About

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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April 16, 2014

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

Culturelytics

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. 

Healthcaregov
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:

http://www.crowdrise.com/nlgbikecesar2013

(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:

"Friends,

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.

June 12, 2013

Privacy vs. Security Survey Interim Results #prism #analytics

This week, one of the big news items is the disclosure of the NSA's Prism program that collects all sorts of our electronic communications, to help identify terrorists and prevent attacks.

I was struck by three things.  One is the recency bias in the outrage expressed by many people.  Not sixty days ago we were all horrified at the news of the Boston Marathon bombings.  Another is the polarization of the debate.  Consider the contrast the Hullabaloo blog draws between "insurrectionists" and "institutionalists".  The third was the superficial treatment of the tradeoffs folks would be willing to make.  Yesterday the New York Times Caucus blog published the results of a survey that suggested most folks are fence-sitters on the tradeoff between privacy and security, but left it more or less at that.  (The Onion wasn't far behind with a perfect send-up of the ambivalence we feel.)

In sum, biased decision-making based on excessively simplified choices using limited data.  Not helpful. Better would be a more nuanced examination of the tradeoff between the privacy you would be willing to give up for the potential lives saved.  I see this opportunity to improve decision making alot, and I thought this would be an interesting example to illustrate how framing and informing an issue differently can help.  So I posted this survey: https://t.co/et0Bs0OrKF

Here are some early results from twelve folks who kindly took it (please feel free to add your answers, if I get enough more I'll update the results):

Privacy vs security

(Each axis is a seven point scale, 1 at lowest and 7 at highest.  Bubble size = # of respondents who provided that tradeoff as their answer.  No bubble / just label = 1 respondent, biggest bubble at lower right = 3 respondents.)

Interesting distribution, tending slightly toward folks valuing (their own) privacy over (other people's) security.

Now my friend and business school classmate Sam Kinney suggested this tradeoff was a false choice.  I disagreed with him. But the exchange did get me to think a bit further.  More data isn't necessarily linear in its benefits.  It could have diminishing returns of course (as I argued in Pragmalytics) but it could also have increasing value as the incremental data might fill in a puzzle or help to make a connection.  While that relationship between data and safety is hard for me to process, the government might help its case by being less deceptive and more transparent about what it's collecting, and its relative benefits.  It might do this, if not for principle, then for the practical value of controlling the terms of the debate when, as David Brooks wrote so brilliantly this week, an increasingly anomic society cultivates Edward Snowdens at an accelerating clip.

I'm skeptical about the value of this data for identifying terrorists and preventing their attacks.  Any competent terrorist network will use burner phones, run its own email servers, and communicate in code.  But maybe the data surveillance program has value because it raises the bar to this level of infrastructure and process, and thus makes it harder for such networks to operate.

I'm not concerned about the use of my data for security purposes, especially not if it can save innocent boys and girls from losing limbs at the hands of sick whackos.  I am really concerned it might get reused for other purposes in ways I don't approve, or by folks whose motives I don't approve, so I'm sure we could improve oversight, not only for what data gets used how, but of the vast, outsourced, increasingly unaccountable government we have in place. But right now, against the broader backdrop of gridlock on essentially any important public issue, I just think the debate needs to get more utilitarian, and less political and ideological.  And, I think analytically-inclined folks can play a productive role in making this happen.

(Thanks to @zimbalist and @perryhewitt for steering me to some great links, and to Sam for pushing my thinking.)

May 20, 2013

"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.

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