We’ve all had arguments about whether customers are going to want this feature or that. Maybe it’s about how the feature might work or even what colour a button might be. It’s an important debate to have, but all too often an important follow-up question is left out: how many customers will this really serve? For whatever reason, discussions about what customers want tend to focus on the functionality, opinions, or individual personas. This might have something to do with humans not being great at statistics. It also might be because it’s easier for us to understand an individual story or see how the functionality will work (e.g. through a mockup). What we all need to get better at and constantly come back to in any conversation about what customers want is quantifying the number of customers the feature or functionality will serve. If you have an existing customer base With an existing customer or user base, this quantification is sometimes straight forward, at least in the first instance. You can access data like 42 customers are using the ABC feature, so if we make an improvement, we’re delivering value to 42 customers immediately. Analytics needs to be an essential first port of call in any conversation about features. From here you can then hypothesize or conduct research to understand how many more customers your ABC feature improvement will benefit. Maybe you have some proxy data, maybe you have interviews or you might need to run a test so you can form a view like “over and above the 42 customers, we think 300 more in our customer base will take the feature up.” Now we’re getting somewhere. We have 42 customers with high confidence, 300 medium confidence. This analysis can then go further. What about in the broader market? How many customers in the total market for ACME software want this feature? This one is harder to answer. You might need to make some guesses, however, I’ve found it can give you a conclusive negative. For example, we recently looked into the total market for a particular major product feature (an integration) and found there were around only 5 companies in total in the product’s target market using the software we were looking to integrate with. This immediately stopped us from pursuing the idea further. If you have a new product or major new feature If you’re setting out with a new product, then you still need to ask the question: how many customers will this feature reach or impact? Early on, the best way to make this meaningful is to focus on how many of the customers you know it will be useful to – these need to be companies you’ve actually spoken to and have a first-hand understanding of. With new products, you might also want to consider the broad market approach I described above. You’ll find the power here of immediate negatives will help you pursue low impact opportunities. not Pro tip: plug this data back into a like RICE. product prioritisation framework

Target

Product Decisions In Large Organisations Don't Have To Be Hard

Public ASX100 APIs: The Essential List

Don't talk to McKinsey, talk to Terem

Nominated for 2022 - HackerNoon Contributor of the Year - Agile

Nominated for 2022 - HackerNoon Contributor of the Year - Agile Software Development

Too Long; Didn't Read

Download free Tech Leaders Productivity Report!

Will The Customers Like This Feature?

About Author

Comments

TOPICS

THIS ARTICLE WAS FEATURED IN

Related Stories

Untitled Story

4 Reasons You Shouldn't Use MoSCoW for Product Prioritization

10 Prioritization Techniques for Agile Product Development

6 Prioritization Techniques to Make You Stop Working on the Wrong Stuff

7 Steps to Keep Commitments at Work and Induce Growth

An Intro to Rio: A Lightweight Job Scheduler in Go

4 Reasons You Shouldn't Use MoSCoW for Product Prioritization

10 Prioritization Techniques for Agile Product Development

6 Prioritization Techniques to Make You Stop Working on the Wrong Stuff

7 Steps to Keep Commitments at Work and Induce Growth

An Intro to Rio: A Lightweight Job Scheduler in Go

Light-Mode

Classic

Newspaper

Dark-Mode

Neon Noir

Minty

HN StartUps