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How to Provide Value to Your Organization in a Developer Relations Role

Published Mar 8, 2024

Updated Mar 8, 2024

4 min read

A few years ago, a multi-billion dollar corporation we all know and love hired what could be perceived as every developer with Twitter influence on the market into a Developer Relations role. Suddenly, everyone you spoke to worked at the said corporation. While many of us were excited to see the value of Developer Relations recognized by a prominent tech organization, many were also left scratching their heads at the alignment, or lack thereof, between what those hired Developer Relations professionals did to “evangelize or build” and how it related to their employer.

A majority of those DevRel professionals were not even creating value in communities related to that organization’s products. An insider look will tell you that the reason is that those hired were just told to “keep doing what they were doing” and not given any additional direction or focus outside of that. Without clearly defined expectations, DevRel professionals cannot provide value to your organization. This is not sustainable and no one wants this.

Below is a list of four ways that those in developer relations can deliver value to their organizations.

Aligning with your Organization's Goals and Objectives

DevRel professionals recognize that all activities should be tangentially related to providing value to the organization and ensure that these activities contribute to the organization's goals and objectives.

The best way to align DevRel efforts with organizational goals is to make sure you understand what the strategic objectives of your organization are. Is it product awareness? Is it customer satisfaction? Is it revenue growth? Is it pushing new concepts in a market? Is it fostering innovation? Depending on the objective, you will be able to better tailor your DevRel efforts to the organization.

Product Awareness as a Goal

For example, if the current goal of the company is product awareness, Developer Evangelism is the best way to provide value. Twilio is still the canonical example of evangelism done right. In the beginning, Twilio needed to spread awareness of their product, and show developers how easy it was to use.

They did this by showing up at events with developer-centric swag, creating the best tutorials for developers to follow, giving workshops on how to use Twilio APIs, giving talks about how to use Twilio, and creating a strong community of Twilio adopters that shared their use cases around the internet. Through this, the perception was that Twilio was the best solution, and considered the easiest, most obvious tool choice to use when developers were trying to build unique communication features and capabilities like voice, text, chat, video, and email into their applications.

Customer Satisfaction as a Goal

If the organizational goal is customer satisfaction, then Developer Community and Developer Experience are the two areas a DevRel professional should key in on. By building a community and creating customer feedback loops where developers can share struggles, and their issues can be heard and resolved directly by engineers and product in a quick manner, customer satisfaction will increase because you are helping solve the pain points of using the product. Creating a community allows other developers adopting the product to bond and share solutions in an open forum.

Currently, most organizations choose Discord as a way to engage developers and build community. As a DevRel professional, you are in a unique position to help with the Developer Experience in a way that engineers may not be able to. For example, the customer satisfaction metrics may be affected by a lack of documentation, which can easily be solved by improving the documentation where you see the pain points. This does not require engineering or product to get involved and can often solve developer problems overnight. There may be an issue with the migration of one technology to another.

Many Developer Relations professionals have built excellent migration tools to help developers ease this pain. Having the flexibility to work outside of the product roadmap and engineering sprints can serve as an invaluable resource for organizations and serving their needs, and the needs of their users.

Revenue Growth as a Goal

If the goal for your organization is revenue growth, then helping get developers into the top of the sales funnel is key to providing value to your organization. This does not have to be done in a way that compromises the other principle of DevRel or the core value either.

One example I’ll bring up again is Test Automation University by Applitools. By providing a free resource that was able to garner the interest of over 150,000 developers through a DevRel initiative (shout out to Angie Jones for leading this initiative), Applitools was able to collect the email addresses of all these individuals and engage them.

Since our focus is DevRel and contributing to the top of the funnel, I won’t speak to the “right way” sales or marketing should engage these developers, but I will say it is critical for DevRel to educate and advocate for marketing and sales to engage with these developers the right way that does not erode brand trust.

Delivering Value to Your Organization

Delivering value to an organization while upholding the core value of authenticity is not difficult. The hardest part is advocating internally to stakeholders within the organization on the approach to developers and helping them understand the “why” behind the DevRel strategy you are employing.

Balancing authenticity and providing value to both the community and to organizations allows DevRel professionals to thrive and achieve mutual success for both developers and the organizations they support.

Through living these core values and principles, Developer Relations is the way to forge a path of innovation, collaboration, and long-term success in the technology.

This Dot is a consultancy dedicated to guiding companies through their modernization and digital transformation journeys. Specializing in replatforming, modernizing, and launching new initiatives, we stand out by taking true ownership of your engineering projects.

We love helping teams with projects that have missed their deadlines or helping keep your strategic digital initiatives on course. Check out our case studies and our clients that trust us with their engineering.

About the author(s)

Tracy Lee
Partner, This Dot Google Developer Expert (Angular, TC39, Web) RxJS Core Team & Lead for RxJS Learning Team Contributor to Angular, RxJS, EmberJS

Key Strengths:
- Teaching developer relations strategies
- Influencer marketing
- Developing brands
- Community strategies and maintenance
- Creating product launch strategies
- Leading marketing operations efforts & standards
- Effective conference presence & speaking strategies
@ladyleet @ladyleet

