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).

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May 29, 2014

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.)


April 16, 2014

Book Review: "Big Data @ Work", by Tom Davenport

I've just finished Big Data @ Work: Dispelling The Myths, Uncovering The Opportunities, by Tom Davenport, the author of Competing On Analytics.  

The book marks a watershed moment in the Big Data zeitgeist. Much of the literature on the topic to this point has been more evangelical, telling us how analytics will make us all taller, smarter, and more handsome.  But the general sense for me has been of stories that are "way out there" for most organizations.  This latest book is much more about how to realize these visions with tactical, practical prescriptions across a range of issues.

Perhaps the most important of these dimensions is having a clear idea of the challenges or opportunities for which Big Data might be a part of the solution.  In Chapter Two, Davenport presents a very helpful series of use cases for using Big Data in several industry applications, including business travel, energy management, retail, and home education. He pushes further to examine the relative readiness of a number of different industries and business functions, including marketing and sales (which are the particular focus of my own upcoming book, Marketing and Sales Analytics). In Chapter Three he builds on these examples and sector assessments to offer a framework for shaping business strategies that leverage Big Data.  He suggests cost reduction, time reduction, new offerings, and decision support as broad objectives for focusing Big Data initiatives, and then further suggests a useful distinction between discovery-oriented application of Big Data (say, for sorting out emergent patterns of behavior to address) and production-oriented usage (applying Big Data to personalize experiences based on which emergent patterns might be worth the effort).

This "ends" focused approach to applying Big Data, in contrast to an "If I build it (my giant Hadoop Cluster) they will come" is an extremely valuable perspective to have introduced at this point in the evolution of this trend, and Davenport has wrapped it in a clean, well-organized package of specific advice executives interested in this space can profit from.

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...

January 17, 2014


I'm working on a book. It will be titled Marketing and Sales Analytics: Powerful Lessons from Leading Practitioners. My first book, Pragmalytics, described some lessons I'd learned; this book extends those lessons with interviews with more than a dozen senior executives grappling with building and applying analytics capabilities in their companies. Pearson's agreed to publish it, and it will be out this spring. Right now I'm in the middle of the agony of writing it. Thank you Stephen Pressfield (and thanks to my wife Nan for introducing us).

A common denominator in the conversations I've been having is the importance of culture. Culture makes building an analytics capability possible. In some cases, pressure for culture change comes outside-in: external conditions become so dire that a firm must embrace data-driven objectivity. In others, the pressure comes top-down: senior leadership embodies it, leads by example, and is willing to re-staff the firm in its image. But what do you do when the wolf's not quite at the door, or when it makes more sense (hopefully, your situation) to try to build the capability largely within the team you have than to make wholesale changes?

There are a lot of models for understanding culture and how to change it. Here's a caveman version (informed by behavioral psychology principles, and small enough to remember). Culture is a collection of values -- beliefs -- about what works, and doesn't: what behaviors lead to good outcomes for customers, shareholders, and employees; and, what behaviors are either ignored or punished.

Photo (16)

Values, in turn, are developed through chances individuals have to try target behaviors, the consequences of those experiences, and how effectively those chances and their consequences are communicated to other people working in the organization.

Photo (15)

Chances are to  culture change as reps (repetitions) are to sports. If you want to drive change, to get better, you need more of them. Remember that not all reps come in games. Test programs can support culture change the same way practices work for teams. Also, courage is a muscle: to bench press 500 pounds once, start with one pushup, then ten, and so on. If you want your marketing team to get comfortable conceiving and executing bigger and bolder bets, start by carving out, frequently, many small test cells in your programs. Then, add weight: define and bound dimensions and ranges for experimentation within those cells that don't just have limits, but also minimums for departure from the norm. If you can't agree on exactly what part of your marketing mix needs the most attention, don't study it forever. A few pushups won't hurt, even if it's your belly that needs the attention. A habit is easier to re-focus than it is to start.

Consequences need to be both visible and meaningful. Visible means good feedback loops to understand the outcome of the chance taken. Meaningful can run to more pay and promotion of course, but also to opportunity and recognition. And don't forget: a sense of impact and accomplishment -- of making a difference -- can be the most powerful reinforcer of all. For this reason, a high density of chances with short, visible feedback loops becomes really important to your change strategy.

Communication magnifies and sustains the impact of chances taken and their consequences. If you speak up at a sales meeting, the client says Good Point, and I later praise you for that, the culture change impact is X. If I then relate that story to everyone at the next sales team meeting, the impact is X * 10 others there. If we write down that behavior in the firm's sales training program as a good model to follow, the impact is X * 100 others who will go through that program.

