Whether you want to give everyone a simple and engaging view of their performance through a dashboard, or delight your sales people with innovative and effective challenges, you will need to import your results into the platform through a "dataset".
Indeed, it is in the dataset that the results of users or teams will be collected and stored. This data will then be used to update the indicators of your challenges or dashboards.
After configuring the basic parameters of the space, you can now create your dataset. Here's how to do it. 👇
I. The first steps
Go to "Administration organization" via the parameters, represented by a cogwheel.
Click on "Manage datasets" and then "+ Create a dataset" in the data tab.
OR
Go to the challenge or dashboard where you want to create a dataset.
In the "Data" tab (at the top of your screen), click on the "Create a dataset" button.
A new page will open and you will need to fill in the following information
The name of the dataset: use a name that is easy to remember and that will allow you to distinguish it from the others if you have several.
The type of data update processing:
Overwrite: When you load a file, its data will replace the previously loaded data.
Incremental: When you load a file, its data will be added to the previously loaded data.
II. Definition of the results owners
Here you define the population for which the results are entered in your file:
If your file brings up the results of a user, check the box: Users.
If your file shows results for a team (role), check the box(es) for the hierarchical levels for which there are results in your file.
Then you can fill in:
The User Field Name:
Fill in the heading of the column where the identifiers of the users with results can be found. If you leave this box empty, the column where the identifiers of the users with results can be found must be named "user".
The Team Field Name:
Enter the name of the column where the identifiers of the result-bearing teams can be found. If you leave this box empty, the column where the identifiers of the teams with results are located must be named "team".
The Name of the ID field:
The row identifier is used to make updates in "Overwrite" mode faster. This identifier allows the platform to analyze the file and to update only the data that has changed. For example, in a dataset containing contracts, the contract reference can be used as a line identifier. At each file import, the platform will not delete all the contracts referenced in the dataset and replace them with all those referenced in the file. The platform will detect the contracts whose reference is already present in the dataset and will add only the new contracts. For this to work, the identifier must be unique (present in only one line). This can work if your file references the results by listing your users line by line.
III. Modeling the file
Finally, you must indicate the different columns of your results file so that the platform can read it correctly.
To do this, two methods are proposed to you:
Automatic addition of the columns: you just have to import your file thanks to the "Choose a file" button. The platform will automatically add the columns and detect the type of data they contain. You will only have to check the accuracy of the information created automatically.
Adding columns manually: you must click on the "Add a field" button to reference each of the columns present in your file.
For each field, you must indicate :
The name: it must correspond exactly to the title of the column in your file (Beware of invisible spaces, lower case letters, capital letters and typing errors).
The type of data: This information allows the platform to determine how it should process the information. There are several types of data:
Number: for numeric fields.
Text: for short texts, like the name of a product, the reference of a contract,...
Multi-line text : for long texts like a comment.
Choice of value : this field is useful when you have a column that allows you to differentiate between different types of contracts for example. By referencing the different types of contracts, you can then use the "Analysis by category" functionality to display the distribution of your sales according to the type of contract. This option is proposed to you when you create an indicator.
Date: must be in "DD/MM/YYYY" format.
E-mail: by selecting this type of data, the platform will make sure that it is an e-mail address and will make the link clickable.
URL: by selecting this type of data, the platform will make sure that it is a link and will make it clickable.
File: by selecting this type of data, the platform allows the addition of an attachment. It can be, for example, a photocopy of an invoice or a quote.
The "Required" box: this box indicates to the platform whether the cells in this column must all be filled in or whether they may be empty in certain lines. If you check it but a cell in this column is empty, the platform will tell you.
Make sure you have added a "Date" field in your dataset. This will allow you to follow the evolution of the indicator and to be able to consult it according to the chosen periodicity (weekly, monthly, etc.).
🔎 Some clarification:
You do not have to reference in your dataset all the columns present in your file. You can just reference only the columns you will need on Incenteev.
You are not obliged to respect the order of the columns in the dataset. The platform will identify the column thanks to the field name.
The platform only recognizes the first tab of a result file. Therefore, if you have several tabs, make sure that the one with the results is the first one in your file. If you ever have multiple tabs that are useful, you will need to either combine them into one tab or separate them into multiple files and create the corresponding datasets.
😃 See you soon on Incenteev! 😃