Advertisements

How self-driving vehicles can fix the San Francisco housing crunch

In the San Francisco Bay Area, home prices have seen significant appreciation the last few years:

Source: Paragon Real Estate Group

Source: Paragon Real Estate Group

In the Bay Area, skyrocketing home prices and rents have driven people out of the area. They look for homes in further-out suburbs and exurbs, extending their commutes to work. And the Bay Area leads the nation in the percentage of people who are mega-commuters (pdf). If you’re raising a family, you accept that long commutes (e.g. hour or more) are the price you pay to have a home for your children.

It’s frankly worrying the way prices continue to rise and people are being pushed out further and further from the employment centers of the Bay Area. Similar types of scenarios are playing out in New York City, Washington D.C., Los Angeles, Chicago, etc. It causes a rising level of stress for working parents, trying to excel both at work and at raising their children.

The general response has been a call for the creation of more affordable housing. Which is a very desirable objective, and needs to be pursued. There is no other near-term relief.

But project yourself forward a few decades. A time when the roads will be dominated by self-driving vehicles. Many benefits to individuals and society will open up. One valuable outcome we will see is this:

————————————————————-

Commute time becomes regular work time

————————————————————-

Once you’re freed from having to drive, having to pay attention to the road, you’re afforded new options. Participate in conference calls. Respond to emails. Write documents. Prepare presentations. Run the numbers. Access files in the corporate drive. Engage in conversations. Read reports. Update project plans. Etcetera, etcetera, etc…

As Bay Area companies compete for workers, I expect that they will recognize the challenge of the housing market. Rather than continue to pay escalating salaries so people can afford to live in San Francisco, Santa Clara, Palo Alto, etc., companies will try a different approach. Hire people who live much further out.

See the map below, outlining Northern California median home prices:

Northern California regional home prices

Let’s take Roseville, CA as an example. Roseville is 106 miles away from San Francisco, with at least a two-hour commute. And look at the home price difference. You can buy a typical home in Roseville for nearly $700,000 less than what it costs in San Francisco. Per Chase Bank’s mortgage calculator, one can afford a house in Roseville with $100,000 in household income. In San Francisco, you need a household income of $266,400.

Look at that from a Bay Area employer’s perspective. You can spend a lot less on workers who live further out. Of course, that comes at a cost in terms of worker productivity. Or does it?

Let’s assume companies get wise to the benefits of hiring people who live far away from the office. You can imagine a worker’s daily schedule looking something like this.

Extreme commuters daily schedule

Such a schedule would provide for:

  • Productive work time during expected work hours
  • In-person face time at the office
  • Parenting time
  • More money in the employee’s bank account
  • Less money spent on compensation by employers

Self-driving cars are really the enabling technology for this schedule. We can look at the much discussed Google Buses for some insight. On Quora, Google employee Mary Xu says she uses the bus commute to Mountain View as productive work time. Which validates the possibilities here. However, in a discussion forum about Google Buses, user gnomatic notes that the shared resource of the bus does restrict worker productivity. Wifi can be overtaxed, and phone calls are considered bad etiquette. Which means that individual vehicles are better for realizing the worker productivity.

Self-driving vehicles will radically change the game for us in the decades ahead, a point made also by Reid Hoffman in a terrific post. I fully expect the nature of where we live and work to be altered by autonomous vehicles.

I’m @bhc3 on Twitter.

Advertisements

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.

Exactly what jobs will self-driving cars satisfy?

On Twitter, I made this observation about the future of self-driving cars:

A moment later, Megan Panatier made this skeptical counterpoint:

This is a great example where it pays to consider the jobs-to-be-done. Self-driving is in the realm of experimentation right now. There’s no hindsight of how obviously this was going to be a success. Self-driving vehicles could end up being the next Segway. An interesting technology that never catches on.

Image via Engadget

How can we begin to know self-driving cars’ fate? Do some outside-in market analysis. Understand what jobs-to-be-done relate to the act of commuting. Know those, and you can determine what opportunities exist for self-driving cars.

To that end, here are four relevant jobs-to-be-done that I see:

  1. I want to get from point A to point B
  2. I want to get work done
  3. I want to improve the environment
  4. I want to enjoy my personal interests

Where can self-driving help? Know that, and you can see how it will fare in the future. In the analysis that follows, self-driving is compared to two common alternatives: regular, manually driven cars; and public transit like buses and trains. Concepts from the Strategyn jobs-to-be-done innovation approach are used to assess the alternatives: outcomes and satisfaction with those outcomes. While a typical job has 50 – 150 outcomes, we’ll focus on a few summary level outcomes here.

Job #1: Point A to Point B

This is the core job of driving. Getting from one place to another. What’s key here is understanding the important outcomes that are desired for this job. The table below shows outcomes for this job, and how well satisfied they are for different transit alternatives.

Outcomes Regular car Bus & train Self-driving
Minimize commute time Satisfaction - medium  Satisfaction - low Satisfaction - medium
Minimize accident risk  Satisfaction - medium  Satisfaction - high  Satisfaction - high
Minimize commute stress  Satisfaction - low  Satisfaction - high Satisfaction - high
Increase driving enjoyment  Satisfaction - high  Satisfaction - low  Satisfaction - low

Reviewing the desired outcomes, where might self-driving vehicles provide an advantage? It’s dependent on how the different alternatives are considered. For instance, self-driving vehicles will not provide better commute times than manually-driven cars. But they are better than what buses and trains provide. Buses and trains are bound by set routes and schedules. These inject delays in commute times. Cars generally have an advantage here because of their direct door-to-door operation.

