The Macbook Pro is the gold standard of laptops among creators.
One fine day, Apple did something strange. They replaced USB C ports with all others. You needed an adapter to connect a memory card or an HDMI cable. The infamous ‘touch bar’ replaced the function keys (F1-F12).
Apple received years of continued backlash for these hardware changes. After talking to their users, they understood the error of their ways.
Apple’s researchers distilled insights from their interactions with various types of users, so they deeply understood different user workflows.
They turned that info into different personas of Macbook Pro users, which they used to tailor products to specific preferences.
Personas help companies create products that customers actually need so they keep coming back for more.
What Makes a User Persona?
A company’s ultimate goal is to create value for its customers. Harvard’s value stick illustrates that customer delight is an integral part of creating value and capturing profit.
So how do firms increase customer delight?
They build personas to understand more about their users. User personas are archetypal users who represent a large group of people with similar:
- Demographics. Age, occupation, gender, education & income
- Psychographics. Lifestyle, goals, needs, motivations, attitudes, frustrations
- Behavior. How do users interact with a product?
Well-rounded user personas incorporate a mix of quantitative and qualitative data. Quantitative data from analytics and surveys helps researchers find trends in data. Qualitative data brings contextual richness — it gives researchers a better understanding of users and their needs.
User personas build empathy. They elevate the voice of the customer, so all product decisions are made with the end user in mind.
Establishing user personas helps companies adopt a customer-centric approach.
Why User Personas Matter
To create products that delight users, you need to understand them first.
Personas aid designers in numerous ways:
- Personifying potential users helps establish user empathy. User personas enable designers to identify who they are designing for. Designers can put themselves in the customer’s shoes. Understanding user needs and expectations, they identify with whomever they’re designing for. All stakeholders develop a better understanding of end users. It helps everyone keep these users in mind when creating a product.
- Personas aid in defining product strategy. Designers can decide which features are and aren’t necessary. They can prioritize features based on how they cater to the main persona. With detailed user information, personas help remove guesswork from decision making.
- Creating products for personas helps designers circumvent common design pitfalls. With specific users in mind, they avoid designing for elastic users. These are loosely defined users with a generic user profile, and mean different things to different stakeholders. Designers can also avoid falling prey to self referential design. This happens when they design a product for themselves, rather than the target users.
User personas help answer important questions about users. What are their interests? What communication channels do they use? What features do they need?
Not knowing what appeals to customers is the equivalent of shooting in the dark.
How Machine Learning Helps with Persona Creation
Marketers and designers used to create personas manually. They’d collect demographic information and conduct interviews to craft user personas. Traditional persona creation had a few blatant downsides:
- The process was tedious and resource-intensive. We’re talking about the big 3 – time, money and people.
- Research methods relied on in-person data collection. Therefore, online behavior wasn’t accounted for. This created a disconnect between the data collected and actual user behavior (most of which was happening online).
- People’s tastes change all the time. Crafting and refining user personas is an ongoing activity. Constantly updating user personas is expensive.
- Researchers relied on guesswork to fill any knowledge gaps. Decisions made based on incorrect assumptions can harm a product in the long run.
Machine Learning (ML) addresses these shortcomings. It revolutionizes user persona creation.
Andrew Hogan, Figma’s Head of Design thinks the AI & Design fields must work in unison:
“If AI is actually going to achieve the things that people seem to think it will, it’s got to be designed well, so that people can use it effectively. Design and research have to play an integral role. It can’t just be about integrating some APIs,” he said.
Learn more about Andrew’s thoughts on the influence of Design on AI.
ML is a branch of AI that creates algorithms and statistical models. Computers continuously learn from data. They make predictions or decisions without being explicitly programmed.
When it comes to crafting user personas, ML can perform heavy lifting. It can analyze large datasets from various sources:
These detail user behavior, preferences, demographics and other relevant information. And help build dynamic user personas. That’s fancy speak for personas that change and evolve with user tastes and preferences.
To analyze all this data, you need a centralized research repository. Learn why Marvin is the one-stop shop for all your research data.
Keep your user personas up-to-date, digital and flexible with ML. It enables designers and developers to build user-centric products. Refining personas is a continuous process that evolves with the product. Every piece of information used to define your customers brings you a little closer to them.
Steps to Create User Personas
Without further ado, let’s explore how to set up a successful user persona:
1. Gather Data
Conduct user research to better understand their mindset, motivations and behaviors. Interview and observe a sufficient number of people who represent the target audience.
All companies are different. Extensive field research may not be possible due to resource restrictions (the big 3). All hope isn’t lost. Create provisional personas from customer support logs, web analytics and competitive intelligence. This acts as a placeholder until you populate a persona with real user data.
Failing to build personas from user research is a big no-no. Avoid creating stereotypical users from people’s understanding or imaginations.
