Why Amazon wins | Innovate the core, innovate to transform

Take a look at your organization’s innovation projects. Are you strategically balancing your efforts between the core business and future growth areas?

In advising companies about innovation, an area I stress is the value of consciously pursuing both little and BIG innovation. Often, companies pursuing innovation can be categorized as either:

  • Seeking small innovations such as operational improvements and existing product enhancements
  • Swinging for the fences to find unique, breakthrough ideas in new markets

In both cases, such myopia limits the value of innovation, focusing too low (incrementalism) or too high (core business withers from neglect). Companies do well to take a holistic look at their innovation efforts, understanding their portfolio of initiatives in the context of both small and big projects.

To understand the value of this approach, let’s examine Amazon’s innovation portfolio. Why? Their efforts are visible, and they’ve got the results to back up their innovation approach:

Amazon stock vs S&P 500

Amazon’s stock performance is no fluke. They’ve done a fabulous job mixing their innovation efforts. They have significantly improved their core, while moving into adjacent and unfamiliar ground.

 

Segmenting Innovation Efforts

To begin with, it’s useful to divide innovation efforts according to their level of familiarity:

Segmenting innovation efforts by familiarity

 

Why use familiarity as the basis of segmenting? Because familiarity manifests itself in two ways when it comes to innovation:

Institutional advantage: Companies are awash in information about current operations. Product or service features, with their strengths and weaknesses, are understood. How they’re delivered is understood. What customers like and what they want to see improved…are understood. This data rich environment makes it easier to focus on what is done today.

Personal advantage: Expertise in a given realm is why you hold your position. Executing on various initiatives related to current operations is how you’re assessed. Keeping your focus on that is a natural outcome, but one that can stymie interest in exploring new areas.

Moving outside the realm of familiarity is tough. Yet that’s where growth for the company will be found. So it takes a conscious effort to step outside the world we know, assess opportunities and try new things. Much of the organization is not geared to do this.

The matrix above characterizes innovation efforts as three types:

  1. Sustain: Innovations that maintain and even grow the core business. While this type of innovation is often dismissed as inconsequential, that;s not true at all. We’ll see that in a moment with amazon.
  2. Expand: Innovations that are adjacent to an organization’s current operations. That can be extending current offerings into new markets, or introducing new products and services to existing customers.
  3. Transform: Innovations here have the effect of changing the identity of the company. To be successful in these new markets, the new offerings are substantially better at satisfying existing jobs-to-be-done for customers.

To show the diversity and power of innovations representing each of these types, let’s look at how Amazon’s innovation have fit this model.

 

Amazon Innovations by Type

One of the more valuable aspects of the Amazon story is that it highlights the value of innovating for core operations as well as entering new arenas. The matrix below maps several Amazon innovations against the different types:

Amazon innovation - segmented by levels of familiarity

 

Sustain

Think sustaining innovation can’t make a difference? Then check out Amazon’s results here.

Collaborative filtering recommendations are those suggested products that display when you’re viewing an item. Collaborative filtering is based on product categories and “people who bought this also bought that”. At one point in Amazon’s history, its recommendation engine was responsible for 35% of company revenues.

1-click checkout is Amazon’s patented innovation that dramatically reduce friction in the buying process. With a single click, your purchase is on its way. This is a significant improvement over the multi-field (even multi-page) process that other sites provided to purchase.

Drone delivery is a futuristic idea: have drones deliver goods within 30 minutes. Bypass the traffic on the roads. Even if it is of dubious potential, it’s a good example where sustaining innovations can be “radical” in the popular sense.

Prime originated as a way for customers to pay a single price for all-you-can-buy shipping. Customers were already paying for shipping on a per order basis; Prime made the shipping a single price, no matter how many transactions. As a sustaining innovation, it has delivered impressive results. Prime members are estimated to spend $1,500 per year vs. $625 for non-Prime customers.

Expand

The two flavors of expansion innovation vary in emphasis: new products and services vs. new customers. But they both are cases of extending what the company already does. The familiarity measure is less, but still meaningful.

Affiliate marketing is a normal web practice now. But Amazon was an early innovator here, although not the first. Through affiliate marketing, Amazon reached new customers with its existing offerings.

Kindle is Amazon’s reading tablet. People who own Kindles download the digital books, and are able to search text and annotate passages. Kindle was estimated to account for 10% of Amazon’s sales in 2012.

Amazon Fresh is a home delivery service for groceries. More than a decade after the failure of Webvan, Amazon is seeking to deliver a new category to customers: perishable groceries. This extends the offerings of Amazon’s existing retail selection, for both existing and new customers.

Transform

Amazon’s growth has included investing in transformative innovation, beyond its initial core business. The risk here is much higher, as the company moves beyond the familiarity of its core business and takes on new competitors.

Amazon Web Services offers cloud computing services to companies. Its origins come out of Amazon’s work to optimize its internal cloud operations. Jeff Bezos decided to turn that work into a new offering. Introduced in 2006, AWS has been a tremendous success. In the 2nd quarter of 2015, AWS generated a profit of $391 million on $1.82 billion in revenue. Even the super secret CIA is using AWS.

Prime is now Amazon’s vehicle for delivering original content programming. While its roots were in making buying products easier, Prime has become a wide range of offers. One of these is Amazon’s entry into the world of original programming, Amazon Studios. Its shows include Alpha House, Betas and Bosch. Oh, and these new content customers are converting to full Prime shoppers.

Fire Phone was Amazon’s entry into the smart phone market, taking on Apple’s iPhone, Samsung’s mobile phones and others. Fire Phone’s notable feature was its 3-D “Dynamic Perspective”. However, it failed to offer anything that delivered better outcomes on people’s jobs-to-be-done. Amazon has stopped its work on the Fire Phone.

 

Amazon: What a Strategic Innovation Portfolio Looks Like

Amazon provides a powerful example of how companies should approach their innovation portfolios. Despite claims that sustaining innovations are a recipe for mediocrity, Amazon has shown there’s plenty of value innovating on your core. What aids Amazon in this case is a clear mission, a sense of what they want to accomplish and a CEO who continually acts on it. Lack of such clarity and leadership is the cause of innovation failure for other companies; it’s not “getting bogged down in incremental innovation”. Note that Google also dedicates significant innovation effort towards its core business.

In the Transform quadrant, Amazon takes on bigger risks. This is the harder area. AWS has turned out to be a hit. Fire Phone was a failure. But the key is (i) understanding the risks of that quadrant; and (ii) making sure efforts there are part of a larger portfolio approach across different levels of familiarity.

What’s the right mix? That will vary by company and the health of its core business. The key is to understand why you’re making the innovation investments you are.

I’m @bhc3 on Twitter.

Avoiding innovation errors through jobs-to-be-done analysis

The lean startup movement was developed to address an issue that bedeviled many entrepreneurs: how to introduce something new without blowing all your capital and time on the wrong offering. The premise is that someone has a vision for a new thing, and needs to iteratively test that vision (“fail fast”) to find product-market fit. It’s been a success as an innovation theory, and has penetrated the corporate world as well.

In a recent post, Mike Boysen takes issue with the fail fast approach. He argues that better understanding of customers’ jobs-to-be-done (i.e. what someone is trying to get done, regardless of solutions are used) at the front-end is superior to guessing continuously about whether something will be adopted by the market. To quote:

How many hypotheses does it take until you get it right? Is there any guarantee that you started in the right Universe? Can you quantify the value of your idea?

How many times does it take to win the lottery?

Mike advocates for organizations to invest more time at the front end understanding their customers’ jobs-to-be-done rather than iteratively guessing. I agree with him in principle. However, in my work with enterprises, I know that such an approach is a long way off as a standard course of action. There’s the Ideal vs. the Reality:

JTBD analysis - innovation ideal vs reality

The top process – Ideal – shows the right point to understand your target market’s jobs-to-be-done. It’s similar to what Strategyn’s Tony Ulwick outlines for outcome-driven innovation. In the Ideal flow, proper analysis has uncovered opportunities for underserved jobs-to-be-done. You then ideate ways to address the underserved outcomes. Finally, a develop-test-learn approach is valuable for identifying an optimal way to deliver the product or service.

However, here’s the Reality: most companies aren’t doing that. They don’t invest time in ongoing research to understand the jobs-to-be-done. Instead, ideas are generated in multiple ways. The bottom flow marked Reality highlights a process with more structure than most organizations actually have. Whether an organization follows all processes or not, the key is this: ideas are being generated continuously from a number of courses divorced from deep knowledge of jobs-to-be-done.

Inside-out analysis

In my experience working with large organizations, I’ve noticed that ideas tend to go through what I call “inside-out” analysis. Ideas are evaluated first on criteria that reflect the company’s own internal concerns. What’s important to us inside these four walls? Examples of such criteria:

  • Fits current plans?
  • Feasible with current assets?
  • Addresses key company goal?
  • Financials pencil out?
  • Leverages core competencies?

For operational, low level ideas inside-out analysis can work. Most of the decision parameters are knowable and the impact of a poor decision can be reversed. But as the scope of the idea increases, it’s insufficient to rely on inside-out analysis.

False positives, false negatives

Starting with the organization’s own needs first leads to two types of errors:

  • False positive: the idea matches the internal needs of the organization, with flying colors. That creates a too-quick mindset of ‘yes’ without understanding the customer perspective. This opens the door for bad ideas to be greenlighted.
  • False negative: the idea falls short on the internal criteria, or even more likely, on someone’s personal agenda. It never gets a fair hearing in terms of whether the market would value it. The idea is rejected prematurely.