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As web developers, we're often working with data from external sources like APIs we don't control or user inputs submitted to our backends. We can't always rely on this data to take the form we expect, and we can encounter unexpected errors when it deviates from expectations. But with the Zod library, we can define what our data ought to look like and parse the incoming data against those defined schemas. This lets us work with that data confidently, or to quickly throw an error when it isn't correct. Why use Zod? TypeScript is great for letting us define the shape of our data in our code. It helps us write more correct code the first time around by warning us if we are doing something we shouldn't. But TypeScript can't do everything for us. For example, we can define a variable as a string or a number, but we can't say "a string that starts with user_id_ and is 20 characters long" or "an integer between 1 and 5". There are limits to how much TypeScript can narrow down our data for us. Also, TypeScript is a tool for us developers. When we compile our code, our types are not available to the vanilla JavaScript. JavaScript can't validate that the data we actually use in our code matches what we thought we'd get when we wrote our TypeScript types unless you're willing to manually write code to perform those checks. This is where we can reach for a tool like Zod. With Zod, we can write data schemas. These schemas, in the simplest scenarios, look very much like TypeScript types. But we can do more with Zod than we can with TypeScript alone. Zod schemas let us create additional rules for data parsing and validation. A 20-character string that starts with user_id_? It's z.string().startsWith('user_id_').length(20). An integer between 1 and 5 inclusive? It's z.number().int().gte(1).lte(5). Zod's primitives give us many extra functions to be more specific about *exactly* what data we expect. Unlike TypeScript, Zod schemas aren't removed on compilation to JavaScript—we still have access to them! If our app receives some data, we can verify that it matches the expected shape by passing it to your Zod schema's parse function. You'll either get back your data in exactly the shape you defined in your schema, or Zod will give you an error to tell you what was wrong. Zod schemas aren't a replacement for TypeScript; rather, they are an excellent complement. Once we've defined our Zod schema, it's simple to derive a TypeScript type from it and to use that type as we normally would. But when we really need to be sure our data conforms to the schema, we can always parse the data with our schema for that extra confidence. Defining Data Schemas Zod schemas are the variables that define our expectations for the shape of our data, validate those expectations, and transform the data if necessary to match our desired shape. It's easy to start with simple schemas, and to add complexity as required. Zod provides different functions that represent data structures and related validation options, which can be combined to create larger schemas. In many cases, you'll probably be building a schema for a data object with properties of some primitive type. For example, here's a schema that would validate a JavaScript object representing an order for a pizza: ` Zod provides a number of primitives for defining schemas that line up with JavaScript primitives: string, number, bigint, boolean, date, symbol, undefined, and null. It also includes primitives void, any, unknown, and never for additional typing information. In addition to basic primitives, Zod can define object, array, and other native data structure schemas, as well as schemas for data structures not natively part of JavaScript like tuple and enum. The documentation contains considerable detail on the available data structures and how to use them. Parsing and Validating Data with Schemas With Zod schemas, you're not only telling your program what data should look like; you're also creating the tools to easily verify that the incoming data matches the schema definitions. This is where Zod really shines, as it greatly simplifies the process of validating data like user inputs or third party API responses. Let's say you're writing a website form to register new users. At a minimum, you'll need to make sure the new user's email address is a valid email address. For a password, we'll ask for something at least 8 characters long and including one letter, one number, and one special character. (Yes, this is not really the best way to write strong passwords; but for the sake of showing off how Zod works, we're going with it.) We'll also ask the user to confirm their password by typing it twice. First, let's create a Zod schema to model these inputs: ` So far, this schema is pretty basic. It's only making sure that whatever the user types as an email is an email, and it's checking that the password is at least 8 characters long. But it is *not* checking if password and confirmPassword match, nor checking for the complexity requirements. Let's enhance our schema to fix that! ` By adding refine with a custom validation function, we have been able to verify that the passwords match. If they don't, parsing will give us an error to let us know that the data was invalid. We can also chain refine functions to add checks for our password complexity rules: ` Here we've chained multiple refine functions. You could alternatively use superRefine, which gives you even more fine grained control. Now that we've built out our schema and added refinements for extra validation, we can parse some user inputs. Let's see two test cases: one that's bound to fail, and one that will succeed. ` There are two main ways we can use our schema to validate our data: parse and safeParse. The main difference is that parse will throw an error if validation fails, while safeParse will return an object with a success property of either true or false, and either a data property with your parsed data or an error property with the details of a ZodError explaining why the parsing failed. In the case of our example data, userInput2 will parse just fine and return the data for you to use. But userInput1 will create a ZodError listing all of the ways it has failed validation. ` ` We can use these error messages to communicate to the user how they need to fix their form inputs if validation fails. Each error in the list describes the validation failure and gives us a human readable message to go with it. You'll notice that the validation errors for checking for a valid email and for checking password length have a lot of details, but we've got three items at the end of the error list that don't really tell us anything useful: just a custom error of Invalid input. The first is from our refine checking if the passwords match, and the second two are from our refine functions checking for password complexity (numbers and special characters). Let's modify our refine functions so that these errors are useful! We'll add our own error parameters to customize the message we get back and the path to the data that failed validation. ` Now, our error messages from failures in refine are informative! You can figure out which form fields aren't validating from the path, and then display the messages next to form fields to let the user know how to remedy the error. ` By giving our refine checks a custom path and message, we can make better use of the returned errors. In this case, we can highlight specific problem form fields for the user and give them the message about what is wrong. Integrating with TypeScript Integrating Zod with TypeScript is very easy. Using z.infer<typeof YourSchema> will allow you to avoid writing extra TypeScript types that merely reflect the intent of your Zod schemas. You can create a type from any Zod schema like so: ` Using a TypeScript type derived from a Zod schema does *not* give you any extra level of data validation at the type level beyond what TypeScript is capable of. If you create a type from z.string.min(3).max(20), the TypeScript type will still just be string. And when compiled to JavaScript, even that will be gone! That's why you still need to use parse/safeParse on incoming data to validate it before proceeding as if it really does match your requirements. A common pattern with inferring types from Zod schemas is to use the same name for both. Because the schema is a variable, there's no name conflict if the type uses the same name. 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How to Provide Value to Your Organization in a Developer Relations Role

Tracy Lee

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