I've noticed something from working with people involved in journey management, journey mapping, and journey analysis. They embrace superficial results for some reason. Perhaps it's one of the following reasons:

• Not authorized to dig too deep

• Not skilled enough to dig deep enough

• Not enough time allocated to the exercise

• Not enough of their team budget got spent this year so it's at risk of being reduced next year. Let's use it!

Maybe you have some other observations. Let me know in the comments. Until then, here are some tips I'd like to share based on my observations

Make sure you can take action

If you're asking end users to rate success, make sure that you would know what you could do for every single metric you ask them to rate. Don't ask questions you can't - or your organization won't - take action on.

Here's an example. Suppose you're a wireless Telco that wants to optimize the journey their prospects sometimes take when they decide to switch from another carrier and consider your brand. In this day and age, consumers have a lot of product options, but they also have a lot of channel options.

Let's say you heard that growth and better experiences come from raising the level of problem-solving abstraction. That means that you can't just study physical store experiences. You need to consider all channels, and the digital era offers more than one of those.

The problem is that your leadership believes in stores. They believe that building stores and infrastructure in rural areas is the pathway to growth. So, while studying a decision journey and a purchase journey across channels seems like a great idea, the insights generated will not be acted upon.

Yea, that's an extreme example, right? 🤫 You thought I'd be talking about questions whose countermeasure would involve some material science thing that didn't exist yet, right. That too!

While this might be frustrating, the benefit of leaving things out is that you have more room to go deep and broad in areas that make everyone comfortable. Don't worry, you will develop insights that invalidate their hypotheses anyway. 😉

Maybe then they'll unleash you into the world of real insights and innovation.

Surveys should not be qualitative

Spend your qualitative research time building a purposeful question framework in order to collect the data you need in a way that can identify value gaps through prioritization. Anything short of this is just theater for individual performers.

Open-ended questions at scale simply amplify what a pain-in-the-ass it is to synthesize unstructured and unmanaged inputs. If you take garbage in, you get synthesis bias on the way out. Maybe AI will save you.

I include hypothetical questions in this category as well. They seem structured and quantitative, but participants are prioritizing something they've never done. So, they are in an imaginary playland at your expense.

What strategic decisions are you supposed to make with this kind of information?

Don't use fancy formulas

It's easy to be fooled by fakery. But who's fooling who? When you're collecting ordinal data, use ordinal models to analyze the data.

You've probably been wowed by metric models that use fancy tools like top-two-box and aggregation before doing any calculations. I'm not saying this is smoke and mirrors ... just loss of signal.

Why not calculate things at the lowest point in the data...the respondent? You can always average up.

The reason to take my suggestion is that you don't lose information in your ranking approach. You keep all of the data and the relationships all the way up the chain.

If you have two 5-point scales and only take two data points from each and then aggregate each scale, you've lost the relationship information between those scales before you've even run it through your magical algorithm.

Is it possible that there could be missed opportunities? How about false positives? Is this what you're looking for?

It's unlikely an experienced data scientist would recommend that approach. But, apparently there are some that do. Just use simple ranking tools as they will tell you want you need to know regardless of how you slice or dice your data.

And, you'll be able to explain it to your stakeholder.

Don't be afraid to capture detail

 

If this is your idea of a customer journey, I'm going to guess you have about 2 years of experience where you were shown an infinity loop on day one and then tasked to spend 2 weeks studying consumers within the context of this loop...and then another 2 weeks...and then another 2 weeks...for a total of 52 cycles. So, you have 2 weeks of experience 52 times.

You simply can't take action on this kind of stuff. Some of the concepts (advocacy) are company-centric, or are not journeys (repeat), and all of them must be studied in great detail, individually.

There is a much better and more insightful approach that is so granular that it used to be out of the reach of traditionally trained insights researchers. In this new model you can get deep and broad, and aggregated up the chain all day long.

I actually have a base toolkit that goes so far beyond that simple diagram that I hate to give away for free, but here it is anyway: https://jtbd.one/data-driven-journeys

Don't tell anyone 🤫

 

I'm working on a fine-tuned way to use this with other complementary downstream methods, but if you want insights, this is the basis for that questions framework I mentioned earlier.

Never assume you understand customer needs

This is what it all boils down to. HiPPO culture assumes that they understand what customers need. Yet, these CX type projects fail at a high rate ... very much like products do. I'm not sure what the contribution level CX has to product failure, but they are two sides of the same coin.

