Posts Tagged ‘complexity’

Scott Berkun reminds us of the value of learning from mistakes

Wednesday, March 3rd, 2010

You may remember my project The Mistake Bank. It’s on hiatus now (isn’t that what broken-up bands say?), and someday soon I’ll be putting up a post on what I learned from that project. (Thanking Cynthia Kurtz for that idea.)

In the meantime, people are still screwing up and, thankfully, learning from those experiences. Most recently there was this post from Scott Berkun: “My Biggest Mistakes.”

Scott, in addition to being a great speaker and blogger, is a first-class mistake learner. His post “How to Learn From Your Mistakes” was an early entry in the Mistake Bank. It’s gratifying to see that he still appreciates the value of reflecting on his past actions, and retains the sense of humor that allows him to do so.

Related post:
Scott Berkun on learning from mistakes

Offshoring telesales reduces close rates – why?

Tuesday, September 29th, 2009

I’ve heard from several friends in call center operations that outsourcing inbound telesales to the Philippines has resulted in close rates below expectations. In at least one case that I know of, a company is re-establishing an internal sales center to try to get to the root of why telesales is harder to offshore than customer care.

After listening to hundreds of sales calls and care calls and helping companies find actionable patterns in them, I’ve got some opinions on the subject.

1) Sales is harder to script than care
– a care call is bounded by the product or service the customer has bought. There’s only so much that can go wrong, and most/all those scenarios are documented and can be scripted into the CRM system. Sales calls are open-ended; they can go anywhere, and can veer off track at any moment. Will the prospect complain about the price? Will they bring up a competitor you’ve never heard from? Any left turn a prospect makes can cause an offshored rep, already managing language complexity and reduced empathy, to panic or lose his place (see “confusion kills sales,” below).

2) It’s easier for a prospect to give up than a customer - anyone who has made a call through an offshored center knows that it’s more difficult to communicate with someone who’s from a different culture, with a different accent and familiar with different figures of speech. That difficulty can breed frustration. A current customer with a problem is more inclined to persevere through the frustration, in order to solve her problem, than a prospect, who can hang up the phone or say, “No thanks” and be no worse off than she was before.

3) Confusion kills sales -
if your sales process has a number of steps, and/or it has options a customer has to understand and select, the rep or the customer is prone to become confused. And if the rep gets confused, the prospect is soon to follow. My experience listening to and finding patterns in sales calls tells me that confusion is a sales-killer. There are enough negative emotions swirling around the buying process that adding confusion into the mix can tip a sale from Yes to No.

What have your experiences been with offshored telesales? Are there other reasons sales is difficult to outsource?

(If you’re interested in getting a deeper read as to why your telesales operation is undershooting its objectives, we can help.)

(Photo by vlima.com via Flickr Creative Commons)

Related posts:
Complex sales: it’s all about the negatives

Innovation moving from initiatives to experiments

Monday, August 17th, 2009

An interesting piece in today’s WSJ Business Insight section (”The New, Faster Face of Innovation” by Eric Brynjolffson and Michael Schrage of MIT) asserts that information technologies are reducing the cost of business experimentation and increasing the speed of rolling out new processes and approaches to the organization as a whole. As a result, more and more businesses are moving to use experimentation as a basis of their innovation programs. Here’s an excerpt:

Innovation initiatives that used to take months and megabucks to coordinate and launch can often be started in seconds for cents.

And that makes innovation, the lifeblood of growth, more efficient and cheaper. Companies are able to get a much better idea of how their customers behave and what they want. This gives new offerings and marketing efforts a better shot at success.

Companies will also be willing to try new things, because the price of failure is so much lower. That will bring big changes for corporate culture—making it easier to challenge accepted wisdom, for instance, and forcing managers to give more employees a say in the innovation process.

There will be even better payoffs for customers: Their likes and dislikes will have much more impact on companies’ decisions. In globally competitive markets, they will ultimately end up getting products and services better tailored to their needs.

