Devon McDonald of OpenView Partners recently wrote a blog post on Scrum Agile Marketing in which she discussed Minimum Viable Marketing. It got me thinking about my clients’ prospects and customers, and it lead me to the following theory, which I am now testing, using a more agile approach:

  1. Prospects don’t want to be sold or marketed to, but most want to be educated.
  2. They’re not looking to get a degree. They just want an answer to a question.
  3. They don’t want to plow through long documents, so the answer has to be easily found.
  4. If they have another question, they want that answered, too.
  5. They are constantly dealing with others’ objections, so new-found knowledge has to be easily shared.

That lead me to create a series of one-minute videos, each designed to answer one question or cover one topic very succinctly. I’ve chosen as a topic, information technology in remote and branch offices. The series is called Branch Office Tech Tips. I’ve posted eight so far, but expect new content frequently, and don’t be surprised if some content is replaced. This is Agile.

As part of this experiment, I’ve hosted the videos on Wistia, because Wistia gives great insight into how many people watch, for how long, and when they stop watching. But seriously, if I can’t keep someone engaged for 60 seconds, then I need to go back to the content for a do-over.

I’ve added another page on this blog, specifically dedicated to Branch Office Tech Tips (BOTT). I’m also going to start making better use of IFTTT. Blog updates will automatically be posted to LinkedIn, Twitter, and other platforms. At least that’s my plan. We’ll see what works.

I do remember enough of my college statistics courses to know that using Twitter to evaluate trends and attitudes introduces significant sampling and reporting bias.   Still, ignoring the obvious data quality issues, I do enjoy reading through my TwitterFall feed every week. Because of my interest in application availability requirements, which drives the business of StorMagic, on whose board I serve as an independent director, I typically set the search terms to “computers” and “down,” but when “She shuts it down like computers” is trending, I’ll switch to searching on “computers” and “free.”

This is just a sampling of the “computers” and “free” tweets I saw this week. Apparently, for the patrons of some quick-serve restaurants, when computers go down, #lifeisgood.


AbbeyVoss just got a free coffee and biscuit at Caribou cause their computers are down #lifeisgood

lilseannn Public Service Announcement : Taco Bells computers are down; free TB!

r0danthony Computers shut down in the cafeteria. Your boy got a free meal. Lololol.


I’ve noticed over the course of the past two years, that Starbucks typically offers up free food and coffee when the computers go down, as captured in this tweet last week.

‏@NDarnell96  Getting free Starbucks cause their computers froze


My guess is that Starbucks gives away coffee when the computers go down, because it’s cheap marketing, and they assume the cost of delivering high-availability applications is too high. Coffee brewers basically turn water to gold, anyway. And if you’ve got as many gold buyers as Starbucks, what’s wrong with an occasional free giveaway when customers are willing to provide free advertising? I’ve never been able to verify this, so if anyone can validate the assumption, please let me know. If that’s the approach, at Starbucks, I get it. But it appears someone wasn’t on the program last week, as I also saw this tweet:

@Spencer_Westley@Starbucks‘ computers are down and WHAT IS LIFE?


This “give it away when the computers are down” approach works fine at quick-serve restaurants, when you only need computers to take payments, but it gets much more challenging if you need the computers to get the orders from the front counter to the kitchen, to apply loyalty credits, to recall the frequently ordered items on the automated order entry system, to actually make the food, to know when to plate the food, or when you take orders over the web, but fulfill them in the restaurant. These days, companies drive efficiency from automated operations and new revenue sources from processes that are dependent upon computers.  They also are driving the perception of customer intimacy by knowing more about their customers’ likes, dislikes, and preferences. Computers matter, not when you’re selling any cup of coffee to the next person in line, but when you are selling this particular, customized cup of coffee to that loyal customer.

I’m a big believer in the value of in-the-field observation. What people do is more important than what people say. What some investors, analysts and IT professionals have been saying recently is, “Nobody wants on-premise IT. Everything is moving to the cloud.” The first part might be true, but the second part is utter nonsense, and ignores what is easily observable. Let me describe a recent purchase to illustrate.

