SimplySfdc in 2019

Happy New Year 2020! To follow the yearly tradition, I would like to share some statistics of SimplySfdc in 2019 -- here is the statistic for 2018.

In 2019, lesser blog posts were written compare to 2018 (40% less), but the total Pageviews and total Sessions increase.

Page	2019	2018	2017	change*
Total New Page	38	63	34	-39.68%
Total Pageviews	210,213	171,249	115,744	22.75%
Total Sessions	185,396	149,574	97,310	23.95%
Pages / Session	1.13	1.14	1.19	-0.88%

* compare 2019 to 2018

Similar to previous years, organic search always contributes to the largest portion of traffic source in 2019, and this year it crosses the 70% mark. Direct traffic, referral, and social weaker in 2019.

Channel Source	2019	2018	2017
1. Organic Search	74.06%	69.63%	67.66%
2. Direct	23.35%	27.15%	25.64%
3. Referral	1.72%	1.76%	4.09%
4. Social	0.82%	1.46%	2.60%

Google, as always is the king of search engine globally, the same applies to SimplySfdc in 2019, it contributes more than 96% of traffic, which is more than 1% lower compared to 2018. Bing and Yahoo gain slightly more percentage, while other search engines show an increase in contribution too.

Top Search Engine	2019	2018	2017
1. Google	96.09%	97.48%	96.46%
2. Bing	2.61%	2.02%	2.66%
3. Yahoo	0.56%	0.47%	0.77%
4. Other	0.74%	0.03%	0.11%

Twitter as the predominant social media for Salesforce #Ohana stays at the 1st spot by contributing more than 39% traffic for SimplySfdc. LinkedIn and Blogger contribution a huge increase for more than 17% compared to 2018.

Top Social Media Source	2019	2018	2017
1. Twitter (#1 in 2018)	39.05%	56.50%	26.42%
2. LinkedIn (#3 in 2017)	31.57%	15.65%	44.81%
3. Blogger (#4 in 2017)	20.21%	4.17%	4.61%
4. Facebook (#2 in 2017)	5.57%	18.00%	7.15%
5. Others	3.60%	5.68%	17.01%

* in 2017 - StackExchange contribute 7.29% and Google+ contribute 7.11%


There is no change in the top 6 countries of the visitors, however, US visitors gain almost 2% of the total number of visitors. All other countries contribute lesser visitors in terms of the percentage of visitors. The top six countries represent more than 78% of the visitors.

Top Visitor Country	2019	2018	2017
1. United States	43.60%	41.84%	49.45%
2. India	22.07%	23.88%	19.56%
3. United Kingdom	4.50%	4.95%	4.61%
4. Australia	3.20%	3.30%	2.58%
5. Canada	2.85%	2.92%	2.38%
6. France	2.09%	2.18%	1.90%

From the cities' perspective, Chicago out of nowhere takes the 1st spot in 2019, while it was not in the top 10 in 2018, I guess probably this is related to intensive Einstein Analytics posts in 2019. New York climb to spot #4, and London drop to #6.  The top 6 cities represent almost 22% of the total visitors.

Top Visitor City	2019	2018	2017
1. Chicago	6.60%	n/a	1.82%
2. Bengaluru (#1 in 2018)	5.27%	6.08%	5.24%
3. Hyderabad (#2 in 2018)	3.27%	3.76%	3.11%
4. New York (#5 in 2018)	2.26%	2.30%	2.80%
5. Pune (#4 in 2018)	2.20%	2.32%	1.97%
6. London (#3 in 2018)	2.14%	2.50%	2.37%

* San Francisco was in #3 in 2017 and #6 in 2018
* Chicago was in #8 in 2017 and not in top 10 in 2018

This statistic includes access from desktop and mobile. Total top 5 web browsers contribute more than 98% of the visitors. All browsers position stay the same as in 2018, but Internet Explorer gains a higher contribution percentage by visitors.

Top Visitor Web Browser	2019	2018	2017
1. Chrome	83.13%	83.34%	81.33%
2. Internet Explorer	7.05%	5.43%	4.65%
3. Firefox	3.57%	4.64%	6.36%
4. Safari	2.91%	3.28%	5.32%
5. Edge	1.58%	1.71%	1.11%

In terms of the operating system, there is no change in the top 5 operating system. There is a slight decrease in Windows users. While mobile and tablet users stay the same with close to 6% contribution of the total visitors, this makes sense because most of the visitors access this blog when they have difficulty in configuring Salesforce using the desktop/laptop.

