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How do you avoid common pitfalls and biases when creating customer personas?

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Customer personas are fictional representations of your ideal customers, based on data and research. They help you understand your customers' needs, preferences, behaviors, and motivations, and guide your marketing, sales, and product strategies. But creating customer personas is not a simple task. You need to avoid common pitfalls and biases that can distort your customer insights and lead to ineffective or even harmful decisions. In this article, we'll share some tips on how to create customer personas that are accurate, relevant, and actionable.

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1 Collect data from multiple sources

The first step to creating customer personas is to collect data from multiple sources, such as surveys, interviews, analytics, social media, and feedback. This will help you get a comprehensive and balanced view of your customers, and avoid relying on assumptions or stereotypes. You should aim to collect both quantitative and qualitative data, as well as data from different segments and stages of the customer journey. The more data you have, the more realistic and detailed your personas will be.

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Sandra Soczówka-Frankowska

FP&A Manager
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Ideally, you also get data about non-users and non-performers (e.g. contacts from lost opportunities to understand if your assumptions represent the full scope of the addressable audience OR only those who were successfully addressed). You should also repeat the data collection exercises on different timelines (e.g. 3 - 4 times during the year) to understand if seasonality or external events affecting your personas' day-do-day life also affects their preferences.

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Mykel G. Larson ?

I create. I build.
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All you need are preference models. Forget personality. That's a construct loaded with problems. Ignore all that and focus on desires - preferences.

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2 Analyze data and identify patterns

The next step is to analyze the data you collected and identify patterns and trends that can help you group your customers into different personas. You can use tools like spreadsheets, charts, or software to organize and visualize your data. You should look for common characteristics, such as demographics, goals, challenges, values, pain points, and behaviors. You should also look for differences, such as preferences, expectations, and motivations. The goal is to find meaningful and relevant ways to segment your customers based on their needs and wants.

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Rajan Ladhwa

Data analytics and Product Operations Leader | Building and leading vision focused teams | Unlocking value and efficiency through data
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Analysis is best done using tools and/or algorithms to avoid bias based on what you want the segment or persona to be versus what it actually is. One thing to note is your actual segment or persona maybe different from the one you design your marketing campaigns for (perceived persona).

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3 Create and name your personas

Once you have identified the segments of your customers, you can create and name your personas. You should create one persona for each segment, and give them a name, a photo, and a brief description. You should also include specific details, such as their background, role, industry, income, hobbies, personality, and lifestyle. You should make your personas as realistic and human as possible, so that you can empathize with them and understand their perspective. You should avoid generic or vague names, such as "Customer 1" or "Business Owner".

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Mykel G. Larson ?

I create. I build.
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No no no no. Personality is useless. Preferences tell you a better story about anticipated benefits based on resources avaliable and prices of goods. Personality models are noise models. Ditch them.

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4 Validate and update your personas

The final step is to validate and update your personas regularly. You should test your personas with real customers and stakeholders, and get feedback on their accuracy and usefulness. You should also monitor your data and market changes, and adjust your personas accordingly. You should keep your personas updated and relevant, so that they reflect the current and evolving needs and wants of your customers. You should also review your personas periodically, and discard or merge any that are no longer valid or useful.

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5 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?

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Rohit D.

Lead - ML & AI
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Most organizations use unsupervised learning algorithms (k-means) to create personas. This tags the customers into same category with every refresh as the centroid values remain static. It is essential to monitor and retrain these algorithms in production to address market and behavioral changes to personas at regular frequency. This will help in using these personas into supervised learning models and will avoid overfitting/underfitting problems.

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How do you avoid common pitfalls and biases when creating customer personas?

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1 Collect data from multiple sources

2 Analyze data and identify patterns

3 Create and name your personas

4 Validate and update your personas

5 Here’s what else to consider

Customer Analysis

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