We’ve had a cold winter in New England with plenty of snow. Almost 2 feet of snow had accumulated on the northern side of my roof. I have experience with ice dams and knew that if I didn’t get the snow off the roof, I would be facing water damage, as the ice made its way into the attic, melted, and dripped onto the ceiling. The tool of choice is a roof rake, of which I had none. I called the local Home Depot, which is about 6 miles from my house, but found that roof rakes were out of stock. No big surprise. I then went online to, and found that there were about 50 at a store only 20 minutes away. I quickly placed an on-line order and requested in-store pickup. I also selected the option that directed the store to text me when my order was ready for pickup, since I didn’t want to drive the round trip unless the order was ready. My order was confirmed, and a few minutes later, I received a text saying my roof rake was available for pickup at the service desk. An hour later, I was pulling mountains of snow off my roof and onto my head, and after a couple of hours chilling work, I successfully avoided thousands of dollars in potential water damage. For me, it was another happy Home Depot buying experience.

Now, imagine for a moment if all of Home Depot’s IT was centralized to the cloud. If “the cloud” went down, would I have been able to order anything online? If the communications link between the store and “the cloud” failed, would I have been able to know the actual inventory in the store? If I placed the order on line, but the link to  the store then went down, would the store have been able to fulfill my order? Would they have been able to notify me that the order was ready for pickup? Would they have been able to avoid a scenario where I ordered on line, but the store sold it to someone else?

Retailers are increasingly combining on-line retail with in-store shopping. This extends to everything from order entry, fulfillment, and payment processing, to returns processing and customer  loyalty programs. Some functions can and should reside in the cloud (or core data center). But for an optimal customer experience, some IT functions will need to remain in the store. And at the edge, retailers need an infrastructure that is both highly available and affordable.

In the interest of disclosure, I am a board member of StorMagic, an enabler of affordable, high-availability infrastructure at the enterprise edge.


If you are with a reseller, you’ve probably heard the dire warnings about the impact of “the cloud” on resellers. Among the warnings are:

  • Look out. Everything is moving to the cloud.
  • If your business is just selling products, your business is going away.
  • If you don’t have a cloud strategy, your business is going away.

Resellers are getting plenty of advice on how to transform their businesses to a recurring-revenue model. Here’s a recent sampling:

Last summer, I had breakfast with a friend; someone I have known for more than 20 years.  When we first met in the 90s, she was with one of the big systems companies, and I was manager of IT Procurement at State Street Bank. Now she is with one of that same systems company’s direct VARs.  For our breakfast, she arranged to have two people from a cloud-gateway company join us.  The company, apparently, had been pitching her current company on the benefits of reselling their gateway to the storage cloud, and she wanted my opinion regarding their technology.

As it turns out, nothing about the company’s technology really mattered. The technology could have been the greatest thing ever to hit the technology road, but she wasn’t going to sell it. What mattered was how she got paid. She had a sales number, and that number wasn’t based upon closing recurring-revenue opportunities. Selling a gateway plus a service was going to get her maybe 1/4 of what she would have gotten staying on her current path of telling customers that “the cloud is unproven and too risky.”

Cycle forward another nine months, and I just met with a local reseller. I was exploring opportunities for one of my clients that offers a software solution to a vexing problem of delivering affordable, highly-available applications in an environment that lacks on-site IT resources. After listening politely, this reseller told me, “Yours would be the last solution I would sell.” Again, it had nothing to do with the quality of the product, but rather, how he got paid. Turns out that he got better revenue and quota retirement for selling a service, than he did for selling a product. Management at this particular reseller was very focused on covering operating costs with recurring revenue streams.

This takes me, now, to a friend, who quotes this Bahamian saying:

Never mind the noise in the market.
Pay attention to the price of the fish.

