Individual statistics/overview


Individual statistics/overview


Individual statistics/overview
 150 150 SimplyDelivery Academy

With our statistics you get a good overview of the statistical data of your business or franchise system.
General information:
You can compile the individual statistics by selecting the store(s). You can also define the time period and even limit it to specific times. Use the dropdown to select the desired statistics.
If you want to make a store comparison of individual statistics, select the option “Individual store statistics”.
You can also filter by source and type of order by selecting the options offered.

Item statistics:
The item statistics show you which items were ordered during which time period. By clicking in the table header, e.g. on quantity, you change the sequence.

Complaint coupons:
The statistics for complaint coupons show you which complaint coupons were issued and when they were redeemed. Complaint coupons must be created as a coupon and can be entered at checkout by clicking on the button “Complaint”.


Outgoing order channel:
With the statistics for “Outgoing order channel” you have a clear presentation of the distribution of your sales. For example, you can see here how many orders are picked up.
If you click on the row for staff, you will obtain a list of staff sales.


Incoming order channel:
In the statistics for the “incoming order channel” you get an evaluation of where your orders come from. Here you can see, for example, how many orders come from your online shop or the order portals.


Order frequency:
The “Order frequency” statistic shows you how many customers have ordered more than once in which period. The aim should, of course, be for you to ensure that existing customers order more often than just one time. To make it easier for you to draw conclusions about the underlying causes, we will show you, among other things, how the delivery times were in the selected period.

Driver statistics:
The driver statistics show you how your drivers perform in comparison to each other.
 
Coupons & discounts:
This statistic shows you all redeemed coupons and discounts in a comparative view. You can easily check the success of your marketing campaign.
 
Heatmap:
With the Heatmap you can check the local distribution of orders and also see the order volume in the various postcode areas. Furthermore, once you have loaded the statistics, you can also display the sales volume in the area around the delivery area. To do this, activate the switches below the dropdown list for the various statistics.
 
Customer area:
The statistics in the “Customer area” shows you the progress of new registrations and usage.

 
Delivery portals:
Here you see the distribution of the orders of the different order portals, e.g. Lieferando.
 
New customers:
The new customer statistics give you an overview of the customers who have ordered from you for the first time.

Store comparison:
The store comparison allows you to compare all your stores in terms of the most important key figures. If you have been using SimplyDelivery for more than a year, it is also possible to see a comparison to the same period for the previous year. To do this, click on the store name from the list.
 
Hours:
Under Hours you see the order volume divided into hourly periods.
 
Daily turnover:
This statistic shows you how each day compares to the other in terms of sales.
 
Sales groups:
If you have set up sales groups, you can perform a relevant evaluation here. By clicking on (item list) in the list you will once again be provided with a list of the invoices on which the item was found.
 
Product groups:
The product group statistics show you at a glance what sales are like in the various product groups. By clicking on the product group a list with the respective items within the product group opens.
 
Product groups (detailed):
In contrast to normal product group statistics, totals are calculated for all product groups of an article, i.e. if an article is assigned to two product groups, the number and total of these are added together with the waiting time of the drivers in two product groups: The statistics
Waiting time of the driver:
The “Waiting time of the driver” measures how long the drivers had to wait on average until you could place the next order. In conjunction with the statistics “Time in the kitchen”, you can measure the efficiency or workload of your kitchen staff.

Example:
Sign in 08:00 AM
First tour 08:15-08:22
Second tour 08:45-09:16
Break from 09:16-09:46
Third tour 09:50-10:12
Shift end around 10:15
 
It is calculated as follows:
The time between all tours:
08:45 – 08:22 => 23 minutes
09:50 – 09:16 => 34 minutes => 57 minutes
 
The break time is deducted from this if it did not take place before the first or after the last tour..
57 minutes – (09:46-09:16 => 30 minutes ) => 27 minutes
The time from the last tour until now/end of shift is not added, even if the driver waited during this time, because the time between two tours is to be selectively evaluated.
 
So, the driver waited twice (number of tours-1 => 3-1 = 2) with a total waiting time of 27 minutes.
So on average the driver waited 27/2 = 13:30 minutes. 
Remarks:
1) If a driver is not logged off in the evening and drives tours again the next day, then he has waited all night for one tour.
2) The second values have been omitted in the examples, but are also taken into account.
3) If drivers drive tours and they only assign the tours to themselves later, then the kitchen times are also distorted.
 
Weekdays:
Compare different weekdays by order volume. By clicking on the respective day, a list with the order periods is opened as well.
 
Payment methods:
In the section “Payment methods” you can see a comparison of the statistics for all the payment methods.
 
Payment types (without tax distribution):
Similar to the normal payment method statistics only without a breakdown after taxes.
Time in the kitchen:
The “Time in the kitchen” statistics show you in different time ranges how long an order took from arrival at the store to being taken by the driver. In conjunction with the ” Driver’s waiting time ” statistics, you can measure the efficiency or workload of your kitchen staff.
Example:
For all delivery orders (no pre-orders, no cancellations) the difference between “invoice number generated” ( =>kitchen receipt printing) and tour start is calculated.
These times are added up and divided by the number of orders.
 
Example (deliveries only):
Order 1:
Re-Date: 08:21
Tour start: 08:31 => 10 minutes
Order 2 (cancellation)
Re-Date: 08:23
Tour start: 08:44 => 0 minutes, because of cancellation
Order 3:
Re-Date: 08:27
Tour start: 08:45 => 18 minutes
Order 4:
Re-Date: 08:35
Tour start: 08:45 => 10 minutes
 
Kitchen time: (10+18+10) / 3 = 38/3 => 12:40 minutes 
Remarks:
1) If a driver is not logged off in the evening and drives tours again the next day, he has waited all night for one tour
2) In the examples, the second values were omitted, but are also taken into account.
3) If drivers take tours and they only assign the tours to themselves at a later point in time, then the kitchen times are also affected and distorted.

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