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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.

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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.

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.

Generate opportunity maps with customer jobs-to-be-done

JTBD Opportunity MapIn seeking to better understand customer jobs-to-be-done, I found myself a bit underarmed. Meaning, I didn’t really have a way to do this. The value of jobs-to-be-done (JTBD) thinking has only emerged recently. It’s still nascent, and there aren’t ready guideposts to follow. However, Tony Ulwick has been at it over two decades. Indeed, his outcome-driven innovation remains a powerful methodology with the structure needed to effectively identify opportunities. It is the JTBD standard.

But in my work, I wasn’t ready to engage external consultants. My project was more low level, relating to a significant enhancement to an enterprise software platform. My needs – and budget – didn’t rise to the level of a full-fledged consulting engagement. Also, I wanted to be the one talking with customers.

So I did what anyone interested in innovation would do. I hacked my own approach.  I wanted a way to elicit jobs-to-be-done that had the following aspects:

  • Accessible anytime I wanted it
  • Low cost (free!)
  • Allowed me to rank  different jobs-to-be-done
  • Created a way of seeing where the opportunities are
  • Deepened my understanding of, and connection with, customers

The presentation below outlines a method to generate opportunity maps with customer jobs-to-be-done:

——-

As you’ll see in the presentation, I consider this an initial blueprint. One that can, and should, be hacked to optimize it. But as the approach exists now, it will provide significant value. And for those who haven’t engaged customers at this level of dialogue, you’ll be amazed at what you learn.

Give it a try and let me know what value you get in talking JTBD with your customers.

I’m @bhc3 on Twitter.

McDonald’s wins on the fast food jobs-to-be-done that matter

Consumer Edge Insight conducted a survey of consumer perceptions about 20 different fast food restaurants. Specifically, how are they ranked by consumer perceptions on different attributes, such as:

  • Good value
  • Convenience
  • Low prices
  • Fast service
  • Great tasting food
  • More…

The customers were asked to rank order the different attributes, and “great tasting food” actually ranked 8th. The first four above were the top ranked attributes.

Which explains this unusual finding when it comes to McDonald’s:

McDonald’s scored very low in “satisfaction with last visit”. Only 22% of respondents were extremely satisfied with their last McDonald’s experience. Highest satisfaction scores went to Chick-fil-A (66%), Long John Silver (56%), and Whataburger (54%).

McDonald’s scored very high in “extremely likely to visit again”. McDonald’s 64% score on that measure was third behind Subway (68%) and Chick-fil-A (67%).

Wha…? Yeah, low satisfaction combined with high intent to visit again. Strange isn’t it? Are consumers masochists? Well, no. The key here is recognizing that customers have a number of jobs-to-be-done when it comes to eating. Translating the four attributes into jobs-to-be-done (using the previously defined structure):

Attribute Context Job Success Metric
Good value When I purchase food… I want to spend the same or less than what I would for preparing the same food myself. Perception that the food quantity and quality is commensurate with the price paid.
Convenience When I need to eat with limited time… I want to find food to eat quickly Decreased time to get to the food that I will eat.
Low prices When I need to feed myself and others… I want food costs that fits within my budget. Food that costs less than [X]% of my daily income.
Fast service When I need to eat with limited time.. I want food that is served quickly after I’ve ordered. Food is served within [2 minutes] after I order it.

On the highest ranked jobs-to-be-done (remember, jobs should be ranked ordered), McDonald’s is at or near the top. What’s interesting is that the “satisfaction with last visit” score was low for McDonald’s. But it turns out that’s not the most important question. Rather, the question should be, “how satisfied are you with the jobs-to-be-done that matter?”

I’m @bhc3 on Twitter.

A Method for Applying Jobs-to-Be-Done to Product and Service Design

Say you’re designing something new for a product or service. Of course, you have your own ideas for what to do. But, how informed are you really about what is needed?

This is a question I faced in thinking about game mechanics used in a social platform. A common product approach is to work up some game mechanics ideas, get them designed and deployed. The source for ideas? My own fertile mind. Inbound suggestions (“in World of Warcraft, you can…”). Competitors. What companies in other markets are doing.

But that wasn’t sufficient. Game mechanics are an evolving, somewhat complex field.  I wanted to understand at a more fundamental level: why game mechanics? So I decided to learn more from our customers. What needs would game mechanics address? Initial question: what’s the best way to go about this?

Jobs-to-Be-Done: Only for game-changing innovation?

