The statistics page in ChannelEngine gives an overview of your total sales/revenue via ChannelEngine, including statistics on when your peak sales periods/times are, your top performers and your cancellation and return rates.
The top section is labeled sales and shows a graph based on the total revenue for a selected period, including the selected timeframe before the current. So if you select 'This year' you will see a blue graph for the revenue of this year, with behind it a grey graph for the previous year. If you select 'This month', it will be compared with the previous month, etc. If there is no period available to compare with, you'll only see the blue graph (for the current period) and the comparison stats will remain empty.
You can change the period and the included channels, both in the top right corner as below the comparison stats.
Total prices on orders are always including VAT, commission and other fees, which is no different for the statistics overview (so the total revenue is the gross revenue, not the net revenue).
When do your customers buy
This 'heatmap' shows in which timeframe and on what day most of your orders take place. This can be useful in order to determine the best time for updates, discounts, etc. As is usually the case with all online sales, the peaks will be located in the evening hours, while the lows will be at night.
This section gives you an overview of your best running marketplaces, your best-selling products, your best-selling brands, your best-selling categories and the countries you sell on most. This can be very useful to get a quick look if a specific marketplace is giving you enough turnover, what products and brands are doing great, etc.
The order section gives a quick overview of the number of total orders and their statuses. This includes the cancellation rate and return rates including the option to compare this with previous timespans.
This section gives an overview of the total amount of returns for the selected timeframe and the supplied return reason. This can be used to get a good sense of common return reason and where you might improve (for example: if you get a lot of wrong size returns including a comment from buyers that the item is too small for size [x], you might need to update your description for this product stating that customers are better off ordering one size bigger than normal).