Summing up, here's a simple set of questions to ask for managing culture change:

  • What specific values does our culture consist of?
  • How strongly held are these values: how well-reinforced have they been by chances, consequences, and communication?
  • What values do I need to keep / change / drop / add?
  • In light of the pre-existing value topology -- fancy way of saying, the values already out there and their relative strength -- what specific chances, consequences, communication program will I need to effect the necessary keeps / changes / drops / adds to the value set?
  • How can my marketing and sales programs incorporate a greater number of formal and informal tests? How quickly and frequently can we execute them?
  • What dimensions (for example, pricing, visual design, messaging style and content, etc.) and "min-max" ranges on those dimensions should I set? 
  • How clearly and quickly can we see the results of these tests?
  • What pay, promotion, opportunity, and recognition implications can I associate with each test?
  • What mechanisms are available / should I use to communicate tests and results?

Ask these questions daily, tote up the score -- chances taken, consequences realized, communications executed -- weekly or monthly. Track the trend, slice the numbers by the behaviors and people you're trying to influence, and the consequences and communications that apply. Don't forget to keep culture change in context: frame it with the business results culture is supposed to serve. Re-focus, then wash, rinse, repeat.  Very soon you'll have a clear view of and strong grip on culture change in your organization.

November 23, 2013

Book Review: "The Human Brand"

October 13, 2013

Unpacking Healthcare.gov

So healthcare.gov launched, with problems.  I'm trying to understand why, so I can apply some lessons in my professional life.  Here are some ideas.

First, I think it helps to define some levels of the problem.  I can think of four:

1. Strategic / policy level -- what challenges do the goals we set create?  In this case, the objective, basically, is two-fold: first; reduce the costs of late-stage, high-cost uncompensated care by enrolling the people who ultimately use that (middle-aged poor folks and other unfortunates) in health insurance that will get them care earlier and reduce stress / improve outcomes (for them and for society) later; second; reduce the cost of this insurance through exchanges that drive competition.  So, basically, bring a bunch of folks from, in many cases, the wrong side of the Digital Divide, and expose them to a bunch of eligibility- and choice-driven complexity (proof:  need for "Navigators"). Hmm.  (Cue the folks who say that's why we need a simple single-payor model, but the obvious response would be that it simply wasn't politically feasible.  We need to play the cards we're dealt.)

2. Experience level -- In light of that need, let's examine what the government did do for each of the "Attract / Engage / Convert / Retain" phases of a Caveman User Experience.  It did promote ACA -- arguably insufficiently or not creatively enough to distinguish itself from opposing signal levels it should have anticipated (one take here).  But more problematically, from what I can tell, the program skips "Engage" and emphasizes "Convert": Healthcare.gov immediately asks you to "Apply Now" (see screenshot below, where "Apply Now" is prominently  featured over "Learn More", even on the "Learn" tab of the site). This is technically problematic (see #3 below), but also experientially lots to ask for when you don't yet know what's behind the curtain. 

3. Technical level -- Excellent piece in Washington Post by Timothy B. Lee. Basically, the system tries to do an eligibility check (for participation and subsidies) before sending you on to enrollment.  Doing this requires checking a bunch of other government systems.  The flowchart explains very clearly why this could be problematic.  There are some front end problems as well, described in rawest form by some of the chatter on Reddit, but from what I've seen these are more superficial, a function of poor process / time management, and fixable.

4. Organizational level -- Great article here in Slate by David Auerbach. Basically, poor coordination structure and execution by HHS of the front and back ends.

Second, here are some things HHS might do differently:

1. Strategic level: Sounds like some segmentation of the potential user base would have suggested a much greater investment in explanation / education, in advance of registration.  Since any responsible design effort starts with users and use cases, I'm sure they did this.  But what came out the other end doesn't seem to reflect that.  What bureaucratic or political considerations got in the way, and what can be revisited, to improve the result? Or, instead of allowing political hacks to infiltrate and dominate the ranks of engineers trying to design a service that works, why not embed competent technologists, perhaps drawn from the ranks of Chief Digital Officers, into the senior political ranks, to advise them on how to get things right online?

2. Experience level: Perhaps the first couple of levels of experience on healthcare.gov should have been explanatory?  "Here's what to expect, here's how this works..." Maybe video (could have used YouTube!)? Maybe also ask a couple of quick anonymous questions to determine whether the eligibility / subsidy check would be relevant, to spare the load on that engine, before seeing what plans might be available, at what price?  You could always re-ask / confirm that data later once the user's past the shopping /evaluation stage, before formally enrolling them into a plan.  In ecommerce, we don't ask untargeted shoppers to enter discount codes until they're about to check out, right?

