What is Salesforce Marketing Cloud?

Written by Alina Makarova, Growth Marketer at DESelect. DESelect is an app for Salesforce Marketing Cloud that makes it easy for marketers to segment without relying on SQL.  

Salesforce Marketing Cloud (SFMC) is a product of Salesforce, which is the biggest CRM platform in the world. It enables marketers to get to know customers, personalize, and engage with them during their customer journey. It provides an opportunity to engage with customers using various digital marketing channels: email, web, mobile, social, or digital advertising. 

What else can you do with Salesforce Marketing Cloud?

First off, it is possible to create unified views of every customer combining known and unknown customer profiles. Moreover, SFMC allows measuring and reporting to optimize your marketing performance and achieve better campaign results while improving your customer relationship. Lastly, you can take your marketing to the next level by enabling Salesforce Einstein’s artificial intelligence capabilities.

What does Salesforce Marketing Cloud consist of?

There are several built-in apps in SFMC that help to get a 360 view of your customers and communicate with them at every level. Here you can see how these apps look like in SFMC itself:

Below you can find a quick description of the main functions of these apps.

Name of the app	What is it used for?
Content Builder	Create content for your emails, in-app messages, push notifications.
Journey Builder	Deliver cross-channel personalized experiences at every step of the customer lifecycle with campaign management.
Interaction Studio	Visualize, track, and manage customer experiences with real-time interaction management—driving valuable engagement at the right moment.
Email Studio	Use data from every department to build smarter email—from basic marketing campaigns to sophisticated 1-to-1 messages.
Mobile Studio	Send consistent SMS, push, and chat app messages in real-time.
Advertising Studio	Use CRM to securely power 1-to-1 advertising across Google, Facebook, LinkedIn, Twitter, Pinterest, and Display at scale.
Social Studio	Listen, publish, and engage to create customer advocates. Connect social to marketing, sales, and service in one platform powered by AI.
Web Studio	Allows you to create Cloud pages.
Einstein	Einstein Email and Web Recommendations power delivery of relevant content based on prior user behavior.
Analytics Studio	Allows to get deep insights into the behaviors and interests of your contacts across channels. You can use these insights to set marketing goals and refine customer journeys.

Beyond the above-mentioned apps, there are many possibilities to expand the capabilities of the Salesforce Marketing Cloud. On AppExchange you can find various apps that can be added to your SFMC instance. For example, DESelect enables you to segment your customers easily without the need to rely on SQL which is the default approach in SFMC. 

Who is Salesforce Marketing Cloud made for?

If you need to create long-term relationships with your customers, then SFMC is a great solution for you. It enables multichannel communication, and possibilities to track, analyze, and report the received data to create a 360 view of an individual customer. It is primarily used for B2C marketing purposes. 

Moreover, SFMC always changes and evolves opening new horizons for marketers to communicate with their customers. Lastly, the apps found in AppExchange make the platform even more powerful, aiming to save marketers time and improve their user experience.

Einstein Analytics: Compare Table functions

Compare Table is another powerful and easy to use feature in Einstein Analytics to meet your business requirements, you do not need to manually construct the SAQL or edit with JSON. It allows you to aggregate data into a table (or chart) based on formulas defined.

You can use familiar SAQL syntax to create your own formula for a column of multiple columns in a table or visualize it to a chart. 

On top of manually building custom formula with SAQL, Einstein Analytics also provide defined windowing functions to analyze data across rows.

Sliding Window

Applies an aggregate function to the current row with respect to a configurable range of rows.

- Column: source data (a column)

- Function: Average, Sum, Min, Max

- Start: start row to analyze, -1 mean 1 previous row from the current row

- End: end row to analyze, 0 mean current rows, 1 mean 1 row after current row

- Reset Group: if only you have more than 1 grouping, you can reset by 

Let us see a sample:

This function compares the current value with 1 previous row (start = -1, and end = 0), then get the Max value. 