The jobs-to-be-done framework struck me as the right approach here. What are customers trying to get done? As legendary professor Theodore Levitt said:

People don’t want to buy a quarter-inch drill. They want a quarter-inch hole.

Similarly, I didn’t believe customers wanted to buy “game mechanics”. They want to buy results, which game mechanics can help deliver. Using the jobs-to-be-done framework, what jobs were game mechanics being hired for?

Yet, in reading the advice on jobs-to-be-done, one gets the impression that eliciting jobs-to-be-done can only be done effectively via intensive in-person interviews. Well, I didn’t have the capacity to fly around for one-day deep-dive sessions to understand the jobs-to-be-done related to game mechanics.

Customer Insightini

And this gets at something related to jobs-to-be-done as it stands today. It’s very much positioned for high-impact, game-changing innovation. Which is awesome, by the way. Firms like Strategyn and ReWired Group are setting the tone here. When the payoff for intensive, expensive efforts like in-person interviews is high-value innovation, you do them.

But my needs were at a lower level than that. In the Customer Insightini™ to the right, I segment the types of initiatives where customer insight can be useful. The bottom is the minor stuff (“it should support colors red, green, blue…”). The top is the game changing endeavors (e.g. surgery stents). My game mechanics inquiry? Right there in the middle. Introduction of something new in the same market.

Since I couldn’t find a good framework to elicit customers’ jobs-to-be-done, I hacked together my own methodology. It’s described below.

  1. Job-to-be-done structure (link)
  2. Focus the effort (link)
  3. Rate satisfaction with fulfillment of each job (link)
  4. Rank order importance of jobs to create an Opportunity Map (link)
  5. Use a website to manage the jobs-to-be-done (link)
  6. Conversations are as valuable as the jobs themselves (link)
  7. Step-by-step plan (link)

Job-to-be-done structure

I needed a common language for the various jobs-to-be-done. Specifically, I needed a reliable format that provided the right information. I came up with the following:

Context: “When I am…”

Job: “I want to…”

Success metric: “Increased…”

Context is important, because I want to understand the background for the job to be done. When did it happen? What was the larger goal? The job itself is the core information to be collected. The success metric told me what the customer valued in an outcome, and provided a basis for measuring whether the idea implemented to fulfill the job was actually doing so.

Focus the effort

Something I’ve learned in the realm of innovation management: focused initiatives outperform post-whatever-you-want initiatives. In other words, participants should be asked for insight on a specific topic. Otherwise the exercise risks spinning off in many directions you’re not ready to pursue.

In the game mechanics effort, I started with a definition of gamification itself. This set the tone for the discussion. I believed there were several areas that could benefit, so I focused the discussion around collaboration, engagement and three other areas. This framed the discussion without corralling customers too tightly in what they wanted to express. It also gave a nice rhythm, as we worked through each of the five areas.

Unsurprisingly in the discussions with customers, they had ideas for applying game mechanics to a job-to-be-done. Since the discussion was focused on their needs and wants, I would take these ideas and add them to a separate idea community.

Rate satisfaction with fulfillment of each job

This step was critical. It wasn’t enough to have the various jobs-to-be-done. Understanding the level of satisfaction with each one is the metric which shows where good opportunities lie.  I asked customers to bucket each job-to-be-done as:

  • Satisfaction with current outcomes = HIGH
  • Satisfaction with current outcomes = MODERATE
  • Satisfaction with current outcomes = LOW

Now, there was a natural bias for customers to provide jobs where their satisfaction was lower. They wanted to talk about things that need to be improved. But I wanted to ensure a broader look at the landscape of jobs. So in the course of the discussion, there were three sources of jobs-to-be-done that came outside of these  low-satisfaction ones:

  1. Seeded obvious jobs: I seeded the site with some of the more obvious jobs, to provide examples.
  2. Jobs provided by previous customers: As I learned new jobs from a previous discussion, I’d add them to the new discussion. This was good to see how customers felt about what others were trying to get done.
  3. Jobs elicited in discussions: After getting one job from a  customer, we’d discuss it. In that discussion, you’d hear another job-to-be-done. While that one may not be low satisfaction, it was important to capture it for a fuller understanding.

As you can imagine, there were varying views on level of satisfaction for the same job across customers. Is the software performing differently for each? No. But each customer was communicating the level of outcome they deemed satisfactory.