In both cases, the lack of perspective about the idea’s intended beneficiaries leads to innovation errors. False positives are part of a generally rosy view about innovation. It’s good to try things out, it’s how we find our way forward. But it isn’t necessarily an objective of companies to spend money in such a pursuit. Mitigating the risk of investing limited resources in the wrong ideas is important.

In the realm of corporate innovation, false negatives are the bigger sin. They are the missed opportunities. The cases where someone actually had a bead on the future, but was snuffed out by entrenched executives, sclerotic processes or heavy-handed evaluations. Kodak, a legendary company sunk by the digital revolution, actually invented the digital camera in the 1970s. As the inventor, Steven Sasson, related to the New York Times:

“My prototype was big as a toaster, but the technical people loved it,” Mr. Sasson said. “But it was filmless photography, so management’s reaction was, ‘that’s cute — but don’t tell anyone about it.’ ”

It’s debatable whether the world was ready for digital photography at the time, as there was not yet much in the way of supporting infrastructure. But Kodak’s inside-out analysis focused on its effect on their core film business. And thus a promising idea was killed.

Start with outside-in analysis

Thus organizations find themselves with a gap in the innovation process. In the ideal world, rigor is brought to understanding the jobs-to-be-done opportunities at the front-end. In reality, much of innovation is generated without analysis of customers’ jobs beforehand. People will always continue to propose and to try out ideas on their own. Unfortunately, the easiest, most available basis of understanding the idea’s potential starts with an inside-out analysis. The gap falls between those few companies that invest in understanding customers’ jobs-to-be-done, and the majority who go right to inside-out analysis.

What’s needed is a way to bring the customers’ perspective into the process much earlier. Get that outside-in look quickly.

Three jobs-to-be-done tests

In my work with large organizations, I have been advising a switch in the process of evaluating ideas. The initial assessment of an idea should be outside-in focused. Specifically, there are three tests that any idea beyond the internal incremental level should pass:

jobs-to-be-done three tests

Each of the tests examines a critical part of the decision chain for customers.

Targets real job of enough people

The first test is actually two tests:

  1. Do people actually have the job-to-be-done that the idea intends to address?
  2. Are there enough of these people?

This is the simplest, most basic test. Most ideas should pass this, but not all. As written here previously, the Color app was developed to allow anyone – strangers, friends – within a short range to share pictures taken at a location. While a novel application of the Social Local Mobile (SoLoMo) trends, Color actually didn’t address a job-to-be-done of enough people.

A lot better than current solution

Assuming a real job-to-be-done, consideration must next be given to the incumbent solution used by the target customers. On what points does the proposed idea better satisfy the job-to-be-done than what is being done today? This should be a clear analysis. The improvement doesn’t have to be purely functional. It may better satisfy emotional needs. The key is that there is a clear understanding of how the proposed idea is better.

And not just a little better. It needs to be materially better to overcome people’s natural conservatism. Nobel Laureate Daniel Kahneman discusses two factors that drive this conservatism in his book, Thinking, Fast and Slow:

  • Endowment effect: We overvalue something we have currently over something we could get. Think of that old saying, “a bird in the hand is worth two in the bush”.
  • Uncertainty effect: Our bias shifts toward loss aversion when we consider how certain the touted benefits of something new are. The chance that something doesn’t live up to its potential looms larger in our psyche, and our aversion to loss causes to overweight the probability that something won’t live up to its potential.

In innovation, the rule-of-thumb that something needs to be ten times better than what it would replace reflects our inherent conservatism. I’ve argued that the problem with bitcoin is that it fails to substantially improve our current solutions to payments: government-issued currency.

Value exceeds cost to beneficiary

The final test is the most challenging. It requires you to walk in the shoes of your intended beneficiaries (e.g. customers). It’s an analysis of marginal benefits and costs:

Value of improvement over current solution > Incremental costs of adopting your new idea

Not the costs of the company to provide the idea, but those that are borne by the customer. These costs include monetary, learning processes, connections to other solutions, loss of existing data, etc. It’s a holistic look at tangible and intangible costs. Which admittedly, is the hardest analysis to do.

An example where the incremental costs didn’t cover the improvements is of a tire that Michelin introduced in the 1990s. The tire had a sensor and could run for 125 miles after being punctured. A sensor in the car would let the driver know about the issue. But for drivers, a daunting issue emerged: how do you get those tires fixed/replaced? They required special equipment that garages didn’t have and weren’t going to purchase. The costs of these superior tires did not outweigh the costs of not being able to get them fixed/replaced.

Recognize your points of uncertainty

While I present the three jobs-to-be-done tests as a sequential flow of yes/no decisions, in reality they are better utilized as measures of uncertainty. Think of them as gauges:

JTBD tests - certainty meters

Treat innovation as a learning activity. Develop an understanding for what’s needed to get to ‘yes’ for each of the tests. This approach is consistent with the lean startup philosophy. It provides guidance to the development of a promising idea.

Mike Boysen makes the fundamental point about understanding customers’ jobs-to-be-done to drive innovation. Use these three tests for those times when you cannot invest the time/resources at the front end to understand the opportunities.

I’m @bhc3 on Twitter.

Consumer adoption of Bitcoin | A jobs-to-be-done analysis

Venture capitalist Marc Andreessen recently did one of his tweetstorms on the topic of Bitcoin, a technology he avidly supports. In 25 tweets, he talked about criticisms people have of Bitcoin. Including this one (#18) about “use cases”:

Let’s acknowledge this: When you’re talking currency and payment systems, the use cases and relevant users are enormous. The whole planet, really, and all manner of transactions. Lots of places where Bitcoin could theoretically make an impact. In this post, I wanted to think through the consumer payment market, one of the bigger targets there is. According to the Federal Reserve, a U.S. consumer makes around 69 payments per month (pdf). Fertile ground for exploiting underserved jobs-to-be-done.

A powerful way to analyze any idea is to apply jobs-to-be-done analysis. Specifically, apply three jobs-to-be-done tests:

  1. Does the idea target an actual job-to-be-done that enough people have?
  2. Is the idea a meaningful improvement over the current way people fulfill their job-to-be-done?
  3. Does the value of the idea to customers exceed the cost of the idea to them?

In the above linked post about the three tests, I note that the output of the tests are really degrees of certainty about an idea. You can think of it as meters:

JTBD degrees of certainty

The more uncertain you are, the higher the adoption risk of the idea by your intended beneficiaries.

For this analysis, we’ll target a specific set of possible beneficiaries:

Bitcoin will become a frequently used currency for U.S. and European consumers, displacing dollars, euros and pounds.

Ok, on to the adoption analysis. Each test includes an assessment of its certainty.

#1: Targets and actual job-to-be-done of enough people

We long ago left barter behind as a primary basis for goods and services exchange. Currency offered many advantages: a way to exchange with you now, even if I don’t have a good you want; store of value of that outlasts many goods; ability to build up a supply of currency for larger purchases; immediate trust because the currency is known, as opposed to the risk of a receiving a deficient product in trade.

Currency won out over barter for many reasons. And to that end, Bitcoin addresses an actual job-to-be-done: a measure of value that can be exchanged for goods and services.

The level of certainty for this test:

Targets actual job-to-be-done
of enough people
Certainty meter- targets real JTBD enough people

 #2: Better than current solution

Any new thing has to provide better outcomes than the incumbent solution. And not just a little better. Studies show people will undervalue the benefits of a new offering, and overvalue the benefits of an existing solution. This reflects the varying degrees of the Possibility Effect and the Uncertainty Effect people have. Providing a strong improvement in outcomes is needed to overcome the inertia of the Early and Late Majorities.

Put yourself into the shoes of a typical consumer:

  • I need to pay for groceries
  • I need to pay for my Amazon purchases
  • I need to pay for school tuition
  • I need to pay for gas
  • I need to pay for rent

All these activities happen today with dollars, euros, pounds and so on. In what way does paying by Bitcoin provide better outcomes for my payment needs than the currency I use today? Among the Bitcoin improvements I’ve seen described:

Payment cannot be repudiated: The blockchain technology locks in the transmittal of the payment. There’s a full record of payment offered, payment accepted. Which is great as a record for the transaction. Except repudiation isn’t a material issue for consumers for today. They by and large don’t feel pain from it.

No centralized government control over the currency: Bitcoin is a distributed currency, with no central authority overseeing and manipulating it. The implied value is that issues like devaluation, inflation and governments tracking your spending are finally put to rest.

But stop and think about that. Who cares about these issues? Go find 10 neighbors. Ask them their level of concern that the money in their wallet is managed by a central authority. Find out what they think about the traceability of their spending. Many payment services companies actually offer traceability as a feature, not a bug (e.g. Mint, Yodlee, American Express, etc.). I haven’t heard much outcry about payment traceability among the general public.

Reduced identity theft fraud: Credit card numbers can be stolen and used by thieves. Marc Andreessen asserts that Bitcoin greatly reduces this risk. And I suspect he’s right, as far as we understand the risks today. But already, creative thieves are figuring out ways to steal Bitcoins. Innovation at its finest.

But getting better about reducing identity theft is a clear opportunity for better outcomes. Credit card companies have become quite advanced with this via big data algorithms, which spot out-of-norm transactions and flag them. Companies are also good at covering the losses resulting from payment identity theft.

Because Bitcoin is still experiencing losses due to fraud, it’s not clear in consumers’ minds that it’s less risky than current currency and payment methods.

