A team has been working on a new idea. A product or service that will no doubt be of great value to customers and will shift the revenue trajectory of the business! They’ve developed some customer personas, mapped out the value propositions that might exist, maybe even worked out a complete business model canvas. The idea has matured into a collection of theories about getting customers to see so much value, they are willing to part with hard-earned dollars for the product. Which theory to test first? The team says technology is the riskiest assumption and wants to prototype right away. This should raise a red flag:
Technical feasibility is NEVER the biggest risk
I repeatedly see product teams gravitate to testing the technology first. It is a red-flag, an anti-pattern in good product development practice, and you should set is as a trigger in your mind: when technical feasibility is proposed as the biggest risk to test in early stages, pause and step-back. Technical feasibility-first is essentially saying ‘we don’t know what the market wants but we’re going to start building right away.’ I’ll clarify why this is bad, explore why teams still propose it, then offer a mantra you can use to redirect efforts towards the true riskiest assumptions about your new product.
There are three reasons why you shouldn’t start with technical feasibility.
One: technology is not the most risky assumption
Walk back through all the theories about this product and its business model and review your assumptions. In my experience — the team typicaly acknowledges other assumptions outside of technology, but either holds them as equivalent to the tech working, or they have failed to elaborate on a dynamic that is far more catastrophic to the product’s chances of success.
Two: technical feasibility is not the cheapest thing to test
Developers are some of the most expensive resources applied to product creation and they are often booked on important projects and enhancements for existing product. Why start with the step that has a high labor and opportunity cost? “But it’s just a two-day architecture spike for a few developers”. It’s never two days, but that’s besides the point. Especially early in product development, there are market tests that require less time than prototyping.
Three: technology is in the solution space, and at early stages you are trying to refine your understanding of the problem space.
Why solve a problem that you haven’t confirmed is a problem customers will pay for? In the early stages of product development, efforts should be focused on exercises that build an understanding of the problem. Otherwise, you are building on shifting sand. “But what if it doesn’t work?” is just a chance to re-direct the team: “Yes, what if the market doesn’t work the way you think?” Finally — let us not forget that good developers are some of the smartest and most industrious people on the team — they thrive on hard problems and excel at finding ways to make things work, or reporting on the technical trade-offs for doing so. Leverage that competency when you know more about what is needed by your customers.
So why do teams do this?
One: they don’t have a pathway ‘out of the building’
Work on product-market fit happens ‘outside of the building’. It is not developed on a whiteboard in some glass tower. But sometimes teams don’t have a pathway out. This is most often about the experience of the team. Perhaps they are limited in their industry or customer contacts. Maybe they don’t have experience with tools like interviews, surveys, lead-generation and marketing tests. Diagnosing the capacity of the team will identify solutions. Introduce them to current customers, get them to industry events, integrate them in business development activities… throw them in the deep end.
Sometimes the pathway is blocked — another group or function owns the market interaction. Perhaps marketing thinks of this as market research, or business development insists on all customer and market contact. Incorporate this group in your efforts. While you may be surprised how far a little collaboration can get you, that collaboration also lets you build the ‘plausible deniability’ you are going to need if the strategy becomes one of ‘forgiveness, not permission’. Above all, do whatever it takes to get real market interaction.
Two: musing on the solution is more fun
The solution-side of the problem is typically a very creative activity. Especially if the prototyping involves wire-framing and user experience. It’s also easier for the team, because it can be done in a vacuum. Before you know it, they are daydreaming about “what would land us on the cover of Fast Company? and what would our cover story tagline be?” Begin at the end right? Wrong. You don’t know enough about the problem at this stage to waste any time thinking about the end.
This can be a symptom that the product development team is tilted towards build staff, and they are just going to what they know and excel at. You can adjust the team composition, or you can train that build staff in product development methods.
Three: their financial backers value feasibility
While less common as a reason for technology-first, it can be the case that the financial backers of the product value a prototype in the early stages as a precondition for further funding. This is suggestive of the same types of issues and misunderstandings, just at a different level. Resolution is a longer game of training those backers on what to value — real market validation and feedback.
My grandfather was fond of saying “The only absolute in life is there are no absolutes!” Despite that sage advice — I encourage teams to ALWAYS reject technical feasibility as the biggest risk to test at early stages. This makes decisions clear and avoids the impression that their project is the one exception. Ultimately this is not about whether an exception exists, it is about which muscle you must develop in product development — the market validation muscle. Do you need better build and prototyping skills? No, you need better market validation skills. That is the muscle you are developing when you turn down technical feasibility early. Fitness there pays long-term dividends.
You. Yeah, you — in the mature business. Things are working great, but you’ve got a sense that the past isn’t a guaranteed representation of the future. You want to explore future growth opportunities, but you can’t do it alone. You need to compel your business with the same desire. The S-Curve exercise is a simple, effective means of drawing a wide set of the business into this conversation.