Once saved, we will see the formula

max(A) over ([-1..0] partition by all order by ('Opty.CloseDate_Year~~~Opty.CloseDate_Month~~~Opty.CloseDate_Day'))

Now, let us add Account Name as 2nd level grouping without adding Reset Group

Because there are 3 accounts on 13-May-2017, the row explodes to 3 lines, but the logic still the same, which is comparing max value between the current and previous row. In this change, the formula will not change.

Now, let us add Reset Group = Opty.CloseDate

The formula will be changed by adding Account Name (in this sample the API name is "Name")

max(A) over ([-1..0] partition by all order by ('Opty.CloseDate_Year~~~Opty.CloseDate_Month~~~Opty.CloseDate_Day','Name'))

Because Close Date added as Reset Group, windowing functions only compare within the same date.

Percentage of Group

Calculates the percentage each row is of its group total, or of the grand total. The percentage only applicable for data shown in the table, NOT for the whole data in dataset (if you apply filter).

The formula: A/sum(A) over ([..] partition by all)

The function can be reset on a grouping defined in the table, this is the same with Sliding Window, you need to have minimum 2 grouping to apply Reset Group.

The formula: A/sum(A) over ([..] partition by 'Opty.CloseDate_Year~~~Opty.CloseDate_Month~~~Opty.CloseDate_Day')

Rank Within Group

There are 4 functions offered by Rank Within Group:

• Rank: assigns rank based on order. Repeats rank when the value is the same, and skips as many on the next non-match
• Dense Rank: same as rank() but doesn’t skip values on previous repetitions.
• Cumulative Distribution: calculates the cumulative distribution (relative position) of the data in the reset group
• Row-number: assigns a number incremented by 1 for every row in the reset group.

Formulas for each function:

• Rank: rank() over([..] partition by all order by A desc)
• Dense Rank: dense_rank() over([..] partition by all order by A desc)
• Cumulative Distribution: cume_dist() over([..] partition by all order by A desc)
• Row Number: row_number() over([..] partition by all order by A desc)

The Order menu determines the direction of ranking based on the values being ranked. Ascending ranks the lowest value as number 1, while descending ranks the highest value as number 1. Same as Sliding Window and Percentage of Group function, we can add Reset Group in Rank Within Group.

Period Over Period

To use Period Over Period, the table must be group by Date field. Period Over Period function compare periods of time to calculate changes in values, for example: year-over-year, quarter-over-quarter, month-over-month, week-over-week, or day-over-day.

Then, we have option to show the result as: % Change or Unit Change. 

Formula for % Change

(A - sum(A) over ([-1..-1] partition by all order by ('Opty.CloseDate_Year~~~Opty.CloseDate_Month~~~Opty.CloseDate_Day')))/(sum(A) over ([-1..-1] partition by all order by ('Opty.CloseDate_Year~~~Opty.CloseDate_Month~~~Opty.CloseDate_Day')))

Formula for Unit Change

A - sum(A) over ([-1..-1] partition by all order by ('Opty.CloseDate_Year~~~Opty.CloseDate_Month~~~Opty.CloseDate_Day'))

Period Over Period do not offer Reset Group.

Change from Previous

Compares the value of the current row with that of the previous row and calculates the difference. This function similar with Period Over Period, but you do not need to group by Date. 

Change from Previous offer Reset Group function and similar with Period Over Period, we have option to show the result as: % Change or Unit Change.

Formula for % Change

(A - sum(A) over ([-1..-1] partition by all order by ('Opty.CloseDate_Year~~~Opty.CloseDate_Month~~~Opty.CloseDate_Day')))/(sum(A) over ([-1..-1] partition by all order by ('Opty.CloseDate_Year~~~Opty.CloseDate_Month~~~Opty.CloseDate_Day')))

Formula for Unit Change

A - sum(A) over ([-1..-1] partition by all order by ('Opty.CloseDate_Year~~~Opty.CloseDate_Month~~~Opty.CloseDate_Day'))

Above table looks very similar with Period Over Period, but Change from Previous can be implemented to any grouping, not just Date over a period.

