Bold BI’s sample Online Food Ordering Analysis dashboard provides an overview of the operations of a restaurant delivery service. With this predictive analytics dashboard, viewers can answer the following questions:
- What is our revenue and what are our expenses?
- How many customers place orders online and through our mobile app?
- What are our busiest days?
- What are our average delivery times for each restaurant?
- Are customers happy with our service?
This dashboard’s data is crucial as it offers insight into both the company’s day-to-day operations and long-term financial success. For example, the “Revenue ,” “Forecast Revenue ,” and “Expense” widgets outline the business’s revenue, profits, and projected revenue, and the “Revenue Forecast  by Month” spline chart supplies a monthly breakdown of anticipated revenue. This information could help the company ensure that they allocate resources appropriately during months that are likely to be busy. Further, it could encourage them to strengthen advertising efforts or offer discount codes to customers to boost sales during months that are projected to be less busy.
The “Mobile App vs Website Orders ” spline chart displays the number of website and mobile app orders received each month. From this chart, we can see that customers are using the app at a rate nearly ten times higher than the rate at which they are using the website. As the app is far more heavily utilized by customers than the website, the company might be inspired to invest more resources in its development.
Similarly, the “Total Orders by Week day” spline chart and “Delivery Time vs Customer Rating by Restaurant” bar graph detail which days of the week are busiest for the delivery service and each restaurant’s average delivery time, respectively. This data could help the company ensure that they allocate drivers and other resources appropriately.
Finally, the “Customer Satisfaction Rate” doughnut chart provides a straightforward analysis of customers’ perceptions of the company. This information could be cross-referenced with other data sets to determine whether a correlation might exist. For example, the service might wish to track whether increased customer satisfaction ratings correspond with decreased average delivery times.
Having all of this information available on a single dashboard is invaluable for team members who need an overview of the company’s financial metrics but also want to track key sales and customer service data to identify trends that develop over time.