, and different barriers to entry that can affect pricing.  On TV and in print, prime ad spots are more limited, so simple supply and demand dynamics drive up prices for the best spots beyond what the equivalency idea might suggest.  

In favor of Meeker's thesis, though representing another short term brake on it, is yet another factor she doesn't speak to directly. This is the relative maturity of the markets and buying processes for different media, and the experience of the participants in those markets.  A more mature, well-trafficked market, with well-understood dynamics, and lots of liquidity (think the ability for agencies and media brokers to resell time in TV's spot markets, for example), will, at the margin, attract and retain dollars, in particular while the true value of different media remain elusive. (This of course is one reason why attribution analysis is so hot, as evidenced by Google's and AOL Platform's recent acquisitions in this space.)  I say in favor, because as mobile ad markets mature over time, this disadvantage will erode.

So for advertisers, agency and media execs, entrepreneurs, and investors looking to play the arbitrage game at the edges of Meeker's observation, the question is, what adjustment factors for demand, supply, and market maturity would you apply this year and next?  It's not an idle question: tons of advertisers' media plans and publishers' business plans ride on these assumptions about how much money is going to come to or go away from them, and Meeker's report is an influential input into these plans in many cases.

A tactical limitation of Meeker's analysis is that while she suggests the overall potential shift in relative allocation of ad dollars (her slide suggests a "~$30B+" digital advertising growth opportunity in the USA alone - up from $20B last year*), she doesn't suggest a timescale and trendline for the pace with which we'll get there. One way to come at this is to look at the last 3-4 annual presentations she's made, and see how the relationships she's observed have changed over time.  Interestingly, in her 2013 report using 2012 data, on page 5, 12% of time is spent on mobile devices, and 3% of ad dollars are going there, for a 4x difference in percentages. In the 2014 report using 2013 data, 20% of time is spent on mobile, and 5% of media dollars are going there -- again, a 4x relationship.  

So, if the equivalency zeitgeist is at work, for the moment it may be stuck in a phone booth. But in the end I'm reminded of the futurist Roy Amara's saying: "We tend to overestimate the effect of a technology in the short term and underestimate its effect in the long term."  Plus let's not forget new technologies (Glass, Occulus Rift, both portable and  large/immersive) that will further jumble relevant media categories in years to come.

(*Emarketer seems to think we'll hit the $30B mobile advertising run rate sometime during 2016-2017.)

 

KPCB Internet trends 2014 from Kleiner Perkins Caufield & Byers

My New Book: #Marketing and #Sales #Analytics

I've written a second book.  It's called Marketing and Sales Analytics: Proven Techniques and Powerful Applications From Industry Leaders (so named for SEO purposes).  Pearson is publishing it (special thanks to Judah Phillips, author of Building A Digital Analytics Organization, for introducing me to Jeanne Glasser at Pearson).  The ebook version will be available on May 23, and the print version will come out June 23.

The book examines how to focus, build, and manage analytics capabilities related to sales and marketing.  It's aimed at C-level executives who are trying to take advantage of these capabilities, as well as other senior executives directly responsible for building and running these groups. It synthesizes interviews with 15 senior executives at a variety of firms across a number of industries, including Abbott, La-Z-Boy, HSN, Condé Nast, Harrah's, Aetna, The Hartford, Bed Bath & Beyond, Paramount Pictures, Wayfair, Harvard University, TIAA-CREF, Talbots, and Lenovo. My friend and former boss Bob Lord, author of Converge was kind enough to write the foreword.

I'm in the final editing stages. More to follow soon, including content, excerpts, nice things people have said about it, slideshows, articles, lunch talk...

#MITX Panel: Analytically Aligned Decision Making in the Multi-Agency Context

I moderated this panel at the Massachusetts Innovation and Technology Exchange's (mitx.org)"The Science of Marketing: Using Data & Analytics for Winning" summit on August 1, 2013.  Thanks to T. Rowe Price's Paul Musante, Visual IQ's Manu Mathew, iKnowtion's Don Ryan, and Google's Sonia Chung for participating!

