How do you measure BI user adoption and satisfaction?

When analytics is accessed with multiple BI tools + PDFs and Excels, there is a need for cross-platform usage analysis in 3 key areas: 1. User behaviour: Share of active users; Analytics users to all employees ratio; Number of new users; Avg number of unique report views per user; User distribution by channels; etc. 2. Reports usage: Total number of reports; Share of active reports; Number of new reports; Avg number of views; Avg report consumption; Share of unused reports; Distribution by channels / departments; etc. 3. Licenses and costs: Total costs (licenses + team); Avg costs per active user; Avg costs per user; Costs per report; Costs by departments; etc.

In my experience leading BI adoption, it's all about where the value is for various audiences within an organisation. Segmenting the BI community, designing solutions based on their needs at different points in time, making their lives easier all work. But the metrics matter most when you capture the 'why'.

In my experience, the usage metrics by team are especially important. It showcases whether the report (in our case Tableau) is being viewed/downloaded by internal staff (e.g. developers) or by the stakeholders. In addition, the duration (are they viewing it for a matter of seconds or minutes).

By using a combination of usage metrics, survey feedback, support tickets, user training, and business impact metrics. This way organisations can gain insight into how well the BI solution is being adopted and received by users.

User metrics are a reliable indicator of a given report's failure, but aren't as predictive of success on their own. So, I find use metrics effective for cleaning out reports on a BI platform that don't add value and might detract from user satisfaction. User metrics are less effective, however, in gauging success in user satisfaction, since users can adopt a report that they find lacking - if it's the best information they have available. So, metrics should be considered in the context of other factors (like satisfaction metrics mentioned below) to gauge relative success among reports that are being actively used.

BI adoption rate serves as the basis for developing an effective data-driven strategy. Any data literacy or BI adoption initiative has to be supported by regular data on changes in the BI adoption rate. Tracking adoption changes allows us to test different approaches and methods, conduct A/B testing (a well-known yet often underestimated analytics method) and identify the most efficient strategies for raising BI and analytics usage in the organisation.

To effectively manage and measure the adoption of a BI application, it is essential to classify users into two categories based on their skill set - power users who can create visualizations and casual users who rely on the BI application for decision-making and data discovery. Next, specific metrics should be tracked to understand the usage patterns of each user class. These metrics can include the number of metrics and dashboards created, the number of edits made to existing dashboards, and the popularity of specific dashboards or metrics. By tracking these metrics, organizations can better understand how their users are interacting with the BI application and identify opportunities to improve its adoption and effectiveness.

Adoption rate is only effective in more mature BI programs that have clearly defined audiences. We work with a lot of organizations who are just starting or advancing their BI programs and many don't segment their target audience sufficiently to make this a very precise metric. For example, a common scenario would be to start a BI program with reports for management and executives, which represent a small subset of the total users - but this group could initially be granted access to a broad range of reports. In this context, a sales performance report or a report assessing operating delays or defects could be very successful even if it's only be used by 10 to 30% of managers and executives who have access to it.

Open-mindedness is key. When one is green, one will grow. When one is ripe, the rotting process will soon start. Believe in scientific facts and evidences. There is no right or wrong but one's perspective, timing and balance.

Our adoption rate is the # of unique users viewing the dashboard/target audience. We have utilized subscriptions to email reports to users for better adoption on mission critical reports. We also compare our development segment to other units within the organization. We pay close attention to adoption from units that do not have the resources or time to build reports through our CRM.

For a developing BI program, these measures may be difficult to translate into actionable improvements. We find that early BI efforts focus on surfacing snapshots of data which produces user satisfaction if that data was previously unavailable, but continued success requires improvements in the timeliness of information, comprehensive aggregations that inform users of a given domain of information, visualizations to spot trends of over time, and mapping actuals against intelligently placed targets (KPIs). These factors may be reflected in lower NPS, CSAT, or CES scores, but can be addressed more directly until you have confidence that these fundamentals are addressed in the design of all of your deployed reports.

Black cat or white cat, it do not matter as long as the tasks is done. Result delivered. I will let other experts to set up the satisfaction metrics. Sometime in order to move forward, one need to reverse first.

Surprisingly, some of the most popular BI tools, such as Power BI, Qlik, and Tableau, do not offer built-in tools to collect Net Promoter Score (NPS) data about dashboards. At the same time, Qlik provides rich API to create your own NPS survey forms as the visual extension, that Qlik developers can place on the dashboard.

Creating success criteria DURING development from a mixed focus group of developers, power users and other stakeholders is critical to capturing satisfaction metrics. Many elements of the design can be influenced by the focus group leading to higher CSAT scores and user adoption.

In reality this is the only outcome that matters in BI - everything else is a fallback metric to use when someone comes questioning the value of their BI investment. Having a tangible business result to point to is worth 100x more than great adoption, volume or speed-to-resolution metrics. Organizations often lament low adoption rates for BI, for example, without identifying WHY a particular role would use BI in the first place. If you don't know how data can improve a given area of the company, don't even think about rolling out BI. Take a big step back and learn how data can help make money, save money or retain customers. Then start your BI efforts. Relying on vanity metrics to justify BI investment is a recipe for disappointment.

Focus groups are critical to developing reports/dashboards that users will adopt. We involve stakeholders to create a standardized, consistent look and feel and success criteria for each report. We also direct users to these reports, subscribe them (in some cases) and incorporate training in our onboarding program. Finally, we advertise these products whenever we are in group meetings to ensure users are aware they exist.