The three personas you must design an analytics app for

There are three broad personas that you should consider in the design of any analytics application.

When you're designing an embedded analytics application it’s important to focus clearly on who will be using your app.

While every product is slightly different, in our experience at Yellowfin there are three broad personas that use analytics apps - executive, analytical and operational users. When creating an application you need to consider these personas and create the application and its content to suit the way they are likely to engage with your product.

1. Executive users want high-level information over a long time frame

Executive users are important because they’re probably the people who will sign your cheques. They want to make long-term decisions about business strategy, so top-level information about how the business is performing, trends that are occurring over time, KPIs and indicators all help them make better decisions. Because their decisions impact the business over a longer timeline they generally need to see at least three years worth of data.

Executives are also very busy, so it’s important to make sure their information is visually clear - they should be able to glance at it and understand what it means. Dashboards, models, and simulations are all useful ways to present information to an executive audience.

The information must also have depth so that they can find answers to questions quickly. Achieving a level of depth is challenging and isn’t often available in existing applications. To add value to their decisions, think about what questions may not be answered by your competitors’ products. For example, early warnings of impending issues or collaborative capabilities that allow users to share commentary about the data can add depth to the information. Provide context and give them the opportunity to explore data within that context if they need more detail.

In summary, for executive users it makes most sense to focus on trends over a longer term timeframe (up to three years). Executive users are busy, so it’s important to make sure this information is presented clearly and is quick to consume.

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2. Analytical users are tech savvy and want to explore the data

The power users of any analytical app are often data analysts. They understand the technology and the business issues so they will require more advanced analytical tools that allow them to drill into the data and work with it in more depth. Things like online analytical processing, ad-hoc queries, and modelling will enable them to slice and dice the data the way they want it. While most analysts are tech savvy, they may need some training so they can build their own content rather than just consume it.

Analytical users often need to make decisions in the medium term (i.e. comparing this year to last), so they will want to go back at least 12 months and ask more questions of the data. Allowing them to create dashboards, scorecards and other content will enable them to produce analysis that can be easily consumed by others.

Analytical users are generally the users who will spend the most time in your product and will care most about your full suite of functionality. They’ll also be most vocal in speaking out about features or changes that they don’t like. Whilst most product managers do a reasonably good job in catering to these power users, it’s important not to forget that these are just one of your user personas. Whilst line of business and executive users are less likely to be vocal, they’re equally critical in building a sticky product that adds value for your customers.

3. Line of business users need to keep the day-to-day business operating

Line of business users use information to enhance how they perform their role day-to-day. These users generally work to a short-term horizon, they may be stock controllers or store managers who are interested in their stock count or daily takings, or sales managers who want to see how their sales representatives are performing.

About 90% of a line of business users’ time is spent managing their business, which leaves only 10% available to look at information that will help them run their business. This means they don’t want to get stuck in the analytics, they just want to understand what they need to do today.

They need information that is concise and doesn’t overwhelm them. It should be easy to comprehend and specific to their purpose. If the dashboards or reports are too cumbersome for them to consume then they simply won’t use them.

For these types of user, reports often come from a variety of sources. Giving line of business users access to the right reports, dashboards and scorecard visualizations helps them track things like sales statistics and customer information. They will need detailed operational metrics that need to be updated daily. It is not uncommon for them to drill down to transactional level details.

To truly understand your users it’s important to build out each of your specific personas. To do this it can be helpful to use these three broad groups. But then there’s still work to do to understand their frustrations, how they use the data, and the specific decisions they are trying to make. This will then help you determine what they need access to, how often, and in what level of detail. Most personas don’t need all of your functionality or all the available data.

A good analytics application gives each user only the slice of information that they need to do their job. Ultimately, that’s why getting your personas right are so important.

Frequently Asked Questions About Designing Analytics Applications for User Personas 

1. What are the three main user personas in analytics applications?

The three primary personas are executive users, analytical users, and line-of-business (operational) users. Each group interacts with data differently and requires tailored analytics experiences.

2. Why is it important to design analytics apps around user personas?

Designing around personas ensures users receive relevant, actionable insights instead of overwhelming data. Persona-based design increases adoption, engagement, and long-term product value.

3. What do executive users need from an analytics application?

Executive users need high-level summaries, long-term trends (often up to three years), KPI tracking, and visually clear dashboards that allow quick decision-making without deep technical interaction.

4. How should analytics apps support analytical (power) users?

Analytical users require advanced tools such as ad-hoc querying, drill-down capabilities, data modeling, and interactive exploration features. They need flexibility to investigate data independently.

5. What do line-of-business users expect from analytics tools?

Operational users need concise, role-specific insights that help them manage day-to-day activities. They prioritize clarity, speed, and actionable metrics over deep analytical exploration.

6. How much historical data should different personas have access to?

• Executives: Often 2–3 years of data for strategic decisions
• Analytical users: At least 12 months for comparative analysis
• Operational users: Daily or real-time data for short-term performance

7. What happens if you design analytics tools for only power users?

Focusing only on analytical users can result in complex tools that executives and operational users avoid. This reduces overall product adoption and limits business impact.

8. How can embedded analytics improve user adoption?

Embedded analytics integrates insights directly into workflows, reducing friction and ensuring each persona receives relevant data within the applications they already use.

9. How do you identify the right analytics persona for your application?

Start by analyzing user roles, decision timelines, technical expertise, and the type of decisions they make. Map these factors to executive, analytical, or operational needs.

10. What makes an analytics application “sticky” across personas?

A sticky analytics product delivers personalized insights, matches decision-making timeframes, avoids data overload, and provides the right level of depth for each persona without unnecessary complexity.

This article is built on the research and content from our new 70-page ebook.
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