The s-curve describes a common trajectory of the mature business. Initial adoption of a product or service is slow but steady as the business builds up product-market fit and momentum. Eventually (one hopes) the business is able to scale that business model, move on from early adopters to a broad customer base, and build revenue quickly through a growth stage. But eventually, that rapid growth starts to stall. Market saturation or segmentation, aging product offering, and/or evolving customer demands all put pressure on the growth rate of the core business.
The successful long-term business reinvests in itself, developing new products and new business models, and repeats the cycle. And the unsuccessful? They take a trip down the backside of the curve like a rollercoaster (except that no one has their hands in the air saying ‘wheee!’)
The s-curve exercise is a simple way to engage a cross-section of the business on where their mature company is at on the curve, and what they could do about it. I use this simplified diagram:
Explain the basic dynamics of the s-curve. Even for business executives steeped in growth metrics, it is good to reiterate the phases:
- A segment early in the curve representing core business model investment (pre- point 1),
- A growth segment where the core offering scales and gains market share (1–4),
- A period where it becomes harder and more expensive to grow at the same rate (4–6),
- A backside of the curve ( 8–9) and a new business model offering (7).
Then, ask the group by show of hands to identify where the business is on the curve. “Raise your hand if it’s at point #1… #2… “). Be sure to remind them there’s no right or wrong answer. At the conclusion of the polling, ask folks to explain their choice. Start with ‘why’ they chose that point. Also, don’t ignore outliers. If the group consensus was #4, and one person said #1 — ask them to explain why, and validate their perspective. Finally, transition the conversation to the following questions:
- What are we doing about this?
- What should we be doing about this?
Expect a wide range of responses, all of which generate a good starting point for an innovation conversation. When I put this in front of a CEO of a half-billion dollar business, he correctly pointed out the significant new revenue the business was winning with it’s core model. #6, #8, and #9 were clearly out. He asked me how he would tell between #3 – #5. Everything about the successful mature business is pre-disposed to think it is earlier on the curve. How could I engage a leader who knew more about the industry and business than I will ever know?
When the answers trends to the early side of the curve, ask about sales pipeline. Are future customers for the core business identified? Are their needs the same as the growth customers (or will they require major investment to win over?), and are there enough of them? This leads to great conversations about sustaining vs. breakthrough/disruptive innovations.
Typically, I’ve found that business development roles tend towards earlier positions on the curve, and internal roles tend towards later positions. This is a function of focus — business development is tuned to new customers and growth, internal folks are tuned to serve existing customers and market. The temptation for both is to focus on sustaining innovations — things that empower the core business model. Use this in the conversation — highlight new business models, yet acknowledge that there are several types of innovation investment all of which are needed for the business.
The s-curve exercise is a level-playing field for any role in the business; it’s easy enough to explain to roles not caught up in sales forecasts and P&L, yet familiar and legitimate to executives. It’s a great way to start the conversation about innovation and investing in the future.
The s-curve has applications in other contexts where the motivation to find the next ‘leap forward’ is key. Whitney Johnson describes an application to personal career, and others have reported using this to inspire and reason about new development methodologies for software delivery.
Great suggestions for learning on this list of 37 learning sites. But the winner for me was in the comments - CodeCombat.com. I don't know how it will address advanced topics (I'm only a few missions in) but the adventure video game style is perfect for teaching basics to my middle schooler's Lego robotics team.
I've stared reading Lean Enterprise at the recommendation of a friend. These books tend to produce a bit of head-knodding and the occasional 'Amen'! I'll admit it, they are preaching to the choir on this topic. But after the introduction and first chapter I'm intrigued. The focus is how high-performing, agile/lean teams can empower innovation.
A key premise of the book is that the flexibility of software can accelerate the innovation cycle, and that people, in the form of high-performing agile teams, are a competitive advantage. I'm glad to see the second point - People. Too often these missives focus on process over people.
Earlier this year, I had to chance to visit the folks at the Australian Medical Council (AMC)*. They are a great case for what a small group of incredibly talented and motivated people can achieve. We had toured their state-of-the-art clinical medical testing facility, reviewed their progress for sharing expertise with other global medical boards, and saw how efforts to digitize and streamline the credentialing process were improving public health protection.
I had the chance to ask Ian Frank, their CEO, what was the secret to his (relatively small) organization's big impact. I was expecting some blend of 'navigating the politics of medicine, securing funding, strategic planning... ' Instead, he said this:
"Hire a small group of absolutely wicked smart generalists - find people with broad and complimentary skills over specific expertise. Then give them all the support and responsibility to move mountains, and expect them, hold them accountable, to succeed." **
Aptitude and breadth of experience trumped specific expertise every time. People quite frankly trump process and create in the AMC an organization that punches way above its weight class.
