What is the Jobs-to-be-Done (JTBD) test and why use it?

The JTBD test is a powerful and comprehensive methodology that helps you identify the most important goals and outcomes for your brand or product category.

What is JTBD?

Created by Anthony W. Ulwick (2016), the Jobs-To-Be-Done (JTBD) Theory illustrates the process of a consumer adopting an innovation to reach goals. Based on this theory, people have goals or outcomes that they wish to accomplish, and they will look for innovations to “get their jobs done”.

For example, people would purchase a vacuum cleaner for several reasons. Some of them buy it to “clean the floor effortlessly”, while others buy it to “clean narrow spaces”. The “jobs” can incorporate emotional and social aspects as well, where some people buy a vacuum cleaner to “make them feel refreshed to have a clean environment”. Identifying the “jobs” that consumers actually want to accomplish allows you to better position the feature prioritisation of your product concepts.

When launching a new product concept, you should focus on fulfilling these desired outcomes to meet consumer needs, i.e. you should answer the question of “what JTBDs can my product help customers achieve?

JTBD testing is a research methodology that allows you to identify the most important outcomes for your brand or product category. Its main advantage is that it can reveal the underlying consumer needs and identify new business opportunities for you.

Key JTBD metrics

You can easily compare the performance of a list of JTBDs and determine the top performing JTBDs for features prioritisation based on the following primary metrics:

  • Preference score: This is a scaleless score that is derived from choice-based questions where respondents pick the JTBDs that motivate them the most to purchase your product. It quantifies how well each JTBD performs relative to each other.

  • Diagnostics: There are two compulsory diagnostic measures to understand customers’ perceived importance of each JTBD and satisfaction with its current solution. These measures can help you discover any unmet needs in the opportunity analysis, which will be discussed below. You can also specify a few other diagnostic measures such as “personal relevance”, “uniqueness” and “value of purchase” for respondents to evaluate on each JTBD. With these measures, you can assess consumers’ perception and attitude towards the JTBDs.

  • Brand associations: You can also test the degree of brand association for each JTBD, where you can see a summary table of what percentage of respondents associate each claim with different brands.

The following table summarises these metrics for each job:

Summary of preference

In the example above, you can interpret that “Managing my time more efficiently” has the highest preference score, which means that it is the most preferred JTBD among respondents. In order to appeal to consumers, Company ABC should incorporate the top JTBDs when launching the app. You can also easily assess and compare the diagnostic metrics across JTBDs.

Brand association table

Based on the above brand association table, you can also identify the extent to which each JTBD is associated with your brand compared to competitor brands. In this case, Company ABC is mostly associated with “Managing my time more efficiently” and “Analysing my habit streaks for further insight”.

Discovering unmet needs as innovation opportunities

You can also look for any unmet needs among consumers as an innovation opportunity to fill in the market gap through opportunity analysis.

By using the two compulsory diagnostic measures (perceived importance and satisfaction), you can derive a powerful metric called Opportunity Score to assess which JTBDs have the biggest gap between its perceived importance and satisfaction with current solutions. This helps you identify any unmet needs among customers for potential innovations.

To illustrate better, you will also obtain an Opportunity Map that charts your JTBDs using the two compulsory diagnostic measures. This chart describes the positioning of each JTBD, and is split into three zones: Overserved, Appropriately Served, and Underserved. JTBDs that fall into Underserved category are those with higher opportunity score - hence, greater opportunity to capture consumers’ attention.

Opportunity Map

Based on the example Opportunity Map above, the red area is the Overserved region, while the yellow area is the Appropriately Served region, and the green area is the Underserved. JTBD2 and JTBD5 fall under the Underserved region, which means that consumers are not satisfied with the current solutions or resources for these JTBDs considering their perceived importance. Therefore, Company ABC can focus on innovations revolving around these two JTBDs to fill in the gap.


In a JTBD test, there are two compulsory diagnostic questions on a 5-point Likert scale to help us identify the opportunity:

  • Perceived importance of a JTBD: How important is the following statement to you?
  • Perceived satisfaction with the current solutions for a JTBD: How satisfied are you with the current tools/resources you have to fulfill the following statement?

