Tag Archives: dashboard


Some common chart and graph types that would be useful for performance dashboards include line charts, bar charts, pie charts, scatter plots, area charts, gauges and indicators. Each type of visualization has its own strengths and suits different kinds of data and metrics. A good performance dashboard brings together different charts and graphs to paint a comprehensive picture of how the business or organization is performing.

Line charts are well-suited for displaying trends over time. They are often used to show how a particular metric is changing each week, month or quarter. Line charts make it easy to see the direction that numbers are headed up or down. Some examples of line charts include tracking revenue over 12 months, comparing website traffic week-over-week, or viewing sales numbers year-over-year. The performance dashboard would include line charts to reveal trends in key performance indicators.

Bar charts provide a simple visual comparison of item categories or values across periods. They are effective for depicting differences in amounts or quantities. Bar charts in a performance dashboard may illustrate a team or division’s monthly sales, compairing branches and regional profitability, or ranking top 5 products by units sold. This allows managers to easily discern which areas are exceeding goals and where improvement may be needed.

Pie charts express numerical proportions by cutting a circle into slices corresponding to different categories or subgroups. They are helpful for showing percentage breakdowns or distributions. For example, a pie chart on a dashboard could indicate what percentage of revenue came from different product lines or departments. Another use may be demonstrating the proportion of services that are completed on time versus late. This gives a clear at-a-glance view of how quantities are divided among different segments.

Scatter plots display numerical values for two variables on the horizontal and vertical axes to reveal any statistical correlation or trend in the relationship between the variables. On a performance dashboard, scatter plots may chart employee performance ratings against productivity metrics. Or they could compare service level agreement fulfilment times with customer satisfaction ratings. This helps identify if improvements in one area may positively or negatively impact another.

Area charts are similar to line charts but fill the space under the line, producing an image that more clearly illustrates changes in magnitude. They are useful when cumulative totals need to be emphasized over time, such as depicting overall sales achieved month-to-date or year-to-date. Area charts on a performance dashboard can succinctly show progression towards key targets as time periods accrue.

Gauges and indicators are graphic displays that present measurements against graduated scales, akin to physical dashboards in vehicles. Circular gauges with needles are commonly used, along with linear progress bars. These visuals are placed prominently on performance dashboards to constantly showcase metrics crucial to management like cash flow, capacity utilization, headcount, customer satisfaction NPS score etc. The “at-a-glance” monitoring promotes quick understanding of whether goals are being achieved or remedial action is necessary.

Combining these different types of charts and graphs allows dashboards to provide holistic insight into business health and direct attention to obstacles or opportunities across multiple dimensions. Well-designed performance dashboards present an assortment of clearly labeled visualizations to facilitate comparison, correlation, trends analysis and informed decision making. Additional graphs may also be integrated such as histograms, tree maps or sunbursts depending on the nature of benchmarks to oversee. The blending of varied charting formats results in dashboards that distill volumes of operational data into actionable strategy recommendations.

Effective performance dashboard views capitalize on line charts, bar charts, pie charts, scatter plots, area charts and gauges to transform raw figures into coherent stories through data visualization. Judiciously applying the strengths of each graphical technique surfaces key insights, flags issues and spotlights successes by functional area, team, product or over time. This empowers leadership oversight of performance metrics indicating where adjustments or new initiatives could propel objectives forward. A dashboard bringing together different charts and graphs creates a comprehensive and intuitive medium to manage business performance.


A dynamic dashboard in Excel allows you to visualize changing data in real-time or near real-time to gain insights and track key performance indicators (KPIs). It allows non-technical users to see their constantly updating data in an easy-to-understand format without needing to regularly refresh or update their reports manually. Creating a dynamic Excel dashboard involves the following steps:

Plan your dashboard – The first step is to plan out what type of data you need to display and the key metrics or KPIs you want to track. Determine things like the data sources, the frequency with which the data will update, the visualizations needed, and how the dashboard will be accessed and updated. Sketch out on paper how you want the dashboard to look and operate.

Setup data connections – You’ll need to connect your dashboard workbook to the underlying data sources. For Excel, common data connection types include connecting to other worksheets or workbooks within the same file, connecting to external data stored in text/CSV/XML files, connecting to external databases like SQL Server, and connecting to online data sources through OData web queries. Use things like Excel’s built-in Get Data tools and functions like power query to automatically import and structure your data.

Automate data refreshes – For a true dynamic dashboard, you need the data visualizations to update automatically as the underlying data changes. This is done by setting up scheduled data refreshes using Excel’s Data Refresh tool. you can refresh the queries and pivot tables on a schedule linking to external data. For example, you may want to refresh the data daily at 6 AM to pull in the previous day’s data. You can also trigger refreshes manually.

Design interactive visuals – The dashboard should display your key metrics through various interactive visualizations like charts, gauges, maps, pivot tables and more. You can use Excel’s wide range of built-in chart types as well as more advanced types through add-ins. Ensure the visuals are formatted properly for readability and aesthetics. Add relevant titles, labels, data labels, colors, tooltips etc.