Or, why not pre-process and cache the answer to the eligibility question the system currently tries to calculate on the fly?  After all, the government already has all our social security numbers and green card numbers, and our tax returns.  So by the time any of us go to the site, it could have pre-determined the size of any potential subsidy, if any, we'd be eligible for, and it could have used this *estimated* subsidy to calculate a *projected* premium we might pay.  We'd need a little registration / security, maybe "enter your last name and social security number, and if they match we'll tell you your estimated subsidy". (I suppose returning a subsidy answer would confirm for a crook who knows my last name that he had my correct SSN, but maybe we could prevent the brute force querying this requires with CAPTCHA. Security friends, please advise.  Naturally, I'd make sure the pre-chached lookup file stays server-side, and isn't exposed as an array in a client-side Javascript snippet!)

3. I see from viewing the page source they have Google Tag Manager running, so perhaps they also have Google Analytics running too, alongside whatever other things...  Since they've open-sourced the front end code and their content on Github, maybe they could also share what they're learning via GA, so we could evaluate ideas for improving the site in the context of that data?

4. It appears they are using Optimizely to test/ optimize their pages (javascript from page source here).  While the nice pictures with people smiling may be optimal, There's plenty of research that suggests that by pushing much of the links to site content below the fold, and forcing us to scroll to see it, they might be burying the very resources the "experience perspective" I've described suggests they need to highlight.  So maybe this layout is in fact what maximizes the results they're looking for -- pressing the "Apply Now" button -- but maybe that's the wrong question to be asking!

Postscript, November 1:

Food for thought (scroll to bottom).  How does this happen?  Software engineer friends, please weigh in!


September 11, 2013

Book Review: "Building A Digital Analytics Organization" by @Judah Phillips #analytics

I originally got to know Judah Phillips through Web Analytics Wednesdays events he organized, and in recent years he's kindly participated on panels I've moderated and has been helpful to my own writing and publishing efforts. I've even partnered with some of the excellent professionals who have worked for him. So while I'm biased as the beneficiary of his wisdom and support, I can also vouch first-hand for the depth and credibility of his advice. In short, in an increasingly hype-filled category, Judah is the real deal, and this makes "Building The Digital Analytics Organization" a book to take seriously.

For me the book was useful on three levels. One, it's a foundational text for framing how to come at business analysis and reporting. Specifically, he presents an Analytics Value Chain that reminds us to bookend our analytic efforts per se with a clear set of objectives and actions, an orientation that's sadly missing in many balkanized corporate environments. Two, it's a blueprint for your own organization-building efforts. He really covers the waterfront, from how to approach analysis, to different kinds of analysis you can pursue, to how to organize the function and manage its relationships with other groups that play important supporting roles. For me, Chapter 6, "Defining, Planning, Collecting, and Governing Data in Digital Analytics" is an especially useful section. In it, he presents a very clear, straightforward structure for how you should set up and run these crucial functions. Finally, three, Judah offers a strong point of view on certain decisions. For example, I read him to advocate for a strongly centralized digital analytics function, rooted in the "business" side of the house, to make sure that you have both critical mass for these crucial skills, as well as proximity to the decisions they need to support.

These three uses had me scribbling in the margins and dog-earing extensively. But if you still need one more reason to pull the trigger, it helps that the book is very up-to-date and has a final chapter that looks forward very thoughtfully into how Judah expects what he describes as the "Analytical Economy" to evolve. This section is both a helpful survey of the different capabilities that will shape this future as well as an exploration of the issues these capabilities and associated trends will raise, in particular as they relate to privacy. It's a valuable checklist, to make sure you're not just building for today, but for the next few years to come.

Here's the book and the review on Amazon.

September 01, 2013

#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!


July 16, 2013

Please sponsor my 2013 NLG #autism ride: 2007 Ride Recap

On July 27, I'll be riding once again in the annual Nashoba Learning Group bike-a-thon, and I'd really appreciate your support:


(Note: please also Like / Retweet / forward to friends, etc. using links at bottom!)

This is a great cause, and an incredibly effective and well-run school.  Your contribution will make a big difference. (And thank you to everyone who'd been so generous so far!)

For kicks, here's my recap of my 2007 ride:


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 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."

June 16, 2013

Organizing for #Analytics - Seven Considerations

We're now in the blood-sugar-crash phase of the Analytics / Big Data hype cycle, where the gap between promise and reality is greatest.  Presenting symptoms of the gap include complaints about alignment, access to data, capacity to act on data-driven insights, and talent.  This September 2012 HBR blog post by Paul Barth and Randy Bean of NewVantage Partners underscores this with some interesting data.