But self-driving vehicles do provide improvements over regular cars on two other outcomes: accident risk and commute stress. A great opportunity in ‘accident reduction’ applies to driving under the influence of alcohol. Self-driving cars will get you home safely. In this sense, they are more akin to what Megan Panatier tweeted. They’re like trains.

Taking those three outcomes together, it becomes clearer that self-driving vehicles will provide greater satisfaction on the Point A to Point B job-to-be-done.

There is one outcome where self-driving cars are a step backwards: driving enjoyment. Think about those commercials with high performance vehicles speedily taking curves on beautiful rural roads. The high performance manually driven car market will still be intact even in a world of self-driving vehicles. People will want that visceral pleasure.

Job #2: Get work done

A recent survey sponsored by Jive Software highlighted that people are working outside office hours more and more. While the causes of this vary, the result is that this has become an important job-to-be-done for many. Let’s look at the key outcomes for this job.

Outcomes Regular car Bus & train Self-driving
Increase digital work completed  Satisfaction - low  Satisfaction - medium Satisfaction - high
Increase availability for conference calls  Satisfaction - medium  Satisfaction - low  Satisfaction - high
Minimize distractions  Satisfaction - medium Satisfaction - low  Satisfaction - high

Self-driving vehicles really shine in this job-to-be-done. They essentially become traveling offices. Fewer distractions and the ability to focus on the work tasks at hand.

The other advantage is better availability for conference calls. Ever tried to be on a work call while driving? Your focus is diverted by driving issues. And you really don’t want to be one of those people who loudly talks on the phone while commuting on a bus or train. When a conference call includes a shared screen, you can participate on that via the self-driving vehicle vs. driving a car.

Getting work done is one of those jobs that you might not associate with commuting. But self-driving opens up the ability to better satisfy this longstanding job.

Job #3: Improve the environment

Improving the environment continues to be an important job-to-be-done for a majority of Americans, and the world. And driving is a critical aspect of environmental impact. Two outcomes are assessed for this job below.

Outcomes Regular car Bus & train Self-driving
Reduce emissions  Satisfaction - low  Satisfaction - high Satisfaction - low
Reduce fossil fuel consumption  Satisfaction - low  Satisfaction - high  Satisfaction - low

When self-driving vehicles are considered as replacements for trains and buses, it’s possible that environmental benefits may be conflated between the two alternatives. Public transit is often touted for its environmental benefits.

But self-driving cars are not public transit. They will still have the same environmental impact of regular cars. Now, as automakers continue to improve the environmental impact of vehicles (electric vehicles, hybrids), then self-driving cars will follow the same improvement curve as regular cars.

However, self-driving vehicles provide no improvement on satisfaction for the key outcomes of the environmental improvement job. Indeed, to the extent they replace public transit (bus, train), they could contribute to increased environmental issues.

Job #4: Enjoy personal interests

Enjoying personal interests is a job that we do everywhere. Read in bed. Crochet during a television program. Engage in physical activity. Video gaming. There are numerous individual jobs-to-be-done here, but we’ll lump them into a summary job for this analysis. Below are two outcomes for this job-to-be-done.

Outcomes Regular car Bus & train Self-driving
Increase time spent on activity  Satisfaction - low  Satisfaction - high  Satisfaction - high
Minimize distractions  Satisfaction - low  Satisfaction - low  Satisfaction - high

Similar to the ‘get work done’ job, this job is well served by self-driving cars. Regular cars really prevent the ability to enjoy a range of personal interests, due to the majority of time spent on…actually driving.

The individually controlled environment of a self-driving car also facilitates more engagement in personal interests. No competing phone calls, loud conversations, crowded space.

Self-driving vehicles will be fantastic for this job-to-be-done.

Conclusions

Based on analyzing the jobs-to-be-done, two conclusions can be drawn about self-driving vehicles.

Target market: urban areas. The jobs and outcomes outlined herein point to a better fit of self-driving vehicles to urban areas and the surrounding suburbs. People have longer, more stressful commutes than in rural and lower population areas. They also tend to have professional employment where digital work and conference calls are more the norm.

Urban areas do not lend themselves to driving enjoyment. Hard to take those curves when there are red lights, sharp corners and lots of traffic around you. So the ‘increase driving enjoyment’ outcome – a weakness of self-driving – is less relevant in these geographies.

Future design. The current look of a self-driving vehicle is essentially that of a regular car. And why not? The technology is being tested and iterated. No need to adjust a car while the technology is on that stage.

Eventually, self-driving technology will be perfected and be ready for broader adoption. Then the jobs and outcomes outlined herein become more relevant. What we currently know for car interiors and shapes will most certainly change. The basis of design changes from optimizing the driving experience (the outcomes) to optimizing for other jobs-to-be-done. One can imagine basic manual override driving capability for vehicles as a back-up in case the self-driving technology fails.

But the focus of design changes. Vehicles will be optimized for existing jobs-to-be-done that can now be newly satisfied via the self-driving technology. And new internal accessories will be developed to take advantage of this expansion of the market through increased jobs-to-be-done. Like a little exercise during the commute? How about a modified stationary bike inside your car?

Self-driving vehicles will be a source of significant new market opportunities.

I’m @bhc3 on Twitter.