Anyway, there are common reasons for failure that include lack of a strategic approach (thanks HiPPO), insufficient customer insights (low resolution lens), and inadequate technology integration (failure to collaborate across the organization).

This 👇🏻 is not the data collection mechanism. It's a multi-layered service blueprint. You see that first row? That's what the CX team produced. It's represented in the Experience layer in the next image. Each row will have it's own drill down.

There are no customer needs depicted anywhere, though. Or, nothing that I would place a bet on. 🤑

 

The problem is that the experience layer has no real data whatsoever. Nor was anything measured. It's just pure workshop material. All text, all ideas, no hard prioritization or segmentation. My toolkit lays the foundation for that kind work.

 

I had to build the rest of the layers out (and pull it all together into a blueprint) with limited time and resources. But, it got done.

If I had done this all myself, we would have had an explicit representation of each segment (separate layers) and their underserved and overserved needs. In great depth and breadth.

By the way, each column in this masterpiece is actually a separate journey (some company-centric). Exploding that out would create completely different sets of these stacks. The tools now exist to do this in a complete new and more productive way.

There's no more excuses about customer needs, whether it's product and service needs, or experience and journey needs. I've got you covered if your interested. Let me know.

To sum things up:

• Don't measure things you can't act on

• Surveys should be quantitative

• If you can't explain it, don't use it

• Fear low resolution insights

• Never assume, always measure

#cx #customerjourney #servicedesign #jtbd #innovation

In this article you will learn:

• What is a Jobs-to-be-Done (JTBD) product innovation strategy

• Should you run a JTBD product innovation strategy?

• What are the criteria for success for JTBD?

• How to implement a JTBD strategy (step by step)

• How to launch a JTBD pilot

In this article I’m going to share my recommendations on how to implement a JTBD function. It’s based on my experience working with the methodology for 15 years, experimentation, advisory, and discussions with my industry peers.

 

Why is Jobs-to-be-Done (JTBD) gaining popularity now?

Product strategy teams are looking for new innovation tactics since every single thing that’s been evangelized to them - while creating nice cottage industries - has failed to perform to their expectations. Product failure rates are as high as ever. While many are looking to Generative AI to save the day, it works with the same data they do - backward-looking. Accelerating and scaling data science misses a key consideration …

Customer Science

JTBD appears to be the best alternative (or complementary capability) to methods and practices that were designed for the solution-space. It’s a great way to focus on customers without falling into the trap of customer behaviors alone. With this approach, product leaders are less likely to burn their budgets by wasting time, effort, and dollars on approaches that have a success rate between 5-10% (and that’s a very generous range).

Imagine a world where our invested dollars weren’t wasted and we could apply what we lose today to more important problems for humanity!

Become a Supporter

What is NOT JTBD?

A lot of product leaders have been in this situation:

“We need to launch JTBD research! Let’s do it!”

• They interview a handful of customers and ask them about a purchase decision, or…

• They pick a canvas template that can run on a cloud-based whiteboard

• They fill a room with internal stakeholders and sometimes customers

• They generate ideas to fill in areas of the canvas

• They admire their work

• Their leadership asks “Now what?”

Then leadership says to the industry, “Yes, we’ve tried JTBD”

While in fact they’ve tried ‘standard ideation.’

Honestly, this is normal:

• JTBD is still an obscure strategy

• There are numerous erroneous and weak online resources. Unfortunately, they are the popular ones.

• And it has recently begun getting popular among product leaders (and marketers)

That’s why I decided to create this complete (but simple) 5 part guide.

So what is Jobs-to-be-Done (JTBD)?

If I had to define JTBD I would start by describing the 5 pillars.

• Choose and Prioritize Customer Jobs - not what they do, what they are trying to accomplish

• Develop value models for the priority jobs - these are stable depictions of the problem space which means you no longer have to field teams of super-excited employees to redo them twice a year.

• Ask the market to prioritize your model(s) - and make sure you’re asking the side of the market that is actually spending the money. Yes, I know social media platforms get money from advertisers, but the advertisers are targeting people that spend money on products. So make sure you satisfy the product … er … the users.

• Analyze the results and develop actionable insights - based on data, not guesses

• Formulate a portfolio of strategic options based on the insights - you should be getting multiple possible strategies from this kind of research. You may not be able to implement all of them, but you should consider all of them.

With these 5 pillars you will understand the true nature of JTBD.