The level of certainty for this test:

Meaningful improvement over
the current way people fulfill
the job-to-be-done
Certainty meter- meaningful improvement over current

#3: Value to consumers exceeds costs of new idea

In this test, you’re asked to look holistically at the costs of a new idea. Monetary costs, yes. But also other costs, such as:

  • Connecting the new solution to existing infrastructure
  • Loss of features you enjoy in current solution
  • Giving up the uncertainty of the current solution
  • New unwanted behaviors

Given the low (non-existent?) value of Bitcoin over current currencies, pretty much any cost will cause a high level of uncertainty for this test.

And Bitcoin has costs. Its current volatility makes it tough to rely on a consistent store of value. You receive $10,000 worth of Bitcoin today, what will that be worth in a month? There’s a learning curve for usage. You need to know how to operate a Bitcoin account, and retrain yourself to think in terms of Bitcoin values (like when you travel abroad and have to mentally calculate the local currency price into your home currency to understand what something really costs).

To the extent that economic cycles will inevitably continue, you need to get used to no central authority intervening to help stabilize things. It’s not clear what a Bitcoin-dominated world would look like in terms of economic stability. Likely, though, this uncertainty doesn’t weigh into consumers’ calculus of costs.

The level of certainty for this test:

Value to consumers exceeds
the cost of the new solution
Certainty meter- value exceeds costs

Wrapping up

It’s hard to see how Bitcoin becomes a regular currency used by consumers. It doesn’t offer sufficient improvement over incumbent currencies and the cost is hard to overcome with any potential value. One possibility: if the lower fraud rates associated with Bitcoin are reliable, perhaps merchants will offer discounts for use of them. That could spur some people to switch to Bitcoins.

The bigger story of Bitcoin is actually the blockchain technology. The ability to ascertain easily, without an intermediary, that a transaction (e.g. document signing, receipt of something, etc.) has occurred seems to offer tangible value over current solutions. That may be Bitcoin’s true legacy.

I’m @bhc3 on Twitter.

Bring customers into the idea review process

Say you’ve got some internal ideas at your company. Who takes a look at them? Assesses them to determine next steps for each idea? Figures out the value and difference the ideas can make?

How about your own customers?

I talk a lot about jobs-to-be-done here, and getting a firm grip on those to understand where innovation and product enhancement opportunities lie. But sometimes that’s not realistic. Ideas come from many sources, and more likely than not, fail to reflect hard analysis of jobs-to-be-done. But customer feedback is valuable. Any idea which can touch on customers’ experience – products, services, support, pricing, deliver, knowledge – can benefit from their perspectives.

The concept sounds right, yes? But it’s also something that’s somewhat scary. I know this because I asked innovation executives for a number of large companies what they thought of it. There was hesitancy to the concept of bringing customers into what is generally an internal – and often murky at best – process of evaluating ideas.

In my post, What if customers evaluated your company’s ideas?, five areas are examined to de-scarify this idea:

  • Differentiating from focus groups
  • Profile of right customers to involve
  • Type of ideas
  • Ways to engage customers in the evaluation process
  • What criteria make sense?

Give it a read, and see if this is something you’d consider.

I’m @bhc3 on Twitter, and I’m a Senior Consultant with HYPE Innovation.

Gmail offers surprising innovation lessons for the Fortune 500

If you’re familiar with the story of Gmail, you know – for a fact – that it was a 20% time employee project by Paul Buchheit. A little bottom-up experimentation that grew into something big.

Surprise! That story is wrong.

It was a desire by Google, the company, to offer its own email. From Harry McCracken’s great piece How Gmail Happened: The Inside Story of its Launch 10 Years Ago:

Gmail is often given as a shining example of the fruits of Google’s 20 percent time, its legendary policy of allowing engineers to divvy off part of their work hours for personal projects. Paul Buchheit, Gmail’s creator, disabused me of this notion. From the very beginning, “it was an official charge,” he says. “I was supposed to build an email thing.”

Gmail’s creation has more in common with innovation inside large enterprises than it does with the start-up world. Read on if you recognize these:

  1. Job-to-be-done thinking
  2. Reports of the death of company innovation are greatly exaggerated
  3. Corporate antibodies are everywhere
  4. Senior executive support
  5. Big Innovation takes time

Job-to-be-done thinking

Yahoo email screenshot

Image via Variable GHZ, “Why Yahoo Mail is Still an Epic Catastrophe

Anyone remember life before Gmail? We had low storage limits. ‘OK’ search. Poor spam control. Yahoo, one of the dominant players at the time, pursued the freemium strategy that required paying for more storage and better controls. Which isn’t unheard of, mind you.

It’s just…

Think of the core job-to-be-done: When I want to update others, I want to send and receive communications. Some key job tasks that define that job include:

  • Easily send pictures to others
  • Read emails from real people and organizations that I care about
  • Find old emails when I need them
  • Expand my usage of email economically

Yahoo, Hotmail, AOL were fine as far as they went, but they each were challenged on these key job tasks. Back when I had a Yahoo email, I remember the spam being awful and it seemed impossible to control.

Google looked at the offerings in the market, and recognized an opportunity to better satisfy people’s expectations for these important job tasks. Larger size limits, stellar spam control, excellent search and ongoing improvements through Gmail Labs.

Lesson: ABI (Always Be Improving) on the customers’ jobs-to-be-done. Think of the entire job flow and determine which areas are ripe for a better service and experience. Big companies can too easily focus on executing what they have rather than thinking about customers need. 

Reports of the death of company innovation are greatly exaggerated

Image via Family Life Resources

Somewhere along the line, a narrative has emerged that pretty much every big company cannot innovate its way out of a bag. Admittedly, the increasingly rapid turnover of the S&P 500 and the fast rise and decline of companies fuels this narrative. But it’s glib to say companies just don’t do it.

Google’s 20% time is espoused as the antidote to this issue. Middle management stifling innovation? Let everyone experiment on their own. But Gmail wasn’t a 20% time project. It was actually something planned and resourced for development for the organization at large.

This is an important point. If companies set their mind to innovate in an area, people will contribute and provide fantastic ways to get there. Tony Vengrove advised on a key element for success here:  “A compelling vision statement describes what the company wants to become in the future. It not only needs to inspire but ideally it should inform the innovation agenda.”

Lesson: Innovation is not dead inside companies. It does require leadership to set a vision that employees can focus on.  

Corporate antibodies are everywhere

Google is rightly perceived as one of the most innovative companies on the planet. Given that, one might assume that the innovation wheels are well greased there. But I was struck by these quotes from McCracken’s story about the birth of Gmail:

“A lot of people thought it was a very bad idea, from both a product and a strategic standpoint,” says Buchheit of his email project. “The concern was this didn’t have anything to do with web search. Some were also concerned that this would cause other companies such as Microsoft to kill us.”

Within Google, Gmail was also regarded as a huge, improbable deal. It was in the works for nearly three years before it reached consumers; during that time, skeptical Googlers ripped into the concept on multiple grounds, from the technical to the philosophical. It’s not hard to envision an alternate universe in which the effort fell apart along the way, or at least resulted in something a whole lot less interesting.

Inquisitor vs. Corporate AntibodyIn those two quotes, you see critiques that aren’t really about specific elements of Gmail, the concept.

In Four Personality Types that Determine Innovation Success or Failure, a distinction is drawn between Inquisitors, who reflect thoughtfully on issues facing an idea, and Corporate Antibodies, who just want the idea dead. Here are hypothetical responses to Gmail by the two different personality types:

Inquisitor: “Won’t we spook people when they see ads related to the email they’re reading?”

Corporate Antibody: “Email has become a commodity. There are other products we should be building.”

Lesson: Corporate antibodies will always be with us. Recognize legitimate probing for faults versus efforts to undermine the idea in total. Spend time figuring out how to get around Corporate Antibodies, not appeasing them.

Senior Executive Support

Senior executives matter in innovation

In a land of radical transparency and holacracy, the traditional top-level support needed for initiatives is a thing of the past. Alas, we are not in that land. For the 99.9% of people who live with today’s reality, top-down support continues to be the effective way things get done.

It does put pressure on top executives then. They are held accountable by the C-suite, the Board and shareholders. Already in this post, senior executives are called on to ensure innovation moves forward in two different ways.

Set the innovation course: Leadership – be it in business, community, military – has a role in establishing the objectives for people. Indeed, set objectives and get out of the way. In Gmail’s case, Larry Page and Sergey Brin saw a future that extended beyond just search. Paul Buchheit was charged to figure out what a Google email app would look like.

Remove obstacles to innovation: We saw previously that Corporate Antibodies are alive and well. But they didn’t stop Gmail’s progress. From McCracken’s article: “Fortunately, the doubters didn’t include Google’s founders. ‘Larry [Page] and Sergey [Brin] were always supportive,’ Buchheit says. ‘A lot of other people were much less supportive.’ “

Lesson: If senior management isn’t paying attention to innovation, it’s a safe bet no one in the company is either. Employees respond to the agenda set by executives. Organic growth comes from a clear focus that involves executives and employees.

Big Innovation takes time

One of my favorite perspectives on innovation comes from Jeff Bezos. In an interview on Harvard Business Review:

ADI IGNATIUS: Jeff, you’ve said that you like to plant seeds that may take seven years to bear fruit. Doesn’t that mean you’ll lose some battles along the way to companies that have a more conventional two or three-year outlook?