The channel equivalent of this is:

Never mind the noise in the market.
Pay attention to how the sales reps get paid.


Over the past month, I’ve been writing about the importance of data and measurement in tracking the success of a startup.  In “Mastering the VC Game,” Jeffrey Bussgang takes the point one step further, when he quoted successful entrepreneur, Gail Goodman, CEO of Constant Contact, who said:

“The single most important thing to do with a board is to keep them really up to date on the business. The good and the bad.”

Bill Chambers, the founder and former CEO of Lefthand Networks, has on more than one occasion been heard using the phrase:

“I was more surprised than pleased.”

It is his gentle, but very direct way of communicating the fact that he doesn’t like surprises. But like many seasoned executives, given a challenge, he is more than happy to step in and provide constructive suggestions to overcome most any challenge.

It doesn’t matter whether the organization is a for-profit corporation or a not-for-profit charity, the CEO or Executive Director needs to be transparent with the Board. As Jeffrey points out in his book, adversity can pull the team together or pull the team apart. The outcome depends on transparency. When challenges are plainly placed on the table and help from the board and outside advisers is sought, options appear and logjams become unstuck. But if, despite obvious challenges, the organization’s head feels a need to hide behind a self-assurance facade, the board will lose confidence and the organization is almost assuredly doomed to fail.

I ended last year by reading two books. The first was Competing on Analytics, The New Science of Winning. The second was 18 Minutes: Find Your Focus, Master Distraction, and Get the Right Things Done. “Competing on Analytics” changed my thinking, and “18 Minutes” changed my life.

A core tenant of “Competing on Analytics” is that in order to make correct decisions, you need data.  Data requires measurement. And, as I’ve come to learn, it requires determining which data, among all of the data that one could collect, is the most important data.

A core tenant of “18 Minutes” is that getting the right things done requires that you decide, among all of the possibilities, the relatively small number goals that you want to achieve over the coming year. These are the things that you absolutely want to get done, and get done right. Another core tenant is that you need to evaluate each of your activities against those goals.  It suggests, among other things, that you start your day by evaluating what is on your calendar, not in your email InBox. Almost immediately upon rising, ask yourself,

What am I scheduled to do today?

Is my schedule consistent with progress against my goals?

If not, what changes do I need to make?

As I write this, I recognize that the above statements may sound very basic to some of you. Some of you already live your lives based upon very specific goals and measurement.  I suspect, however, that many more only think you live your lives this way, and haven’t actually taken the time to determine your personal goals, separate from everyone else’s goals. Nor have you decided what specific data will tell you whether you are making the correct choices in achieving your goals. I recently made that embarrassing admission to myself. It was especially embarrassing for me, given that I love science and studied, among other things, mathematics in college.

I won’t bore you with my own specific goals for this year, but suffice it to say that by measuring my time against my priorities, I have determined how long it takes me to complete certain tasks. I’ve learned what work is profitable and what work is less profitable than I had previously thought. I’ve learned the direct impact of diet and exercise on weight and heart rate. And I’ve learned that if I schedule things as soon as I make a verbal commitment, that I almost always keep my commitments.

For the 3rd time this month, Mark Leslie was mentioned in a conversation or presentation. The most recent was at TiE Boston’s Annual VC Outlook Dinner. Actually, mentioned, is way too soft. He was cited, praised, quoted, and otherwise venerated. Mark has a remarkable story. He took VERITAS from 12 employees and $95,000 in annual revenue to over 6,000 employees and $1.5B in revenue, before the company merged with Symantec on July 5, 2005.

I first met Mark, when I hosted IDC’s StorageVision conference in San Jose in May 2000, a year in which we recruited Steve Luczo, then CEO, and now Chairman of Seagate, and Joe Tucci, then the newly appointed President and COO  of EMC.  Mark wasn’t the best dressed (that was Joe) or most poised (that was Steve), but he gave, by far, the best talk. The talk that was discussed three times this month wasn’t, however, his StorageVision speech. That honor goes to this talk: “It Always Takes Longer and Costs More.” On Page 15 of the presentation, Mark begins a discussion of the Sales Learn Curve (SLC), which is the sales equivalent of the Manufacturing Learning Curve (MLC). The SLC is critical, Mark argues, to knowing when to step on the gas with sales.