I agree with Brynjolffson and Schrage that experimentation-based innovation will have tremendous impact on improving products and reducing companies’ innovation costs. But while they credit IT enablers, I think there’s another crucial reason that experimentation is growing in popularity. Schrage touches on it in this video companion to the article:

Schrage mentions that companies need to be creating “a culture of experimentation rather than a culture of grand planning and initiatives.” And when he uses these terms, I start thinking about the Cynefin framework, devised initially by Dave Snowden and first published in a mind-blowing article in the IBM Systems Journal (Cynthia Kurtz and Dave Snowden, “The New Dynamics of Strategy: Sensemaking in a Complex and Complicated World“) .


The framework is useful for lots of purposes: knowledge management, strategic planning, managerial action (the subject of Snowden and Boone’s HBR article, “A Leader’s Framework for Decision Making“). But here I’m discussing applying it to thinking about innovation.

Schrage’s casual comment illuminates innovation’s relationship to Cynefin. “The culture of grand planning and initiatives” is dominant in most companies, and shows that they view innovation initiatives in the Complicated domain of the Cynefin framework (I find the original terminology from Kurtz and Snowden helpful–”knowable”). Knowable or complicated systems are ones where cause and effect are related–but may be separated in time. You often need expertise to diagnose and act on a situation, but once the system is solved, the way forward is clear.

Traditional innovation initiatives treat the interactions between companies, customers and markets as a Complicated system. Innovation projects are expensive and time-consuming – you often hire consultants to lend their expertise. There is a solution–certain objectives and expectations that the initiatives must meet (these are often encoded into business plans and pro forma P&L’s). Of course, you only need to be involved in one such initiative to know that they never deliver to plan. Innovation initiatives are always surprises–sometimes delightful upside surprises, but more often long, expensive failures. This is because they treat a Complex problem (in the Cynefin definition) with a tool suited for the Complicated domain.

In the Complex domain, cause and effect are not observable in advance–”grand planning” is not productive. The outcomes of a complex process seem logical – but only in retrospect. Why did Twitter evolve the way it did? Why was iPod/iTunes so revolutionary and so successful?

“The culture of experimentation,” on the other hand, acknowledges this complexity – that the objectives of innovation – creating interesting, popular, valuable and attractive new products and getting them into the hands of customers – are not straightforwardly attained and cannot be planned. When customers buy certain products, when they linger on certain web pages over others, when they flock to some brand-new platform, they are exhibiting behavior best described by the language of complexity. And the way to achieve progress in this domain is to use experimentation: generate lots of ideas – perhaps even some deliberate mistakes. Try them out. If something works, spread it around. If it doesn’t, kill it quickly and move on. Iterate. [The Toyota Production System applies this experimental thinking to manufacturing innovation.]

So, is the ability of information technology to make experimentation fast & cheap responsible for the increasing use of experimentation to achieve innovation’s goals? Yes, in part. But a great deal of the reason lies in the fact that the old way of innovating, “grand planning,” isn’t the right tool for the task.

[Another viewpoint on innovation and experimentation is in McGrath and MacMillan's recent book, "Discovery-Driven Growth." While the book doesn't use the Cynefin terminology or share the complex adaptive systems roots, it nonetheless focuses on the uncertainty of the innovation process and emphasizes the need to cheaply and quickly experiment, allowing successful projects to emerge.]

(Image source: Wikipedia article on the Cynefin Framework)

Related posts:
On deliberate mistakes
On “Discovery-Driven Growth”
An example of “safe-fail” experimentation

The complexity of sales & marketing… it’s comedic

Tuesday, June 2nd, 2009

Peering up at the title, you might be expecting this post to be funny. It probably won’t work out that way…

I read Matt Ruby’s Sandpaper Suit comedy blog regularly, and yesterday’s entry was really terrific. He discussed Conan O’Brien’s first show as host of “The Tonight Show” and pointed out some really important characteristics of good comedy:

[O'Brien spoke in an interview about the challenges in hosting a nightly TV show:] “…There are 35 variables every night — what comedy do we have? What’s the audience like? Who are the guests? What time of year is it? What’s my mood? You need 15 cherries to line up to pay out the jackpot. And, every now and then, the stars align. And you keep chasing after that feeling.”

