Well-built user personas are a great guiding star for any communication-type of project. How can we extend their lifespan beyond normal use?
We use personas to guide the creation of websites, marketing campaigns, mobile apps, and many other modern experiences. Once we launch, we gauge the success of our creations for these fictitious user groups. And eventually, we create feedback loops that allow us to improve our creations using the perspective of real users. This real-time, actual customer and user feedback becomes the guiding insights for our work.
But subjective feedback is just one part of the user persona picture. For data-driven organizations, what can we do with interaction data, between user and brand, to improve our customer understanding?
Let’s explore three types of persona building and maintenance to learn how to implement real-time customer interaction data to augment and improve user personas.
Before we go any further, let’s set our definition of a user persona. Personas are basically a model of a group of people based on collective research about their traits in context to the objectives at hand. Here’s a longer read about personas and their history, if you’re interested.
For web developers, user personas might represent different people and how they would use a particular website and its various pages and functions.
For marketers, user personas might represent different kinds of buyers and the behaviors they engage in with your brand.
Different contexts and objectives will define how these user personas are built. In the end, most support the creation of an end result that helps achieve those objectives.
Each project might have more than one persona. The number of intended uses or activities you want your customers and users to do could determine the types of user personas to plan for.
Just as there are different types of users to solve for, there are also a few different ways to create and maintain your user personas.
I’ve built, rebuilt, recommended, used and (wrongfully) abandoned my fair share of user personas across content creation, software development and product support in my career so far. Here are a few standout models.
These personas are built from primary and secondary market research, including organization-run focus groups and user testing. While rich in behavioral and interaction data at first, these personas represent one snapshot in time.
This snapshot is typically enough to get your project going and solve for basic usability. But after kick-off and the creation of your product, service, or content, the relevance of this snapshot starts to wane.
Let’s use a simple support scenario as an example:
Let’s say a multi-player mobile game has a user persona named Jen. The game built the Jen persona based off of support forum research and what they’ve learned from past tickets and product reviews.
Jen runs into a lot of issues and is no stranger to contacting customer support. She’s loyal and pretty happy with the game because they’re so proactive in fixing her troubles. Customer support leaders use the Jen persona to craft better support content and customer service experiences.
While customer support reps know what qualms Jen and customers like her have with the service through phone and email logs, that’s only one part of the story.
Description: Millennial, plays the game during commuting hours and during some evenings.
Expectations: Unparalleled uptime with the game and no bugs throughout the user interface. She often makes micro-transactions in-game and wants peace of mind that they go through without trouble.
Challenge: As a power gamer, she's using many nooks and crannies of the game and its interface. Use her UI troubles as a compass to service errant parts of the game.
Primary and secondary research are a great first step toward building user personas. But, what about using all of the data that marketers collect from customers and users across tools and campaigns?
Data-augmented personas are progressive and continue to improve as more user data is collected. They’re great for two primary reasons:
Many brands with digital presences collect data on the daily. These metrics can tell us how certain product features or campaigns perform—but is that all?
Let’s return to our example.
As Jen and users like her continue to use the support and service UI, the game continues to collect interaction data. This includes where users like Jen fall off of the UI or give up, how long they spend in the service portions of the UI, and even how much money they spend on micro-transactions.
There’s more to a user’s story than the number of times they contact support about an issue. How they use a product or service (e.g. where and how they navigate it, where they get stuck, and where they succeed) can tell a brand a lot more about their users.
Finding a way to collect, connect, and unify all of this data, like with a Customer Data Platform, can create new ways to improve user personas and our understanding of customer behaviors.
Continuously aggregating this kind of customer and user data and reinvesting insights back into personas improves overall understanding, whether you’re running a mobile game or a huge e-commerce brand.
Data-first personas progressively integrate customer and user interactions and behaviors into the persona creation. This ensures we build new product, service, or content using actual, aggregated customer behaviors.
Using interaction data can add behavioral layers to persona creation that outweigh simple demographic and declarative data-based ones. New behavioral layers that tell us things like how frequent and with what intensity personas interact and use certain parts of brands can deepen our understanding of customers.
This doesn’t mean we abandon market research and other declarative data. It’s through the combination of that and interaction and behavioral data that we can create a universal, holistic understanding of users.
Brands large and small use a data-first approach to improve their products, services, and content. Netflix is a large proponent of integrating a hybrid of subscriber declarative data and an immense amount of behavior and interaction data to fuel their predictive content engine and streamline their user interface.
But even smaller brands, like the flash-sale e-retailer The Clymb, use progressive, data-first user personas and profiles to fine-tune their web retail and email marketing experiences.
There’s so much to gain from paying attention to how customers interact with your brand and having the means of attributing like behaviors with similar customer groups. These common groups are basically progressive, real-time personas.
Serving customers and users relevantly and effectively based on a progressive understanding can lead to increasing engagement, activity, purchase, and, most important of all, loyalty.
I wrote this piece because I want to tell data-driven marketers that there’s so much more mileage they can get out of user personas. They get this mileage by adding user intent, through interactions and behaviors with content, to how they define personas.
I’m attempting to serve the very persona that we built during the kick-off for this blog. In other words, we started with a basic research-driven user persona.
If my hypothesis is incorrect, I’m going to take significant findings (declarative and behavioral user data) and adjust not only my content creation, but also my user persona so I don’t make the same mistake again. This is the transition into the data-augmented user persona.
If I’m correct (and I hope so), the intent data that I gather from real users can be used to evolve and improve existing user personas. This could improve the way Lytics builds other content, how we present related topics within our app and other operational improvements, which would be the creation of data-first user personas.
You may have built a great product or content with a guiding star built from research. Imagine how much better the work can become with the collective power of actual user interactions from multiple communications channels like email, web, and mobile.
Modern sales and marketing teams typically have some kind of centralization of customer data and understanding. CRMs and support databases often keep all kinds of customer information for business reps to use to work more efficiently and customize sales and support experiences for customers.
This is a prime scenario to incorporate a progressive, data-first user persona.
Customer Data Platforms let marketers and other communications professionals combine these kinds of data with the interaction data from their apps, products, and marketing campaigns. Unified data leads to universal understanding of customers and users.
Identifying customer and user intent can improve your communications workflow:
But, it requires an appetite to unify data to create that better understanding:
Use these kinds of unified insights to add new levels of user empathy and understanding to your personas. This extends user personas after product or campaign launch. They can continue to be a relevant, accurate archetype to build new content with.
Keep them around and use the collective power of your unified customer data to maintain their relevancy. The more these behavior-driven personas evolve, the more likely they will keep your product or service relevant and purposeful.
For the data-driven professional, the contemporary buyer persona isn’t enough anymore. Those willing to take the journey to unify and enrich their customer understanding will find more engaged, responsive customers in the future.
Curious how Lytics can help you with this data-driven approach? Request a demo today. We'd love to show you how it works.
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