How do you measure the impact and effectiveness of your Tableau data stories?

Before starting anything it's important to have a defined goal that allows us to figure out where we are, how to navigate, evaluate our success, and even when to stop. To craft a data story goal, like unveiling customer behavior insights in an e-commerce platform. Objectives include pinpointing popular products and assessing marketing effectiveness. Identify audience - executives, marketing teams—and tailor insights accordingly. Aim for outcomes; e.g., motivate marketing teams to refine strategies based on consumer behavior. Your story should evoke specific prompt actions aligned with strategic decision-making. By clarifying these goals, your data story gains purpose, ensuring relevance and impact for your intended audience.

Para medir el impacto de las historias de datos en Tableau, analiza las interacciones de los usuarios, recoge su feedback y revisa los cambios en los KPIs relevantes

Ultimately by understanding how well they are helping business users answer their questions, alongside measures such as the adoption rate/ engagement. There is a process to follow to get there that starts with understanding the audience / business question being asked - what do they want to see/ hear/ what action will they drive off the back of it/ how will they best consume it. This is the basis of your story. Determine the kpis that will best support the story, determine the data required to enable it, source / ingest and then craft your dashboard with the user in mind. Often iteration will then happen, either in the refinement/ extension of the story.

Focus on three key areas: User Engagement: Track how often and how deeply users interact with your dashboards (e.g., clicks, views, time spent). Decision Impact: Assess whether the insights provided are being used to make informed decisions or changes in strategy. Feedback and Iteration: Collect user feedback on the utility and clarity of the visualizations and iteratively refine them for better understanding and actionability.

Example could be a marketing team that wants to track performance of an ad campaign to understand how the campaign influenced website traffic and user behavior. They might create a Tableau data story that includes website visits, page views, bounce rates, and conversion rates. Maybe also segment data by different dimensions, such as time, location, device type, to gain deeper insights into user behavior. They might care about specific metrics, such as the number of new users acquired or the revenue generated from the campaign. To measure the impact the they might set specific goals, such as increasing website traffic by 10% or improving conversion rates by 5% or track how their audience reacts to the data story. #ChooseDataForYourTeam

In my experience, adoption is key. You can build the most beautiful Tableau story and yet there are no regular users. One of the best approaches is to keep an intuitive and simple MVP version which speaks about a very critical yet basic KPI. A dependency is built as soon as businesses use this dashboard for some of their regular reports. Now, you can add iterations to make the dashboard more comprehensive while always retaining the core function that made it mission-critical for the business.

One quick and definitive way to check the traction for a Tableau dashboard is to check the number of views it has gotten. Another way of knowing if it is being used is if stakeholders are suggesting changes or enhancements which means that they are interacting with the dashboard. If it is a big organization, one can also run surveys to gauge awareness and usability amongst other aspects of a Tableau dashboard.

Leveraging the concept of input + output analytics is also a great way to articulate hard value in analysis. This entails taking leading indicators (inputs) and measuring their against lagging indicators (outputs). So for example: measuring the # of sales opportunities as an input metric and the corresponding revenue figure as an output metric. This allows for a cohesive way to construct impactful reporting.

One of the most important things to do when you have all your ideal dashboards built is to look at how often they are viewed! If a dashboard is not being utilized, ask key stakeholders if they are aware of the dashboard. If they are not even aware, then it is not the quality of the dashboard that needs to be improved, it is the collaboration and communication with organizational leaders.

One unique feature in Tableau that stands out for analyzing and visualizing data is VizQL (Visual Query Language). VizQL translates your drag-and-drop actions into data queries and then visually presents the results. This allows you to interact with your data intuitively and see immediate visual feedback. With VizQL, you can quickly build complex visualizations by simply dragging fields onto the canvas. For example, you can create interactive charts, graphs, and maps without needing deep technical knowledge. This feature makes it easier to explore data, spot trends, and uncover insights on the fly. VizQL's ability to transform queries into visuals seamlessly helps users of all skill levels analyze and understand their data.