You should focus on developing strategic options that closely align with your internal capabilities to deliver. You have to take into account capabilities you have today, capabilities you could build or develop in the near-term, and capabilities you could acquire and leverage profitably. Just because you have uncovered insights does not mean they are actionable today.

It’s different than idea-first approaches to innovation which attempt to develop ‘valuable’ ideas and then test them - expensively or inexpensively - in the market with no way of knowing what the value is, or if there is value at all before having to invest ’s.

 

Should you run a JTBD product innovation strategy?

You might not realize this, but JTBD can be a long and manual process. Historically, these projects have taken 4 to 6 months and hundreds of thousands of dollars to complete (with consultants who know how to do it properly). So, the question you should ask is: is it worth it? Here’s what you need to analyze in order to make that decision:

Time invested compared to potential return on investment

 

1. Do you have experience with the JTBD method or access to experienced practitioners?

   • Yes → Proceed to question 2

   • No → Consider the cost of education or acquiring external expertise. Is it within budget?

     • Yes → Proceed to question 2

     • No → JTBD may not be feasible at this time

2. Is the cost to educate your team or acquire external expertise justified by the expected market improvement or disruption?

   • Yes → Proceed to question 3

   • No → Explore other innovation strategies

3. Will the insights from JTBD lead to a product or service that significantly improves your market presence or disrupts the market?

   • Yes → Proceed to question 4

   • No → JTBD may not align with your innovation goals

4. Can you achieve a quick Time to Value (TTV) with JTBD insights?

   • Yes → JTBD is a good tactic for your innovation strategy

   • No → Consider if the longer TTV aligns with your strategic timelines

5. Is there a more cost-effective method that aligns with your innovation goals and provides a quicker TTV?

   • Yes → JTBD might be a secondary priority

   • No → Implement JTBD as it aligns with your strategic goals and resources

📌 Note: You should measure the cost of initial attempts to hire or learn about JTBD strategy and take that into consideration. However, you should also ask yourself what the outcome could look like spread across all future innovation research investments. If you invest to learn it, and do it right the first time it will be a game-changer for your business and everyone else will be wondering what you know that they don’t know.

 

By now, you should know if JTBD is the right approach for your innovation strategy. The next step is to understand exactly: How to implement a JTBD strategy (step by step)?

The JTBD Process: from ‘executive demands’ to market disruption

In this section I’m going to break down my simple step-by-step process for thinking through the implementation of JTBD for innovation work. Here are the 8 steps.

1. Identify the group of people (job performers) that are the focus of your research

2. Identify a set of a jobs that this group of people performs

3. Prioritize the jobs based on company objectives or market prioritization

4. Construct one or more value models from quantitative research

5. Prioritize the model(s) in the market with people who perform, or have recently performed the job

6. Analyze the results and develop a portfolio of strategic options

7. Prioritize the options and perform fast, easy, and cheap experiments

8. Begin the implementation of your final option(s)

 

 

 

 

 

 

1a. Identify a group of people your stakeholders are most interested in (Priority)

When you are working for others - which most of us are - their interests should be your top priority. They have reasons for wanting to focus on specific customer groups and use cases that you may not be aware of. But you should definitely become aware of them!

The objective of this step to begin the framing of an innovation market. This is very different than what marketers do. We cannot afford to make up personas that do not exist in the real world. We must define a real group of people who are trying to get the same job done.

In most cases, you are not starting from scratch. After all, you’re involved in a business and probably already have products and/or services. If not, we’ll address that in the other options.

Here’s a simple (very simple) decision framework you can use:

 

Create a simple decision framework to collaborate with your stakeholders

• Given your organization’s existing portfolio, or capabilities, elicit a description a group or groups on which your stakeholders want you to focus

• Confirm that the characteristics you elicit are aligned with the goals or capabilities (you can take action on) of your organization

• Give the group a common name. If you can’t do that, you need to revisit the characteristics

• Do you want a more specific group, or a less specific group?

  • [Pipefitter] or [Electrician]?

  • [Electrician] or [Carpenter]?

  • [Electrician, Carpenter, Pipefitter] or Tradesmen? ←—- perhaps they all have some jobs in common

1b. Find Job Performers Based on an Industry and Sectors that You’ve Cataloged

If you are given the latitude to craft your own research, you may want to do so within the industry that is paying your salary. What I’ve found is that jobs at the industry level are too vague and/or high level. I’ve often had to drill down 3 or 4 levels before I found something worth pursuing.