Rank order importance of jobs to create an Opportunity Map

After the jobs were sorted into the LOW, MODERATE, HIGH satisfaction buckets, I asked the customer to rank them in relative importance, highest to lowest. This was their chance to tell me just what they valued most. Even if their satisfaction was moderate with a job’s outcome, it was valuable to know what they deem most important. Useful for fuller understanding of why customers buy. Because you want to avoid being one of these companies:

The vast majority of companies have no clue what their customers value in the products and services they buy.

Here’s what you get after you’ve done this: an Opportunity Map as determined by the customer’s expressed needs.

JTBD Opportunity Map

After talking with each customer, you’ll be able to generate an Opportunity Map. By aggregating the responses by customers, you have a broader Opportunity Map    that begins to reflect something of the market.

Use a website to manage the jobs-to-be-done

The preceding elements of eliciting jobs-to-be-done are the method. The method needs a place to execute it. For my gamification project, I used the free post-it note site Listhings. With that site, I can add unlimited canvases (for different customers) and individual notes (for the jobs). You can color code the different sub-topics within the question you are asking.

Examples of Listhings culled from an actual customer discussion:

Listhings example

Conversations are as valuable as the jobs themselves

In the process of collecting the jobs-to-be-done, I found the discussions around each job-to-be-done to be incredibly valuable. And this is an important point: customers want to talk to you about what they’re trying to get done! The discussions were intelligent and gave me insight into their world. An insight I cannot get from data about usage or the user stories I write in a vacuum.

This experience is what makes me cringe when I read others throwing around the old Henry Ford chestnut: “If I had asked people what they wanted, they’d have said faster horses.” It assigns customers to the Dumb Bucket.

Interesting experience in doing these with 8 different customers. I would schedule a one-hour session with our admin leads at each. Inevitably, we’d run out of time in that first session. Every single customer was up for a second session. One customer even wanted a third and fourth session, bringing actual end users into it for their perspective.

Perhaps this can be a good path for a design thinking approach, as it really boosts your empathy for what customers are trying to get done as you discover their jobs-to-be-done.

Step-by-step plan

OK, that was a lot of information about individual parts of this jobs-to-be-done methodology. Let’s put it together into a plan:

  1. Establish a topic you want to explore more deeply with customers.
  2. If the topic is somewhat broad, break it down into sub-themes.
  3. Sign up for a site where you can collect jobs. Criteria for such a site:
    • Each job is visible in full on screen
    • Ability to segment the jobs by sub-themes
    • Ability to categorize jobs by level of satisfaction
  4. Seed the site with some obvious jobs that your product/service provides.
  5. Select customers to engage.
  6. Use a web/screen sharing tool to run the discussion (e.h. WebEx, GoToMeeting, Skype, Google Plus).
  7. Plan for an hour, expect to need a second one.
  8. As you talk with the customer, post the jobs in real time so she sees them on screen.
  9. Run through the satisfaction bucketing (LOW, MODERATE, HIGH)
  10. Rank order the jobs within each satisfaction bucket
  11. Collate and aggregate the responses after you have finished the customer discussion. I used Excel for this.
  12. Identify the jobs that were most important and received LOW or MODERATE satisfaction across your customers.
  13. Plumb your notes from the discussions for additional insight that will be useful

The other thing you’ll gain from this are customers who have a demonstrated interest in the phases that follow in the design and development process: ideas, prototypes, early purchasers.

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

Net Promoter Score is a window into jobs-to-be-done fulfillment

I’m a big advocate for better understanding customer needs, particularly in the jobs-to-be-done form. Companies should spend more time on this, instead of the all-too-common approach of implementing someone’s vision in a near vacuum. Although I admit it isn’t easy to do. Focus groups are a start, but are both logistically and financially hard to scale, and fraught with their own issues. So the state of getting customer insights is still fairly immature.

So I was interested when I received this email invitation from American Express:

See there? AmEx wants to better understand my needs. Charge cards fall into that overall payment realm which includes PayPal, Square, Stripe, Google Wallet, etc. It’s a deep part of our society and something we can all relate to.

Sure, the most basic job-to-be-done is:

When I am purchasing something, I want to provide payment to the seller. Success means I complete the purchase.

But is that all there is? No, of course not. I have jobs around paying off AmEx, understanding my spending habits, merchandise assurance, along with emotional ones like a feeling of assurance I can buy when I want to. AmEx even works on fulfilling a status-based job-to-be-done. Extending out from there, there are adjacent jobs related to the purchase decision process before the transaction and better understanding my financial activity after the transaction. Heck, I’ll bet AmEx could come with more areas where I have relevant jobs-to-be-done.