Can the large enterprise replicate this 'people factor'? People don't scale (and in fact - that's what process is for). Enterprises can be good at process - scaling. But the small organization (read: startups) will always have ability to create the advantage on people. We'll see what the authors of Lean Enterprise say as I read the rest of the book.
In the meantime, I'm continuing to lean toward Ian Frank's advice. Hire the absolute best people, favor polymaths and generalists, and value the intellectual and emotional aptitude for success. If you're interested in why you should *be* a polymath for tomorrow's economy, you'll find a recent TechCrunch article interesting.
* the AMC are a client and partner of my current employer Pearson VUE
** Not his precise quote of course, but to the best of my memory his words and spirit.
The future is impossible to predict. Readiness is as much about resiliency as it is about strategy. Exploring the future with a cross-section of your business is one way to improve resilience. Teams discuss business challenges while learning about the perspectives of other disciplines in their business and forging relationships with other leaders.
Recently two colleagues and I were asked to lead our Global Leadership Team in a 'Future of the Business' exploration (hat-tip to Jason and Martyn). We invented this gamestorming exercise and it worked so well I thought I'd add it to the webosphere.
Gamestorming is brainstorming tools for "innovators, rule-breakers, and changemakers". We wanted a game that let participants explore the future challenges and not just runaway success or business as usual. And we wanted to provide practical experience for participants in 'storytelling' the business. Storytelling as effective business communication is highly valued in our organization and many of us are reading and improving our storytelling as communication technique.
This gamestorm includes a twist guaranteed to force teams to grapple with business challenge and disruption!
Title: Back to the Future / Back to the Board
Set-Up: It's five years in the future. Your team is the executive leadership of Company X. You are presenting on the last five years of progress to the board of Company X, and how you've gotten Company X to where it is today. Who are our key customers? what is new to our business? what were the big surprises? what changes hit us (and did we see them coming)?
Prepare your summary to the board in the form of business stories - no presentation slides allowed!
Your story should touch on three aspects of the Company X business:
You should not just focus on positive outcomes - the strongest stories will openly address negative challenges and how they were weathered or overcome.
Execution: Rough timing guidelines - 10 minutes of instruction, 75 minutes of gamestorming, 6 minutes of story time per team at the end. We gave prizes for best success story, best comeback from failure, and best storytelling. These are optional.
The Twist: 20 minutes into the gamestorm - play is interrupted and each team is given two Black Swans. Black swans are unexpected events of business disruption. The team is instructed to incorporate at least ONE of the black swans into their story. The team has NOT been instructed about black swans or any disruption to the game at this point. Example black swans include:
- a competitor buys a key business partner
- several members of leadership depart to start their own company
- government or charitable foundations announce major investments that threaten or empower company X
- a major client departs
The intent is to force teams to grapple with challenges they hadn't anticipated. Two swans are provided to give teams some flexibility ensuring the exercise isn't so hard teamplay crumbles.
- Develop swans relevant to your business - be specific, be tough, think about the things your business is actually worried about. Our swans were specific enough that they can't be repeated here!
- Balances swans - organize the swans in pairs that don't overlap. The swans will impact clients, people, finance, competitors. Give each team two distinctly different swans. If one swan greatly impacts a customer, make the other swan about an internal factor.
- Facilitator float - teams need to gel in the first 15-20 minutes. We found that by floating around to teams we could answer questions about the game, and aid the teams in getting on a track before the swans were introduced.
- Make storytelling the focus - we didn't express this enough in our instructions, but feedback after pointed out that the best way to get teams rolling is to get them to focus on the main element of a story - the conflict. Everything falls into place around that.
Storytelling 101: A slide deck we used to introduce the game can be found here. The intro concludes on the key piece of advice for the game. Teams should focus on determining the conflict first. "Throw life out of balance". Teams that start here will find discussion on how they got to the conflict (the build up) and how they resolved it (the resoluton ) will be much easier to reason about.
Our Experience: This gamestorm worked extremely well. All teams were able to incorporate a swan, and many of them actually found swans that fit and stretched them to expand their analysis and story. Tough topics were addressed in almost all team stories - the exercise rewards grappling with the hard stuff, and thinking about what the outcomes could be.
Surely you've heard this joke:
- Wikipedia: "I know everything!"
- Google: "I have everything!"
- Facebook: "I know everybody!"
- Internet: "Without me you are nothing!"
- Electricity: "Keep talking, bitches"
LinkedIn is electricity.
Content providers may know everything, learning platforms may teach everything, assessors may confirm learners learned what was taught, and credential-ers (both traditional and digital like Parchment and YourAcclaim.com) may communicate that learning. But keep talking... it is LinkedIn's skill-to-job, and employee-employer ecosystem in the end.
