I lead Force Five Partners, a marketing analytics consulting firm (bio). I've been writing here about marketing, technology, e-business, and analytics since 2003 (blog name explained).

Email or follow me:

January 04, 2016

My 2016 Prediction: The (First Serious) Year of #VR

In case you missed it, Facebook's Oculus announced today that it starts taking pre-orders for its Rift VR headset on Wednesday.

Serious predictions include objectively verifiable outcomes and clear deadlines. As reported in Fortune, Piper Jaffray's Travis Jakel forecasted in May of 2015  that a little over 12 million VR headsets would be sold in 2016, with Oculus accounting for about a quarter of them. (The report is very thorough, well-worth reading.)

How reasonable is this bet?

The most popular benchmark is likely to be the iPhone:

Statistic: Global Apple iPhone sales from 3rd quarter 2007 to 4th quarter 2015 (in million units) | Statista
(Find more statistics at Statista)

Oculus (and competition) has been around for about three years. By year three post its Q3 2007 launch (so Q3'09-Q2'10, the iPhone (alone) logged ~31M units. Android phone unit sales exploded similarly

Some arguments to calibrate Jankel's estimate versus the iPhone benchmark:

  • con: Phones were around before the iPhone, so the category was already established and iPhone was an easier to grasp / adopt improvement on separate pre-existing technologies (phone+PDA+laptop)
    • perhaps, but content is also a pre-existing category and VR just represents a Magic Leap ahead
  • con: Communication's "need to have"; VR's "nice to have"
    • maybe, but iPhone sold 30 million units only a year after the '08 crash
  • con: "The technology's not there yet"
    • Latency -- sub 20 millisecond refresh rate when you move your eyes, or >60-90 FPS capture rate for video -- seems to be the main issue
    • Most people don't have a PC fast enough to render graphics at this rate, for interactive content
    • Capture's expensive and complicated, but folks are working on this (e.g., 360fly camera)
  • con: Content's still limited, will be a drag on adoption
    • true, but expect furious investment at all levels of sophistication here in 2016; authoring tools are surprisingly more available and affordable than I imagined they'd be
  • pro: Travel's perceived to be riskier, more people will prefer to do it virtually
  • pro: People learn better when shown than told

I conclude he's got the order of magnitude right.  But I haven't tried the new Rift headset yet, so I'm inclined to be a little conservative (Caveat emptor: I was conservative about Facebook, too).  Put me down for between 9-11 million (non-"cardboard") VR headsets sold in 2016. (If you've found a prediction market for this, let me know!). What's your guess?

link to my poll if you have an adblocker and can't see it here

What should you do?

If you're an individual on a tight budget, get Google Cardboard or something like it, to begin to get an inkling of the VR experience. Here are some videos you can check out. (Of course, watching is only the tip of the iceberg...)

If you're a potential author -- educator, entertainer, marketer -- it's probably a good idea to brainstorm users and use cases in the first half of 2016. If you convince yourself that some make sense for you, you might commission 1-2 $10-50k pilots for the back half of the year.  Because 2017's going to be the year your boss and your board ask what your "VR strategy" is.

It's not unprecedented.




September 11, 2015

@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?

August 17, 2015

Ten Things I Think*: Thoughts on The #Amazon Workplace in @nytimes @jodikantor @DavidStreitfeld

On Saturday the New York Times published a piece by Jodi Kantor and David Streitfeld titled, "Inside Amazon: Wrestling Big Ideas in a Bruising Workplace". Here's the link:


The article provoked a large number of comments and showed up in several of the social media feeds I follow. Plus, my business is helping organizations build their capabilities to use data to drive results.  

Let's stipulate that the article presented facts accurately and in balance. Here's ten things I think (*Thanks to @SI_PeterKing). 

1. What a great litmus test for values. But don't listen to what people say, watch what they do.

You live your values every time you shop there, or anywhere else. I'm reminded of the mission statement of ArsDigita, where I once worked:

...Beyond that, we don't worry about corporate culture. We have a certain set of customers. We have a certain set of people. We have a certain set of tools. Discussions or theories won't change any of those things. If any ArsDigita member wants to change ArsDigita, he or she need only add to the customers, add to the people, or add to the tools.