I know what your thinking: “How does he do that?”

Well…

I cheat!

Many of you know that I developed an AI-based architecture for qualitative research. When I suggested above that it takes 2-3 hours vs. 4-6 weeks I was not kidding!

📚 If you want to do it the old way, I’ve offered a lot of help in the past. Here are two options for you:

• How To Get Results From Jobs-to-be-Done Interviews | by Mike Boysen | JTBD + Outcome-Driven Innovation - the polished guide to real Jobs-to-be-Done interviews

• A Framework of JTBD Questions - This is the original (now in Notion) which helped a lot of people - if all of those anonymous google doc avatars mean anything. It even has the original JTBD canvas at the end.

For this particular exercise you can use the following prompts, which were designed and trained specifically on ChatGPT 4 (or OpenAI). This is a Notion document…

Access Notion Document

You can duplicate this Notion template if you have an account. Otherwise feel free to copy and paste them into your favorite note-taking or database app.

 

1c. Find Job Performers Based on a Functional Breakdown of Jobs You’ve Already Cataloged

This is an approach that can be useful for those of you who have already built a value model If you think of jobs as a pyramid with your value model in the middle somewhere, going lower in the pyramid means treating each step as a job.

Or, if you’ve identified contexts for the job (I cover that in my Masterclass) you can also break your job down into more focused contextual jobs (and maps). That will make your pyramid multi-dimensional but the world is like that, so be prepared to deal with it. 😂

Going higher means considering your current job as an step (it could be in any of the 9 phases) in a higher context job. Some ways to look for that:

• Review Related Jobs - These are adjacent jobs that you should be capturing during the development of any value model. They occur before, during, and after your current job. These are good candidates for adjacent steps in a higher context job

• Narrower (lower) - You can use this prompt to tighten the scope based on your current job model. This is useful when you’re trying to improve customer experience.

Narrower

• Broader (higher) - You can use this prompt to loosen the scope based on your current job model. This is useful when you’re looking for significant growth opportunities.

Broader

 

Learning JTBD is still important, but how long will this really be necessary? 😉

2. Identify the Jobs for this Group of Job Performers

Doing it the Old Way

Traditionally, we’ve done one-on-one interviews (or focus groups) with job performers (whether they used a solution, cobbled multiple solutions together, or did it themselves). These are laborious, time-consuming, and can be expensive if you are interviewing highly trained professionals. Here are some additional concerns with this process:

• People trained in qualitative research struggle - they’ve been trained with a completely different end in mind, therefore they ask the wrong questions, listen to the wrong things, and they tend to interpret most things they hear as paint points. We’re not looking for pain points.

• People with a lot of experience have elevated bias - they get too comfortable with what they know. Since this is an innovation exercise that has a much higher impact of failure than a marketing experiment, it’s what you know that isn’t so that becomes problematic. But, try convincing anyone of that.

• Every human comes with different baggage - we all have different experiences and perspectives, and these hinder our ability to think and act objectively. Give two experienced practitioners the same challenge and a different set of subjects, and you will get completely different results, guaranteed!

I linked to the definitive guides on how to conduct these interviews earlier. There is nothing else like them on the Internet.

Doing it the New Way

Imagine having access to an unlimited number of subjects - all having deep experience and knowledge in the space you are researching. Compare that to 8-10 subjects that are randomly selected, have limited experience and expertise (regardless of screening), and are being asked to validate your strawman more so than being asked to build the model through the interview.

Yes, this is a problem!

I know! You, and your colleagues, are the one exception to this. But, I clearly haven’t met you or seen your work. Call me! 📞

While I do have biases myself, I am also a student of the rules of jobs to be done. At least, I studied under an extremely rigorous method and helped to refactor all of the training content. So, they got burned into my brain.

While I do break rules - I like to call them experiments - I have designed rules into my AI prompts so that I can expect to get pretty consistent results (when using a single Large Language Model). It took over a year of designing and testing, and I have made many changes since I published them (sorry), and they do more than a good enough job to replace those biased humans. They could disprove Clay Christensen’s theory of disruption … they’re that good.

 

After all, we have more important things to do than just interview people. If that’s what you do, my sincere apologies.

If you’d like to spend a month doing interviews, have at it. I can do that work, organize it, and be ready to field a survey in a day or two. And I’m talking complex Jobs-to-be-Done surveys - not customer satisfaction surveys.