[Ruby writes:] All those variables are what make standup [comedy] so fascinating. So many things play a role: the room, the PA, the crowd, the host, your confidence, the placement of each word, little variations in timing, etc. It feels almost impossible to come up with a fixed formula because there are so many moving parts. But yeah, when you hit it and really lock in, there’s nothing quite like it.

This is a good a description of complexity in business as I’ve read anywhere. Just like in comedy, when you release a new product, or enter a new market, there are 35 variables, none of which you can control. Ruby’s comment is worth repeating: “It feels almost impossible to come up with a fixed formula because there are so many moving parts.”

So, like the comedian, the salesperson or the product manager or the channel manager has to deal with those 35 variables, work within them, try stuff out, sense what’s working and continue that, abandon quickly what’s not working, and try to get the stars to align. Failure is not only an option, it’s possible and even likely in some circumstances. Business these days is not easy, but when it comes together, it’s beautiful, not least because it’s so difficult to do well, like a comedy act or “The Tonight Show.”

Now, did you ever hear the one about the traveling salesman…?

Duncan Watts: “If it’s too big to fail, it’s too big”

Wednesday, May 20th, 2009

(Funny, my wife made this point months ago.)

Duncan Watts, principal research scientist at Yahoo Research and expert on human networks and complexity, makes this point in the June Harvard Business Review (”Too Big To Fail? How About Too Big To Exist?“).

Watts looks at the financial market as a complex system and compares it to another complex system: the power grid. As an outage in one power plant can cascade and cause outages regionally, so the financial system failures (such as the collapse of Lehman Brothers) cascaded to a general meltdown in credit and prompted unprecedented governmental intervention.

He points out that in a complex system, the actors (financial firms, power system components) affect each other to the point that one’s own risk profile can change dramatically depending on what happens to others. Meaning, your risk department’s calculations are dependent on assuming the other guy is stable and rational (risky assumptions those are).

Government coming in after a disaster and resuscitating the surviving firms is one approach. A better approach, according to Watts, is to make certain that each actor is small enough that its failure has a limited effect on the other actors.

This discussion reminded me of the evolution of robustness in computer systems in the past thirty years. In the 1980’s, the best way to achieve robustness was to build a huge computer with redundant components and very complex software. Such computers were protected in military-style data centers with concrete walls and fire suppression systems. In case a piece of the computer failed, the software helped the machine use other pieces to continue operating. Tandem (now part of HP) was the market leader here.

Of course, relying on one huge computer (too big to fail) exposed you to lots of other risks. For example, what if the power went off? What if there was a localized weather disaster? etc. There were limits to the “too big to fail” computer architecture–exposed most notably in the 9/11 disaster, where reliance on Lower Manhattan data centers put the stock markets and other financial markets on hold for days till their data services could be relocated.

Another approach to computer redundancy was created in the internet space, perfected by Google. Rather than having one or two huge servers with complex software managing redundant everything, Google has created a worldwide network of hundreds of thousands of small, pretty dumb servers, and software that allows transactions to be moved across those servers depending on their health. If a Google server goes down, nobody notices because its traffic is quickly spread over the remaining zillion servers that are working.

And that seems like a better model for our financial systems, too. I agree with Watts: too big to fail is too big.

Related post:
On Duncan Watts’ “Big Seed Marketing” idea

Thinking about processes as “science” and “art”

Monday, March 30th, 2009

One of my most gratifying but ultimately unsuccessful work assignments was to create an offering to open up an attractive new market segment. It was gratifying because many things went well–we developed a strong brand, quickly took up a position of authority and insight, and sold several important deals. It was unsuccessful mainly because we struggled to deliver the deals we’d won. The operations team, rather than celebrating these new wins, came to dread them. They wanted more certainty and definition–I countered that this was new stuff which we couldn’t pin down yet.