JEFF BEZOS: Well, maybe so, but I think some of the things that we have undertaken I think could not be done in two to three years. And so, basically if we needed to see meaningful financial results in two to three years, some of the most meaningful things we’ve done we would never have even started. Things like Kindle, things like Amazon Web Services, Amazon Prime. The list of such things is long at Amazon.

2014 2019Note that he’s referencing Big Innovation. Concepts that are market changers. There are plenty of opportunities for small-ball innovation (or improvements). But for the really big stuff, executives need to back away from the notion that it can be done in one year.

This was seen with Gmail as well. It was in the works for three years before it was launched to consumers. Continual effort was applied to the product features, the user experience, the business model and the infrastructure to support it. During this time, the project was assailed internally, but as noted previously, senior management supported its ongoing development. Similar to the way Bezos sticks with groundbreaking projects for the long term.

Lesson: Senior management must recognize the magnitude of the innovation it seeks and commit the right time horizon, resources and support to it. This applies for small ball innovation and Big Innovation.

Google, of course is now a HUGE company, on par with the biggest in the world. Its Gmail experience provides valuable lessons for Fortune 500 firms seeking to innovate.

I’m @bhc3 on Twitter, and I’m a Senior Consultant with HYPE Innovation.

Is it innovation or just an improvement? Does it matter?

On the LinkedIn Front End of Innovation group, I saw this post:

Interesting (and heated) discussions @ Unleashing Innovation Summit in Amsterdam earlier in the month: Incremental innovation is NOT innovation – it’s just marketing. REAL innovation is breakthrough/transformational… Agree or not?

I’ve seen this debate before. Attempts to finally, once-and-for-all establish just where improvement ends and innovation begins. People end up with a Maginot Line that fails to defend the sanctity of innovation. Quick: Amazon 1-click purchasing…improvement or innovation?

Does it matter that we define innovation? I once collected a bunch of people’s definitions of innovation to celebrate the multiple ways people think about it. That was a nod to the different ways people think about it. It was divergence, not convergence.

But there are times people want a clearer line between innovation and improvement. Let’s see how some smart folks have articulated the difference.

Perspectives on defining innovation

Scott Berkun: Innovation is significant positive change. This is a high bar, and it should be. What does significant mean? I’d start with the invention of the light bulb, constitutional governments, wireless radio and maybe web browsers. Perhaps you could say significant is a 30% or more improvement in something, like the speed of an engine or the power of a battery. If you know the history of your profession you know the big positive changes people made over the last 50 years, giving you perspective on the scale of brilliance you need to have to be worthy of that word. (#)

Alan Lepofsky: Both innovation and improvement are important concepts, but unfortunately the two terms are often used interchangeably. Innovation reimagines an existing process or market, or creates a brand new one. Improvement enhances an existing process or market, but does not create disruption.  (#)

Chris AndrewsI think your point highlights something important: there’s a pretty fine line between business-as-usual product improvements and real innovation, and it’s important not to confuse the two. (#)

Jon Van Volkinburg: I don’t see innovation as something that merely creates value for a customer and/or for the provider. Expanding or adding a service, feature, or function is not innovation, and these things create value. These things are growth, novelty, and invention. They are great, necessary, and can lead to innovation if the environment and timing is right. If you want, I guess you can call it incremental innovation… but I wouldn’t, to me the term “incremental innovation” is an oxymoron. (#)

What if we actually settled this debate once and for all?

Assume for a moment that we, as a society, agree on what constitutes Innovation. Then what? What is the logical flow of events and decisions that follow such a conclusion?

The reality it that doesn’t matter what something is called ex post facto. It only matters what impact it has on the consumers of the improvation.

Innovation or improvement comic

Before seeking improvation:

  • Understand the problem you’re addressing (no easy task itself)
  • Develop a sense of the magnitude of what’s required (shave a few $1,000? develop a $1 billion market?)
  • Be prepared to follow through on the ideas generated at a level commensurate with their scale

Here, it is important to understand how you define what you’re seeking. And it doesn’t matter whether you call it an improvement or an innovation. Afterwards, after the idea has become real? Again, it doesn’t matter what anyone calls it. It’s about how well it addresses the job-to-be-done. Call it what you want.

You like to-may-to,
And I like to-mah-to…

I’m @bhc3 on Twitter, and I’m a Senior Consultant with HYPE Innovation.

 

Will customers adopt your innovation? Hope, fear and jobs-to-be-done

When will a customer decide your innovative product or service is worth adopting? It’s a question that marketers, strategists and others spend plenty of time thinking about. The factors are myriad and diverse. In this post, let’s examine two primary elements that influence both if an innovation will be adopted, and when it would happen:

  1. Decision weights assigned to probabilities
  2. Probability of job-to-be-done improvement

A quick primer on both factors follows. These factors are then mapped to the innovation adoption curve. Finally, they are used to analyze the adoption of smartwatches and DVRS.

Decision weights assigned to probabilities

Let’s start with decision weights, as that’s probably new for many of us. In his excellent book, Thinking, Fast and Slow, Nobel laureate Daniel Kahneman describes research he and a colleague did that examined the way people think about probabilities. Specifically, given different probabilities for a gain, how do people weight those probabilities?

Why?

Classic economics indicates that an outcome has a 25% probability, then 25% is the weight a rational person should assign to that outcome. If you’ve taken economics or statistics, you may recall being taught something along these lines. However, Kahneman and his colleague had anecdotally seen evidence that people didn’t act that way. So they conducted field experiments to determine how people actually incorporated probabilities into their decision making. The table below summarizes their findings:

Decision weights vs probability

The left side of the table shows that people assign greater weight to low probabilities than they should. Kahneman calls this the possibility effect. The mere fact that something could potentially happen has a disproportionate weight in decision-making. Maybe we should call this the “hope multiplier”. It’s strongest at the low end, with the effect eroding as probabilities increase. When the probability of a given outcome increases to 50% and beyond, we see the emergence of the uncertainty effect. In this case, the fact that something might not happen starts to loom larger in our psyche. This is because we are loss averse. We prefer avoiding losses to acquiring gains.

Because of loss aversion, an outcome that has an 80% probability isn’t weighted that way by people. We look at that 20% possibility that something will not happen (essentially a “loss”), and fear of that looms large. We thus discount the 80% probability to a too-low decision weight of 60.1.

Probability of job-to-be-done improvement

A job-to-be-done is something we want to accomplish. It consists of individual tasks and our expectation for each of those tasks. You rate the fulfillment of the expectations to determine how satisfied you are with a given job-to-be-done. This assessment is a cornerstone of the “job-to-be-done improvement” function:

Job-to-be-done improvement function

Dissatisfaction: How far away from customers’ expectations is the incumbent way that they fulfill a job-to-be-done? The further away, the greater the dissatisfaction. This analysis is really dependent on the relative importance of the individual job tasks.  More important tasks have greater influence on the overall level of satisfaction.

Solution improvement: How does the proposed innovation (product, service) address the entirety of the existing job? It will be replacing at least some, if not all, of the incumbent solution. What are the better ways it fulfills the different job tasks?

Cost: How much does the innovation cost? There’s the out-of-pocket expense. But there are other costs as well. Learning costs. Things you cannot do with the new solution that you currently can. The costs will be balanced against the increased satisfaction the new solution delivers.

These three elements are the basis of determining the fit with a given job-to-be-done. Because of their complexities, determining precise measures for each is challenging. But it is reasonable to assert a probability. In this case, the probability that the proposed solution will provide a superior experience to the incumbent solution.

Mapping decision weights across the innovation adoption curve

The decision weights described earlier are an average across a population. There is variance in those. The decision weights for each probability of gain in job-to-be-done will differ by adoption segment, as shown below:

Decision weights across innovation adoption curve

The green and red bars along the bottom of each segment indicate the different weights assigned to the same probabilities for each segment. For Innovators and Early Adopters, any possibility of an improvement in job-to-be-done satisfaction is overweighted significantly. At the right end, Laggards are hard-pressed to assign sufficient decision weights to anything but an absolutely certain probability of increased satisfaction.

Studies have shown that our preferences for risk-aversion and risk-seeking are at least somewhat genetically driven. My own experience also says that there can be variance in when you’re risk averse or not. It depends on the arena and your own experience in it. I believe each of us has a baseline of risk tolerance, and we vary from that baseline depending on circumstances.

Two cases in point: smartwatches and DVRs

The two factors – decision weights and probability of improved job-to-be-done satisfaction – work in tandem to determine how far the reach of a new innovation will go. Generally,

  • If the probability of job-to-be-done improvement is low, you’re playing primarily to the eternal optimists, Innovators and Early Adopters.
  • If the probability of improvement is high, reach will be farther but steps are needed to get later segments aware of the benefits, and to even alter their decision weights.

Let’s look at two innovations in the context of these factors.

Smartwatches

SmartwatchSmartwatches have a cool factor. If you think of a long-term trend of progressively smaller computing devices – mainframes, minicomputers,  desktops, laptops, mobile devices – then the emergence of smartwatches is the logical next wave. Finally, it’s Dick Tracy time.

The challenge for the current generation of smartwatches is distinguishing themselves from the incumbent solution for people, smartphones. Not regular time wristwatches. But smartphones.  How much do smartwatches improve the jobs-to-be-done currently fulfilled by smartphones?

Some key jobs-to-be-done by smartphones today:

  • Email
  • Texting
  • Calls
  • Social apps (Facebook, Twitter, etc.)
  • Navigation
  • Games
  • Many, many more

When you consider current smartphone functionality, what job tasks are under-satisfied? In a Twitter discussion about smartwatches, the most compelling proposition was that the watch makes it easier to see updates as soon as they happen. Eliminate the pain of taking your phone out of your pocket or purse. Better satisfaction of the task of knowing when, who and what for emails, texts, social updates, etc.