Running Total

Calculates the total value of the current row summed with all previous rows. 

Running Total formula without Reset Group: sum(A) over ([..0] partition by all order by ('Opty.CloseDate_Year~~~Opty.CloseDate_Month~~~Opty.CloseDate_Day'))

Running Total also offer Reset Group if you have more than 1 grouping in table. Let us see in sample with and without reset Group:

Reference:

• Add Custom Formulas to Columns
• Window Functions Available in the Compare Table
• Windowing Functions

Einstein Analytics: computeExpression return Date

In some scenarios, we need to return a Date field from computeExpression node. 

Date value can be null

sample:

case when ClosedDate_sec_epoch > date_to_epoch(now()) then toDate(ClosedDate,"yyyy-MM-ddTHH:mm:ss.000Z") end

or

case when ClosedDate_sec_epoch > date_to_epoch(now()) then toDate(ClosedDate_sec_epoch) end

Date or Date/Time

For a Numeric field, Precision and Scale are required, while for a Date field, Date Format is required in computeExpression. Einstein Analytics by default will "explode" a Date field into:

- Year

- Quarter

- Month

- Week

- Day

- Hour

- Minute

- Second

- Epoch days

- Epoch seconds

Let us see some samples of computeExpression returning Date type. In this sample, ClosedDate is a Date/Time field in Salesforce:

(1) SAQL Expression: toDate(ClosedDate,"yyyy-MM-ddTHH:mm:ss.000Z")

Date Format: yyyy-MM-ddTHH:mm:ss.000Z

--> this will create DateTime field in Einstein Analytics

(2) SAQL Expression: toDate(ClosedDate_sec_epoch)

Date Format: yyyy-MM-ddTHH:mm:ss.000Z

--> this will create DateTime field in Einstein Analytics

(3) SAQL Expression: toDate(ClosedDate,"yyyy-MM-ddTHH:mm:ss.000Z")

Date Format: yyyy-MM-dd

--> this will create Date field in Einstein Analytics

(4) SAQL Expression: toDate(ClosedDate,"yyyy-MM-dd")

Date Format: yyyy-MM-ddTHH:mm:ss.000Z

--> this will cause an error because ClosedDate in this sample is a date time.

(5) SAQL Expression: toDate(ClosedDate_sec_epoch)

Date Format: yyyy-MM-dd

--> this will create Date field in Einstein Analytics, similar to (3)

(6) SAQL Expression: ClosedDate

Date Format: yyyy-MM-dd

--> this will cause an error, we can't use the field directly, even it is a date field 

(7) SAQL Expression: now()

Date Format: yyyy-MM-dd

--> this is okay, it will return the UTC date

As the screenshot above, the date format determines how the values are stored in Einstein Analytics. But, let us drill further:

Even hour, minute, and second are not added in the date format then not shown in the date field, Einstein Analytics still maintains hour, minute, and second in the exploded fields.

yyyy-MM-ddTHH:mm:ss.000Z is similar with yyyy-MM-ddTHH:mm:ss.SSSZ and similar with yyyy-MM-dd'T'HH:mm:ss.SSS'Z'

Now, let us try to use Date field from Salesforce, instead of Date/Time field. CloseDate here is a Date field in Salesforce:

(1) SAQL Expression: toDate(CloseDate,"yyyy-MM-dd")

Date Format: yyyy-MM-ddTHH:mm:ss.000Z

--> this will create DateTime field in Einstein Analytics

(2) SAQL Expression: toDate(CloseDate_sec_epoch)

Date Format: yyyy-MM-ddTHH:mm:ss.000Z

--> this will create DateTime field in Einstein Analytics

(3) SAQL Expression: toDate(CloseDate,"yyyy-MM-ddTHH:mm:ss.000Z")

Date Format: yyyy-MM-dd

--> this will cause an error, because CloseDate is a date field, not Date/Time field in Salesforce

(4) SAQL Expression: toDate(CloseDate_sec_epoch)