So, shop at Amazon, or don't, or, since life's complicated, change the mix of where you shop, according to some rules that make sense to you. Like, "I'll start by looking for competitively priced and appropriately convenient and accountable alternatives, whose operating and employment practices are more consistent with my beliefs about how to treat people. But if I can't find one, I'll still shop at Amazon."

Or, if you work there, and the current culture doesn't work for you, either accept the fact that it doesn't fit and leave, or work to change it as far as you can.



2. Everything's relative.

The article calls out Amazon, but doesn't explicitly contrast it with any other retailers, and examine the trade-off in performance with these retailers: prices for consumers, returns for shareholders -- which of course include many pension funds holding the savings of a wide range of investors. (Since Amazon competes in more categories than retail, and it's an icon of the global economy, it's worth extending this comparison of course, but you have to start someplace.)

Is Amazon an anomalous white-collar sweatshop? Why stop at researching the question? Here's an idea for a motivated someone: create a "Shopper's Bill of Expectations" service.  The service would articulate and promote a set of values / standards / expectations its members would like to see: for example, paternity leave, compliance with environmental regulations and trends, living wage, working conditions, etc. Then it would invite members to subscribe to an auditing service, like Consumer Reports, and invite retailers who agree to comply with the service's expectations to enroll in it. Finally, it would provide some sort of tracking mechanism, manual or automated, so it and its enrolled retailers and members can see how much dollar volume the service sends from members to retailers. There's a huge data play in here, too! I have no objections to this service leveraging that in ways that are transparent and which don't unduly compromise or pressure its core proposition. 

Naïve? Seattle's embraced Fair Trade for coffee-growing campesinos, why not for highly-compensated ecommerce executives? (Uh, well...) 

3. This isn't me.  

Whether I expect emails and texts to be answered at midnight depends on the circumstances -- and they have to be pretty dire.  If someone takes a planned vacation which business conditions support, and thoroughly plans coverage, and says he or she will be out of contact, I respect that choice. If someone has a short-term or longer-term health crisis, I do what I can to support that person as far as practically possible, and then beyond as far as I can.  I don't believe in stack-ranking and firing the lowest decile; I believe in setting and continuously raising performance expectations, being direct and thorough and fair in evaluating people against them, and encouraging anyone who doesn't consistently clear those goals to find something else, with compassion, and endorsement for the stronger things they do. I believe in courtesy and kindness. I believe in teamwork, not beggar-thy-neighbor advancement.

4. This is me.  

I'm objective about performance. I prefer a focus on ends, not means, as long as means are pursued ethically, which = Golden Rule to me. I believe in coaching and development, but I expect hard work and demonstrated progress and positivity and enthusiasm in return. I expect to care, and I expect my colleagues to care as well and as much. And, if I'm asked to be up late busting my behind to help someone under pressure, I do expect him or her to be responsive to late-night emails and texts. I don't care for passive-aggressive cultures whose members play nice superficially but work at cross-purposes behind the scenes; in this regard Amazon's directness and openness to public disagreement seems much healthier.

5. Context doesn't justify, but it matters to the assessment.

Amazon is a product of a super-thin-margin industry. It was founded by a man with the characteristics to succeed in that unambiguous, unprotected environment, and it has thrived by attracting managers with similar profiles. If and when you contrast a culture like Amazon's with some other avatar of corporate humanitarianism, it may be worth a look at the glass-house bulwarks supporting the profitability that enable those practices. These may take the forms of de facto technical standards the firm has established, or a monopoly in its market(s), or legal or regulatory actions -- for example, easy monetary policies -- or hard-won, carefully-cultivated brand strength. Assume the bulwarks weren't there: how would things change? Many successful firms would do well to ask this of themselves.