Almost every company I’ve talked to measures the cost of sales. But where organizations differ is in the costs that are included in the cost of sales. Mark includes:

  • Marketing
  • Product management
  • Product marketing
  • Product  support
  • Sales engineering
  • Sales

Some of these costs are more fixed than variable. Marketing, product management and product marketing don’t need to grow linearly with sales. Support, sales engineering, and sales costs are, however, more directly proportional to revenue.

Since more sales means that the fixed costs in the cost of sales calculation decline as a percent of sales, many startups are tempted to hit the gas early on deploying sales and sales engineering resources. But until they have gone through the iterative process of perfecting the sales process, this approach just burns cash.  Instead, he stresses the importance of  investing for learning in the early stages, making iterative improvements in the sales process. When the sales process is perfected, then, and only then, should a company put the “Pedal to the Metal,” making significant investments in sales and sales engineering.

In last week’s post I talked about the importance of a start up knowing the percentage of customers that would recommend their product or their company. There’s actually a name for this metric. It’s called the Net Promoter Score or NPS. Here’s an article that provides a way to calculate your NPS.  In order to have a high NPS, you need more than the right product. You need the right customer experience.

I’m always a little shocked by companies that see unhappy customers and fail to take immediate action. My wife recently took her car into the local dealership for routine service.  Between a post-doctoral fellowship, an active private practice, and several non-profit board seats, she’s very busy. So she wanted to know how long the service would take. The answer was, “No more than an hour.” After a two and a half hour wait for her service to  be completed, she was obviously steaming. She complained to the service manager, who apologized and then sent her on her way.

When the post-service customer satisfaction survey call came, she gave candid answers. She was not satisfied. Less than 24 hours later, she received a call from the service manager apologizing profusely, telling her to call him directly, the next time she had her car serviced. He then offered her a complimentary car detailing service along with a polite recommendation that she wait until the winter season was done, in order to get the maximum benefit from the detailing. The coupon for the free detailing arrived the next day in the mail. My wife was somewhat calmed by the gesture, but still doesn’t mind repeating the story of the dealership’s poor service.

Now, imagine how different her reaction might have been, had the service manager, to whom she expressed her dissatisfaction before she left the dealership, had then offered her a discount, or a free detailing service. Her anger would probably have been assuaged immediately. What if it didn’t require the service manager’s involvement at all, but the clerk at the service desk had been empowered and taken the initiative to make things right? What if the dealership’s response had been as it was for me at a local restaurant, when the waitress offered my meal for free, because I had to wait too long to be served. Rather than a detractor, I became a promoter, for her willingness to proactively do the right thing.

I met a few weeks ago with a friend who is the CEO of a startup company based in the Boston area. He’s not a first time CEO, and he’s had at least one successful exit, selling his company to a major system company. How good was the exit? Lets just say that he didn’t have trouble raising money, when he was ready to do his next venture. When he did his last round, it was significant and at a very nice valuation, by IT infrastructure standards.

I think data is important, and I like to know what corporate executives care about, so, now, every time I meet with a startup company, I ask the CEO what they measure. Typically on the finance side, I’ll get answers like revenue, cash flow, burn rate. On the sales side, they tell me they track new customers, number of deals, repeat sales or renewals, and average deal size.  And on the development side, they will track the total number of bugs, sometimes by criticality, and the bug retirement rate. These are all good things to measure, so, if you are measuring these things, good for you.  And if you’re not measuring them, time to break out a yardstick and some monitoring tools.

My very successful friend gave me one more number to track before all others. Given his track record, I paid attention. The most important number for him is the percentage of customers that say they would recommend the  solution to a colleague. He actually has someone call every single customer and ask only one question:

Would you recommend our solution to a colleague?