This was recently hammered home in a talk given by Dan Shapero - LinkedIn’s head of career products. LinkedIn’s mission is pretty clear. But for those folks still scratching their heads over endorsements, or thinking the LinkedIn mission is just about being the best networking database, let me enlighten.
LinkedIn wants to create the world’s *economic graph* - where they link every person, every company, and every piece of professional knowledge. They believe access to the best talent is the main catalyst for economic growth, and they intend to help the individual grow their career. Absorb all the data, interpret it, define the relationships.
LinkedIn is in a renewed push for data acquisition, data they hope to use to make money by helping individuals grow their skills, and by helping companies find those skilled individuals. What could they do with this data?
Consider credentials and credential valuation. There are two related outcomes LinkedIn is capable of driving. First essentially, anything and everything can be a credential. And second, the dollar value of a credential in the economic graph CAN BE KNOWN. In the past, recognized brands following established standards for achievement created credentials. The University of Minnesota gave an A for a Project Management class, the State of Minnesota trade regulatory board issued a credential linked to apprenticeship, an IT company like Microsoft or Cisco gave a professional credential for passing a series of tests. One of the barriers to entry for alternative or smaller credentials, is how they obtain value in the marketplace if they aren’t known.
LinkedIn plans to determine that value, and in doing so not only tell you what you stand to gain economically by investing time and money in that IT certification, but also providing economic feedback on a whole host of other ‘non-traditional’ credentials you could earn. My guess is that they only have to obtain salary data to start drawing interesting conclusions about how credentials of all kinds relate to career growth and opportunity, and therefore the market value of that credential. Credentials could be anything, and everything, and the most valuable ones will win. LinkedIn is Electricity.
LinkedIn doesn’t believe credentials alone will define the individual. Starting at 1:07, Dan provides his five-year look into the future, which could be interpreted as a LinkedIn roadmap. Among the more interesting ideas…
Reputation matters. Credentials (again, of all kinds) won't be the only factor - reputation will be a key component. LinkedIn is interested in online reputation and will also improve it's own understanding of your reputation. Endorsed for iOS skills? Great. But endorsed by a recognized expert in iOS, even better. Dan proposed several ways in which this could work, so while we don't know how they plan to implement, they believe it will be a key factor. Their professional network graph and endorsements gives them a huge start.
Proof-of-work will count for something. Portfolios, writing samples, work examples that can be shared and discussed... these will form another part of the employability picture for an individual. “I could tell you about my achievements, or I could show you what I can do as well.”
Dan Shapero, LinkedIn's Head of Career Products, gave the opening keynote two weeks ago at ATP. ATP is the largest testing industry conference for professional assessment and certification. The keynote was provocative, challenging, and very interesting.
I think the keynote says a lot about how LinkedIn views itself, and where it's going. I'll digest in a future post, but folks wanting to review the source material can find it online here.
Skip to 40:24 to avoid ATP administrivia and jump right into the kick-off.
Wired magazine has a nice piece on skating pioneer-turned-creativity hacker Rodney Mullen. As a kid I wanted to skate like him. Though obviously not enough to get over the novice hurdle.
'Innovation happens at the intersection of disciplines'
-- Krisztina Holly, entrepreneur-in-residence, Los Angeles Mayor's Office
A summation that explains his new career as thought-instigator and is a reminder about why we are fascinated and inspired by masters of one discipline capable of supplying inspiration to another.
Last year, I spent some time with one of our largest customers talking about Big Data. Leaders there have deep ties to statistical analysis and research methods. They were after all, part of the psychometric old guard that had invented and refined some of the most advanced aspects of high-stakes testing.
Big Data hype would lead you to believe it would cure cancer and create world peace. They were not falling for it.
At issue was the idea that your data could tell you something. 'Data doesn’t tell you anything, you have to ask it questions, you have to have a theory you are testing.’ I don't disagree with this science and statistical fundamental, and I certainly feel that the hype cycle on big data is nearing historical proportions.
One thing we agreed on was that often data can be explored to develop interesting questions to ask. The recent "Ballghazi” scandal involving under-inflated footballs provides an interesting example of how this might happen.
Football data analyst Warren Sharp delivered a thought-provoking analysis of NFL Patriots performance on one game aspect that might have relation to football inflation: fumble turnover prevention. In a nutshell, the Patriots are better at this game statistic than any other team. But not just better as in top-of-the-bell-curve, better as in ‘off-the-bell-curve', literally off-the-chart.
Take a few minutes to read, and you’ll not only be treated to an interesting spelunking of NFL data, but you can see how data can be explored to identify interesting topics to further theorize about. Develop a theory for WHY the patriots are so good at this statistic, and you can go and test that. Against the data.
UPDATE: There is a detailed critique of the original data analysis here. Further reminders about data interpretation, and not over-reaching on theories.
Simplify. The effect is powerful.