 

"How to Engage Consumers in a Multi-Platform World?" See you May 22 @APPNATION bootcamp panel in NYC

Sponsorpay's Global Sales SVP Andy Bibby kindly asked me to join his NYC Internet Week APPNATION panel on Wednesday, May 22 2:15-3p at 82 Mercer.  Hope to see you there, watch this space for a recap of the conversation.

Book Review: Converge by @rwlord and @rvelez #convergebook

I just finished reading Converge, the new book on integrating technology, creativity, and media by Razorfish CEO Bob Lord and his colleague Ray Velez, the firm’s CTO.  (Full disclosure: I’ve known Bob as a colleague, former boss, and friend for more than twenty years and I’m a proud Razorfish alum from a decade ago.)

Reflecting on the book I’m reminded of the novelist William Gibson’s famous comment in a 2003 Economist interview that “The future’s already here, it’s just not evenly distributed.”  In this case, the near-perfect perch that two already-smart guys have on the Digital Revolution and its impact on global brands has provided them a view of a new reality most of the rest of us perceive only dimly.

So what is this emerging reality?  Somewhere along the line in my business education I heard the phrase, “A brand is a promise.”  Bob and Ray now say, “The brand is a service.”  In virtually all businesses that touch end consumers, and extending well into relevant supply chains, information technology has now made it possible to turn what used to be communication media into elements of the actual fulfillment of whatever product or service the firm provides.  

One example they point to is Tesco’s virtual store format, in which images of stocked store shelves are projected on the wall of, say, a train station, and commuters can snap the QR codes on the yogurt or quarts of milk displayed and have their order delivered to their homes by the time they arrive there: Tesco’s turned the billboard into your cupboard.  Another example they cite is Audi City, the Kinnect-powered configurator experience through which you can explore and order the Audi of your dreams.  As the authors say, “marketing is commerce, and commerce is marketing.”

But Bob and Ray don’t just describe, they also prescribe.  I’ll leave you to read the specific suggestions, which aren’t necessarily new.  What is fresh here is the compelling case they make for them; for example, their point-by-point case for leveraging the public cloud is very persuasive, even for the most security-conscious CIO.  Also useful is their summary of the Agile method, and of how they’ve applied it for their clients.

Looking more deeply, the book isn’t just another surf on the zeitgeist, but is theoretically well-grounded.  At one point early on, they say, “The villain in this book is the silo.”  On reading this (nicely turned phrase), I was reminded of the “experience curve” business strategy concept I learned at Bain & Company many years ago.  The experience curve, based on the idea that the more you make and sell of something, the better you (should) get at it, describes a fairly predictable mathematical relationship between experience and cost, and therefore between relative market share and profit margins.  One of the ways you can maximize experience is through functional specialization, which of course has the side effect of encouraging the development of organizational silos.  A hidden assumption in this strategy is that customer needs and associated attention spans stay pinned down and stable long enough to achieve experience-driven profitable ways to serve them.  But in today’s super-fragmented, hyper-connected, kaleidoscopic marketplace, this assumption breaks down, and the way to compete shifts from capturing experience through specialization, to generating experience “at-bats” through speedy iteration, innovation, and execution.  And this latter competitive mode relies more on the kind of cross-disciplinary integration that Bob and Ray describe so richly.

The book is a quick, engaging read, full of good stories drawn from their extensive experiences with blue-chip brands and interesting upstarts, and with some useful bits of historical analysis that frame their arguments well (in particular, I Iiked their exposition of the television upfront).  But maybe the best thing I can say about it is that it encouraged me to push harder and faster to stay in front of the future that’s already here.  Or, as a friend says, “We gotta get with the ‘90’s, they’re almost over!”

(See this review and buy the book on Amazon.com)

My New Book: Pragmalytics

I've written a short book.  It's called "Pragmalytics: Practical Approaches to Marketing Analytics in the Digital Age".  It's a collection and synthesis of some of the things I've learned over the last several years about how to take better advantage of data (Big and little) to make better marketing decisions, and to get better returns on your investments in this area.  