Date Format: yyyy-MM-dd

--> this will create DateTime field in Einstein Analytics

(5) SAQL Expression: toDate(CloseDate,"yyyy-MM-ddTHH:mm:ss.000Z")

Date Format: yyyy-MM-ddTHH:mm:ss.000Z

--> this will cause an error, similar to (3), CloseDate is a date field, not Date/Time field 

(6) SAQL Expression: toDate(CloseDate,"yyyy-MM-dd")

Date Format: yyyy-MM-dd

--> this will create Date field in Einstein Analytics 

In summary:

• for SAQL Expression, it is easier to use toDate(xxxx_sec_epoch) 
• for Date Format, use yyyy-MM-ddTHH:mm:ss.000Z for Date/Time, or use yyyy-MM-dd for Date field

JSON:

If you download the dataflow JSON, you will see an additional \ before " in saqlExpression, but you can simply ignore that in the dataflow UI.

sample:

          "saqlExpression": "toDate('CreatedDate', \"yyyy-MM-ddTHH:mm:ss.000Z\")",

          "format": "yyyy-MM-dd",

Side Note: can we refer to a field in computeExpression within the same note?

Yes, as long as the field referred to is located above (in order) from the field call it, see screenshot below, CloseDate_2 can use CloseDate_1, but not CloseDate_4 and CloseDate_6.

Einstein Analytics: Hidden Step

Hidden step probably not the official term in Einstein Analytics, but you probably heard when having a conversation with Einstein Analytics experts with many years of experience, furthermore, Salesforce no longer calls it "step", but now it calls "query".

So, what is the hidden step? it is a query that used to support other queries, but not directly uses for widgets. The query can be in compact form, Salesforce Direct, SAQL, and SOQL.

As mentioned above, we can use a hidden step to support other queries, in this case, usually, we use "result" binding, remember selection binding will be trigger when user change/select something.sim

Sample SOQL query:
 "QueryLoginUser_1": {
                "groups": [],
                "numbers": [],
                "query": "SELECT Name,Id FROM User Where Name = '!{User.Name}'",
                "selectMode": "single",
                "strings": [],
                "type": "soql"
            }

use it in a "real" query:
         "filters": [
                        [
                            "OwnerId",
                            "{{cell(QueryLoginUser_1.result,0,\"Id\").asString()}}",
                            "in"
                        ]
                    ]


Sample SAQL query:
q = load "Quote_History";
q = filter q by 'StatusChange' == "Created to In Progress";
q = group q by all;
q = foreach q generate avg('Cycle_Time') as 'avg_Cycle_Time';
q = foreach q generate avg_Cycle_Time as 'ave_seconds', floor(avg_Cycle_Time / 3600) as 'hours', floor((avg_Cycle_Time / 60) - (floor(avg_Cycle_Time / 3600) * 60)) as 'minutes';
q = foreach q generate ave_seconds as 'ave_seconds', hours as 'hours', minutes as 'minutes', floor(ave_seconds - ((3600 * hours) + (60 * minutes))) as 'seconds';
q = limit q 2000;

If you open in JSON, it would become
"cycle_time_1": {        
"query": "q = load \"Quote_History\";\nq = filter q by 'StatusChange' == \"Created to In Progress\";\nq = group q by all;\nq = foreach q generate avg('Cycle_Time') as 'avg_Cycle_Time';\nq = foreach q generate avg_Cycle_Time as 'ave_seconds', floor(avg_Cycle_Time / 3600) as 'hours', floor((avg_Cycle_Time / 60) - (floor(avg_Cycle_Time / 3600) * 60)) as 'minutes';\nq = foreach q generate ave_seconds as 'ave_seconds', hours as 'hours', minutes as 'minutes', floor(ave_seconds - ((3600 * hours) + (60 * minutes))) as 'seconds';\nq = limit q 2000;"}

use it in a "real" query
"text": "{{column(cycle_time_1.result, [\"seconds\"]).asObject()}}",



Reference:

• Einstein Analytics Tips – Hiding columns in Tables Dynamically
• Einstein Analytics: Result Binding