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The Analytics Use Case canvas is inspired by the Value Proposition Canvas.

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Many analytics projects fail due to lacking user acceptance and / or little added value. They've simply been developed without really targeting the user's needs. The Analytics Use Case canvas helps you to better understand these needs. Subsequently you can develop analytics solutions which really add value.

An use case is defined by providing answers to the following three questions:

  1. What's the user's problem?
  2. What's the solution from the user's perspective (not from a mere technical or analytical point of view)?
  3. What's the solution's benefit for the user?

Therefore an use case describes the desirability of a solution.

In contrast a business case answers the question regarding the viability on top of that. This also includes costs, risks and the (financial) benefits of a solution. For this the Business Model / Case canvas can be used.

Looking at an analytics solution the benefit reveals itself by supporting, optimizing or even completely automating (decision) processes to reach certain (business) objectives. Here analytics serves as a tool to get from data to relevant information which assists in setting the right course of action. Meaning deciding to go for those actions which deliver results which in turn contribute to reaching the objectives:

Data ➡ Analytics ➡ Information ➡ Decision ➡ Action ➡ Results ➡ Objectives

Hence, to design a value-adding analytics solution, we need to first understand a user's objectives and key results (OKR). Furthermore we need to examine which decisions and actions can be taken by the user respectively which he / she is allowed to take. Finally we need to answer the question what's currently preventing the user to take better decisions (pains) and what would support him / her to take these (gains).

Only after that we can meaningfully think about the information and features an analytics solution needs to provide and what's the (professional, personal and / or economical) benefit of all that. Subsequently the Analytics Use Case canvas is divided into two parts:

  • right-hand side: the problem / user view
  • left-hand side: the solution / provider view

In the end both sides must fit together which is either called problem-solution-fit or product-market-fit. Here the Analytics Use Case canvas follows the structure of the famous and successful Value Proposition Canvas.



Start with the right-hand side and put the analytics solution's user in the middle (i.e. the field User). The user might be a concrete person or - most likely - a certain role or position in your company: for example a marketing-manager or a managing director. Use the blue cards (neutral color) for the user.

In case you are not sure who is going to be the user you can use the Stakeholder Analysis canvas to identify potential users, buyers and decision-makers and to discuss them. Possibly there is more than only one user.

Alternatively you can use the Business Model / Case canvas: the Analytics Use Case canvas is zooming into the Business Model / Case canvas.


Next you discuss with your team and ideally also with one or two potential users the user's desired objectives: what does he / she wants to achieve? Which are the main (working) results which contribute to achieve his / her objectives? To do so it's crucial to understand how the user's performance is measured. To get this understanding the following artefacts can help:

  1. Agreements on objectives & labor contracts
  2. KPI / value driver trees (KPI: Key Performance Indicators)
  3. OKR lists (OKR: Objectives & Key Results)

Use blue cards to document the objectives and results.

Examples for objectives and results are:

  • To generate a certain amount of leads by starting different campaigns.
  • To increase a production facility's efficiency by x % by reducing its unplanned and planned maintenance by y % and z %.


As the third step you derive, coming from the objectives and results, the decisions and actions which a user needs to take respectively which he is allowed to take. To do so it's important to understand the user's freedom of action as well as his / her decision-making-authority. This is to provide the user with the information which is relevant to come to a decision.

For this step you also use the blue cards.

Examples for decisions and actions are:

  • Which campaign should be started on which channel, addressing which target group?
  • When should which machine be maintained?


Turn now to the red cards and ask the question which problems and risks the user is facing while achieving his / her objectives, reaching results, taking actions and finding for the best decisions. Which are the user's efforts, costs, obstacles, problems, uncertainties and so on, in short the user's pains which keep him awake at night? Place the corresponding red card close to the related objective, result, action or decision.

Important: Scrutinize the pains by asking five times "why?". The question "Why is that a problem?" works in both directions: What's the problem's root cause? What's the problem's (negative) consequence? Try to understand the problem's root causes as good as possible in order to solve exactly those root causes, and not the symptoms, with the analytics solutions. However, you should also know about the negative consequences in order to be able to prioritize the problems according to their negative impact.

Examples for answers to the questions regarding the pains are:

  • The source of the leads is not known because this information is not available in the CRM
  • Outage risks of the production machinery in dependence on its lifetime is unknown because this has never been documented


Next are the green cards. These represent the gains meaning helps and incentives which are desired by the user or even expected. Here it needs to be distinguished between so called push and pull factors. Push factors have a supportive effect whereas pull factors a motivational one.

Place the green cards just as the red cards (pains) close to the related decisions, actions, results and objectives. Again, scrutinize the gains as well: why do they help the user?

Avoid that the gains only mirror the pains, meaning they only represent to positive formulation i.e. the solution of the problem.

Examples for gains are:

  • Performance benchmarks of the recent campaign comparing it to similar campaigns of the past
  • Quick overview as well as detailed insights about past outages of the machinery

When finishing off with the right-hand side it's recommended to discuss each and every single card once again, prioritize if necessary and to focus on the most critical and / or urgent objectives, results, decisions, actions, pains & gains. For this you can use the Priority Matrix canvas. It's really crucial that you and your team have a good and common understanding regarding the user.


Next you are turning your attention to the left-hand side and think about potential (analytical) solutions. Most probably this will require some repetitions: first you collect high-level solution proposals like:

  • Weekly campaign report
  • Real-Time KPI Dashboard
  • Automated notification for low-performing campaigns
  • Automated campaign optimization
  • ...

If the analytics maturity of the solutions differs substantially, e.g. a mix of descriptive and prescriptive analytics, you can use the Analytics Maturity canvas to sort the solutions according to their analytics maturity.

During the next repetitions you focus on one solution and further detail it:

  • Which information does the solution provide? For example: number of leads per campaign and channel, ROAS (Return-On-Ad-Spending), ...
  • Which features does the solution have? Examples are: a segmentation of measures according to target groups, pausing of campaigns,...

Use blue cards for the features and the information.

Speaking from experience, it's a good practice to use a flip chart on top of the canvas. This is to sketch the user interface as a first wireframe. Make it as concrete as possible.


Now think about the benefit for the user having the information and features of the solution in your mind. Place the respective proposals on green cards. Again, be honest to yourself and scrutinize the proposals: does this information or feature really provide this benefit? Why is it like that?

Examples for the benefit of an analytics solution are:

  • Identify low-performing campaigns.
  • Being able to better assess the risk of a machinery outage.

In case there is a lot of information and / or a high number of features which lead to an unclear value proposition, just switch over to the Value Curve canvas: This one helps to focus on the essential features and their benefits.


As the last step you compare the value proposition (left-hand side) to the user's pains & gains (right-hand) side. Doing so you can for example mark all benefit cards which solve a pain and / or bring along a gain by putting a tick on them. Furthermore you can do the same for all pain and gain cards which are covered by the solution.

At the very end you have an overview how good your solution proposals cover the user's needs. Subsequently you can also compare different solution options against each other or combine them. In most cases it will be a system of solutions which delivers the desired outcomes and success.

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Design value adding analytics solutions from a user's perspective.

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Entwerfen Sie wertbringende Analytik-Lösungen aus Nutzersicht.


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