Top Visitor Operating System	2019	2018	2017
1. Windows	74.19%	75.13%	73.36%
2. Macintosh	17.63%	17.12%	17.93%
3. Android	3.28%	3.15%	2.63%
4. iOS	2.57%	2.80%	4.10%
5. Linux	1.65%	1.36%	1.23%

As mentioned in last year's blog, screen resolution 1536x864 probably is 1920x1080 resolution with the display set to 125% zoom, and 1280x720 is 150% zoom of 1920x1080. In total, 1920x1080 is contributing to more than 42%. I have no idea with 1024x768, but some of the contributions probably are tablets and mobile.

Top Visitor Screen Resolution	2019	2018	2017
1. 1920x1080	24.38%	21.87%	20.87%
2. 1366x768	15.62%	20.09%	21.63%
3. 1536x864	8.88%	8.53%	8.12%
4. 1280x720 (#5 in 2018)	8.75%	6.63%	4.63%
5. 1440x900 (#4 in 2018)	7.95%	8.23%	8.44%
6. 1024x768 (#8 in 2018)	6.35%	3.63%	n/a

Top 5 Popular Page

For popular pages, 3 of 5 top pages from 2018 still stay in 2019. And, 3 of 5 top pages in 2019 are written in 2018.

1. Salesforce: List View Mass Action in Lightning Experience ~ 6,265 hits [2018] (#4~2018)

2. Salesforce: How to export Attachments? ~ 6,023 hits [2014] (#1~2018; #1~2017; #4~2016; #4~2015; #5~2014)

3. Salesforce: Convert ID from 15 to 18 characters with Excel formula ~ 4,215 hits [2018] (#15~2018) 

4. Salesforce Lightning: Log a Call button does not appear in Activity component ~ 4,125 hits [2018]

5. Salesforce: Activity Controlled by Parent ~ 2,970 hits [2015] (#5~2018; #17~2017)

Top 5 Referral Site

1. Salesforce Ben ~ 10 Most Popular Salesforce Admin Blogs

2. Kelsey Shannon ~ EA Certification Study Guide Part 1: Data Layer

3. Kelsey Shannon ~ EA Certification Study Guide Part 5: Security

4. SrinuSFDC ~ Admin 201 Sample Questions 1 - 20

5. Kelsey Shannon ~ EA Certification Study Guide Part 6: Administration

Reference:

• SimplySfdc in 2018
• SimplySfdc in 2017
• SimplySfdc in 2016
• SimplySfdc in 2015
• SimplySfdc in 2014

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 Cloud Einstein or High Velocity Sales license, then Einstein Lead Scoring is available for you to use.

menu availability depends on the licenses provisioned

Step to enable and configure Einstein Lead Scoring

1. Add Remote Site Settings
example:
Remote Site Name CS41
Remote Site URL https://cs41.salesforce.com

2. Permission Set
You can use the standard Sales Cloud Einstein or High Velocity Sales User permission set, or create a custom permission set and make sure to enable Use Einstein Lead Scoring permission under App Permissions. Assign the users with the Permission Set.

3. Enable Einstein Lead Scoring
Go to the setup menu and type Assisted Setup, it should be under Einstein Sales. Follow the wizard start with set up for Einstein Lead Scoring.

It may take up to 24 hours for Einstein to score the Leads. Once finished, you will get a notification.

You will find Einstein Scoring component available on the Lead Lightning page.

Reference: Prioritize Leads with Einstein Lead Scoring

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. have Einstein Prediction Builder license, you should see Einstein Prediction Builder in the setup menu.

There is a few blogs have shared on Einstein Prediction Builder use cases and how to configure it:
- Einstein Prediction Builder. The Artificial Intelligence by Salesforce
- Einstein prediction builder to predict
- Einstein Prediction Builder

Einstein Prediction Builder comes with a wizard-style configuration, you need to select an object, a field for prediction, a set of records as "example", and field to store the prediction result.

Currently, it only supports to predict a checkbox field, while the result would be in number between 0 to 100. In summary, here are the steps:

1. Object
Select a Salesforce object you would like to use for prediction.

2. Segment
Do you want to use the whole data of the object for prediction and learning? Select:
- No segment, to use all data, or
- Yes, enter filter of fields to be included
Make sure you have a minimum of 400 records (after filter if you segment the data).