I was thinking about this experience while reading “When Should A Process Be Art, Not Science?” in the March 2009 Harvard Business Review. The authors, Joseph Hall and Eric Johnson of the Tuck School of Business, argue that while many processes benefit from a scientific, methodological approach (such as McDonald’s formula for frying burgers), other processes defy standardization and, in fact, are better off not being standardized. The authors call these “artistic” processes and cite such widely dispersed examples as the creation of a Steinway piano, auditing, and customer service. Complex sales, channel management, new business development, requirements gathering are other examples of artistic processes.

Most simply, Hall and Johnson call artistic processes those with high variability and, crucially, value of variability to customers [in this case also meaning internal customers]. In other words, a process that yields different results to a customer that wants consistency isn’t an artistic process, it’s a mess.

The “artistic process” argument parallels the definition of the Cynefin framework, defined in Kurtz & Snowden’s paper “The New Dynamics of Strategy: Sensemaking in a Complex and Complicated World” and discussed in Snowden and Boone’s 2007 HBR article, “A Leader’s Framework For Decisionmaking.” The scientific processes defined by Hall and Johnson fit into Cynefin’s Known or Simple domain, while the artistic processes sit in the Complex domain.

Six Sigma adherents would claim that the segmentation of processes into scientific and artistic subsets merely excuses obstinate “artists” who don’t wish to constrain their freedom by submitting to any defined process (salespeople and sales managers are frequent targets of this accusation). Helpfully, Hall and Johnson discuss how they would propose measuring artistic processes–by harnessing customer feedback. They write:

An artistic process has to rely on external measures of success. Artists need continual exposure to customer feedback, which prevents them from constructing their own idiosyncratic notion of quality. Sometimes this feedback must come from a broad swath of customers. For example, medical professionals obviously have to work closely with all afflicted patients to diagnose and treat complex diseases – to obtain a complete picture of their symptoms and track their reactions to remedies. With other processes, including those used to product Steinway’s high-end pianos, feedback from a select group of customers can suffice.

Meaning: to check how you’re doing on non-mechanized processes, it is necessary to query the customers of the process and draw conclusions from their feedback about the process’ effectiveness. This means getting deeper feedback than we are accustomed to. For sales, it means not only tracking that a deal was won or lost, but why it was won or lost, and what could/should have been done differently, in the customers’ eyes. A lot of the work I’ve been doing in the past year has focused on this–measuring how a company is doing in telesales, or customer service, or account management by gathering customer stories and finding patterns in them revealing what customers value, or deep issues they have. [Now I have more help to describe the value of this work!]

Back to my new-business assignment. In looking back on that experience, one serious issue we had was the collision of artistic processes (marketing, sales, solution development) and scientific ones (operations, call center management, etc.). What seemed at the time to be misunderstanding or lack of teamwork may have been a poorly defined interface between the artistic processes of innovation and business development and the scientific ones required to deliver value to real customers.

Related posts:
Buyers, tell companies why they lost your business
Leaders need to manage complexity

(Photo by a hundred visions and revisions via Flickr Creative Commons)

“Discovery-Driven Growth” – a vital handbook for developing new business

Wednesday, March 18th, 2009

There’s nothing more fun for me than building a new business, whether it’s a startup or a new line of business within an existing company. New businesses are the life-energy of capitalism, the “creative” part of “creative destruction.”

But like most everyone who’s been involved in starting up new ventures, I’ve endured my share of missed opportunities, strikeouts and horror stories. Anyone who’s been in this business for a while knows there are no sure things.

As a result, it’s been gratifying to tune into the developing literature in managing innovation, whether it’s finding out how P&G does it, how to brainstorm more effectively, or the ever-expanding Clayton Christensen library. And I’ve been eagerly awaiting “Discovery-Driven Growth: A Breakthrough Process to Reduce Risk and Seize Opportunity” by Rita McGrath and Ian MacMillan.

McGrath’s article “The Value Captor’s Process” was one of my favorite of 2007. And in “DDG” she and MacMillan describe an entire new-business development process using the same pragmatic thinking.