But improvement in this task comes at a cost. David Breger wrote that he had to stop wearing his smartwatch. Why? The updates pulled his eyes to his watch. Constantly. To the point where his conversational companions noticed, affecting their interactions. What had been an improvement came with its own cost. There are, of course, those people who bury their faces in their phones wherever they are. The smartwatch is a win for them.

If I were to ballpark the probability that a smartwatch will deliver improvement in its targeted jobs-to-be-done, I’d say it’s 20%. Still, that’s good enough for the Innovators segment. I imagine their decision weights look something like this:

Decision weights - Innovators

The mere possibility of improvement drives these early tryers-of-new-things. It explains who was behind Pebble’s successful Kickstarter campaign. But the low probability of improving the targeted jobs-to-be-done dooms the smartwatch, as currently conceived, to the left side of the adoption curve.

DVRs

DVRDigital video recorders make television viewing easier. Much easier. Back when TiVo was the primary game in town, early adopters passionately described how incredible the DVR was. It was life-changing. I recall hearing the praise back then, and I admit I rolled my eyes at these loons.

Not so these days.

DVRs have become more commonplace. With good reason. They offer a number of features which improve  various aspects of the television viewing job-to-be-done:

  • Pause a live program
  • Rewind to watch something again (your own instant replay for sports)
  • Set it and forget it scheduling
  • Easy playback of recorded shows
  • Easy recording without needing to handle separate media (VCR tape, DVD)

But there are costs. If you’ve got a big investment in VCR tapes or DVDs, you want to play those. It does cost money to purchase a DVR plan. The storage of the DVR has a ceiling. You have to learn how to set up and work with a DVR. It becomes part of the room decor. What happens if the storage drive crashes?

My estimate is that the DVR has an 80% probability of being better than incumbent solutions. Indeed, this has been recognized in the market. A recent survey estimates U.S. household adoption of DVRs at 44%. Basically, knocking on the door of the Late Majority. I imagine their decision weights look like this:

Decision weights - Late Majority

On the probability side of the ledger, they will need to experience DVRs themselves to understand its potential. For the Late Majority, this happens through experiencing an innovation through their Early Majority friends. They become aware of how much an innovation can improve their satisfaction.

On the decision weight, vendors must do the work of addressing the uncertainty that comes with the innovation. This means understanding the forces – allegiance to the incumbent solution, anxiety about the proposed solution – that must be overcome.

Two influential factors

As you consider your product or service innovation, pay attention to these two factors. The first – jobs-to-be-done – is central to getting adoption of any thing. without the proper spade work there, you will be flying blind into the market. The second factor is our human psyche, and how we harbor hope (possibility) and fear (uncertainty). Because people are geared differently, you’ll need to construct strategies (communication channels, messaging, product enhancements) that pull people toward your idea, overcoming their natural risk aversion.

I’m @bhc3 on Twitter, and I’m a Senior Consultant with HYPE Innovation.

Collecting and analyzing jobs-to-be-done

via the Daily Mail

I’ve previously written about collecting jobs-to-be-done from customers. Because I was analyzing a broad topic across the entire innovation lifecycle, it was a good way to get a breadth of insight. However, it doesn’t work as well in the more common situation for product managers and innovators: analyzing a specific flow. In that case, there are three requirements for collecting jobs-to-be-done:

  • Comprehensive capture of job elements
  • Map collection as closely as possible to the actual job flow
  • Understand importance and satisfaction of individual tasks

Comprehensive is important, because you can’t address what you don’t know. A limitation of my previous effort was that it was not comprehensive. Actual job flow is a powerful framework. Needs captured in context are more valuable, and it’s critical to follow the steps in the job. Importance and satisfaction become the basis for prioritizing effort.

To address these requirements, I’ve put together a process to understand customers’ jobs-to-be-done. The major elements are:

1 Job flow 2 Job task 3 Collect job tasks per activity
4 Job canvas 5 Task importance & dissatisfaction 6 Number of customer interviews
7 Create affinity groups 8 Label the groups 9 Calculate group importance and dissatisfaction

For purposes of this write-up, assume you’re an automotive product manager. You’re tasked with understanding people’s needs to get work done on the commute to the office. Note this is a job that becomes more readily enabled by self-driving cars.

Start with the job flow

A job-to-be-done has a flow. For example, take this job:

When I commute to the office, I want to get work done.

A job flow consists of the job’s major activities, in sequence. The job flow looks something like this:

Job flow

The purpose of the flow is to provide a framework for capturing specific tasks. Putting this together is primarily the responsibility of the product manager (or innovation team). By stating the major activities that define the job, expect a much more comprehensive capture of all the job elements.

Job task

Each activity consists of a series of tasks. Task are what the customer actually does. They are independent of specific features, although may often be intertwined. Here’s an example task:

Job task

Previously, I’d focused on including context in job statements. But when these tasks are organized according to the job flow, the context is readily known. So task statements don’t include a context element.

But they do include an expectation statement. For every task we do, we have an expectation for it. It defines whether we consider the current experience wonderful or painful. This expectation is formalized for each task, captured in the customer’s own words. It’s valuable to know what the customer expects, as that becomes the basis of design.

Collect jobs tasks for each activity

Next step is to conduct the actual customer interviews. Whether done in the customer’s environment (best) or via a web conference call (acceptable), the job flow provides a familiar framework to the customer.

Job activity + tasks

When I worked at eFinance, I conducted brown paper sessions with clients to understand their commercial credit processes. A staple of the Capgemini consulting model, the brown paper is a step-by-step process flow of what the customer does today. Collecting the job tasks is similar here. Similarities and differences:

  • Brown papers are conducted with groups of people together. Job-to-be-done capture will more often be solo interviews.
  • Brown papers are done in a strict step-by-step flow, captured visually on a wall. If doing this for job-to-be-done interviews works for you, go for it. But a simpler post-it note capture style works as well.
  • After capturing the steps in a brown paper, the group is invited to post stickies describing points where improvement is needed. In the job-to-be-done interviews, each task includes a statement of what the customer expects for it.

A key element of the interview process is to probe the responses of the customer. In a perfect world, they will lay out the individual tasks and easily express their expectations. But likely, customers will talk a lot about features. Which is valuable in its own right. But the objective here is to capture what they are trying to get done. So apply the simple question why. Not in a robotic way. But make sure to probe past the expression of features. These are the tasks – versus features – to place on the job canvas.

Here’s an example of the approach:

Customer: I want a 4G internet card.
Interviewer: Why do you need that?
Customer: So I can connect to email and the web.
Interviewer: What is your expectation for connecting to email and the web?
Customer: Always-on internet.

One tip: Use different color sticky notes for each major activity’s group of tasks. This color coding will help later in identifying where the tasks occur in the job flow.

Job canvas

For each major activity, job tasks are collected onto that customer’s job canvas. An example (with fewer tasks than would actually be there):

Job flow + tasks

In reality, there will be  a large number of tasks per customer interviewed. Strategyn’s Tony Ulwick states there will be between 50-150 outcomes collected from multiple customer interviews. Gerry Katz of Applied Marketing Science sees 100 or more needs collected as well. Sheila Mello of Product Development Consulting says it’s not unusual to extra several hundred images from the customer interviews.

Top tasks by importance and dissatisfaction

Once the job tasks have been captured, the customer selects the tasks:

  • That are most important
  • That are least satisfied

The customer will select the 3-5 tasks that are most important for each major activity in the flow (e.g. there are 3 major activities shown in the job canvas above). These tasks will be assigned points. For example, assume three tasks are identified as important. The most important task would receive 3 points, the next most important 2 points, and so on.

The customer will also select the 3-5 tasks that are least satisfied for each major activity in the flow. Assuming three selected tasks, the task that is least satisfied receives 3 points, the next least satisfied task receives 2 points, and so on.

Job tasks - importance and satisfaction

Keep this insight handy, but separate from the collected stickies (or however you’ve collected the job tasks). We’ll come back to how to use this information.

Note: it will help to apply unique numbers to the individual job tasks. You’re going to want to know the most important and least satisfied tasks across multiple customers later in the process.

Number of customer interviews

A general rule of thumb is that 15-20 customer interviews will provides solid coverage of customers’ needs. You can take it further, as George Castellion advocates 40 interviews. Each interview starts with a blank canvas containing only major activities.

Create affinity groups

After conducting multiple interviews, you will have a large number of job tasks, with information on which ones are most important and least satisfied. Working with a large number of statements by people is a challenge that others have faced. They key is to reduce the large number to a manageable set of insights. There’s a proven approach called the KJ-Method to systematically abstract hundreds of statements into a few key groups.

UX expert Jared Spool provides a detailed series of steps to run the K-J Method. I’ll use his description here.

Bring together group of people do the affinity grouping

The first step is to determine who will do the affinity grouping with you. Try to keep this group at 5 people or fewer. Draw on people from different disciplines.

Put all the job tasks on a wall

In a single space, all the job tasks should be visible and accessible. They need not be laid out in the job flow, which might introduce a bias to the grouping. The color of the stickies will be the basis for knowing where the tasks fall in the flow.

Group similar items

The next step is for the team members to group like  job tasks together. The process is one of looking at pairs of tasks, and determining if they share characteristics. This how Jared instructs clients to do this:

“Take two items that seem like they belong together and place them in an empty portion of the wall, at least 2 feet away from any other sticky notes. Then keep moving other like items into that group.”