6. Data-driven, to a fault?

If what gets measured gets managed, we also manage as we measure. I see a spectrum: we ignore data at one end, we are slaves to it at the other end.  Both extremes are dysfunctional. Ignorance is of course not bliss, but in very few cases do we have enough clean, ungamed data to put our faith in it exclusively.  Digital commerce may be one such exception, but employee behavior -- measured as described in the article, and here -- is not.

In most cases data are "Platonic Shadows" for what's happening in the business. To be useful they need to be looked at holistically, and across as much time as is available to distinguish signal from noise. I believe in watching for emergent patterns from machine learning, but also in humans having their heads in the game with hypotheses and explicit sensitivity to biases of many kinds.

7. Development requires opportunity and demands responsibility.

It may be dysfunctional at the margins, but I admire the empowerment Amazon offers its managers to get things done, and its expectation that they will get things done.  There's nothing sadder than well-educated and qualified managers who feel blocked and just go through the motions for a paycheck. Well, actually, there is: employees with extremely limited choices who are ethically and illegally exploited.

I appreciate Amazon's bias for action, and for matching analysis to the stakes and uncertainty associated with a decision.

8. Is this a cult of personality?

What happens beyond Jeff Bezos? Paging John Galt!

9. How do you compete?

Amazon competes on utility and service, tightly defined and realized based on the firm's extreme degree of customer focus. Their managerial ranks thus reflect this.  If you don't want to join them, or can't, or want to leave, then beat them.  This means playing a different game. For example, Amazon doesn't create brands so much as it amplifies them.  Likely you will need to create a brand along an emotional dimension that Amazon under-serves, then partner with Amazon in ways that extend but don't erode it. What opportunities to speak to or reflect someone's actual or desired identity can you reinforce through an online-retail service? Maybe some sort of emo-oriented Edgio blending content and commerce in curatively creative ways?

10. On reflection...

There are older and fouler things than Amazon in the deep places of the world. But Amabots and Amholes at Amazon and beyond, remember: In the end, the love you take is equal to the love you make. 




July 07, 2015

My Q&A With @chiefmartec's Scott Brinker

One Marketing Analytics Myth? The Perfect KPI... published July 6, 2015

NLG Bike-a-thon 2015 Appeal and Bonus from the Archives: 2007 Ride Recap

Apropos of enlisting a crack team (Jane Repetti Chang, David Chang, and Caroline Repetti) to ride with me in this year's NLG Bike-a-thon (please support us, and thank you to those who have!), I thought it fair to warn them for what they're in for.  This recap of the 2007 ride seemed just the thing:


Thank you all for being so generous on such short notice!   

Fresh off a flight from London that arrived in Boston at midnight on Friday, I wheeled myself onto the starting line Saturday morning a few minutes after eight 
.  Herewith, a few journal entries from the ride:

Mile 2:  The 
peloton drops me like a stone.  DopeursNever mind; this breakaway is but  le petit setback.  Where are my domestiques to bring me back to the pack?

Mile 3:  Reality intrudes.  No 
domestiques.  Facing 47 miles' worth of solo quality time, I plot my comeback... 

Mile 10: 1st major climb, L'Alpe de Bolton (MA), a steep, nasty little "beyond classification" grade.  I curse at the crowds pressing in.  'AllezAllez!' they call, like wolves.  A farmer in a Superman cape runs alongside.

Mile 10.25: Mirages disappear in the 95-degree heat.  (First time I've seen the Superman dude, though.  Moral of this story: lay off the British Airways dessert wines the night before a big ride.) 

Mile 10.5: Descending L'Alpe de Bolton, feeling airborne at 35 MPH

Mile 10.50125: Realizing after hitting bump that I am, in fact, airborne.   AAAAARRH!!!

Mile 14: I smell sweet victory in the morning air!

Mile 15:  Realize the smell is actually the Bolton dump

Mile 27: Col d'Harvard (MA).  Mis-shift on steep climb, drop chain off granny ring.  Barely click out of pedal to avoid keeling over, disappointing two buzzards circling overhead. 