Executives' anxiety about this gap is also at its peak.  Many of them turn to organization as their prime lever for solving things. A question I get a lot is "How should we organize our analytic capabilities?"  Related ones include "How centralized should they be?", and "What should be on the business side, and what belongs in IT?"  

This post suggests a few criteria for helping to answer these questions.  But first, I'd like to offer a principle for tackling this generally:

Think organization last, not first.

A corollary to this might be, "Role is as role does."  Too much attention today is paid to developing and organizing for analytic capability.  Not enough attention is paid to defining and managing a portfolio of important business opportunities that leverage this capability.  In our work with clients, we focus on building capability through practice and results.  Our litmus test for whether we're making progress is a rule we call "3-2-1": In each quarter, the portfolio of business opportunities we're supporting with analytic efforts has to yield at least three "news you can use" insights, two experiments based on these insights, and one "scaling" of prior experiments to "production", with commensurate results.  (The specific goals we set for each of these varies of course from situation to situation, but the approach is the same.)

Approaching things this way has several benefits:

  • You frame "Analytics" and "Big Data" requirements in terms of what you need to solve specific challenges relevant to you, not in terms of a vendor's list of features;
  • You stay focused on the result, and not the input, so you don't invest past the point of diminishing returns;
  • By keeping cycles short and accountable to this rule, you hedge execution risk and maximize learning;
  • Your talent recruitment, development, and organization are done in the context of explicit opportunities, and thus stay flexible and integrated around concrete business results and not abstract concepts for what you need;
  • The results-oriented management of the capability helps build confidence that the overall ROI expected will be achieved.  Momentum is strategic.

Now, two critiques that can be made of this approach are, first, that it's too ad hoc and therefore misses opportunities to leverage experience beyond each individual opportunity addressed, and second, that it ignores that most people are "tribal" and that their behaviors are shaped accordingly.  So once you've got a decent portfolio assembled and you're managing it along, here are some organizational considerations you can apply to help decide where folks should "live":

  • For the business opportunities you're faced with, how unique is "local knowledge" -- that is, intimate knowledge of the specific market dynamics or operational mechanics that generate the data and shape the necessary analytics -- to each of them?  The more so, the more it will make sense to place your analysts in the groups responsible for those areas.
  • To what extent does the type of analysis you are pursuing require a certain degree of critical mass? It's hard for a single person or even small groups to manage and mine a Big Data capability, and if you sprinkle Big Data analysts throughout your firm to support different groups, you overwhelm each of them and under-serve the opportunity. Plus, each of them ends up with different Hammers Looking For Nails based on the particular tools and techniques they learn, rather than picking the best ones for different jobs.
  • How important is enterprise leverage to the business case for your capability?  If it is, centralizing your analysts so that purchasing efficiencies and idea sharing and reuse are maximized will be more important.
  • Are you concerned about objectivity?  When analysts get embedded deeply with business teams, there's a risk they can "go native", either because they fall in love with the solutions they're part of developing, or because of pressure, subtle and otherwise, to prove these solutions work.  This phenomenon is well-documented in scientific fields, even with peer review, so it's certainly more problematic in business.  
  • Are you, for whatever reason, having trouble keeping your analysts and their efforts aligned with your key priorities? For example, if one group needs to quickly get a product into market to grab its share of a high-growth opportunity, and then evolve it from there, and your analysts work in a group whose norms and objectives are more about "perfect" than "good enough", you may need to move folks, or get different folks in place.
  • How's your analyst-marketer relationship? If they're talking and working together productively, and the interpersonal karma is good, you can worry less about whether their boxes on the chart are closer or further apart.
  • Finally, which of these four "C's" describes the behavior you're trying to encourage: communication, coordination, collaboration, or control?  At the communication end of the spectrum, you just want folks to be aware of each other's efforts.  Coordination, for example, can mean "Hey, I'll be running my test Tuesday, so could you wait until Wednesday?"  Collaboration may require formal re-grouping, but it might only be temporary.  Control can be necessary for effective execution of complex projects.  The more analytic success relies on such control, rather than being satisfied by the "lesser" C's, the more you may solve for that with organization.

In our work we'll typically apply these criteria using scoresheets to evaluate either or both the specific business challenges we're solving for or the organizational models we're evaluating as possible options.  Sometimes we just use "high-medium-low" assessments, and other times we'll do the math to help us stay objective about different ways to go.  The main things are to keep attention to organization in balance with attention to progress, and to keep discussions about organization focused on the needs of the business, rather than allowing them to devolve into proxy battles for executive power and influence.


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Cesar Brea