The book, in page after page, honors innovation as a complex process. Failure of new projects is not a result of poor planning; it is a necessary component of the uncertainty of interplaying markets, technologies, customers and competitors. The tragedy of innovation is not failure, it’s risking huge investments and careers before the business learns whether the potential in the venture can ever be realized.

The typical tools of managing innovation have been detailed, upfront planning, hurdle rates and stage gates. By contrast, McGrath and MacMillan propose an iterative prototyping approach to managing innovation: do as much on paper as possible before “breaking ground” on expensive investments; document assumptions and launch inexpensive experiments to validate or refute them; create rough financial models and refine continuously; monitor progress frequently; compare results to expectations; disengage when the likely outcome of the project falls short of alternative uses of capital and resources; extract value from the results of failed projects.

The managers who use the DDG prescription speak almost like acolytes of the Cynefin framework:

As we go into markets where we’re trying to learn, what we’ve gotten better at is trying small things. If they are successful, we spread them like wildfire. If not, we kill them quickly. (p. 162)

And, in the spirit of “giving it away,” McGrath and MacMillan offer all the tools they reference in the book on their website. This lifts “Discovery-Driven Growth” above the level of a book and into the realm of a vital business resource.

Using this approach may not increase the yield of ideas to successful initiatives–as McGrath and MacMillan write, “remember the plan was uncertain to begin with, so there can be no question of failure–how can you fail if you had no idea of the outcome to start with?” But it is certain that a company that dedicates itself to implementing “Discovery-Driven Growth”’s ideas will improve the yield–possibly dramatically–of profit to investment for new ventures.

Customers are talking: turning points in telephone sales calls

Monday, February 2nd, 2009

I’m working on a project to listen to telephone sales calls and help the client find patterns explaining why some calls end up in a sale and others don’t. Each call is a story, complete with emotion, conflict, and turning points. Listening to dozens of these, pictures begin to emerge of how people buy, and how, even when they like the product and may want to buy, don’t. And it has nothing to do with logic.

One turning point I’ve experienced is the moment when a call turns from being headed to a close, to not. On the calls, it’s very subtle: a pause, a change of subject, perhaps an additional question from the prospect. But afterward, a call that seemed to be heading toward a sale instead is, at best, a promise to call back.

The best way to explain it is to relate a personal story.

I’ve been a customer of Verizon Wireless for more than five years. I got a telemarketing call from them today, offering inducements to renew my service contract early. I’ve been evaluating this for a while now (this is a subtext of my posts on the Blackberry Storm), and after discussing it at some length with my wife, we’re headed toward renewal.

This call, then, could have been Verizon’s way of closing the deal. I was pretty ready, although I was thinking of doing this in March. If the deal was good enough, perhaps I would pull the trigger today. The call went something like this:

“Mr. Caddell,” the rep said, “we are offering some extras today if you want to renew your contract early. You might be able to get a discount on a new phone.”

“When does my contract expire?”

“The end of July.”

“I thought it was the end of March.”

(turning point 1) “That’s the time when you are eligible for an early equipment upgrade. Your contract expires in July.”

“OK, what are you offering?”

(turning point 2) “100 extra minutes per month.” (This wasn’t attractive to me at all. We don’t use the minutes we have now.)

“How much off the phone?”

(turning point 3) “Well, you’ll be eligible for that at the end of March.”

“Earlier you said I could get a discount off a phone.” (I didn’t tell her that Verizon had already sent me two mailings offering me phone discounts for renewing now.)

“I said you might be.”

There was no way was I going to renew then. At each turning point, in fact, I became farther from renewing than I had been before the call. Instead of feeling happy, encouraged, eager to get a new phone, I felt frustrated, annoyed, and that I had wasted time even picking up the phone.

It wasn’t the rep’s fault. She was given a difficult product to sell (competing, in fact, with the company’s own mailings). When I began to ask pointed questions, the pitch fell apart. There was probably no rep on earth who could have closed me with that offer.

Which is a significant learning from this project for me. Selecting and training reps is only a part of the formula for success in telesales. The product must be useful, and the offer must be made attractive. And that work happens far outside the call center.