These two diagnostic metrics will be summarised as a top two boxes score, which is the proportion of respondents who chose 4 or 5 on the Likert scale. Using the top two boxes scores, you can derive an Opportunity Score for each JTBD, which represents the gap between the perceived importance of a JTBD and the perceived satisfaction with its current solutions. The equation of the calculation is as shown below:

$$ \textrm{Opportunity Score} = 10 \times (\textrm{Top2Box}_\textrm{Importance} + max(0, \textrm{Top2Box}_\textrm{Importance}-\textrm{Top2Box}_\textrm{Satisfaction})) $$

As seen in the equation, the score is often multiplied by 10 for easier interpretation, resulting in a metric ranging from 0 to 20. A high Opportunity Score indicates a greater opportunity to target consumer needs for a certain JTBD.

Determining the priority list of JTBDs that maximises audience reach

The JTBD test can also help you acquire more customers by determining the priority list for launching JTBDs incrementally, with the use of Conjoint.ly’s TURF simulator to simulate the reach for the respondents.

Specifically, the TURF simulator will look for the singular best JTBD that has the highest reach to the customers. With the first JTBD established, it will search for the next best JTBD that (combined with the first JTBD) leads to the highest reach. The same calculation process is done to determine the subsequent best JTBDs to increase the reach until the number of JTBDs you specify.

For example, if you specify ten JTBDs, then you will obtain a priority list with 10 JTBDs, with the first JTBD leading to the highest reach, and the subsequent JTBDs contributing to the reach incrementally. This gives you an indication of which JTBDs you should prioritise over the others to acquire the majority of consumers.

TURF output

As seen on the example TURF output above, JTBD1: Managing my time more efficiently is the best performing JTBD to reach 29% of respondents. Subsequently, we see that prioritising JTBD2: Reminding me to complete my daily routine together with JTBD1 will contribute to an additional 19% of reach. With the top ten JTBDs being prioritised, the total reach will be 82%.

Segmenting consumers based on their needs

You can further utilise the outputs above to perform customer segmentation based on their JTBD preferences. This provides you an insight on the types of consumers with different needs to target.

In practice, customer segmentation is done by performing an analysis technique called K-means clustering, whereby respondents will be repeatedly assigned into k number of segments (usually from 3 to 7) using the individual preference for the JTBDs as the inputs, until the segments become optimal and stable.


The resulting customer segments will have distinct preferences for certain JTBDs, and you can utilise this information to optimise the features of your products to target each segment. Additionally, you can perform profiling analysis to understand the demographic and common characteristics of the members from the same segment for more insights.


In overall, the process of customer segmentation still requires a lot of human touch to refine the results. If you would like to tap into our expertise for clustering analysis, please feel free to approach us for a custom project.

Try the JTBD test today

Do you want to efficiently test JTBDs to identify customer appeal, fit with brand, and any other diagnostic questions of your interest? Conjoint.ly’s JTBD testing is a powerful and comprehensive methodology that helps you determine the most important features for your brand or product category.

Please feel free to book a call with us to launch your first ever JTBD test!

Written on 27 April 2022 by:
Jun Jie Chow image
Jun Jie Chow
Market Researcher

Read these articles next:

State of the Consumer Product 2019

State of the Consumer Product 2019

Case studies Strategy and innovation 24 December 2019

We explore the top consumer product trends in 2019 across several markets, covering: Digital Disruption, Sustainability, Health, Online Habits, Chinese Market. View article

Unpacking Subscription Box Market Trends

Unpacking Subscription Box Market Trends

Strategy and innovation 30 March 2020

Many businesses now offer subscription boxes as part of their marketing strategies and distribution methods. We analyse this trending category and discover why it so popular in 2020. View article

Understanding Brand Preference through Market Research

Understanding Brand Preference through Market Research

Strategy and innovation 21 September 2020

This article examines the importance of establishing brand preference and brand equity and explores how market research helps businesses shape their brand to fit their target audiences. View article