Mile 33:  Whip out Blackberry, Googling 'Michael Rasmussen 
soigneurto see if I can score some surplus EPO

Mile 40:  I see dead people

Mile 50:  I am, ahem... outsprinted at the finish.  Ride organizers generously grant me 'same time' when they realize no one noticed exactly when I got back."

February 20, 2015

My latest article in CMO.com: "Analytics Is Too Important To Be Left To Analysts"

"Analytics Is Too Important To Be Left To Analysts", published February 20, 2015

February 03, 2015

My article in Programmatic Advertising on DSP Requirements

"How to select a programmatic advertising solution in 2015", published February 2, 2015

January 31, 2015

My article on Organizing For Analytics in The CMO Club Solution Clubhouse

"Organizing Marketing Analytics Capabilities: Seven Considerations"

January 15, 2015

"When Big Data Isn't That Big": my talk at MIT

Philip Greenspun kindly invited me to present a talk on database models for evaluating digital advertising in his class, "6.S186: Special Laboratory Subject in Electrical Engineering and Computer Science -- Relational Database Management System and Internet Application Programming" on January 28, 2015

December 03, 2014

My article in Programmatic Advertising on Buying TV

"AOL brings programmatic to TV: how it works, and why you should care"

November 18, 2014

My article in Forbes CMO Network Blog

"CMOs, Here Are The Three Types Of Analytic Talent You Need To Hire Right Now"

November 01, 2014

Data Analytics Week Las Vegas: "Prediction Modeling For Business Leaders"

McGraw Hill Education VP R&D and Analytics Alfred Essa and I will present a hands on workshop for senior business leaders and the senior analytics executives who advise them.  The session will blend case studies and lessons from practical experiences with a tour through getting, prepping, exploring, and modeling data for insights, using the Python programming language.

October 29, 2014

Analytics Week Boston: "Web, Mobile, Analytics - User Tracking and Experience Creation in the Age of Backlash"

I'm moderating a panel at Analytics Week Boston on November 6 at 4pm at the Boston Courtyard Hotel.  participants include: Jim Barrett, VP Marketing, Rue La La; Andy Dubickas, Senior Solutions Consultant, VisualIQ; Sara Radkiewicz, Director / Product Management, Localytics; Lawrence Schwartz, VP Marketing, Attunity.

Session description: "One of the biggest opportunities in marketing is the ability to recognize and target an individual user across all the devices he or she might use to proceed through an experience.  But the foundational mechanisms for this -- cookies and the information we can get from mobile devices -- are increasingly limited in their utility and availability.  Botnets are compromising the quality of the cookies advertisers target.  Recent compromises of photo archives and credit card records, and other data by both official and criminal entities  have driven mobile device suppliers to tighten up privacy and security controls and features. Where does this leave us today, and how (well) can we continue to realize seamless experiences in this newly-fraught environment?"

October 28, 2014

My article in iMedia Connection: "What the future of #programmatic buying will entail"

How marketing planning will evolve into "Marketgramming"

Driving Social Engagement With Sentiment Analysis: Text Analytics Summit West

McGraw Hill VP of R&D and Analytics Al Essa kindly invited me to join him in delivering this workshop in San Francisco on November 3.  Hope to see you there!

October 07, 2014

My Interview in EContentmag.com: "Which Analytics Tell The Best Story?"

http://bit.ly/watbs-econtmag, published October 3

September 29, 2014

My Article in CMO.com: "How To Manage Your #Analytics Portfolio"

http://bit.ly/htmyap-cmo, published September 26

July 30, 2014

Organizing for #Analytics, Part II: The Chart

A year ago, while doing research for my new book Marketing and Sales Analytics I wrote a post titled "Organizing For #Analytics -- Seven Considerations".  I argued that you should "think organization last, not first", and presented, well, seven considerations that would shape the structure you end up with.

But I made an important mistake in writing that post.  Enquiring minds still want the boxes.  So, based on the 15 case studies I presented in the book, as well as other experiences over the past decade with even more firms, this post offers a "reference structure" and suggests how it might be tweaked under different circumstances.