 I click on the Begin Survey button in the email, passing through three set-up screens first:

Now, if you look at those three screens, that’s a good amount of setup. An intro screen, a list of “helpful hints” and finally confirmation of the product I’m using. Feels like I’m about to undergo a weighty exercise.

Well…no. Rather, this is the meat of the entire survey:

A net promoter score question. NPS has become more and more popular, and is a simple report card on how your customer perceive your product or service. Customers who click 9 or 10 are called Promoters, 7 or 8 are Passives, and 1 to 6 are Detractors (read more on net promoter scores). For the record, I picked ‘7’. AmEx is a fine card, but in my wallet I can’t really distinguish it from my Visa or Mastercard. It’s the only charge card Costco takes, so I can recommend it based on that.

But a broader question occurred to me. What will AmEx do once they have all the NPS’s collected from customers? Say the NPS comes in averaging ‘9’. They’re done, right? Pretty much nailing it. If it comes in around ‘7’, they’ll wonder what is wrong, why people aren’t more gung ho.

Assuming they will take action based on the NPS collected from us customers, I can see a few paths here for what happens next.

Marketing focus: Product is fine as-is, it’s the messaging around it that we need to improve. Also, we’re not reaching customers where they are. TV is declining, we need new ways to get across why our card is better. That will get our NPS’s up.

Internal product development focus: Work on ways the card features and experience can be improved. Smart people work at AmEx, and they can come up with some interesting approaches. Focus group the ideas after they’ve honed them down to a few. That is, get customer insight after the fact.

Jobs-to-be-done focus: As Steve Blank espouses, there are no facts inside your company’s walls, get outside of your purely internal focus. Initiate a program of exploring jobs-to-be-done by customers. Incorporate customer wants into determining the design of new products. This is insight before committing to any ideas.

Indeed, the NPS is a great first cut on identifying customers to approach about getting deeper insight. Each person’s NPS is essentially a window telling you how well AmEx Green matches their particular jobs-to-be-done.

What do you think AmEx will do next?

I’m @bhc3 on Twitter.

Jobs-to-be-done’s place in a customer-centric organization

On Twitter, I asked this question:

I asked it, as I had a conversation in recent days with a fellow from a large corporate. Customer-centricity was recently adopted as an internal mantra, but the manifestation of that was…wait for it…sentiment analysis.

It’s a start, right? But is it really a difference-maker?

I’ve written recently about jobs-to-be-done. As in, what customers hire your product to do. Those jobs have a tendency to (i) be hidden from you; and (ii) change over time. Knowing, and acting on, jobs-to-be-done (JTBD acronymized) is probably one of the most customer-centric things a company can do. You’re getting deep into what someone is buying your product for.

While I don’t work for a large corporate, I am integrating jobs-to-be-done in some work on next generation gamification elements for the Spigit platform. Why? Because there are many different types of game mechanics that can be applied to a platform. But why would you add any of them? To better deliver on what your customers hire you to do. To accomplish this, I’m using the Listhings site – online post-it notes – to collect and socialize these. I follow my own format for JTBD: context, job, success metric. An actual (blurred-out) example is below:

You know what? Customers love talking about their jobs-to-be-done. Seriously.  I usually schedule an inital hour to talk about them, and every single company has wanted to continue to the conversation for another hour. The conversations are not just good customer relations, which they are. They are leading to areas where the Spigit platform can apply game mechanics to improve their outcomes.

But apparently, this approach is sort of radical. As only 7% of firms are deemed to be customer-centric.

Where would JTBD fit?

Which got me thinking. What exactly are companies doing today, at least in the product and service development arena? Where would customer jobs-to-be-done fit with existing approaches? The graphic below is my take on what’s happening out there:

The center blue area represents the work of ideating, designing and producing products and services. The top grey boxes floating around up there? Those are the current factors influencing the product/service development process.

Market Analysis: Classic input for product development here. What are the trends? What are competitors doing? What’s going on in adjacent markets? You’re got to do this. It’s a source of ideas, and evidence of what customers are gravitating toward.

Executive Fiat: Does this really happen??? Heh, just joking of course. This will be a reality forever, and it’s actually appropriate in mild doses.  The thing to watch is the bull-in-the-china-shop approach, where that product is gonna get done, I’m not listening to anyone! Perhaps too many executives subscribe to the Steve Jobs-attributed notion that customers don’t know what they want (“So I’m going to give it to them!”).