What? You don’t have a year to design and test AI prompts to identify the jobs of a group of people - and don’t know any of the rules?

Here, I’ll give you a hand. This AI Prompt is very general but will set you on the right path. It will generate a list of jobs and describe the for a Job Performer you provide. You can control how many jobs you’d like it to identify for you.

Identify Jobs of a User

3. Prioritize the jobs based on company objectives or market prioritization

You can tackle this one in a number of ways. Since your work-shoppers are going to be looking for things to do soon, you could use their skills to put together a structured decision-making workshop whose underlying theme is to eliminate as much noise and bias as possible. While many professionals have an approach to eliminate bias it’s likely that very few of them have an approach for eliminating noise. Even fewer have put the two together.

• Description of workshop - use an approach like the Mediating Assessment Protocol (MAP) as a quick and structured approach to evaluating and prioritizing the customer jobs in the market. The two factors to consider are how customers would prioritize these jobs in their lives (in the minds of the participants - who are not customers) and the capabilities of the organization to deliver solutions on high-priority jobs.

• Visualization of a market scan survey - Field a relatively simple survey to ask real job performers a number of basic questions about the list of Jobs you’ve developed. No need for job mapping since we’re just trying to determine whether there is a reasonable opportunity to add value. Rate things like Importance, Difficulty, Frequency, Spend, etc.

 

Not all priority jobs are within your reach to deliver new value:

• From the top jobs, which are closest to your current capabilities?

• Which require capabilities that you can develop or acquire in the near term?

• Which are well outside your capabilities for the foreseeable future?

4. Construct one or more value models for quantitative research

You may opt to do this the hard way, or the easy way.

 

Regardless of your approach, Jobs-to-be-Done is not about interviewing customers for their pain points. This approach always results in a recital of Job Stories that conflate functional, aspirational, experiential, and emotional concepts together. This may sound great to executives who like to hear stories after one week of research. But, the evidence shows an increasing lack of trust - due to a lack of results - in this cottage industry approach to Jobs Theory.

Results: As with a training curriculum, it’s a bad input to ask students what they thought of your course at the end: “I just loved how I could fill out the wheel, it was beautiful!”).

It’s a much better input to ask their boss how the training impacted the results of their work a few months later: “We increased market share by 20% in 3 months and now I trust JTBD!”. 😉

I realize I won’t convince everyone. As Samuel Clemens said “It’s easier to fool someone than it is to convince them that they’ve been fooled.”

• There is no shortcut to actionable insights.

• However, the laborious path to actionable insights can be obfuscated without producing an inferior results. This is the next digital transformation!

I hesitate to even point you to the work I’m doing because every single day something new emerges that makes me re-think not only my approach, but what the outcome is. “I picked a helluva year to quit drinking!” 🎥✈️

5. Prioritize the model(s) in the market with people who perform, or have recently performed the job

One of the reasons we see so much love for workshops is because relatively speaking they are quick, easy and cheap.

And, you get what you pay for.

So there are two ways to prioritize customer success (or struggles, depending on how you look at it) and one new way that is being studied by academics

1. Internal Workshops - Once again, if you’re going to do them, at least put some effort into reducing bias and noise by using a structured decision-making approach like MAP. While this will not reflect the actual market from an unmet needs perspective (although you should include it) it does facilitate an ability to deliver perspective. Approaches like this help you get closer to the correct decision. You could also develop a capability model approach.

2. Appropriate Survey Instrument - I say appropriate because many will default to letting their inexperience drive this, or worse, allow the marketing department to design it! I’ve already addressed my opinion on this several times, but here’s a link to one of those rants. I’ve been working on a streamlined approach with some friends to craft and field these at scale while also considering the structure of the output for lightning fast data model construction for analysis. It’s pretty cool!

   

   Asking the Right Questions of the Right Audience in Jobs-to-be-Done Research

   Mike Boysen

   ·

   July 30, 2021

   Read full story

3. Synthetic Respondents - I’m amenable to considering this because I’ve had so much success using them to develop entire value models in minutes instead of weeks. However, there is a difference here. In a prioritization survey, we’re actually asking for an opinion, or perspective on a set of measures of success. How to ensure that we provide every possible combination of situational factors and contexts is paramount. While academic research is happening and showing an unbelievable amount of success, the research may not align to what we’re doing here. So, as better tools emerge I will be doing a lot of testing against benchmark research

• The benchmark has to be consistent - e.g., generate models that are devoid of bias from humans

• The proximity will need to be established - what is the best way to determine how close synthetic respondents are to real respondents and what level will be considered adequate (that will likely vary by market segment).