Filter and slice data – Enable users to filter the visuals by parameters to drill-down into subsets of the data. For example, allow filtering a chart by region, product, date range etc. You can add slicers, filters or combo boxes linked to pivot tables/queries for this.

Add KPIs and metrics – KPIs are critical data points that need to be prominently displayed and tracked over time. Use gauge charts, traffic lights, meter charts etc to visualize KPI values against targets. Add relevant background colors, icon graphics and call-outs. Power BI also allows building KPI scorecards from Excel data.

Format for mobile – Consider if dashboard needs to be accessed on mobile screens. Use responsive design principles like auto-fitting charts, larger text, fewer/simpler elements on mobile views. Explore tools like Power BI for reports accessible on any device.

Protect and share – Password protect or restrict access to the file if needed. Publish Power BI dashboards securely online. Share workbook links for read-only external access. This allows distributed teams to monitor metrics remotely.

Test and refine – Thoroughly test all the interactivity, refreshing, formatting on different systems before implementing the dashboard for actual use. Monitor for issues, get feedback and refine design iteratively based on user experience. Consider automation add-ins for enhanced formatting, lay-outing and governance.

Maintain and evolve – As needs change, the dashboard should evolve. Streamline the maintenance processes by version controlling the file, documenting procedures and changes. Train others to extend, refresh or make modifications as required. Monitor dashboard usage and determine if new metrics/visualizations need to be added or obsolete ones removed over time.

This covers creating a robust, dynamic Excel dashboard from planning to implementation to maintenance. Some key advantages are easy creation without coding for business users, familiar Excel interface, interactive data exploration within the sheet itself and mobility across devices. With latest tools in Excel and Power BI, sophisticated dashboards can now be built directly in Excel to drive better business decisions through data. Regular refinement keeps the dashboard aligned to the evolving needs.


Customer churn or customer attrition refers to the loss of customers or subscribers for a product or service of a business or organization. Visualizing customer data related to churn can help decision-makers gain meaningful insights to develop engagement and retention strategies. Some key visualizations that can beincluded in a churn prediction dashboard include:

Customer churn rate over time (line chart): This line chart shows the monthly or yearly customer churn rates over a period of time. It helps identify trends in the rates of customers leaving the business. The dashboard can allow selecting different cohorts or customer segments to compare their churn rates. This chart is often one of the first graphs seen on a churn dashboard to give an overview of how churn has changed.

Customer retention rate over time (line chart): Similar to the above chart, this line shows the retention rates of customers (customers who have not churned) over monthly or yearly intervals. It provides an alternative view of how well the business is retaining customers. Both retention and churn charts together give management a holistic view of customer loyalty patterns.

Customer churn by acquisition cohort (horizontal bar chart): This chart segments customers based on the year or time period they were acquired. It shows the churn rate of each acquisition cohort side by side in an easy to compare manner. It can help identify if older customers have higher churn or if certain acquisition periods were more successful at retaining customers. Making informed decisions about re-engaging past cohorts can help reduce churn.

Customer churn by subscription/plan type (pie or donut chart): When the business has multiple subscription or plan types for the product or service, this chart shows the distribution of customers who have churned according to their subscription type. It helps understand if particular plan types have inherently higher churn or if there are engagement issues for customers on specific plans.

Customer churn by various attributes (table or datasource filter): This interactive filtering view shows churn counts and rates according to various customer attributes like industry, region, size of business, etc. Management can select these filters to drill down and understand how churn varies according to different customer profile properties. Insights from this help create churn reduction strategies targeted at specific customer segments.

Customer behavior over time by churn status (dual line chart): This chart compares behavioral metrics of customers who churned (lines in red) versus those who were retained (lines in blue) over a period leading up to their churn/retention time. Behavioral metrics can include usage frequency, purchases made, support requests, etc. This visualization is very effective in identifying differences in engagement patterns between the two customer groups that can be monitored on an ongoing basis.

At risk customers (gauge or meter chart): This view depicts the count or percentage of customers identified as ‘at risk’ of churning by the prediction model in the near future (say 3-6 months). Seeing this number change over time helps assess the effectiveness of any new retention programs or incentives in keeping at-risk customers from real churn. Reducing this number remains a key measure of success.

Prediction accuracy over time (line chart): As the prediction model is retrained over time on new customer behavior data, this chart indicates how accurate it has become at identifying churners vs retainers. A rising blue line showing an increased percentage is ideal. Tracking model accuracy helps confirm it is learning as intended from ongoing customer interactions and past churn behavior.

These are some of the effective visualizations that can be incorporated into an insightful churn prediction dashboard. Proper filters and crosstabs need to be provided to allow drilling down and comparing across different sub-segments of the customer base. With regular monitoring and refinement, such a dashboard becomes a valuable management reporting solution for reducing churn. Key decisions around retention best practices, high-risk customers, acquisition campaign effectiveness and prediction model performance can all be made more data-driven with these visual analytics.