In constructing an analytic organization, you've got two objectives to serve that should guide you.  One is answering questions.  The other is answering them better the next time.

To answer questions, once again you've got two things to balance.  One is "business intimacy" -- how close you need to be to the business you're serving to understand what's going on, so your analysis is grounded in that reality.  By what's going on, I mean things like terminology, the particular patterns and quirks in the data the business generates, and the options for acting on insights that the operational infrastructure, people, and process of the business make possible.  

Balanced against business intimacy is "analytic capability".  This means having a critical mass of skills for supporting decisions appropriately.  If a decision requires a sophisticated model using lots of data, for example, having a bunch of business-intimate MBAs who last ran a regression a few years ago in business school using Excel might not cut it. So you might need one or more specialists who can do that sort of thing. Or, if it requires specialized knowledge about, say, how digital analytics work, you might need folks who know about things like tag management systems, or inferred matching across devices for multi-screen tracking.

In implementing these two requirements, you then face -- wait for it -- another two choices.  One is how much to distribute into the field, or into business units, or aligned to functions, versus how much to centralize.  The second will be how much to separate analysis from reporting. With respect to the former, a lot depends on how different or similar the needs in each business / field location are (the more different, the closer you want support to them, and the less leverage there is from centralization anyway). As for the latter, My thinking and research has suggested that separate "report generation" groups are a bad idea -- they encourage conveying data rather than insight, and thus the development of a second-class citizenry in analytics groups; they disconnect report generation from report usage, resulting in the proverbial pallets of unused reports that consultants on cost-hunts feast on; and, they discourage  automating reporting (because most folks don't like to work themselves out of a job).

To answer questions better the next time, you need some folks thinking about everything from whether there are better techniques and associated tools, to whether there's more useful data, and how to better organize and provision the data you've already got.  There are going to be -- yes, of course -- two options for how to proceed here.  One is a "rapid prototyping" approach, and the other is a more traditional "waterfall" method.

Wrapped around all this is "governance", only with a capital "G".  In most cases, governance is applied to data, but not to the prioritizations of the questions and decisions that analysis will answer and support. Capital G governance also sets analytic priorities, and recruits, develops, and evaluates senior analytic leadership.

So, here's your org chart:

Analytic Org ChartOK, now that you got what you came for, here's the price:  You have to consider the design above not an immutable multi-year reality, but rather a delicate, dynamic compromise that balances the objectives and tensions mentioned above.  Where a group should live, as the footnotes suggest, and how relatively large each group becomes, should be a function of a constantly evolving portfolio of opportunities and challenges your governance process should determine. Conceiving of this not as a static chart prevents a pattern too often repeated -- a guardrail to guardrail "centralize versus distribute", "locate in the business versus locate in IT (or finance)" swing, where each successive move trashes the logic of the last. But that's a topic for another post...

What's your chart look like? Why? How often has it / does it evolve?



July 24, 2014

The Revolution Will Not Be #Mobile -ized

Via the WSJ, Emarketer's latest forecast has mobile ad spending eclipsing newspapers and radio (individually, not together, yet):

Ad spending on smartphones and tablets will this year eclipse radio, magazines, and newspapers, according to research firm eMarketer. Spending on mobile, which includes smartphones and tablets, will jump 83% in 2014 to nearly $18 billion, eMarketer predicts. Newspapers and radio, on the other hand, will draw nearly $17 and $15.5 billion respectively.

And yet, you will not be able to get full value from your investment, because the Revolution will not be Mobilized... until we can track and properly attribute the impact of advertising in this channel.

By various estimates, mobile now accounts for 30% of web traffic, but only 10% of ad revenues.  Some, like Mary Meeker, make the case that this means mobile advertising spending still has lots of room to go.  Here's my critique of her argument a couple of months ago. In that post I described demand- and supply-side factors that inhibit the "equivalency thesis" she outlines.  One of these is understanding the properly attributed influence of mobile in a customer experience.