 

6,7,8 Coordinating between problem-space research and solution-space work

We’re moving further and further away from JTBD research and into areas that are closer to implementation. Many of you are probably masters at this, and this is a completely different competency I do not wish to short-change in a simple guide.

Here are my high-level thoughts on what needs to happen.

Analyze the results and develop a portfolio of strategic options

• You will generate an overwhelming set of data points - so make sure you address your research questions back in step 0

• Identify the insights that will answer your primary questions - theme them, if necessary, into groups

• Identify potential root causes for the theme (or insights) and actions to close the gap - these are useful inputs into concept design, experimentation, and strategic road-mapping

• Do not believe there is only one strategy - kick that person out of the room!

Prioritize the options and perform fast, easy, and cheap experiments

• Consider all of your options - you won’t be able to take action on everything - at least not in the near term - since every organization is constrained on a resource level. But, you should consider all of your options.

• Prioritize your options - this will be more than just customer prioritization, you need to consider your capabilities

• Conceptualize solutions for priority options - let your design team use the inputs you provide to see what they come up with

• Determine the best option / concept through experimentation - there may be more than one that you can implement in the near term. Design fast, easy, and cheap experiments to test the concepts you’ve developed.

Begin the implementation of your final option(s)

I don’t know anything about your business, so I must end this here!

All the Best!

Mike

Become a Supporter

This is the first post in a series of posts about constructing Jobs-to-be-Done surveys. I’m sure many of you have done plenty of surveys. However, based on the reactions I’ve seen from seasoned veterans when these are put in front of them, they are aghast at the length and complexity.

Hey, you do what it takes to get the best information you can get, and the traditional ways are only good if they validate your bosses opinion. That’s not the goal here.

 

Analysis Plan

Having an analysis plan in place before initiating research serves as a structured blueprint that guides the data collection, evaluation, and interpretation processes. It ensures that the research team aligns on key objectives, methodologies, and expected outcomes, thereby enhancing the rigor and focus of the investigation. An analysis plan minimizes the risk of going off course, prevents wasted resources on irrelevant data, and ensures that the research effectively addresses specific questions or challenges. This pre-planning stage makes the subsequent stages more efficient and ensures that the research outcomes are both actionable and relevant, thereby maximizing the impact of the study.

The following is a basic breakdown for a study that focuses on consumers who have switched wireless telco providers recently. While I’ve done this study, this is not the analysis plan that was used. This essentially helps you define the approach to your research by keeping the end in mind. So, make sure you get all of the questions your stakeholders want answers to so you can make sure you have the data you need to answer them.

I recommend a workshop exercise called Question Storming. If you can’t find that on Google, ChatGPT will outline the entire process for you 😏

The following is a basic outline for an analysis plan…

Project Goals

Your goals might be different. This is just an example. But, each goal you have should be broken down into a set of bullets that provide more detail. Also, this needs to be socialized with your stakeholders to make sure they understand what they’re paying for, and that you have their agreement on the direction and outcomes of the study.

1. Understand Reasons for Switching Providers

2. Identify Customer Pain Points in the Switching Process

3. Examine Customer Satisfaction with New Plans and Phones

4. Analyze Competitive Differentiators

Understand Reasons for Switching Providers

• Conduct a qualitative analysis of consumer interviews to identify the steps they must accomplish when switching, and the customer success metrics they use to evaluate the process

• Perform a quantitative analysis of survey data to prioritize the qualitative model

• Use data mining techniques to identify patterns or trends among those who have recently switched

Identify Value Gaps in the Switching Process

• Leverage the prioritized Jobs-to-be-Done metrics to quantify the importance and satisfaction of various "jobs" in influencing customer decision-journeys.

• Understand the emotional and social aspects of the switching process to influence differentiated messaging

• Understand other (related) jobs a consumer is trying to accomplish before, during and after switching, to finds ways to get help them get more jobs done at the center of a switching decision

Examine Customer Satisfaction with New Plans and Phones

• Determine what the key drivers are around plan satisfaction and dissatisfaction

• Determine what the key drivers are around device satisfaction and dissatisfaction

• Determine what the influencing factors are that drive consumers to leave a brand, what pulls them into a new brand, what keeps them in the funnel until the switch is complete

• Determine how consumers learn about brands, plans and devices and which they trust most

Analyze Competitive Differentiators

The beautiful thing about real Jobs-to-be-Done is that it establishes common performance benchmarks for competitors. This makes the concept of brand-developed feature matrices laughable. There is no hiding behind that first column of green checkmarks any more.