Usage Vectors: Once you have product out there, you learn what people are using, what they value in the existing product features. And you continue to develop along those vectors. It’d be irresponsible to do otherwise. Just watch getting stuck on those vectors and missing the market shifts.

Customer Service Tickets: As people use your product, they’re going to file requests and report issues. These items are some clues to what people are trying to get done. They suffer from being narrow, focused on a specific interaction point and grounded in what they know of the current product. But you can divine some of what people want to get done from these.

Customer Surveys: Surveys get you closer to customers. Polling people’s preferences for difference attributes and behaviors. Good input as you consider a product or venture. Problem with surveys is that the questions are set ahead of time. Whoever puts them together has to decide what the key factors are. But that leaves a huge hole in understanding what customers themselves value.

Focus Groups: A favorite activity of large companies is to get some random people in a room for a couple hours and ask them about some concept being tested. In that these sessions have actual people talking, they are nominally useful. But common critiques of these are that

  • Participants tell researchers what they want to hear
  • The format is unnatural – forced face-to-face interactions with strangers for two hours in a closed room
  • Alpha personalities sway things
  • What’s discussed are already-decided concepts, not insight on what customers are looking to get done

As was stated in this 2003 Slate article, “The primary function of focus groups is often to validate the sellers’ own beliefs about their product.”

Jobs-to-be-done fills a gap

In all of the popular bases for developing products and services, one can see a gap. Most are a triangulation to understanding what customers want. Now some are quite useful in a customer-centric sense: usage vectors, customer service tickets, surveys. But they’re also piecemeal.

They represent the hope that you’ve got a bead on customer needs and wants.

Why the reluctance to actually talk directly with customers? Seems plain talk in a (not overly) structured way will give you a better sense of where opportunities lie. Aside from the product/service tools listed above, there are the social media engagement practices of today (react to tweets, have a Facebook page, sentiment analysis). All have their place, but they fall short.

Want to be customer-centric? Try talking to your customers.

I’m @bhc3 on Twitter.

It’s the Jobs-to-Be-Done, Stupid!

I do product management for Spigit. I’ve done product management for other companies as well. And let me tell you, the easiest thing in the world is to fall into the trap of focusing on how customers are using your product. Product forms your relationship with customers. It’s how you know them. They will tell you about your product, and the features they want improved. You can’t not listen to that. Of course, you’re going to improve your product.

But don’t confuse that with understanding what your customers need.

Just because you’re on top of what you’re customers need from your current product, doesn’t mean you’re on top of market changes. Two titans of the television industry remind us of that. They have, in recent weeks, been dismissive of a rumored Apple HDTV:

Sharp isn’t paying much heed to rumors that Apple is developing an HDTV. Nor does it have much reason to, says Kozo Takahashi, head of the company’s operations in North and South America.

All Things D

“TVs are ultimately about picture quality. Ultimately. How smart they are…great, but let’s face it that’s a secondary consideration.” – Samsung AV product manager

TechCrunch

And there you have it. Apple HDTV? Whatever.

Of course, one might be reminded of the comment by Palm’s CEO before the Apple iPhone was introduced: “PC guys are not going to just figure [phones] out. They’re not going to just walk in.” Ouch!

What we’re seeing is incumbents falling back on the thing that got them to their position: features. This is feature-led innovation. It’s got its place in the market, but relying only on it puts companies at risk for missing either (i) critical market shifts; or (ii) emerging needs that will drive organic growth.

Divergence between Product Features and Jobs-to-Be-Done

In the graphic below, a typical scenario for feature-led innovation is depicted. What happens is that over time, companies lose touch with where the market moves, with customers’ changing jobs-to-be-done.

When a company “makes it” in the market, it has the features that meet what customers are trying to get done. On the graph above, that’s set as “Time 0”, where features match Job 1. Given this is the ticket to success, a company will of course continue to develop these features. And the people who were looking for Job 1 fulfilled will follow along as the new features are rolled out.

Somewhere along the line, a new job-to-be-done emerges. Call it Job 2. New jobs enter the market all the time, via what Re-Wired Group’s Bob Moesta calls the “push” force. After Job 2, Job 3 emerges. And on and on.