“Feel free to move items into groups other people create. If, when reviewing someone else’s group, it doesn’t quite make sense to you, please feel free to rearrange the items until the grouping makes sense.”

“You’re to complete this step without any discussion of the sticky notes or the groups. Every item has to be in a group, though there are likely to be a few groups with only one item.”

Label the groups

Each participant then gets to label each group. This entails looking at the grouped job tasks and determining the common theme. Again, here’s how Jared instructs teams on this process:

“I want you to now give each group a name. Read through each group and write down a name that best represents each group on the new set of sticky notes I just gave you.”

“A name is a noun cluster, such as ‘Printer Support Problems’. Please refrain from writing entire sentences.”

“As you read through each group, you may realize that the group really has two themes. Feel free to split those groups up, as appropriate.”

“You may also notice that two groups really share the same theme. In that case, you can feel free to combine the two groups into one.”

“Please give every group a name. A group can have more than one name. The only time you’re excused from giving a group a name is if someone has already used the exact words you had intended to use.”

Note that part of the exercise in this step is to give one more consideration to the groupings. If, upon trying to determine a label one finds that the groups doesn’t actually make sense, the groups can be split up as needed.

I’ll add this caveat to Jared’s instructions. For purposes of this affinity group work, lots of different labels for each group of tasks are not important. It’s OK to go with one person’s good label for a group, a point to emphasize more strongly.

Here’s an example of labeling a group of job tasks:

Job tasks grouped

Once done, you’ve organized a solid group of job tasks into major themes for what customers are trying to do.

Calculate the importance and dissatisfaction score for each group

Remember asking the customers to rate the three most important and three least satisfied job tasks? Now it’s time to use those ratings. In each group, calculate the following for both importance and dissatisfaction:

  • Total points
  • Average points per task

For each group, you’ll have something like this:

Job task groups with scores

The Total score gives a sense for where the customer energy is. Large scores on either metric will demand attention. The Average score is good for cases where a group has only a few, highly scored job tasks. It ensures they don’t get overlooked.

Prioritize roadmap

You now have major groups scored with customers’ view of importance and dissatisfaction. Within each group are the tasks and expectations that customers have. This is the good stuff, the insight that fuels design efforts. It’s also the data-driven, factually based input that helps clear the fog when tackling a new area for development.

The expressed customer insight – what they want to do, what is important, what is not satisfied – becomes the foundation for constructing a roadmap. The team can layer on other considerations: business strategy, adjacent initiatives that impact the effort, internal priorities. Balance these with what customers actually value. Anything that ignores this hard-won customer insight needs a compelling reason, and an understanding of the higher risk it entails.

I’m @bhc3 on Twitter.

The Product Manager is the Chief Customer Development Officer

If pressed, what would you say is the secret to product success? Certainly there are a number of things that go into making and selling products. Prioritization, design, manufacturing frameworks, marketing, service, cost of production, etc. Each of these elements needs to be optimized, and there are people, practices and tools that do just that.

Despite rigor in much of the product process, there’s still too high a failure rate for products. I’ll bet you’ve seen this in your own company: proposed products that received a lot of internal resources only to be killed off, or that launched and didn’t hit the mark with customers. As you can see, there’s a story to it:

Product development success and failure

Consider that first panel for a moment. A third of launched products fail. That doesn’t include the projects that were killed before launch. 32% of development resources are spent on products that get scrapped or fail in the market. To put that in perspective, imagine similar levels of failure in other venues:

  • We  miscalculated 32% of the accounting entries
  • 32% of our inventory purchases were wasted
  • Our marketing initiatives fail to sell anything or raise brand awareness 32% of the time
  • 32% of our manufacturing capacity is chronically unavailable

Those levels of performance would be unacceptable in companies. Yet they’re considered part of the ‘art’ of innovation when it comes to product. The cost of doing business. Which is pretty sweet if you’re a product person…

OK, forget that. Let’s assume rational, ambitious people want to do better.

What works? Survey says…

In a survey of B2B firms, people were asked to identify the causes of failed products. The top answer was ‘lack of market analysis’. As in, did the market have the need, did the feature address it if so, and did it do so better than competing products? The next answer was that the ‘product didn’t satisfy customer needs’. There’s a pattern here.

Flip the analysis…what are the top success factors? All three  are specifically rooted in understanding customer needs:

  1. Product directed at customer needs
  2. Staying close to the customer
  3. Product adds value to the customer

Notice that pattern again? Products that succeed are designed and developed with customer insight.

Factors that make customer involvement successful

Researchers in Sweden conducted an analysis of firms’ product development efforts, classifying the products as successful or not successful. They tracked these product outcomes against the types of interactions the firms have with customers. Note they tracked product development efforts as incremental innovation. They separately tracked radical innovations as well.

For incremental innovation – i.e. the daily work of product managers – they were able to identify three factors that separated successful products from the rest. Factors that affected the “absorptive capacity” of the company to assimilate customer needs.

Engagement frequencyEngagement frequency: The more often a company communicates with customers, the more successful were it product releases. Communication can be oriented toward understanding needs, or for feedback on design iterations.

Two-way directionTwo-way communication: The nature of the communication dictates its value. If the company does all the talking, it’s not going to learn much. The more the communication is a dialogue, the better the outcome for the product.

Needs in contextNeeds in context: The more the insight is captured as part of a broader view of the activity, the better that insight is. Top insight is gathered as the customer experiences using the product. It’s also valuable to understand the why for insight. If the suggestion or need is in isolation, it can be hard to understand the core need.

Now, who is in charge of getting this insight?

Chief Customer Development Officer

Think about this. For marketing, it’s clear who owns that activity, and you can see processes, systems, people and priorities for it. Same goes for manufacturing / development. And design. And supply chain management. And distribution. And financial analysis. And human resources. And so on…

But where are the comparable processes and people dedicated to understanding the customers’ needs? Who plumbs the jobs-to-be-done and analyzes the key outcomes customers are seeking? The work of understanding customer needs, in one sense, is everybody’s responsibility. It’s what makes the company grow. But if something is everybody’s responsibility, it’s really nobody’s responsibility.

It’s an important question, because the degree to which one stays close to the customer is a primary basis of success or failure in product development. As a function, what would you call this work? Customer Needs Whisperer? Voice of the Customer-ologist? Actually, Steve Blank has it covered with customer development:

Before any of the traditional functions of selling and marketing can happen, the company has to prove a market could exist, verify someone would pay real dollars for the solutions the company envisions, and then go out and create the market. These testing, learning and discovery activities are at the heart of what makes a startup unique, and they are what make Customer Development so different from the Product Development process

Steve Blank, The Four Steps to the Epiphany

While Steve Blank’s excellent book is targeted at entrepreneurs who need to do the hard work of validating an idea, the mindset underlying customer development is well-suited for the need to stay close to customers. Hence, the notion of the Chief Customer Development Officer. And the product management team sits at ground zero in the customer development activity.

What distinguishes customer development from the current mentality in most companies? Cribbing from a Jack London quote:

You can’t wait for customer insight to come to you. You have to go after it with a club.

This is a change in mindset for many. Be proactive in understanding customers. Make communicating with customers a meaningful percentage of the weekly schedule. Don’t settle for inbound inquiries. Or only focus groups on an already-designed product. Or quarterly customer council meetings. Really own the customer development activity.

It’s worth it, as here are five concrete benefits of employing the customer development mindset:

  1. Develop understanding of what success looks like for the customer
  2. Customer becomes invested in the success of the product
  3. Elevate customers’ awareness of what’s coming
  4. Discover opportunities for growth due to underserved JTBD
  5. Reduce uncertainty due to lack of information

You only get these by being a proactive customer development officer.  In a future post, I’ll examine the different ways engage customers in the product development process. Because there are many.

I’m @bhc3 on Twitter.

10 examples of fabulously flawed product-first thinking

In talking about jobs-to-be-done here, I sometimes think that all I’m doing is stating the obvious. I mean, isn’t it obvious that you’d create something that helped fulfill a need or desire? What else would you do?

But I’ve seen in my own work experience, and across a multitude of initiatives in other industries, cases where that’s not necessarily the case. Invention was the thing. I mean that in this sense:

Invention creates. Innovation changes.

Exercising creative chops was the focus, with a thought that customers would have to take up this amazing thing invented. But unfortunately, that’s not generally the case. The invention is not adopted, and thus nothing changes for the target market. Innovation does not occur. The invention either does not address a job-to-be-done or the proposed solution was nowhere near satisfying the specific outcomes of an applicable job-to-be-done.

To illuminate how this “product-first” dynamic is a pervasive dynamic, I’ve collected ten examples of it. While the plural of anecdote is not data, see if you recognize similar examples in your own experience.

1. Because Apple, Microsoft, Google did it!

Context

Kareem Mayan wrote a great post Why only fools write code first. In it, he stated, “I have a confession to make: I’m 35, and until last year, I started building companies by creating a product.” The post describes on his evolution in thinking, focusing first on customer needs before building anything.

Product-first thinking

In the comments, someone wrote:

“Almost all of the successful startups I know of built a product first, simply because the founder wanted. Apple, Microsoft, Google, Dropbox — some of these are famous even today for never doing user surveys.”

This argument expressed skepticism about Kareem’s point.

