Network Summary Dashboard

Network Summary Dashboard compares your performance across your entire client network AND compares you against the entire ServiceChannel platform. This dashboard is helpful for not only regular Provider Performance Reviews, but also to check in on your performance health and see where you might need to make some changes. Quickly evaluate your performance of your client network on repair work orders measured in five different categories: All, Speed, Quality, Engagement, Price (US).

Navigating the Network Summary Dashboard

The Network Summary Dashboard data is split into 3 sections:

1. Performance Category Tabs: Click on each tab to get a deeper look at each performance category.
   
   1. ALL shows the overview of each performance category.
   2. Speed  specific look at 9 speed KPIs based on repair work orders.
   3. Quality specific look at 4 quality KPIs based on repair work orders.
   4. Engagement specific look at 6 engagement KPIs based on Fixxbook activities.
   5. Price specific look at 6 price KPIs based on median invoice amounts.
      
      

2. Pie and Bar Charts: Located at the top of each tab, show how you rank against the entire platform for each Subscriber in terms of bottom, below average, average, and top. The numbers in each slice or bar indicate how many subscribers you are performing for at that level. You can click in and explore on each chart except on Engagement and Price.

   When you filter to a specific data point, your specific data will be ranked against the entire ServiceChannel platform not just that specific data in the platform. Ex: If you filter to see the performance of your plumbers, the dashboard will show how your plumbers rank against the entire platform. It will not compare your plumbers against all plumbers. Drill down into trade, region, and market specific comparisons using the Benchmarking Dashboard.

3. Table: View all filtered subscribers and your scores for them side by side in the table. Like most tables in analytics, you can sort, filter, and drill into the associated data.