The main point of the book is the need for orchestration.  I see too much of the focus today on "If we build It (the Big Data Machine, with some data scientist high priests to look after it), good things will happen."  My experience has been that you need to get "ecosystemic conditions" in balance to get value.  You need to agree on where to focus.  You need to get access to the data.  You need to have the operational flexibility to act on any insights.  And, you need to cultivate an "analytic marketer" mindset in your broader marketing team that blends perspectives, rather than cultivating an elite but blinkered cadre of "marketing analysts".  Over the next few weeks, I'll further outline some of what's in the book in a few posts here on my blog.

I'm really grateful to the folks who were kind enough to help me with the book.  The list includes: Mike Bernstein, Tip Clifton, Susan Ellerin, Ann Hackett, Perry Hewitt, Jeff Hupe, Ben Kline, Janelle Leonard, Sam Mawn-Mahlau, Bob Neuhaus, Judah Phillips, Trish Gorman Clifford, Rob Schmults, Michelle Seaton, Tad Staley, and my business partner, Jamie Schein.  As I said in the book, if you like any of it, they get credit for salvaging it.  The rest -- including several bits that even on the thousandth reading still aren't as clear as they should be, plus a couple of typos I need to fix -- are entirely my responsibility.

I'm also grateful to the wonderful firms and colleagues and clients I've had the good fortune to work for and with.  I've named the ones I can, but in general have erred on the side of respecting their privacy and confidentiality where the work isn't otherwise in the public domain.  To all of them: Thank You!

This field is evolving quickly in some ways, but there are also some timeless principles that apply to it.  So, there are bits of the book that I'm sure won't age well (including some that are already obsolete), but others that I hope might.  While I'm not one of those coveted Data Scientists by training, I'm deep into this stuff on a regular basis at whatever level is necessary to get a positive return from the effort.  So if you're looking for a book on selecting an appropriate regression technique, or tuning Hadoop, you won't find that here, but if you're looking for a book about how to keep all the balls in the air (and in your brain), it might be useful to you.  It's purposefully short -- about half the length of a typical business book.  My mental model was to make it about as thick as "The Elements of Style", since that's something I use a lot (though you probably won't think so!).  Plus, it's organized so you can jump in anywhere and snack as you wish, since this stuff can be toxic in large doses.

In writing it amidst all the Big Data craziness, I was reminded of Gandhi's saying (paraphrased) "First they ignore you... then they fight you, then you win."  Having been in the world of marketing analytics now for a while, it seems appropriate to say that "First they ignore you, then they hype you, then you blend in."   We're now in the "hype" phase.  Not a day goes by without some big piece in the media about Big Data or Data Scientists (who now have hit the highly symbolic "$300k" salary benchmark -- and last time we saw it, in the middle part of the last decade in the online ad sales world, was a sell signal  BTW).  "Pragmalytics" is more about the "blend in" phase, when all this "cool" stuff is more a part of the furniture that needs to work in harmony with the rest of the operation to make a difference.

"Pragmalytics" is available via Amazon (among other places).  If you read it please do me a favor and rate and review it, or even better, please get in touch if you have questions or suggestions for improving it.  FWIW, any earnings from it will go to Nashoba Learning Group (a school for kids with autism and related disorders).

Where it makes sense, I'd be very pleased to come talk to you and your colleagues about the ideas in the book and how to apply them, and possibly to explore working together.  Also, in a triumph of Hope over Experience, my next book (starting now) will be a collection and synthesis of interviews with other senior marketing executives trying to put Big Data to work.  So if you would be interested in sharing some experiences, or know folks who would, I'd love to talk.

About the cover:  it's meant to convey the harmonious convergence of "Mars", "Venus", and "Earth" mindsets: that is, a blend of analytic acuity, creativity and communication ability, and practicality and results-orientation that we try to bring to our work. Fellow nerds will appreciate that it's a Cumulative Distribution Function where the exponent is, in a nod to an example in the book, 1.007.