Analysis

A good example of the ongoing pervasiveness of product-first thinking. It really is everywhere. Here, the commenter displays a classic example of the survivor bias. A focus on only those companies that made it, and what they do. Ignoring that perhaps dozens of competitors also charged ahead with their own product-first approaches. And were nowhere near as successful.

It’s like looking at the ways lottery winners live, and saying that’s the way you should live too. They’re not connected.

Of course, it’s also possible the commenter actually has no idea what those companies do in terms of understanding customer needs…

2. The “what you can do for us” attitude

Yahoo home page 2002, via All Things D

Context

Way before Marissa Mayer joined Yahoo, the company was a case study in mediocrity. From its glory days in the 90s, it had managed to become a bloated collection of media properties, without a coherent strategy due to a succession of changing executives and business models.

Product-first thinking

As reported by Kara Swisher on All Things D, Yahoo’s home page became increasingly overrun with links. To cram more stuff above-the-fold, font sizes shrunk. It became a nasty hodge podge of links that no longer related to what users wanted.

As Yahoo’s Tapan Bhat, SVP of Integrated Consumer Experiences noted,  “It had nothing to do with the user, but what Yahoo wanted the user to do.”

Analysis

What Yahoo wanted the user to. What a wonderful expression of the approach. It’s such a pernicious mode, where the needs of the company eclipse those of the customers. Call it inside-out thinking. When the company’s, not the customers’, needs drive product and service decisions, it’s a good bet customers will turn elsewhere. It’s a great opportunity for competitors.

3. Dazzled by the invention

Source: NBC Bay Area

Context

Anyone remember the hype over Dean Kamen’s project code named Ginger back in 2001? Turned out to be the Segway, that amazing triumph of technology that allowed people to travel on a motorized two-wheel scooter. It really is amazing, with its self-balancing mechanism, easy navigation and smooth ride.

Product-first thinking

It was hailed as the next coming of great technology. No really, it was. Here are quotes by both Steve Jobs and Jeff Bezos prior to its launch:

Jobs: “If enough people see this machine, you won’t have to convince them to architect cities around it; it’ll just happen.” (#)

Bezos: “You have a product so revolutionary, you’ll have no problem selling it.” (#)

Wow! So what happened? Well, have you taken your Segway out for a spin today? It  missed the mark in terms of how frequent the job-to-be-done was. For me, Segways are what tourists rent to travel around Golden Gate Park in San Francisco.

Analysis

My own perspective is that Segway is an optimum mode of transport for journeys where walking would take more than 10 minutes and less than 30. And where you don’t need to carry anything heavy or bulky. And where weather would be OK for the journey. Steve Jobs, who did heap praise on it, was prescient about what needs it didn’t fill.

“Jobs said he lived seven minutes from a grocery and wasn’t sure he would use Ginger to get there. Bezos agreed.” (#)

So Jobs and Bezos were full of praise, but in a hard analysis couldn’t quite say what mass job-to-be-done the Segway fulfilled. And it turns out most of the market couldn’t either. Sometimes the invention is so dazzling, we’re blinded to understanding what need it actually fulfills. Invention first thinking.

4. Same template, different market

Via Bloomberg Business Week

Context

Ron Johnson did a fantastic job of creating the Apple stores. They’re enjoyable to visit, full of all the latest in cool technology Apple has to offer. The clean vibe, the on-the-spot purchasing, the Genius Bar. Clearly he brought some of the experience from his Target (aka “Tar-jay“) days to the job.

Based on this, the Board of JC Penney installed him as CEO to restore a retailer that had lost its luster.

Product-first thinking

Johnson put in place a number of changes to reinvigorate the retailer. He stopped the discounting, going for a low price everyday approach (like Target). He developed brands that would be exclusive to JC Penney (like Target). Trained employees to help people shopping (like Apple).

Ultimately, however, his changes didn’t take. Perhaps the most telling insight came from another executive:

Ron’s response at the time was, just like at Apple, customers don’t always know what they want,” said an executive who advocated testing. “We’re not going to test it — we’re going to roll it out.”

There it is, product-first — or maybe vision-first — thinking.

Analysis

It’s tempting to look at this as the hubris of being smarter than customers. But I don’t think that’s the lesson to draw. Rather, this is a case of previous success with a format in other markets (Target, Apple), and applying it to a new market. Without understanding the customers in the new market. The fact that Johnson didn’t feel the need to run the new strategies by JC Penney’s customer base was due to his success with the template previously. Why test? You know what customers want.

But in this case, it led to overlooking existing customers and what they outcomes were being fulfilled by JC Penney. This alienates the core customer base, while potential new customers ponder why they’d switch from Target to JC Penney. Unsurprisingly, the stock dropped 55% during his tenure, with a horrendous 32% drop in same-store sales in the critical holiday 4th quarter of 2012.

5. Blaze a new trail

Context

Tired of people saying you should listen to the marketplace, Dan Waldschmidt advocates something different. He argues that most of the time, people don’t know what they want. In making his argument, he references both American slavery and Martin Luther’s religious reformation.

Product-first thinking

Here is how Dan puts it:

One of the things business experts tell you when you are considering changes to your sales strategy is the idea that you need to “listen to your marketplace”. That you need to take your idea and run it by the people around you to get some feedback. Instead, blaze a new trail. Think about where you want to lead your market.

Analysis

Perhaps the key phrase is lead your market. That, in and of itself, is fine. Lead your market in sales. In profits. In innovations that resonate. But in the context of (i) ignoring the marketplace; and (ii) blazing a new trail, it comes across as advice to tell the market where it needs to go. Which actually is nice if you can accomplish it. Alas, the business landscape is littered with folks who tried to tell the market where to go. The market can be fickle that way.

To be fair, it is important to separate the jobs-to-be-done from the potential solutions. That’s a better way to think about Dan’s advice.

6. What Steve Jobs said

Via Inc. Magazine, 1989

Context

Perhaps you have seen this quote by Steve Jobs:

“You can’t just ask customers what they want and then try to give that to them.”

Run a search on that exact phrase, and 687,000 results are returned. It’s a sentiment from one of the all-time greats that clearly has caught on.

Product-first thinking

Interpretation is important here. When you read a number of articles that reference the quote, the context is one of divining products that no one in the market would come up with. Use your inner genius to do this. As written about Jobs  in Fast Company:

He is a focus group of one, the ideal Apple customer, two years out.

And he was quite good.

Analysis

But for most of us, we’re not an ideal focus group of one. That’d be the dangerous lesson to draw from his quote. If every corporate product person, or innovator, or strategist decided to channel his inner focus group of one, there’d be a lot of  wasted resources. Actually, there are a lot of wasted resources

The other thing to note is the quote in its fuller context. Here’s more from Jobs in the 1989 interview with Steve Jobs where he first said that quote:

“You can get into just as much trouble by going into the technology lab and asking your engineers, “OK, what can you do for me today?” That rarely leads to a product that customers want or to one that you’re very proud of building when you get done. You have to merge these points of view, and you have to do it in an interactive way over a period of time—which doesn’t mean a week. It takes a long time to pull out of customers what they really want, and it takes a long time to pull out of technology what it can really give.”

Sound like he’s advocating to ignore your customers?

7. My business model demands your attention

Facebook Home user ratings

Facebook Home user ratings

Context

A few months ago, Facebook introduced Facebook Home. This app for Android became the user interface of the phone. In so doing, it dominates the experience on the device:

Designed to be a drop-in replacement for the existing home screen (“launcher”) on an Android device, the software provides a replacement home screen that allows users to easily view and post content on Facebook along with launching apps, a replacement lock screen that displays notifications from Facebook and other apps, and an overlay which allows users to chat via Facebook messages or SMS from any app.

Note that the existing Facebook app was still available, allowing you to get your Facebook updates via the phone.

Product-first thinking

What Facebook Home does is elevate Facebook above all others on the phone. It was a play to get Facebook front and center in your daily experience. There would be access to all your other apps, but the path to them would go through Facebook each and every time.

Globally, the average smartphone user has 26 apps on their phone. For Facebook Home to be popular, the typical user would rank Facebook above all other apps. The games. Email. Twitter. Instagram. And on…

Analysis

Ultimately, Facebook Home withered in the market. I can understand why. In 2012, mobile time spent on Facebook surpassed time on the Facebook website. From a user experience perspective, Facebook wanted to make mobile even easier. From an advertising perspective, Facebook needed to establish a way to present more mobile ads. Imagine serving up an ad every time someone turned on their Android phone.

But the problem is that Facebook was solving a job that most users were already satisfied with. The Facebook App works well for its purpose. It also imposed new friction on using one’s mobile device. The burden of navigating through Facebook to get to your other 25 apps. As Joseph Farrell, EVP Operations at BiTE interactive, said:

“Facebook Home solves Facebook’s needs for more user data, but what does it solve for its users?”

8. Solution in search of a problem

Context

In a post, entrepreneur Ramli John talked about lessons he’s learned from failed startup efforts. Specifically, the experience gained with Lesson Sensei. Lesson Sensei didn’t make it.

Product-first thinking

Ramli states plainly the trap he fell into:

“Don’t lose focus of the problem. That was one of the biggest mistake I made in my previous failed startup, Lesson Sensei. About a few weeks in, we realized that we really don’t have a problem to solve. But, we had this awesome solution. So we started pivoting on possible problems we can solve with our solution. Each week, we tried a new problem to solve. Each time, we found a flaw with our assumption. Then, we started losing steam. Always start with validating a problem before you validate the solution. The other way around just takes up too much time and energy.”

