Sensai Customer Journey
You will receive a Welcome email with login instructions.
Click on URL - https://sensai.cloudapps.com
Enter username and temporary password and press the Login button
On your first login, you will be prompted to change your temporary password. Change and press Submit button.
You will then be presented with the Sensai Home Page
Upload Closed Opportunity File
We need to load some Closed Opportunity information to train our deep learning model.
For this training example, you can use the following file - later we will be using your own files or integrating directly with your instance of salesforce.
- Download the SF1_Closed_Opportunities.csv file from here https://drive.google.com/file/d/1NiLNeJNsdV04-KL9fPDx2hV_KNcTTM_t/view?usp=sharing
- Press the Options button in the top left of the Navigation Bar and select Files
- Press the Upload File button
- Choose SF1_Closed _Opportunities.csv and press the Upload button
- It may take a few seconds to upload the file and for it to appear in the list
Your file is now loaded
At this point, we will define a Schema with only one Entity, Opportunity.
- Press the Options button in the top left of the Navigation Bar and select Schemas
- Press the 'Create Schema' button
- Give the Schema a name - e.g. SF1 Opportunity
- Under the 'Source' field, use the picklist and select 'Other'
- Press the 'Add Primary Entity' button
- Add the Primary Entity information - in this case the Opportunity then press Add Entity button
- Press the Next button.
- There are no relationships to add at this point so please just press the Create button
You have now created the Schema
- Press the Options button in the top left of the Navigation Bar and select Datasets
- Press the 'Create Dataset' button
- Give the Dataset a name and then select the Schema you made earlier
- Choose your uploaded file by typing in 'SF1_Closed_Opportunities.csv' then select the file from the picklist.
- Press the Create button
Your Dataset is now created
- Press the Options button in the top left of the Navigation Bar and select Models
- Press the Create Model button
- Give the Model a name and choose your 'Architecture Type'. In this example, we are using a Binary Classification
- Then select your Dataset
- Select the Target of ISWON
- Then select the Opportunity fields you would like to include in the Model - suggest the 11 fields shown in the screenshot
- Then press the 'Create' button
- You may need to refresh the page for the new Model to appear in the list
- Once the Status changes to CREATED, press the dropdown button for the Model you have just created and select 'Train Model'
The deep learning model will be created - this should take about 5 mins but may vary depending on the volume of data and number of fields selected
Predict Open Opportunities & Download
- Click on the Options button in the top left of the Navigation Bar
- Select 'Files'
- Import the SF1_Open_Opportunities.csv file - https://drive.google.com/file/d/1CCMxeHfIrnv-lcla91byP8_J46xX1NMx/view?usp=sharing
- Create a new Dataset “SF1_Open_Opportunities” from the SF1_Open_Opportunities.csv using the SF1 Opportunity Schema.
- Press the Options button in the top left of the Navigation Bar and select Predictions
- Select the Request Predictions button
- Give the Request a name, choose your Model & Dataset and press the 'Request' button.
This may take a few minutes to create and appear in the list - if it does not appear after a few minutes you may need to refresh the page to see the completed list.
- Once completed drill into the Prediction and you will be presented with buttons to Download Prediction and Download Contributions. You will also see 2 charts, the first is a ranked list of the importance of Features and the second is a ranked list of values inside these Features.
- Click on the 'Download Prediction' from the dropdown menu button.
This will download the file in a csv format with 3 columns - ID, PROBABILITY and PREDICTION. This can then be imported into either your CRM system or into a BI tool of your choice.
Please sign in to leave a comment.