 

 

Gaming Facebook Sponsored Stories #fb #sponsoredstories

Facebook's Sponsored Stories feature is one of the ad targeting horses the firm's counting on to pull it out of its current valuation morass (read this, via @bussgang).  

Sponsored Stories is a virality-enhancing mechanism (no jokes please, that was an "a" not an "i") that allows Facebook advertisers to increase the reach of Facebook users' interactions with the advertisers' brands on Facebook (Likes, Check-ins, etc.). It does this by increasing the number of a user's Facebook friends who see such engagements with the advertisers' brands beyond the limited number who would, under normal application of the Facebook news feed algorithm, see those endorsements.

Many users are outraged that this unholy Son-Of-Beacon feature violates their privacy, to the point that they sue-and-settle (or try to, oops).

What they are missing perhaps is the opportunity to "surf" an advertiser's Sponsored Stories investment to amplify their own self-promotion or mere narcissism.

Consider the following simple example.  Starbucks is / has been using this ad program.  Let's say I go to Starbucks and "check in" on Facebook.  Juiced by Sponsored Stories (within the additional impressions Starbucks has paid for), all of my Facebook friends browsing their news feeds will see I've checked in at Starbucks (and presumably feel all verklempt about a brand that could attract such a valued friend). 

Now, what if I, savvy small business person, comment in my check in that I'm "at Starbucks, discussing my <link>NEW BOOK</link> with friends!"  I've pulled off the social media equivalent of pasting my bumper sticker on Starbucks' billboard.

I need to look more closely into this, but as I understand it, the Sponsored Stories feature can't today prevent users from including negative feedback in their brand engagements, where such flexibility is provided for.  So if they can't handle the negative yet, it may still be that they can't prevent more general forms of off-brand messaging.

I'm sure others have considered this and other possibilities. Comments very welcome!  Meanwhile, I'm off to Starbucks to discuss my upcoming NEW BOOK.

 

 

Wanted: Marketing Analytics Director, Global Financial Services Firm (Mid-Atlantic) # Analytics

I've been working with a global financial services firm to develop its marketing analytics / intelligence capability, and we're now building a highly capable team to further extend and sustain the results and lessons so far.  This includes a Marketing Analytics Director to lead a strong team doing advanced data mining and predictive modeling to support high-impact opportunities in various areas of the firm.  Here's the job description on LinkedIn.  If you are currently working at a large marketer, major analytics consulting firm, or advertising agency, and have significant experience analyzing, communicating, and implementing sophisticated multi-channel marketing programs, and are up for the challenge of leading a new team in this area for a world-class firm in a great city, please get in touch!

Congratulations @marissamayer on your new #Yahoo gig. Now what? Some ideas

Paul Simon wrote, "Every generation throws a hero at the pop charts."  Now it's Marissa Mayer's turn to try to make Yahoo!'s chart pop.  This will be hard because few tech companies are able to sustain value creation much past their IPOs.  

What strategic path for Yahoo! satisfies the following important requirements?

• Solves a keenly felt customer / user / audience / human problem?
• Fits within but doesn't totally overlap what other competitors provide?
• Builds off things Yahoo! has / does well?
• Fits Ms. Mayer's experiences, so she's playing from a position of strength and confidence?
• As a consequence of all this, will bring advertisers back at premium prices?

Yahoo!'s company profile is a little buzzwordy but offers a potential point of departure.  What Yahoo! says:

"Our vision is to deliver your world, your way. We do that by using technology, insights, and intuition to create deeply personal digital experiences that keep more than half a billion people connected to what matters the most to them – across devices, on every continent, in more than 30 languages. And we connect advertisers to the consumers who matter to them most – the ones who will build their businesses – through our unique combination of Science + Art + Scale."

What Cesar infers:

Yahoo! is a filter.