Analysis

This is a classic issue. There’s a hazy sense of what an idea could address. It’s not nailed down yet, but there’s the rush of starting on the solution anyway. To be fair, there is some merit in this. You could be 50% there in terms of product-market fit, and the initial product can help elicit the right iterations. But as Ramli notes, that can be an expensive approach. It burns time, energy and money. And depending on how hazy that view is of the actual job-to-be-done and its attendant outcomes, you may be entirely off track.

9. We’ll get to those customers at some point

Context

Robin Chase is the founder and former CEO of Zipcar (acquired by Avis in 2013). After Zipcar, she founded GoLoco, a carpooling app. Unlike Zipcar, GoLoco didn’t make it. She is now leading Buzzcar, a peer-to-peer car sharing service.

Product-first thinking

Robin is open about the failure of GoLoco:

“With my second company, GoLoco – social online ridesharing – we spent too much money on the website and software before engaging with our first customers.”

Analysis

In some ways, this is a similar situation to Ron Johnson at JC Penney. Having been successful in getting Zipcar going, Robin had a confident attitude about her new endeavor. That confidence led her to develop first, worry about customers later. As she notes, this was backwards. The spade work of understanding customers’ needs is a critical first step.

10. Dazzled by the innovator and the hot trends

Color website is deadContext

Remember Color? This app would let you take pictures. These pictures were then visible to anyone with the Color app within 100 feet of you. It was a way for friends or strangers to participate together in some close proximity.

Color is no more. It didn’t fare well.

Product-first thinking

It was a can’t-miss app. It was started by an energetic, persuasive entrepreneur whose previous company was bought by Apple. It was SOcial. It was LOcation-based. It was MObile. It was SoLoMo!

With that combination, Sequoia Capital and Bain Capital felt confident investing $41 million. Product-first thinking.

Analysis

Presumably, the entrepreneur’s previous success was a good-enough proxy for understanding the target market’s jobs-to-be-done and attendant outcomes. However, as seen with GoLoco above, previous success doesn’t automatically grant the ability to divine customer needs. There’s still the work of understanding the market you intend to tackle.

GigaOm’s Darrell Etherington gets props for identifying the flaws of Color right at its launch:

“But I think it’s more likely this is a prime example of how, when it comes to apps, 1+1+1 does not always equal 3. An app can’t just hope to profit by being at the intersection of a number of promising mobile trends. Developers still have to think intelligently about how those trends integrate, and remember that user experience, especially the one following first launch, is still the key to wide app adoption.”

Remember this next time you see another startup in an overhyped space, say Big Data. What job-to-be-done does it fulfill?

Wrap-up

Perhaps not surprisingly given my work experience and interests, these examples have a heavy technology orientation.  One can imagine similar cases in financial services, apparel, consumer product goods, etc. Hopefully the examples here will be useful as you look at your own world. And in your own work. I’ll admit to being guilty of product-first thinking. The creative muse is a strong human characteristic. But recognize when that muse is taking you down a path you shouldn’t go.

I’m @bhc3 on Twitter.

Uncover latent needs with a simple question

After publishing Latent needs are overplayed as an innovation dynamic, I got a lot of feedback. Plenty of agreement, but also some good counterpoints. And in reading through some of them, I realized that there is something to this. A lot of people are convinced that whole markets are waiting to be built based on people not really understanding their own needs.

Or if not whole markets, at least new products that can find success based on unrealized needs.

I submit that there is an antidote to this problem. That the issue is not that customers either do not know or cannot articulate their needs, or jobs-to-be-done. It’s that follow-up is required.

Are you asking ‘why’ enough?

The antidote to the scourge of latent needs is simple: ask ‘WHY?”

As you interview a customer, you’re seeking their jobs-to-be-done, along with the associated outcomes that are needed to be successful in that job. Here’s the thing: you’re going to get superficial responses initially. Not because people don’t realize their own needs. Rather, they’re fixated on current processes and product features (this is the ‘faster horses’ issue). We go with what we can recall the easiest. It’s a natural phenomenon, documented well by Daniel Kahneman in his book Thinking, Fast and Slow. But it can result in limited insight into what they really value and seek to accomplish.

When interviewing a customer, listen for your internal voice that says, “this need is not the real one”. You’ll know it, because it will be deeply entwined in the current product features. That’s when delivering a well-timed ‘WHY?’ will make a difference.

Faster horses - WHY comic

This is not a novel concept. Indeed, it’s part of the lean six sigma methodology, used to get at the root cause of issues (here’s a Jeff Bezos example). But it’s perhaps not so obvious to use WHY in the pursuit of latent needs.

I have found asking ‘WHY’ to be a great method for penetrating the “what I easily know” bias. Two examples below – one from a former HPer, one from me – relate the value of understanding ‘WHY’.

HP large format graphic plotter

Spigit innovation management platform

Related by Marvin Patterson, President Dileab Group, and formerly with Hewlett-Packard From my own experience as VP Product – Spigit
We were asked to figure out how to get HP into the large format graphic plotter business. In one customer visit after another we were told that accuracy was the critical requirement. The current product to beat utilized a magnetic x-y motor moving over a precisely grooved steel surface that was mounted on a super-flat granite slab weighing the better part of a ton. This $50,000 product had accuracy that was hard to beat.

During a visit to a semiconductor design company, I asked them to show us how they used these highly accurate drawings. We were ushered into a room where engineers were verifying the design of multiple chip layers. They did this by taping the drawings, each roughly 3×4 meters, to a large light table, with each drawing carefully aligned with the one below. They would then crawl around on top of the light table, literally on their hands and knees, sighting down through the layers of transparent Mylar, and checking the alignment and design of each layer.

“See,” said our host, “that’s why we need the accuracy.” But, in fact, this application did not use the accuracy at all. It depended, instead, only on repeatability between one drawing and the next. Repeatability is fairly cheap and easy to accomplish. Accuracy is really, really expensive. After a thorough survey of the market, we decided that repeatability was the crucial specification in most applications, so we traded off accuracy for lower cost. The resulting product employed a radical new plotting mechanism that delivered extremely good repeatability, fairly poor accuracy, and sold for under $15,000. Within three years its sales exceeded 50% market share.

I performed a jobs-to-be-done exercise with multiple customers. One job that several talked about was the need to get more people ‘down-voting’ ideas. What they were seeing was that people tended to be positive only, or they didn’t rate an idea at all.

More down votes JTBD

Hence the desire for more down-votes. But I asked for more than that. “Why?” Because they wanted better distinguishing of the good ideas from the bad, and getting only up-votes made that hard.The real need was better ways to distinguish ideas. The request for ways to increase down votes was the way they expressed that.Customers were providing feedback on the current process/features when they talked about more down-votes. But pressing them to understand why unveiled the real need. And there are a lot of other ways to stratify ideas besides increasing down-votes.

Marvin got to the real need here by pursuing customers’ responses through ‘why’. Note that repeatability wasn’t an unrealized need. Customers were doing this with every design! They indeed realized they needed to do it. It’s just that they were caught up in the current process they used when expressing This insight was used later in the product roadmap to address the real need. Rather than push to get users to do something they were uncomfortable with – down voting – there are ways to leverage what they actually do.

In both cases, customers were providing feedback about current process and product features. But with some digging, the root job-to-be-done was secured. Nothing latent or unrealized. Just some work penetrating the natural way people think: starting with what they can easily recall. Dig deeper with WHY.

I’m @bhc3 on Twitter.

Latent needs are overplayed as an innovation dynamic

Reading this thought piece from the Silicon Valley Product Group, The End of Requirements, I saw this point about latent needs:

Unrealized needs (also called “latent needs”) are those solutions where customers may not even be aware they even have the need until after they see and experience the solution. Examples include digital video recorders, tablets, always-on-voice, self-driving cars, etc.

In other words, customers often don’t know what they want. This is essentially another version of the Henry Ford quote, “If I asked people what they wanted, they’d have said faster horses.”

I want to differ with the Silicon Valley Product Group here. People do know their needs, it’s incumbent on companies to understand them. Then it’s appropriate to try out ideas that can better satisfy those jobs. This diagram illustrates the two separate dynamics:

Decoupling customer JTBD from solutions

In their post, they use self-driving cars as an example of “latent needs”. Two issues with that. First, self-driving cars are not yet in the market, so it’s not possible to say that was a latent need, as described by the Silicon Valley Product Group. The second issue is that self-driving cars will actually address known jobs-to-be-done. I wrote a whole post on that, Exactly what jobs will self-driving cars satisfy? In that post, I outline several jobs-to-be-done and some key outcomes desired:

Job-to-be-done Outcomes
I want to get from point A to point B Minimize commute time | Minimize accident risk | Minimize commute risk | Increase driving enjoyment
I want to get work done Increase digital work completed | Increase availability for conference calls | Minimize distractions
I want to improve the environment Minimize emissions | Minimize fossil fuel consumption
I want to enjoy my personal interests Increase spent on activity | Minimize distractions

The point here is that these are not latent needs. Some are needs that people do not think about now in the context of commuting in a vehicle. But they are not latent needs.

I do agree there are some needs that can be hard to discover, or which become more important as societal norms and expectations change. Sure, there are some needs that are not obvious and may indeed become more visible in the face of a potential solution. But these are exceptions, not the norm.

Making product and innovation decisions based on the thought that, “Well, people don’t really know what they want” is a recipe for a lot of wasted effort. It’s not a sustainable basis for growth.

Agree? Or think I’m oversimplifying things?

I’m @bhc3 on Twitter.