But many companies are never aware of this. There are too many customers. Product is selling. You know your company’s product, and you’ve gotten lots of feedback for improvements. Systems are in place to reward and nudge you further along the path that fulfills Job 1. When they do solicit feedback from customers, it’s all Net Promoter Scores, focus groups for new features, surveys, customer service ticket analysis. Believe me, I really can appreciate how companies get lulled into this cycle of feature-led innovation. Professor Freek Vermeulen of the London Business School calls this the innovation “success trap”.

Meanwhile, customers cast about for ways of fulfilling their new jobs-to-be-done. They improvise. They settle. They experiment. They’re open to new entrants that meet their emerging jobs. And this is how it happens to companies.

Let’s look back at what the Samsung product manager said: “TVs are ultimately about picture quality. Ultimately. How smart they are…great, but let’s face it that’s a secondary consideration.”

Here are three jobs I’d personally like fulfilled that aren’t about picture quality:

Situation Job to Be Done Success Metric
When I turn on my TV I want a set of recommendations
based on my viewing habits
Increased awareness of
shows that interest me
When I want to share a moment I want a link to post to
Facebook or Twitter
Decrease steps it takes to
share on social networks
When I’m watching a sports
event
I want to order food for delivery Decrease time it takes to find
food and place order

The first two of those jobs have emerged based on new technologies in other arenas (recommendation engines, social networks). The third is a tried-and-true job that’s been around forever. Might there be a play to improve that via my TV?

All three of those jobs-to-be-done are divergent from the ongoing focus on picture quality espoused by the incumbent TV leaders.

Parable of Digital Cameras

The feature race of the HDTV manufacturers has a parallel in the digital camera industry. A key feature of digital cameras has been the megapixels. The higher the megapixels, the better the image quality. It has been escalating so much in recent years, Consumer Reports ran a piece wondering when the megapixel arms race would cease.

But in another case of new jobs emerging, lower end digital cameras are seeing their sales decline. Why? As the L.A. times noted in December 2011:

According to a survey by NPD Group, 27% of photos and videos taken this year were shot with smartphones — up from 17% last year.

Wait a minute. Are you telling me that with all that megapixel firepower, we’re gravitating toward phone cameras? What’s wrong with people these days?

Nothing actually. There’s always been the job-to-be-done of capturing moments. It’s just that lugging around a separate camera everywhere you go is a pain. But people want to be connected – talk, messaging, email, surfing – and will gladly carry their phone with them. Which is quite sufficient to fulfill the job of capturing moments. Megapixels be damned. Of course, the megapixels are getting better on smart phones too. Clayton Christensen must be amused by the ongoing disruptive innovation.

Sharp, Samsung…heck, all companies…are you listening? How well do you know the emerging jobs-to-be-done by your customers?

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

On the Utility of Thinking in Terms of Jobs-to-Be-Done

Cottonball clouds In a recent post examining the future of retail, I used the jobs-to-be-done approach to break down the industry. And I’ve been using it more in other ways. It’s quite useful as a basis for innovation.

The premise of the jobs-to-be-done approach is that it provides a much better basis for innovation. The focus is on unmet needs of customers. Compare this to asking wide open, pie-in-the-sky types of questions.

I thought about this when I saw this question posted on Quora:

What currently nonexistent websites would you want to be created?

Wow. Talk about an open ended question. I don’t know about you, but that question doesn’t help me. I get brain freeze. I need a prompt to come up with something. Wide open questions like that are somewhat divorced from what people actually need. And will generate a lot of ideas off the mark, or none because it’s too divorced from what people are thinking about (although one guy has an idea there).

Now I’ll describe a different situation. For Spigit, I often find myself needing to come up with a new idea to show off the system functionality. If I used that question from Quora, I’d find myself straining to generate ideas that pass the smell test.

So instead, I’ve been using the jobs-to-be-done framework. I think of my own jobs-to-be-done. Here’s one I actually used to come up with an idea for a client demo:

When I’m traveling with my family on vacation, I want to keep the kids entertained happily the entire trip.

From this job-to-be-done, I came up with an idea for a long haul family SUV (or could be a minivan). It’d have storage for games, and a flat surface for playing them. A refrigeration unit on board to keep beverages and food fresh. Multimedia for videos, music and games. It would take some design genius to develop. But it’s a vehicle I’d actually take a good look at.

[tweetmeme source = “bhc3”]

And that’s the point. The jobs-to-be-done approach is incredibly useful for generating ideas that are relevant and actually have potential. You’re plumbing the depths of what people really feel and what they actually want to accomplish. A powerful head start on innovating.

OK, let’s take this one out with a little Holiday Road.

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.