A couple of weeks ago I listened to Visual IQ's Solutions Consulting lead Andy Dubickas crisply describe three approaches for tracking a user across the desktop-mobile divide, for purposes of attribution analysis:

  1. if your user logs in to one of your digital properties through both your mobile interface and your desktop interface, then you can use the common user ID associated with these log ins to associate the different devices through whatever device tracking technique you use (cookie-ing browsers, or watching for returning hardware- or software-level device IDs, for example). Here's a terrific article by digital analytics guru Justin Cutroni ( @justincutroni ) on how this works.
  2. if you use two devices, but they seem to travel suspiciously together, like logging in from the same place at the same time, and looking at the same thing, then it's possible to guess with some confidence that these two devices both have you behind them. Here's a good article by Tom Manvydas at Experian that explains this a bit further. Experian has a service called AdTruth that provides this capability.
  3. you may be able to get device-to-user match data from other services who do this themselves, if you have a relationship with them and PII issues are addressed. For example, Twitter also collects this information; Mother Jones covered this well late last year.

If you're not ready for this, you can at minimum set up your mobile ad buy with some variation in it -- waves, spikes, whatever -- that allow you to see, either with your naked eye or through modeling, the incremental direct and indirect effects of your investment.  In fact, there's no reason the tighter-tracked approaches described above can't peacefully co-exist with this simple approach, even if it's just a cross-check.

(Apologies to Gil Scott-Heron)


July 21, 2014

"Lionel Messi Is Impossible." How About Your Salespeople? @skepticalsports #Analytics

Warning: due to author sloth, this post is dated.  Most of you have likely moved on with your lives.  Me, my Heart Is Full for los albicelestes, despite a disappointing outcome, but I still have a lingering case of futbolytics to get over.

Fivethirtyeight.com contributor Benjamin Morris had a fascinating article a couple of weeks ago that examined Argentine footballer Lionel Messi's play in recent years.  Until this year's World Cup of course, Messi had come under some criticism for, against the backdrop of his ethereal play for Barcelona, under-delivering for Argentina in recent years.  The article -- warning, 4600+ words long, with charts and videos and hyperlinks and footnotes, as in "Ask not, Bill Simmons, for whom the bell tolls..." -- explains persuasively that Messi is an outlier among outliers, even in his performances for the Argentine national team.  

The analysis of his shooting is fascinating enough.  But what really caught my attention, was the analysis of his passing and other influences on the game. In particular, here's a fascinating chart from Morris' article that makes the point neatly.

My friends at the multi-channel marketing attribution analytics firm Visual IQ are fond of a soccer metaphor to explain what they do.  "Giving all the conversion credit to the last-touched marketing channel is like giving Mario Götze all the credit for his goal in the 113th minute of the World Cup final," they say (with a zesty cruelty that borders on the sociopathic).

In the sales world, it's a holy grail to get to this kind of dynamic, or even an understanding of where on Morris' chart members of your team would be. It can be a thorny path to get there though, because unlike in Messi's world, a helping hand in sales can be harder to observe, and even if you try to measure it, often it can be (will be) gamed mercilessly and unhelpfully.

One way you can track this sort of thing is through online and offline knowledge sharing by members of your team.  Winning proposals, presentations, good answers to FAQs, and then views of these by others can all be tracked in relatively painless, game-free ways. A number of years ago when I worked at ArsDigita, we worked with Siemens to build ShareNet, a global sales and marketing knowledge management system that for many years was a poster child for applications of its kind (see here for the HBR case study).   The secret behind Siemens' success with ShareNet was the flexibility with which it could adapt what was captured, and how, to make it easy for people to contribute and consume.  Today, fortunately, the tools and costs for building capabilities like this are far more accessible. And now as attribution analysis moves closer to the center of the marketing analytics agenda, we have the opportunity to put the resulting data to work in a way that moves the dominant motivation for this kind of behavior beyond altruism to proper credit.

So if you'd like to improve your organization's gol-orientation, perhaps it's time to compile and publish your own assist chart?


Books by
Cesar Brea