3. Field to Predict 
This is a very important field, Einstein will do the prediction based on historical value of this field, and it needs to be a checkbox or formula that returns checkbox.

In this blog, I have a picklist field called "Status" with value: Sent, No Show, and Attended. No Show and Attended is historical data, while Sent is new records, and Einstein will help us to predict the attendance for all Sent records.

Because it needs to be a checkbox field, let us create a formula field that returns checkbox called "Attend", ISPICKVAL(Status__c,"Attended"), this field will return True if the Status field is Attended.

4. Define a set of data as examples (for learning)
As mentioned that Einstein needs historical data to predict, we need to define historical data for learning. Einstein will not predict records set as examples, but use it to learn the value of "field to predict".

The more samples data (historical) is a better prediction, but make sure to have a minimum of 400 example records, with both Yes and No records should have a minimum of 100 records.

From the above sample screenshot:
- 407 is the historical data for learning, which are records with Status does not equal to Sent
- 188 of them is Yes <-- this means Attend value is True
- 219 of them is No <-- this means Attend value is False
- 3399 records with Status = Sent, this needs to be predicted for the attendance

The below image explains dataset scope:

- Green: whole data in an object
- Yellow: data filtered after segment, data outside yellow will not be used for prediction and learning
- Dark Yellow: historical data set for learning (Status = No Show and Attended)
- Light Yellow: new data set for prediction (Status = Sent)


5. Set a field to store prediction values
Once the predictions enabled, the values will be range from 0 to 100.


Depends on your data size, Einstein may take up to 24 hours to build the prediction, then you can review the scorecard. Once you enable the prediction, Einstein will start to populate the prediction field with a score, including new records.



Reference:

• Einstein Prediction Builder
• Einstein Prediction Builder Trailhead
• Introduction to Predictions - Einstein Analytics (YouTube)

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.

Then, let us group it by Account Source

Notice from the above screenshot, Accounts with null Account Source do not show at all, the same if we change it to Compare Table.

In Values Table, Account with null Account Source still appears.

But, if we sort by Account Source column, the rows will no longer show.


So, instead of keeping it null, we can transform the value as N/A in Einstein Analytics. We can do this from sfdcDigest note in Dataflow.
Open the sfdcDigest node for Account and click the pencil icon next to Account Source.

In the Default Value, populate N/A, and click the Save button.

Notice the pencil icon next to Account Source is changed, this tells us that something has been updated for that field, click Save button again to close sfdcDigest window.

If you check the dataflow JSON, defaultValue is added

"sfdcDigest_Account": {
    "action": "sfdcDigest",
    "parameters": {
      "fields": [
        {
          "name": "Id"
        },
        {
          "name": "Name"
        },
        {
          "name": "AccountSource",
          "defaultValue": "N/A"
        }
      ],
      "object": "Account"
    }
}


Now, update the dataflow and run it again, then group the lens with Account Source, notice N/A added for all null values Account Source.

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 properties, under "Background Image" section, click Upload Image then select an image
- Set the Image Scale and Image Alignment properly

Link
- Add link widget in the container widget added
- Resize the link widget size, to cover the whole container widget
- Set "Link To" to a dashboard or lens or page or URL as needed
- In the "Text Style" section, change the color as transparent, by click Custom and drag the slider to the far left

- Same for "Widget Style", set the Background Color to transparent too, make sure it is "rgba(x,y,z,0)" with the last number is 0.

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 permission set "Einstein Analytics Plus User" or "Einstein Analytics Plus Admin", or custom permission set include the "Can Run Einstein Data Insights" permission.


Analyze Report Data
1. The report must be in tabular or summary report type, matrix and joined report is not supported. If you use the summary report, make sure the "Detail Rows" are shown.
2. Report column: 2 - 50 columns.
3. Report row: 50 - 500,000 rows, even report only display 2,000 rows, einstein will analyze the whole data, not just 2,000 records shown.


Insights button
You will not see the Insights button or the button will be disabled in the report if any of the above criteria are not fulfilled, from the license, user permission, report format, number of rows and columns.

Start Analysis
You can create an analysis by:
- record occurrence
- a number field in the report

Then tell the system if you would like to Maximize or Minimize before analyzing the report, in my case, I would like to maximize Won Opportunities. You will get the analysis almost immediately, depends on the report rows result and number of columns.

Click "learn more" below each box to get the detail of the analysis. You also can click the title to get a bigger view of the analysis.

Reference: Analyze Reports with Einstein Data Insights