1. Enable Opportunity Team
2. Enable Opportunity Split and Add Opportunity Split Type (optional)
For this blog, I am adding Overlay opportunity split type
3. Enable Forecast, Add Forecast
Type (optional), and Enable Quota
enable forecast
adding forecast type
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Salesforce: Setup Forecast Quota
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Salesforce: Forecast with Opportunity Split
This is the continuation of previous blog Setup Forecast Quota, in this blog, we will share about Forecast with Opportunity Split Overlay.
1. Opportunity Team and Opportunity Splits
Here is the scenario:
Two users, both users have been enabled for Forecast
1. Johan Forecast is the manager
2. George Mann report to Johan Forecast
Two closed-won opportunities with Overlay split:
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Einstein Analytics: Result Binding
There are 2 types of binding in Einstein Analytics:
1. Selection: the query result based on user selection
2. Result: the query result based on the changes in other steps
We have discussed selection binding in the previous blogs: here, here, and here. Now let us looks at result binding.
Here is the sample for this blog:
- We have 2 datasets which are not really linked, but both have user Id and
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Salesforce: Query Multi-Currency Field
When you have multi-currency enabled in your Salesforce org., by default you will see the converted value in the page layout, list view, and report.
Page Layout
My user currency is USD and Corporate currency is USD too.
If I change my currency to SGD
List View
The same goes to List View, it will show the amount in the opportunity currency and the value in your currency in brackets.
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Einstein Analytics: Transpose multi columns to single column
In the table-1 below; Data-1, Data-2, and Data-3 are stored as individual columns.
Here is what we can get from Table-1
But, we can't have a total of A, B, and C as a single bar chart for easy comparison, the easiest option is to change the dataset into Table-2.
Solution: use dataflow to expand the rows and create new fields with combine values
Notes:
- ceData1,ceData2,
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Salesforce: SOQL Picklist Values & API Name
As you are aware that we can have different names between Picklist Values & API Name in Salesforce, see this screenshot:
When users enter the data or run a report, they will only see Values and not API Name.
When admin or developer do a query with SOQL, the result is API Name
SELECT Id, Name, AccountSource FROM Account WHERE AccountSource <>''
If you need to get the values from
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Salesforce Einstein – Where to start to experiment and understand Machine Learning?
by Jean-Michel Mougeolle, Salesforce MVP hall of fame, Salesforce Einstein Champion, SharinPix CEO.
What is the Einstein Champion program?
Let me start this blog with the Einstein Champions Program. The Einstein Champions Program is for Trailblazers that are passionate about the Einstein Platform and want to share their advanced knowledge with peers and evangelize the power of Einstein.
I
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Einstein Analytics: Transpose from row to column
You know, in life you face multiple weird things, the same when you work as an Einstein Analytics consultant. In the previous blog, we share how to transpose data from multiple columns to rows.
In this blog, it is the other way round, the requirement is the other way rounds.
Background: we need to show child records on the parent level, the number of rows in the table should follow the
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Einstein Analytics: Auto-filter dashboard based on User
You can set initial selections based on the following Salesforce user tokens: user.id, user.name, user.rolename, and user.roleid. At runtime, Analytics retrieves the values of these tokens from Salesforce environment variables—these values don’t come from datasets.
Sample-1:
"Owner_Name_1": {
"datasets": [
{
"id": "
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Einstein Analytics: Binding serialization functions asString() or asObject()
We discussed the quest for here and the syntax here, and you may see we use asString() and asObject() in the samples, as per Einstein Analytics documentation:
asString() Function
Serializes a scalar, one-dimensional array, or two-dimensional array as a string. Escapes double quotes in strings.
asObject() Function
Passes data through with no serialization. Returns data as an object (an array of
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Einstein Data Insights: Analyze Reports Data
Einstein Data Insights requires the Einstein Analytics Plus license. Einstein Data Insights analyze your report data and provide you with insights, complete with charts and explanations.
Setup
1. Enable Einstein Data Insights
From Setup, enter Einstein Data Insights in the Quick Find box. Under Einstein Data Insights, click Getting Started.
2. Grant User Access
Users need to have the standard
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Einstein Analytics: Use Image as Link
In Einstein Analytics, link by default only offers text. But instead of only text, can we use an image as a clickable link?
Yes, the trick is to use a container then add a link widget on the container, then make the link widget background as transparent and the text as transparent too.
Container
- Add a small container widget with the size similar to image size for the dashboard
- From widget
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Einstein Analytics: Working with Null Dimension with Default Value
A dimension field in Einstein Analytics is equal to a string field. However, by default Einstein Analytics does not show NULL value in grouping or sorting.
From the sample report below, we have 1004 accounts, and 5 of them do not have Account Source.
With dataflow, we get all the Account from Salesforce to Einstein Analytics, this clearly shows the number of Accounts in the dataset is 1004.
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Einstein Prediction Builder: Getting Started
Einstein Prediction Builder allows you to easily build predictions across any Salesforce standard or custom object with clicks, no code! It predicts a field based on a set of historical data.
If you have Einstein Predictions or Einstein Analytics Plus license, then Einstein Prediction Builder is available for you to use, source Einstein Analytics Pricing (as of 31 Dec 2019).
If you org.
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Einstein Lead Scoring: Getting Started
Einstein looks at your company’s past leads, including any custom fields, to find patterns in your successful lead conversion history. Einstein Lead Scoring then determines which of your current leads fit your success patterns best. Each lead receives a score indicating how well it fits your patterns, along with insights about which of the lead’s fields affect its score most.
If you have Sales
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SimplySfdc in 2019
Happy New Year 2020! To follow yearly tradition, I would like to share some statistics of SimplySfdc in 2019 -- here is the statistic for 2018.
In 2019, lesser blogs where written compare to 2018 (41% less), but the total Pageviews and total Sessions increase.
Page
2019
2018
2017
change*
Total New Page
37
63
34
-41.27%
Total Pageviews
210,213
171,249
115,744
22.75%
Total Sessions
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Einstein Analytics: Grouping in Dataflow
After the blog to transpose data from columns to rows, and from rows to columns. Today I have another challenge to group data based on a date.
Here is the data
I know the recipe offers this functionality to group data easily, however, reluctant to put a recipe in between of to dataflows, as it will cause maintenance nightmare in the future.
But, can we do this in dataflow? Dataflow does
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Einstein Analytics: Using EdgeMart object from Salesforce Direct
In Winter '20 release, Einstein Analytics introduces Salesforce Direct, read this release notes for complete info of Salesforce Direct.
However, Salesforce Direct offers you to get data more than just Salesforce objects, but data in Einstein Analytics too, one of them is EdgeMart, please not to confuse with the edgemart node in Dataflow.
Let's have hands-on, make sure you are in the Production
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Einstein Analytics: Precision and Scale
Precision and Scale are important and required for computeExpression node that returns Numeric in Dataflow, otherwise, your dataflow rum will fail.
For numeric, as per this article External Data Metadata Format Reference
precision: the maximum number of digits in a numeric value, includes all numbers to the left and to the right of the decimal point (but excludes the decimal point character).
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Einstein Analytics: Dataflow Performance Best Practice
Performance is critical for Einstein Analytics dataflow, e.g. an optimized dataflow may take only 10 minutes, while the same dataflow with a poor design may take 1 hour (this includes sync setup) to run. Therefore, without great architected dataflows, it will be hard to maintain and sustain Einstein Analytics as a whole, as the company evolved.
Here are a few items noted based on my personal
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