• Determine well the top brands compete using a common set of core success metrics

• Determine important decision factors that no brand is talking about

• Identify important areas that need improvement, improve them, and then message to them

• etc.

Analyses

These are typical Jobs-to-be-Done categories. Feel free to adapt and extend.

• Top struggle / effort / difficulty

• Unmet needs

• Unique success measures (rank diff)

• Competitive analysis

• Derived importance

• Willingness-to-pay

Additional Data Cuts to Produce

If you need to go beyond the basic Jobs-to-be-Done analysis (e.g., needs-based segmentation), you will need to take that into account when determining your sample size. I’m not a statistician, so you can follow their advice and get cuts at n=30. But people tend to have more confidence in larger numbers. Of course, you’ll pay the price.

• Segmentation by age and demographic variables

• Segmentation by type of plans chosen (e.g., prepaid vs postpaid)

• Etc.

Visualizations

We live in a visual world. Most people making strategic decisions don’t want plots of data, they want answers, and the generally want them supported by great stories. So, maybe a story board is the best visualization, supported by the items below, if necessary.

• Prioritizations (heatmaps) of customer success metrics

• Needs-based segmentation landscape

• Prioritization and Segmentation tables

• Dynamic dashboards for specific analyses

Formulate Strategies Based on Needs

Identify what types of strategies you want to, or need to formulate based on the reason driving the survey. These are just suggestions.

High-level Recommendation Categories - Marketing and Sales

• Target existing offerings at segments and needs

• Message existing product strengths

• Improve your marketing communications

High-level Recommendation Categories - Messaging

• Borrow features from other products

• Differentiate with new features

• Build out the ultimate platform/channel to get all job steps done

Within One Segment (Unique Opportunities)

• Recommendation to address the most underserved need - the metric is underserved by a significant margin

• Recommendation to address a group of underserved needs that are thematic - e.g., quality of customer service

• Recommendation to address a group of underserved needs that are within a job step - porting of existing number

• Recommendation to create a new platform as all needs are underserved - comprehensive self-service portal, or mobile app

Within Multiple Segments (Common Opportunities)

• Recommendation to address the most underserved need - .e.g., network coverage (need is underserved by a significant margin)

• Recommendation to address a group of underserved needs that are thematic - e.g., cost and billing transparency

• Recommendation to address a group of underserved needs that are within a job step - e.g., ease of reaching customer service

• Recommendation to address a group of underserved needs that are thematic in one segment, and on the border of another - e.g., device upgrade options

• Recommendation to address a group of underserved needs that are within a job step in one segment, and on the border with another - e.g., quality of customer service

• Recommendation to create a new platform as all needs are underserved - unified app for all customer needs

Sample Plan

An appropriate sample plan is crucial for the integrity and validity of a consumer survey, serving as the foundation upon which reliable and generalizable conclusions are drawn. A well-designed sample plan ensures that the population surveyed is representative of the larger target audience, mitigating the risks of bias or skewed results.

It outlines key parameters like sample size, selection criteria, and sampling method, which together determine the survey's margin of error and confidence level. Without a carefully structured sample plan, there is a heightened risk of collecting data that is not only misleading but also incapable of addressing the research objectives effectively, thereby compromising the entire study.

There are many sources available that will help you to define a sample plan, so I won’t revisit them here. The following is one way you could describe this to a 3rd party research organization given the example study we’re using.

Overall sample frame

We will survey 1500 adult cell phone users in the USA to rate the desired outcomes they are trying to satisfy when deciding to switch to another telco provider.

The sample is designed as a random sample with quota groups to ensure representation across subregions of the US, ethnicity, population density, and wireless telecommunications provider.

Consumption job sample minimums: 1500 adult cell phone users

 

These may not be the dimensions that you want to know about, so feel free to add in your own. What we do know in Jobs-to-be-Done research is that while these may be somewhat helpful for certain marketing activities, customer needs do not break down by these categories. In fact, on average everyone in these groups will appear to be adequately served.

That’s all for this one. There are generally 8-9 sections in a survey, but I will only be covering 8 of them, so 7 more to come.

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