Click here to learn how market research tools help gather data to drive business growth.
2. Analyze Patterns in Research Data
During this stage, look out for patterns in the data that makes it possible to group similar people together into user groups. Here’s how:
- Observe how user behavior differs across participants. List the behavioral variables.
- Map these variables to real life attributes.
- Find sets of people with similar attributes. Grouping these people together forms the foundation of each distinct persona.
3. Use ML to Create User Groups
ML Algorithms can conduct a high level clustering of audiences. Segmentation takes place based on demographics, purchase patterns and behavior.
Clustering begins with mining large datasets. Algorithms find similarities between variables and groups similar data into sets. Once AI identifies persona segments, ML can auto-fit customer datasets against them. As new data becomes available, machines constantly learn and tweak group information.
This enables businesses to keep up with changing user tastes and preferences. It allows them to demarcate between persona segments and target them contextually using the right channel.
Learn more about ML-enabled clustering in the persona tools section below.
4. Introduce Personas to Scenarios
The buck doesn’t stop at developing personas. You’ve got to deploy them. Let us explain.
By themselves, user personas are just detailed user profiles. As designers and developers, we’re interested to see users operate in various scenarios of interaction.
Scenarios are hypothetical situations that describe how users would interact with a product. With their end goals in mind, this helps designers understand a user’s requirements. From these, they design solutions to help users achieve these goals.
Learn how to incorporate AI into every stage of the research process.
Don’t forget to share your findings across the organization. It’ll raise awareness of the user journey and keep users at the forefront of everyone’s minds.
Our CEO shares his tips for creating a culture of customer obsession.
5. Helpful Information for Persona Creation
Keep these useful pointers in mind during the persona creation process:
QUESTION: How many user personas do I need to create?
There isn’t a hard-and-fast rule here. In a perfect world, you’d be covering 100% of your customers. That’s not realistic.
A product may have four or five persona segments attached. Yet, each segment doesn’t hold equal weight. One or two will be the primary personas, the users you’ll prioritize during design. The remainder are secondary personas, whom you should accommodate.
Traditionally, designers created less than 10 personas. So as to not overwhelm decision makers with information. AI adds search, filter, generate and recommend functionalities to the persona creation process. This raises the number of personas possible.
Focus on rocking a decent coverage(%) of your customer base. Taking into account your own resource constraints.
PRO TIP(S): Give your personas a name. It makes them instantly recognizable. Make attributes the same across the board to easily differentiate between personas.
WARNING: Don’t use any real names or user details in personas. This adds bias and compromises objectivity. You’ll tend to focus on the person, rather than the needs of the user group they represent.
REMINDER: Like plants in a garden, these personas need constant care. Leverage AI and ML technology to constantly update and refine user personas.
Benefits of Using ML to Create User Personas
So what are the pros and cons of using machine learning in persona creation? Here are some of the benefits of creating AI personas:
Speed & Efficiency
AI helps streamline the persona creation process. It generates customer insights rapidly. Traditional research methodology is time-intensive and expensive. Researchers manually carry out participant recruitment, interviews and analysis. AI automates these cumbersome tasks, freeing up a researcher’s time. It has the ability to process millions of data points swiftly. Systems scour behavioral and transactional data to help create and refine user personas.
Scalability
AI processes large amounts of data, so companies can scale up their operations quickly. UX professionals suddenly have more time on their hands. Attack AI with unlimited questioning. Gather extensive feedback without time constraints. This allows for a more comprehensive understanding of user behaviors. AI makes it possible to maintain personas for extensive datasets or diverse audiences.
Here’s 5 essential practices to scale design and research operations.
Data-Driven Decision Making
Persona creation tools integrate with data analytical tools such as Google analytics. User personas update in real-time with the latest available data. This ensures a dynamic and up-to-date representation of user trends and preferences. AI persona generation enables agile iterations. It allows refinement of strategies in real-time. These are crucial in a fast-paced business environment.
Personalization
Last, but certainly not least. AI is highly effective in aligning web content with search intent. This means that AI learns what consumers like. What content do they want to see? What channels are most effective for reaching them? Crafting more personalized marketing messages helps attract users to a product. AI can help designers create user experiences that feel tailormade to various personas.
Companies that integrate AI into persona creation have a significant competitive advantage. Future-proofing their technology stack allows them to meet the evolving needs of customers.
Limitations of Using ML to Create User Personas
Reader beware. Look out for these downsides of using ML in the persona creation process:
Lack of Human Involvement
AI doesn’t have human intuition or judgment. It struggles to grasp emotional nuances of user behavior. With little to no understanding of the context, it’s liable to misinterpret situations. Failure to grasp the full context of user behavior leads to inaccuracies in insights.
