We segment developers into "personas" based on their development workflow.
It's very easy to assume, as somebody who works on developer productivity, that one knows all about software development—after all, one is a software developer! However, it turns out that different types of development require very different workflows. If you've never done mobile development, web development, or ML development, for example, you might be very surprised to learn how different the workflows are!
One of the most common mistakes that developer productivity teams make is only focusing on the largest group of developers at the company. For example, many companies have far more backend server engineers than they have mobile frontend engineers, and so they assume that most (or all) of the developer productivity work should go toward those backend engineers.
What this misses out on is the importance to the business of the various different types of developers at the business. You might only employ a few mobile engineers, but how much impact does their work have for your customers? Similarly, Machine Learning Engineers have a very different workflow and set of pain points from backend server developers—in fact, there's often no overlap at all in their pain points and commonly-used tools. But in many companies today, machine learning and AI are key to the success of their business.
If your developer productivity team has been focusing on only one type of developer, and it seems like some parts of the company are very upset with you, this might be why.
We segment developers into broad categories by their workflow. Obviously, each developer works in a slightly different way. But you will find groups that have large things in common in terms of the type of work that they do.
For example, you may see that there are a large group of developers who all work in Java making "backend" servers. They might use different frameworks in Java, different editors, different CI systems, or even different deployment platforms. But you'll find that a lot of their tooling is common within the group, or fits into 2-3 categories (like one team uses The New CI System and another team uses The Old CI System).
Since we use this data for survey analysis (as described below) we try to have our Persona groups be large—at least 200 people, so that if only a small percentage respond to the survey, we can still get statistically significant data about the persona as a whole. Of course, if your company is smaller, you might be doing interviews instead of surveys, in which case the personas should be whatever size makes sense for you. The key is: don't have too many personas, because that makes survey analysis hard. We currently have ten developer personas for an engineering org with thousands of people in it, and that number has seemed manageable.
Of course, if you have a small or medium-sized engineering team, you won't be able to get groups of 200 people (that might be larger than your whole engineering team!). If so, segment them out by the workflows that are most important to the business.
People often ask if they can split the personas down even further and have "sub-personas." Sure, you can totally do that. For example, you might have one overall persona for people whose job it is to maintain production systems (called SREs, DevOps Engineers, or System Administrators). However, you might have one large sub-group that works on creating tools for production management, and another large sub-group that works on handling major incidents in production. Those could be two sub-personas, because their workflows and needs are very different, even though they have some things in common.
The key here is to not make your survey analysis too complex. If the person doing the survey analysis thinks there is value in separating out feedback and scores for the SRE persona into these two categories, that's fine. However, you might want to still review both of these sub-personas in the same meeting or document or whatever process you decide on for doing your survey analysis.
Once you have some idea of what categories exist, you'll likely want to do some initial research into the current workflows of those engineers. Don't go too overboard with this. It often is enough just to interview a set of engineers (not just managers or executives) who are members of that persona. You'll want to ask them about their workflows and what parts of that workflow are the most frustrating. This should be a live dialogue, not an email or a survey, so that you can ask follow up questions to clarify. You will also learn what questions you should ask to this persona in future surveys.
Then you can synthesize the collected information into a document, and present this document to the relevant stakeholders.
One thing that can be useful here is simply describing what the usual workflow is for this type of developer. This is useful because when a tool developer wants to support all the personas at the company, they can start off by reading the descriptions that you've written, instead of having to figure out those workflows themselves all over again.
You'll also want to try to get a count of how many people are in each persona, even just an approximation, as this question comes up frequently, in our experience.
Now you will need some sort of system that categorizes developers into personas. That is, some system that will tell you what personas a person is part of. Don't create a system where managers or engineers have to fill out this data manually. It will get out of date or be missing data. Instead, there are multiple data sources you can combine to figure this out:
- A person's title.
- A person's management chain.
- The "cost center" that they belong to (usually tracked by your Finance team).
- Which systems / tools they use (possibly taking into account how much they use them).
- What codebases or file types they contribute to (or review).
When you're starting off, it is simplest to start off with whatever data is easiest to get, and accept some percentage of inaccuracy in the system. (For example, we accepted a 10% inaccuracy in the early days of our Personas system, and it didn't cause any real problems.)
Note: One developer can have multiple personas, that's fine. Sometimes people ask if we should have a "master" persona for each person, but usually when we investigate, we discover the requester has misunderstood the purpose of personas, or they are trying to work around a limitation in some other system. Usually when a person has multiple personas, it's because they genuinely work in all those workflows. Of course, it's always possible a valid use case for the "master persona" concept comes up at some point in the future.
Here are some of the personas we have defined at LinkedIn:
- Backend Developer
- Data Scientist
- Machine Learning / AI Engineer
- Android Developer
- iOS Developer
- SRE
- Tools Developer
- Web Developer
Plus we have two other personas that are unique to how our internal systems work (they are not included here because that would require too much explanation for too little value).
For us, the Developer Personas system is used primarily as part of our survey analysis, to split out pain points by developer persona.
We have a person called a "Persona Champion" who is a member of that persona (for example, for the "Backend Developer" persona, the Persona Champion is a backend developer). They do the analysis of the comments and the survey scores, and work with infrastructure teams to help them understand the needs of that Persona.
It is helpful to have a person who is familiar with the tools and workflow of the persona doing the analysis, because they can pick up important details that others will miss, and they have the context to "fill in the gaps" of vague or incomplete comments. (Or, they know who they can go talk to to fill in those gaps, because they know other developers who share their workflow.)
For more about persona champions, see the detailed description of their duties (which go beyond just survey analysis).
We have found minimal value in splitting our quantitative metrics by persona. Even when it seems like you would want to do that, there is usually another dimension that would be better for doing analysis of the quantitative metrics.
For example, let's say you want to analyze build times for Java Backend Developers vs JavaScript Web Developers. Splitting the data by persona would get you confusing overlaps—you might have some full-stack engineers who write both Java and JavaScript. It makes the data confusing and hard to analyze.
What you would want to do instead in that situation is analyze the build speed based on what language is being compiled. Then you would have actionable data that the owners of the build tools could use to speed things up, as opposed to a confusing mash of mixed signals.
Next: Persona Champions