This is not the same question as “Would you be a press reference or allow us to do a case study on your installation?” That brings with it the usual baggage of legal and PR approval processes. No, for an early stage company, this question, “Would you recommend our solution to a colleague?” encapsulates the only truly important metric into it. It answers the question, “Am I building the right product?”

A meeting this week with Amy O’Connor, Senior Director of Analytics at Nokia and author of the Im AmyO blog, has led me down an interesting path at the end of the year. Normally, I might spend the last day of the year in self-reflection: Am I happy with how I spent the past year? Do I feel good about the results? What will I resolve to do differently next year? This year, however, instead of self-reflection, I’ve decided to end the year in a little self analysis. What’s the difference between reflection and analysis? Data.

To help me with that, I’m re-reading “Competing on Analytics: The New Science of Winning,” written by Thomas Davenport and Jeanne Harris and published by Harvard Business School Press back in 2007. The first thing that became abundantly clear was that I didn’t have enough data on myself, my activities, and the results of those activities.  So, I decided to collect some. As a starting point, I decided to analyze my activity publishing content on Wikibon.

I posted my first article, “StorMagic Announces SvSAN and Offers Free Download,” on Wikibon on February 19, 2009. It’s the only article I published that year, and it was an experiment. It was also, admittedly, a little self serving, since I’m a non-investor director on the board of StorMagic. Upon analysis, the results of the posting were pretty good. It’s been viewed over 3000 times and received a community rating of 4 out of a possible 5. Perhaps it was ranked a little lower, because the article was a little self serving, though defensibly 100% accurate. Given the results, you’d think I might have published more, but I didn’t.

In 2010, I posted 18 articles on Wikibon, and I posted another 6 in 2011, despite an amazing amount of disruptions, which I won’t go into here. So over the almost three years, I’ve posted a total of 25 documents. The total views across all of my documents is almost 48,000, the average number of views is a respectable 1900+ and the average community rating is 4.8+, despite my lower starting point. I guess I’ve improved with age.

The documents were all relatively short (at an average of 525 words, a very quick read) and designed to be actionable. Personally, I think articles are best, when they spark a dialogue or provoke a comment, and I’m sorry to say that the average number of comments per post was just over .7 and more than half had no comments.  That’s something  that’s worth figuring out how to improve.

Reporting on minimums, maximums, totals, and central-tendency are interesting first steps. But they are just that: Reporting. The key now is to get to the next level, and evaluate the impact of article length, keywords, topics and themes on views.  If anyone can suggest an open-source text-analytics tool, I would be very grateful.

Over the next year, I have resolved to write more, measure more, and analyze more. Expect to see more articles published by me on Wikibon, because I like the team, and it’s an easy platform to use. I enjoy the exposure to end-users that Wikibon affords me, and I like the fact that when I publish content there, I know how much it’s being read. I also enjoy the opportunity to have an occasional conversation with an IT industry executive, with whom I have no current business relationship. I’m a curious and rather social guy, so it doesn’t have to all be about my business and potential business opportunities.

I also plan to learn more about the rapidly developing field of data analytics, currently promoted under the term “Big Data,” which is either a subset or super-set of analytics, depending on your point of view.  I’ve always enjoyed mathematics and analysis, but back when I was a math major (along with majors in Physics, Education, and Psychology), about the only opportunity for a B.S. graduate in Mathematics, outside of academia or education, was to become an actuary at an insurance company. Frankly, I didn’t want to spend my life figuring out morbidity rates. But the life of a data scientist, especially when that skill can be applied to making better products and creating more jobs, is significantly more interesting.

Finally, Amy O’Connor tells me that Nokia plans to re-invigorate a local Big Data user group that has been meeting at the Microsoft offices in Waltham. So you can expect to find me there. I’ll post details as soon as I get them. I hope to see you there.

Best wishes for a happy and healthy new year.

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