Over Reliance on Quantitative Data
Machines learn more about users from their clicks, scrolls and general online behavior. This data is largely quantitative. That’s only one side of the coin. ML is yet to break into analyzing qualitative data as effectively. You still need a human being to unpack and decipher meaning from qual data. This Deloitte study clearly illustrates why.
Data Quality
Data output is only as good as the input. The quality and reliability of input data directly affects the quality of results. Training data is more than likely biased. This can impact the accuracy of generated user personas. Biased input data may perpetuate and amplify these biases.
With these limitations in mind, it’s essential to combine AI’s capabilities with human judgment. Remember:
User Persona Creation Tools
Check out these seven tools to help you create solid user personas with AI:
- QoQo. Designers will love this! This Figma plugin allows you to create different cards based on a user’s goals, needs, motivations, frustrations and tasks.
- Xtensio. Includes highly customizable AI-driven persona templates. The tool facilitates real-time collaboration.
- Make My Persona. Created by HubSpot, this free web tool integrates seamlessly with HubSpot’s CRM application. Step by step, it guides you through the process. Add details to each profile to craft well rounded and exportable user personas.
- UserForge. Create personas for specific scenarios. Besides user persona templates, they offer scenario-backed modeling with dynamic persona creation. This makes it easier to tailor UX design to varying needs.
- Persona.ly. A ML driven tool for persona creation. Conducts behavioral analytics and generates insights that are viewable on a customizable dashboard.
- Up Close & Persona. Uses AI to analyze interviews and generate persona reports. Provides a nuanced understanding of the target audience. The name may be a bad pun. But their product is on the money.
- Datagran. A no-code tool that helps you build AI and ML workflows, an ordinarily complex and time consuming process. Check out their tutorial on setting up a clustering algorithm here.
- Delve AI. This AI-enabled tool helps you create personas from social media, Google Analytics and your CRM. Additionally, perform a competitive analysis. Delve can construct personas of your competitors from its AI intelligence data.
Learn about the various kinds of persona creation tools, and their pros and cons here.
Here’s our list of the best AI-enabled tools for UX research and Design.
AI-Generated Personas
In the era of AI, a new type of user persona is emerging.
AI-generated personas are digital characters built from swathes of public and private data. Created entirely by AI, it imitates the behavior and qualities of a real human.
Also known as synthetic users, AI-generated personas act as ‘digital stand-ins’ for real users. They present an alternative option for companies to create user personas. The end goal is the same – to understand more about a company’s customers.
Generating AI personas is cost-effective, as compared to traditional market research. There’s no restriction on how many users you can generate, so it’s completely scalable. AI algorithms produce more accurate results than traditional methods in far less time.
Use Cases for AI-Generated Personas
When it comes to creating user personas, nothing replaces real user data. However, there are some use cases for businesses to create and use AI-generated personas:
- During initial research. Use personas to segment customers and understand different user journeys.
- Startups or small businesses. These companies don’t have the resources to conduct their own research. Generating personas is helpful during initial stages of product development.
- Fast-paced businesses. These companies need to adapt quickly to changes in user needs and preferences.
- Marketing campaigns. Businesses use AI-generated personas to fill in any information gaps. They do this to better understand the spectrum of users.
- Conversational interfaces. AI-personas are now commonplace in chatbots, search and more. They provide an opportunity to create interactions and gather valuable user data.
Challenges with AI-generated Personas
Proceed with caution before using AI-generated personas. Watch out for these inherent challenges:
- Recognizing Context. AI personas need to have an understanding of the context to generate meaningful responses. Currently, they don’t possess the contextual processing ability of humans.
- Complex Interactions. AI still struggles to comprehend complex interactions and human emotions. This information is important for researchers to grasp how people react to a product.
- Algorithmic Bias. While AI reduces human bias, it’s likely trained on a biased dataset. This can trickle its way down into research findings and skew results.
- Data Ethics. Companies must not misuse people’s personally identifiable information (PII). Follow data compliance laws and regulations to protect user data. Marvin is SOC2 and GDPR compliant, so your user data is always protected. Learn why UX Designers are the driving force behind Responsible AI.
AI-generated personas are a starting point. A high quality “first draft” of user personas. It’s vital to add your own research (using rich data) to user behavior analysis.
Here are some tips and tricks for integrating AI into your UX workflow.
Outstanding Experiences Originate from Personas
Everyone wants personalized content. That’s why we love our Netflix and YouTube home pages. They reflect our tastes and show us stuff we’d like to watch. Experiences that seem tailor made for us.
Personas are a powerful tool that help make that happen. Using AI in the persona creation process simplifies data collection and segmentation.
Personas guide product ideation. Understanding user personas deeply helps designers create products that solve actual customer problems.
User personas ensure that designers keep the user at the heart of every decision.
