The Definitive Guide to Building a Product Persona

Introduction

Demographic Personas are dead! You might be wondering what I mean by that.

As we saw in Module 3 the entire concept of PQL and PQA is based on identifying which self-serve sign up users belong to a Medium or large enterprise

As Elena Verna puts it plainly

“... new accounts that fit your monetization model should be a top priority. It is important to measure free to paid conversion rates on these relevant accounts only. Paid conversion events can be either self-serve or sales-assisted. These high value accounts are typically using their work email or have indicated during the onboarding process that they are here representing their company.”

While this method is great for companies above a certain ARR threshold where most of their monetization is coming from enterprise accounts.

So far Product teams have been responsible for identifying personas based on the job the user has to get done but as mentioned above in Module 3 Revenue Teams now also need to not only be extracting value from a robust free user pool but be careful in nurturing it carefully.

What does this mean? It means that as Marketing and Growth people your PLG nudge campaigns and journeys need to be built from the users point of view.

As show here Revenue teams need to identify everyone that does not want to be contacted let's call this person ‘Talk to no one Tom’ or others like ‘White Glove Wendy’ that are looking to for support or Meticulous Monica someone who is particular about the feature usage and likely comparing your product’s feature set with your competitors.

In any PLG company there are going to be a large number of ‘No activity Nick’s’ those that are signed up and did not do any activity.

One group that is highly useful are groups of users that sign in with the same domain name but create different accounts.

Building Personas

So, what's the alternative? Building Product Personas! Let me explain what they are.

A product persona is a fictionalized representation of your ideal customer that helps you understand and design for the needs, goals, and behaviors of your target audience. Unlike market segments that are based on demographic characteristics such as age, gender, income, and location, product personas are based on how people use your product and what they are trying to accomplish with it.

As shown in the picture which has been shared often where we show two different people who have the same demographic segment but may not have the same pains and gains. This is a stark contrast to highlight why building personas is critical to be in sync with the user’s needs.

When it comes to building personas, a strategic approach is key to truly understand your target audience. One effective method is to create three hypothetical personas and write down their fears, anxieties, hopes, and dreams. This initial approach can then be improved upon by conducting interviews with users that fit the bill. It is also important to identify and write down at least one anti-persona to ensure that you are taking into account users who may not be using your product as intended.

Let's talk about anti-personas. Anti-personas, also known as negative personas or exclusionary personas, are a valuable tool for product teams to identify types of users that the product is not intended for or that the company does not want to target. By defining anti-personas, GTM teams can avoid promoting outreach to users with features that do not align with what they are looking for and instead focus on the needs and goals of the intended audience. Additionally, this approach can help the company avoid competing with other products that serve a different audience or meet different needs.

Let's take an example and walk through in more detail how this works. Here we show the persona created for a Fitness app called Fit bod.

Here we have created 5 personas and 2 anti-personas. For each persona we’ve written down the best guess at what the Fear, Anxiety, Hope and Dream is for that group of users. The first time you fill this grid out it needs to be informed based on your understanding of the pains and gains of the user base.

In this example, we have also created two anti-personas that help identify the needs and wants of users that can be assumed to be users of this product but the product is not built to meet their needs just yet.

Building anti-personas helps you crystallize who you are building for and who you know won’t be satisfied with the product. These are underserved users and a logical set to include in the product once you have served the users that are the current focus.

As you can imagine creating personas is not a one-time event. It's a continuous process that requires refining and validating your personas over time with data and customer research. This approach will ensure that you're not only meeting the needs of your current users but also attracting new ones.

Max Nimaroff, GM of DashPass provides a three step process:

1. First, you need to know your early cohorts are people that are comfortable with the friction of sign-up that the product needs. You know they're happy with the experience that you're offering. But as time goes on you realize that you've kind of captured all of those people. And so you need to evolve your product personas to identify new segments of people that are interested in what you might be doing.
2. This means you need to offer new benefits, and new value propositions that cater to them. Building anti-personas as described above is a good way to start understanding the needs and wants of new users that your product does not address so far.
3. Third, you need to really be honing in on why people are leaving your product. Uncover insights where you know the friction that you have in your product today might inform potential solutions that can help retain those people and then become value propositions for the next cohort of users.

So it's sort of a slow build over time. You don’t have to get it right from the start, but you have to keep learning and improving. You throw something at the wall, you see what sticks and then you learn from that information about why people are leaving, which then informs your product personas you build and then it becomes a cycle.

Personas at Notion

To further understand persona building let’s do a deep dive with how the team at Notion created personas

They identified a product persona called the "heavy collaborator." This persona had fears of not being able to easily share information and collaborate with others, anxieties about being limited by existing tools, hopes of being able to collaborate and make progress on projects faster, and dreams of being able to move everyone in their team faster and more collaboratively. To design for this persona, the team at Notion used data on user activity, such as comments and shares, to identify and classify heavy collaborators. They also identified anti-personas, such as "heavy sheet workers," who may misunderstand the value of Notion and compare it to traditional spreadsheet tools. By understanding both ideal and anti-personas, the team at Notion built features and go-to-market strategies that align with the needs and goals of their target audience.

Overall, the success criteria in building personas is congruence. If after creating the personas they look cohesive together you know you are on the right track. As mentioned above the next step is to identify and interview the right users and refine the fears, anxieties, hopes and dreams. You also need to make sure that what you identify is at the right level of granularity so that it can help you to build product features.

