Hey everyone. I’ve been missing in action, but I wanted to run something by you all.
In the JTBD world we always talk about needs-based segmentation, where a segment (or a group of segments) could have dozens of unmet needs. But isn’t it the reality that we only have limited resources, so addressing all of them is impossible? How many times have you heard an Executive ask what the one thing is that they should do now that will have the desired impact?
What if you could simply find the Top n needs that would address the largest part of the market? Would that interest you? If so, I’d like to hear your thoughts…and check this out!
That would be interesting to know if there was a better and faster way to determine the needs than needs-based segmentation, especially with the limited resources that smaller companies have. The post mentions that if you don't have the data, they can coach you on how to get it. That is the big question, how do you collect that data and how do you know that the results that it produces are accurate? There is what I want but if the data shows that a set of customers won't produce that then which set of customers will produce that. Maybe my time is better spent focusing on the first set and changing my business model, but how do I know this? Bottom line is generating revenue, I just want to be sure that I understand which customer segment and which needs, if served by a product fit, will generate above average revenue with the most efficient allocation of resources relative to the alternative options.
* I see three approaches - interviews, surveys, and experiments.
How do you know it's accurate?
* Interviews produce weak signals, surveys are more robust but still produce weak signals, and experiments produce strong evidence.
How can we be confident which customer segments and needs will generate above-average revenue?
* We run a mock sales experiment with 250 people.
Here's how it could look in practice:
* 10 Interviews to discover needs (weak signals that are useful for widening our options)
* 500 survey participants to assess the needs on opportunity metrics (more robust but still weak signals - proper for reality testing our assumptions and screening for the most critical needs)
* 250 mock sales experiments to test at least three variations of our ideas against the critical needs.
From our interviews, we might discover 50 needs. From our survey, we might find the seven that are critical. We use the Opportunity Ladder to guide us. We then develop ideas that reduce customers' time, cost, and effort to fulfill their needs. Finally, we take the three most promising concepts and run our mock sales experiment.
We might learn:
Concept 1: 10% revenue difference
Concept 2: 20%
Concept 3: 30%
We then have strong evidence of segments, needs, and products and produce above-average revenue at an acceptable cost and risk.
Should we focus on improving the product, service, or business model? All these elements exist to serve customer needs. This process helps us to move from being uncertain to closer to knowing.
What if we don't have the resources to hold interviews and run surveys? Then, above else, run experiments. Don't settle for less!
There are many experiment options. An example is a mock sale experiment.
For an online product:
* Create a simple landing page
* Insert concept descriptors and price options
* If clicked, show a “we’re not ready yet” pop-up with an email signup form
* Integrate web analytics to collect our data
* Drive traffic to the page
* We review the analytics to see how many viewed, clicked and signed up with their email
It works for physical products in retail, too. We create a high-fidelity prototype and intercept folks in-store before they exit with their mock purchase.
This is just one of several experimental options. Interaction prototypes, crowdfunding, landing pages, pop-up stores, etc.
There is almost always an option to experiment in a way that fits a budget.
It all depends on who's asking the question, and the level of fidelity needed in order to answer it.
The data mentioned is fairly traditional ODI data and it comes from a fairly traditional job map and outcomes (however you decide to frame them). The difference here is that instead of doing all of the segmentation experiments and analyzing the results, and correlating to complexity or situational factors, some people may already have a product and just need to know what 2 or 3 things they can do that 75% of the market will value.
That would be interesting to know if there was a better and faster way to determine the needs than needs-based segmentation, especially with the limited resources that smaller companies have. The post mentions that if you don't have the data, they can coach you on how to get it. That is the big question, how do you collect that data and how do you know that the results that it produces are accurate? There is what I want but if the data shows that a set of customers won't produce that then which set of customers will produce that. Maybe my time is better spent focusing on the first set and changing my business model, but how do I know this? Bottom line is generating revenue, I just want to be sure that I understand which customer segment and which needs, if served by a product fit, will generate above average revenue with the most efficient allocation of resources relative to the alternative options.
Dennis, Happy Friday, and good to meet you.
How do you collect the data?
* I see three approaches - interviews, surveys, and experiments.
How do you know it's accurate?
* Interviews produce weak signals, surveys are more robust but still produce weak signals, and experiments produce strong evidence.
How can we be confident which customer segments and needs will generate above-average revenue?
* We run a mock sales experiment with 250 people.
Here's how it could look in practice:
* 10 Interviews to discover needs (weak signals that are useful for widening our options)
* 500 survey participants to assess the needs on opportunity metrics (more robust but still weak signals - proper for reality testing our assumptions and screening for the most critical needs)
* 250 mock sales experiments to test at least three variations of our ideas against the critical needs.
From our interviews, we might discover 50 needs. From our survey, we might find the seven that are critical. We use the Opportunity Ladder to guide us. We then develop ideas that reduce customers' time, cost, and effort to fulfill their needs. Finally, we take the three most promising concepts and run our mock sales experiment.
We might learn:
Concept 1: 10% revenue difference
Concept 2: 20%
Concept 3: 30%
We then have strong evidence of segments, needs, and products and produce above-average revenue at an acceptable cost and risk.
Should we focus on improving the product, service, or business model? All these elements exist to serve customer needs. This process helps us to move from being uncertain to closer to knowing.
What if we don't have the resources to hold interviews and run surveys? Then, above else, run experiments. Don't settle for less!
Eric, you might want to elaborate on what experiments look like.
There are many experiment options. An example is a mock sale experiment.
For an online product:
* Create a simple landing page
* Insert concept descriptors and price options
* If clicked, show a “we’re not ready yet” pop-up with an email signup form
* Integrate web analytics to collect our data
* Drive traffic to the page
* We review the analytics to see how many viewed, clicked and signed up with their email
It works for physical products in retail, too. We create a high-fidelity prototype and intercept folks in-store before they exit with their mock purchase.
This is just one of several experimental options. Interaction prototypes, crowdfunding, landing pages, pop-up stores, etc.
There is almost always an option to experiment in a way that fits a budget.
It all depends on who's asking the question, and the level of fidelity needed in order to answer it.
The data mentioned is fairly traditional ODI data and it comes from a fairly traditional job map and outcomes (however you decide to frame them). The difference here is that instead of doing all of the segmentation experiments and analyzing the results, and correlating to complexity or situational factors, some people may already have a product and just need to know what 2 or 3 things they can do that 75% of the market will value.
https://www.linkedin.com/feed/update/urn:li:activity:6956315546619445248/
In case the link above is unclickable