Tutorials

How to add sparklines to Salesforce using the Process Builder

Sylvain Giuliani
Sylvain Giuliani July 06, 2020

Syl is the Head of Growth & Operations at Census. He's a revenue leader and mentor with a decade of experience building go-to-market strategies for developer tools. San Francisco, California, United States

Our previous blog post got a lot attention, and people asked us how to implement sparklines when they don't have access to an external data source like a warehouse or if the data is coming from another app, like Marketo's Score for example.

I'm pleased to say you can achieve a similar result by relying on the ISCHANGED() and PRIORDATA() functions in the Salesforce Process Builder. 🎉

💡 Please be aware, you won't be able to use historical data to populate the graph. It will only work for future data. This is the main drawback of not using an external data warehouse as the source of the data.

How to set it up

In our example, we will create a Process that automatically stores the previous value of a field (PQL_Score__c)  in another text field  (historic_score_data__c). We will only store the last six values. Finally, we will use a formula field and the IMAGE() function to display a sparkline. 📈

1. Trigger the process when the field changes

First, you need to create a process builder that will trigger every time the field gets updated. In our example, we are using a PQL Score field on the Contact Object.

We will then create a condition called "Score Updated?" using a formula to check if it needs to continue.


ISCHANGED([Contact].PQL_Score__c ) = true

💡 ISCHANGED() is a powerful function you can use to check if a field was updated. You can learn more about it in Salesforce's documentation

2. Store the previous score value in another field

Thank you, Henry Mizel, for getting me back into Salesforce formula 😭

We will then add an immediate action on the Contact Object to store the prior value in our Historic Score Data field by using the following formula:


IF(
/* Check if there are already 6 values in the field */
(LEN([Contact].historic_score_data__c ) - LEN(SUBSTITUTE([Contact].historic_score_data__c ,",",""))+1) > 5,

/* If there are 6 values, remove the oldest one... and append the new score */

SUBSTITUTE([Contact].historic_score_data__c ,LEFT([Contact].historic_score_data__c , FIND(",", [Contact].historic_score_data__c )),'')+","+text(PRIORVALUE([Contact].PQL_Score__c )),

/* If we have less than 6 values, append the latest score */

PRIORVALUE([Contact].historic_score_data__c  )+","+text(PRIORVALUE([Contact].PQL_Score__c )))

💡PRIORVALUE() is a super helpful function that lets you get the previous field value during a process or a workflow.  You can read more about this function in the documentation.

I would love to hear from you if you have any ideas on making the formula prettier or shorter. Ping us on Twitter  🐦

3. Display a sparkline in a field on our Contact

Here we can follow the instructions from our previous post.

We will be using Quickchart to generate the sparkline by using the IMAGE() function in a formula field to render the chart.

In our example, we have a score_progression__c formula field where we will display the sparkline. The formula is straightforward.


IMAGE( 'https://quickchart.io/chart?bkg=white&c={ type: "sparkline", data: { datasets: [{ data: [' + historic_score_data__c + '] }] }}', 'score progression', 20, 100)

Congratulations, you now have sparklines in a Salesforce object that automatically chart the changes in a field value over time. 🎊

Let us know how you are using sparklines in Salesforce; we would love to hear your use cases!

Related articles

Product News
Sync data 100x faster on Snowflake with Census Live Syncs
Sync data 100x faster on Snowflake with Census Live Syncs