Grounded qualification: an emergent approach to assessing sales positioning

Wednesday, January 14th, 2009


For the past eight years, I’ve worked with helping midsized IT companies sell their products into a maturing telecom market. This is so different from the earlier times of unbounded growth that it doesn’t even feel like the same industry anymore.

In the old days (i.e., before 2000), there were so many new telecom companies sprouting up that a company did not have to be a leader to be successful. They just had to be good enough.

Today, telecom vendors circle prospects like hungry dogs around a restaurant dumpster. The biggest and strongest elbow their way to the front, and the midsize guys try to keep from starving.

Some midsize guys do survive, though. They have enough of the right kind of customers, and gain enough new customers to keep making profits. How? The only way is to be very careful in planning and deploying their limited sales resources. Which gets down to a question of qualification.

In a B2B world, companies narrow down their range of prospects by deciding which sales opportunities they wish to pursue and which they don’t. This process is called qualification. Strong sales organizations that I’ve seen are really good at qualification, and poor ones are really bad at it. Successful midsized companies have to be good at it, because they don’t have enough resources to compete on all fronts and win. Stretching out their resources by definition is a failing strategy.

Good qualification means that you deploy your sales resources on opportunities that are large enough, profitable enough and winnable enough. In a virtuous circle, deploying lots of resources on good opportunities means that you have more likelihood of winning those opportunities compared to a company that spreads its resources over both good and “bad” opportunities.

One sales qualification methodology I’m familiar with segments the process into the following categories: “is there an opportunity?” “is it worth pursuing?” “can we compete?” and “can we win?” The first two categories are based on objective data–i.e., the company size, defined project budget, identified executive sponsor, etc. The final two are almost entirely subjective–are we positioned well? are our allies powerful? etc.

The challenge for midsized companies is that the subjective answers to the final two categories can make the difference between an opportunity worth pursuing and one to no-bid. Most salespeople, in my experience, hate turning down opportunities and so have an unconscious bias toward over-rating the subjective categories, resulting in lots of weak pursuits rather than a few, well-chosen, strong pursuits.

As a different approach, is it possible to create some criteria that are more observable and objective that nonetheless help answer the “can we compete?” and “can we win?” questions?

I propose the answer is yes, and we can call these items “grounded” qualification criteria. (Grounded theory, from the Wikipedia definition, is “a systematic qualitative research methodology in the social sciences emphasizing generation of theory from data in the process of conducting research.”)

What I’m trying to say is this: when a company wins an opportunity, there are reasons why–they may be emotional, logical, cultural. Similarly in a loss. The company can use grounded theory methods to gather winning and losing examples, to sort them out and generate from them several insights as to signals of potential wins and losses. Those signals can then be used as part of the qualification of new opportunities.

By way of example, a former employer of mine had a product that was functionally adequate but which was built on a technology architecture that had fallen out of fashion. It had few references. Not surprisingly, most of our sales pursuits were failures. Yet the company made several strategic sales of this product. (As a middle manager, I was surprised by these wins.) If we’d deeply examined those wins and compared them to our losses, grounded theory would have helped us understand that the company’s executives were very well connected to certain telecom ventures, and those connections were vital to our winning that business. Knowing this, we could have planned and evaluated opportunities based on our executives’ connections, and possibly found more strategic wins (at minimum, we could have spent less time on sure losers).

Doing a grounded theory assessment means deeply understanding why companies that bought your product did so, and why those that didn’t made that decision. (See an earlier post on the value of detailed prospect loss reviews.)

It’s important to point out that competitive and market positioning is a complex system (per the Cynefin Framework), and therefore positions and qualification rules will shift over time. The grounded evaluation is therefore something that needs to be updated continuously.

One of the benefits of grounded theory is that it can generate new and unexpected areas of opportunity and unveil hidden dangers. Midsized companies need to “rifle shoot” opportunities and put sufficient resources into the very best opportunities in order to be successful. Grounded qualification is a potentially important tool in these companies’ arsenals.

(Acknowledgement to Cynthia Kurtz for first exposing me to grounded theory.)