Here are some big things the Internet helps us do:

• Find
• Connect
• Share
• Shop
• Work
• Learn
• Argue
• Relax
• Filter

Every one of these functions has an 800 lb. gorilla, and a few aspirants, attached to it:

• Find -- Google
• Connect -- Facebook, LinkedIn
• Share -- Facebook, Twitter, Yahoo!/Flickr (well, for the moment...)
• Shop -- Amazon, eBay
• Work -- Microsoft, Google, GitHub
• Learn -- Wikipedia, Khan Academy
• Argue -- Wordpress, Typepad, [insert major MSM digital presence here]
• Relax -- Netflix, Hulu, Pandora, Spotify
• Filter -- ...

Um, filter...  Filter.   There's a flood of information out there.  Who's doing a great job of filtering it for me?  Google alerts?  Useful but very crude.  Twitter?  I browse my followings for nuggets, but sometimes these are hard to parse from the droppings.  Facebook?  Sorry friends, but my inner sociopath complains it has to work too hard to sift the news I can use from the River of Life.

Filtering is still a tough, unsolved problem, arguably the problem of the age (or at least it was last year when I said so).  The best tool I've found for helping me build filters is Yahoo! Pipes.  (Example)

As far as I can tell, Pipes has remained this slightly wonky tool in Yahoo's bazaar suite of products.  Nerds like me get a lot of leverage from the service, but it's a bit hard to explain the concept, and the semi-programmatic interface is powerful but definitely not for the general public.

Now, what if Yahoo! were to embrace filtering as its core proposition, and build off the Pipes idea and experience under the guidance of Google's own UI guru -- the very same Ms. Mayer, hopefully applying the lessons of iGoogle's rise and fall -- to make it possible for its users to filter their worlds more effectively?  If you think about it, there are various services out there that tackle individual aspects of the filtering challenge: professional (e.g. NY Times, Vogue, Car and Driver), social (Facebook, subReddits), tribal (online communities extending from often offline affinities), algorithmic (Amazon-style collaborative filtering), sponsored (e.g., coupon sites).  No one is doing a good job of pulling these all together and allowing me to tailor their spews to my life.  Right now it's up to me to follow Gina Trapani's Lifehacker suggestion, which is to use Pipes.

OK so let's review:

• Valuable unsolved problem for customers / users: check.
• Fragmented, undominated competitive space: check.
• Yahoo! has credibly assets / experience: check.
• Marissa Mayer plays from position of strength and experience: check.
• Advertisers willing to pay premium prices, in droves: ...

Well, let's look at this a bit.  I'd argue that a good filter is effectively a "passive search engine".  Basically through the filters people construct -- effectively "stored searches" -- they tell you what it is they are really interested in, and in what context and time they want it.  With cookie-based targeting under pressure on multiple fronts, advertisers will be looking for impression inventories that provide search-like value propositions without the tracking headaches.  Whoever can do this well could make major bank from advertisers looking for an alternative to the online ad biz Hydra (aka Google, Facebook, Apple, plus assorted minor others).

Savvy advertisers and publishers will pooh-pooh the idea that individual Pipemakers would be numerous enough or consistent enough on their own to provide the reach that is the reason Yahoo! is still in business.  But I think there's lots of ways around this.  For one, there's already plenty of precedent at other media companies for suggesting proto-Pipes -- usually called "channels", Yahoo! calls them "sites" (example), and they have RSS feeds.  Portals like Yahoo!, major media like the NYT, and universities like Harvard suggest categories, offer pre-packaged RSS feeds, and even give you the ability to roll your own feed out of their content.  The problem is that it's still marketed as RSS, which even in this day and age is still a bit beyond for most folks.  But if you find a more user-friendly way to "clone and extend" suggested Pipes, friends' Pipes, sponsored Pipes, etc., you've got a start.

Check?  Lots of hand-waving, I know.  But what's true is that Yahoo! has suffered from a loss of a clear identity.  And the path to re-growing its value starts with fixing that problem.

Good luck Marissa!