Lastly, it is also important to identify product metrics that allow you identify these personas by looking at product activity.

Let us look at how Notion decided which metrics were important in the early days? In the early stages of a product, it can be difficult to determine which metrics are most important for driving growth and revenue. Notion's approach was to rely on customer feedback and insights from their customer success team to understand which features and use cases were most valuable to users. As they gained more data on user behavior, they used data science techniques like correlation analysis to identify the actions and features that were most strongly correlated with growth and retention. This allowed them to focus on the metrics that were most important for driving long-term revenue. It's important to note that these metrics may change over time as the product and user base evolves, so it's important to continue to analyze and understand the impact of different metrics on revenue.

To identify personas, Notion’s data science team conducted a large distribution analysis early in the user journey. They capture every action in their product, and there are many actions that are hardly ever used. They focus on the features and actions that are being taken most prominently by benchmarking them against the user base. Notion then segmented them based on spend and other demographic data to identify concentrations of users. They found that heavy collaborators were very heavy on commenting and sharing, while 'view only' users were also a significant concentration. Notion’s products are being used for wiki or reading use cases, which is why they are trying to turn them into editors.

By looking at the top features being leveraged and identifying trends in user behavior within any given account, Notion found correlations between user roles and their behavior. This helps categorize users into specific personas. The categorization typically happens early in the user journey and allows us to understand the needs and preferences of different user groups. It's important to note that personas may evolve over time as the product and user base change.

Jobs to be Done

Once you have your personas defined, it is important to understand the job they are trying to accomplish, as it can help you design your product better. It is best to break down these jobs to be done across the entire customer journey of the product to gain clarity.

As shown in the example below for the fitbod app. The customer journey is on the x-axis and the personas are on the y-axis. The customer journey is defined as Exploring, Testing and Continuous use. These are very typical customer journeys stages that could also be true for many other products.

Now if we look at the customer journey for Notion, we can identify three key phases. First, the user tries to collaborate with their existing toolset. Then, they experience some sort of challenge or come up against a wall. Finally, they sign up for Notion and are able to extract value from it. These three phases become the journey for a new user considering using Notion.

It's important to understand how personas like ‘Heavy Collaborator Hari’ are coming to Notion and what they want to accomplish. For instance, Heavy Collaborator Hari wants to collaborate with their existing toolset and maximize the speed of collaboration while minimizing integrations or migrations that need to happen. They probably don't want to use multiple tools with cross-functional partners. Then, they experience some sort of challenge and start looking for all-in-one solutions that help them consolidate their tool of choice. That's when they discover Notion and sign up, wanting to extract value quickly and work seamlessly with others.

Knowing all of this informs Notion's GTM strategy. They need to focus on quick value extraction and highlight templates, out-of-the-box solutions, and sharing modules. They also need to highlight how Notion lowers friction to entry and makes it easier to work with others and collaborate. For instance, sharing a feature like duplicating a template makes it easier for new users to get started and recommend the most relevant templates. I’m sure you get the idea how the personas work can inform and optimize outreach.

Current challenges in creating Personas

An effective growth strategy demands precise triggers to inspire registered users to maximize value from the product, reducing abandonment. To build an automated and efficient system requires a combination of user activity tracking and payment information along with simple multi-channel nudging tools for growth and marketing teams.‍

Intercom, founded in 2011, is a natural tool of choice because of the richness and robustness of the solution for PLG companies. While analytics tools such as Amplitude and MixPanel allow the creation of segments and tracking behavior over time, integrating payment or other data can be challenging. Segment also enables profile  creation, but the starting price may be prohibitive along with its long installation & instrumentation time, and profiles are not easily available in other tools like Intercom. Moreover, ingesting payment data into Segment is not straightforward.

As time marches on, the sheer amount of segmented data from precise event filtering can quickly spiral out of control. There is also the problem of replicating the filters between analytics tools and Growth marketing tools. Lastly, filters are a measurement of where a user might have been in time, versus personas that show the efficacy of your outreach over time as a user progresses from newbie to power user. Data-drive personas that show user journey & product adoption, allow for stronger marketing & revenue campaigns, increase conversion rate, drive profitability, and create better feedback loops through sales, customer success, growth, marketing, and product.To effectively manage this complexity, an ideal solution requires several features listed below.

1. Ease and Richness of creating personas: Creating useful segments requires significant cognitive load. Filtering is necessary but not great at creating personas that allow you to drive revenue goals. You need to be able to review and verify metrics, create distributions (these are described in Module 3 earlier), and create data-driven, but humanized personas with a click of a few buttons.
2. Trends: Once a persona is created it's important to be able to track it over time and see how it's behaving, to quickly test & validate your growth experiments.
3. Organization: Its important to be able to easily organize the persona's into Stages such as Trial, Active, Churned and Never Converted to build your PLG User Journey, exactly where you organize the personas that are taking the journey.
4. Dark Pipeline: In the persona creation exercise it's also important to know how many users are not in any persona, to create additional campaigns to interact with this untapped, unengaged group of future users.
5. Inter-Persona Movement: It's also important to know the change over time across persona's and change per stage
6. Synchronization: Once created in a tool these personas should be easily available in any other marketing tool automatically in a single click.

Finally, Revenue teams need the ability to connect personas to an outcome that they want to drive. So for example if you want to drive a 1% increase in engagement which persona(s) should you be targeting, which tool should be used, when should that outreach occur, and who should do that?