For years, working with high-quality data in real time was an elusive goal for data teams. Two hurdles blocked real-time data activation on Snowflake from becoming a reality: Lack of low-latency data flows and transformation pipelines The compute cost of running queries at high frequency in order to provide real-time insights Today, we’re solving both of those challenges by partnering with Snowflake to support our real-time Live Syncs, which can be 100 times faster and 100 times cheaper to operate than traditional Reverse ETL. You can create a Live Sync using any Snowflake table (including Dynamic Tables) as a source, and sync data to over 200 business tools within seconds. We’re proud to offer the fastest Reverse ETL platform on the planet, and the only one capable of real-time activation with Snowflake. 👉 Luke Ambrosetti discusses Live Sync architecture in-depth on Snowflake’s Medium blog here. Real-Time Composable CDP with Snowflake Developed alongside Snowflake’s product team, we’re excited to enable the fastest-ever data activation on Snowflake. Today marks a massive paradigm shift in how quickly companies can leverage their first-party data to stay ahead of their competition. In the past, businesses had to implement their real-time use cases outside their Data Cloud by building a separate fast path, through hosted custom infrastructure and event buses, or piles of if-this-then-that no-code hacks — all with painful limitations such as lack of scalability, data silos, and low adaptability. Census Live Syncs were born to tear down the latency barrier that previously prevented companies from centralizing these integrations with all of their others. Census Live Syncs and Snowflake now combine to offer real-time CDP capabilities without having to abandon the Data Cloud. This Composable CDP approach transforms the Data Cloud infrastructure that companies already have into an engine that drives business growth and revenue, delivering huge cost savings and data-driven decisions without complex engineering. Together we’re enabling marketing and business teams to interact with customers at the moment of intent, deliver the most personalized recommendations, and update AI models with the freshest insights. Doing the Math: 100x Faster and 100x Cheaper There are two primary ways to use Census Live Syncs — through Snowflake Dynamic Tables, or directly through Snowflake Streams. Near real time: Dynamic Tables have a target lag of minimum 1 minute (as of March 2024). Real time: Live Syncs can operate off a Snowflake Stream directly to achieve true real-time activation in single-digit seconds. Using a real-world example, one of our customers was looking for real-time activation to personalize in-app content immediately. They replaced their previous hourly process with Census Live Syncs, achieving an end-to-end latency of <1 minute. They observed that Live Syncs are 144 times cheaper and 150 times faster than their previous Reverse ETL process. It’s rare to offer customers multiple orders of magnitude of improvement as part of a product release, but we did the math. Continuous Syncs (traditional Reverse ETL) Census Live Syncs Improvement Cost 24 hours = 24 Snowflake credits. 24 * $2 * 30 = $1440/month ⅙ of a credit per day. ⅙ * $2 * 30 = $10/month 144x Speed Transformation hourly job + 15 minutes for ETL = 75 minutes on average 30 seconds on average 150x Cost The previous method of lowest latency Reverse ETL, called Continuous Syncs, required a Snowflake compute platform to be live 24/7 in order to continuously detect changes. This was expensive and also wasteful for datasets that don’t change often. Assuming that one Snowflake credit is on average $2, traditional Reverse ETL costs 24 credits * $2 * 30 days = $1440 per month. Using Snowflake’s Streams to detect changes offers a huge saving in credits to detect changes, just 1/6th of a single credit in equivalent cost, lowering the cost to $10 per month. Speed Real-time activation also requires ETL and transformation workflows to be low latency. In this example, our customer needed real-time activation of an event that occurs 10 times per day. First, we reduced their ETL processing time to 1 second with our HTTP Request source. On the activation side, Live Syncs activate data with subsecond latency. 1 second HTTP Live Sync + 1 minute Dynamic Table refresh + 1 second Census Snowflake Live Sync = 1 minute end-to-end latency. This process can be even faster when using Live Syncs with a Snowflake Stream. For this customer, using Census Live Syncs on Snowflake was 144x cheaper and 150x faster than their previous Reverse ETL process How Live Syncs work It’s easy to set up a real-time workflow with Snowflake as a source in three steps:

Best Practices
How Retail Brands Should Implement Real-Time Data Platforms To Drive Revenue
How Retail Brands Should Implement Real-Time Data Platforms To Drive Revenue

Remember when the days of "Dear [First Name]" emails felt like cutting-edge personalization?

Product News
Why Census Embedded?
Why Census Embedded?

Last November, we shipped a new product: Census Embedded. It's a massive expansion of our footprint in the world of data. As I'll lay out here, it's a natural evolution of our platform in service of our mission and it's poised to help a lot of people get access to more great quality data.