Data Management Archives

Automatically Update Contact Owners to Match Account Owners in Salesforce

Recently I was perusing the Salesforce IdeaExchange for interesting features to upvote and came across a common issue with over 4k upvotes, “Automate contact owner update when account owner changes.”

Now, I would LOVE for this to be a native Salesforce feature of course, but there is a simple solution to this that I couldn’t find documented anywhere.

So let’s get to it!

The problem with Account and Contact Owner mismatches

Depending on how your sales team manages Account and Contacts, Account and Contact Owner mismatches can cause a lot of headaches.

In most Salesforce orgs I’ve worked with, the Account Owner is responsible for nurturing all Contacts within the Account, but if Sales Rep A owns the Account and Sales Rep B owns half of the Contacts under the Account it can cause issues.

Those issues include:

Sales Rep A missing Contacts when viewing reports
Account Engagement “Create Salesforce Task” completion actions get assigned to the wrong user
Contact Activities get missed by the Salesperson who needs to act on them
Multiple Sales reps nurturing the same Contact at the same time

To solve for this common issue we’re going to build two items:

Formula Field on the Contact
Schedule-Triggered Flow for Contacts

Identify Owner Mismatches

The first step is identifying records with an Account and Contact Owner mismatch. To do so, we want to build a Formula Checkbox field.

Navigate to Setup > Object Manager > Contact > Fields & Relationships
Select New
Choose Formula, then Next
Name the field “Owner Mismatch”
Select Checkbox, then Next
For the Formula, enter “IF((Account.OwnerId = OwnerId), FALSE,TRUE)”
- If you are not familiar with Salesforce formulas, what this essentially says is if the Account Owner’s ID equals the Contact Owner’s ID the checkbox should be False, or unchecked. If the two Owner IDs do not match, the checkbox should be True, or checked.
Enter an Description so other users know what this field is for, and select Next
Select the users who should have access to this field and the page layouts you’d like the fields added to, then select Save

Automatically Update Contact Owners

Next, we’ll create a flow to automatically update your Contact Owner to match your Account Owner. This flow is very simple, so don’t abandon ship if you are not yet a Flow super user!

Navigate to Setup > Flows
Select New Flow
Select Schedule-Triggered Flow
Fill in the “Set a Schedule” details as below
- Start Date: Today’s date
- Start Time: I recommend you set this flow to run outside of business hours. For my flow, I chose 12:00 AM
- Frequency: Daily

Next, select + Choose Object
Fill in the Configure Start details outlined below
Select Contact
Enter the condition as below
- Field: Owner_Mismatch__c
- Operator: Equals
- Value: True

Under your Start element, select the plus symbol to add a new element
Select Update Records
Label the Element “Update Contact Owner”
Under “Set Field Values for the Contact Record” set the Field to OwnerID and set the Value to {!$Record.Account.OwnerId}

Select Done
Select Save from the top right hand corner and name your Flow “Contact – Update Owner to Match Account”

Select Save
Finally, Debug your Flow to ensure it works as expected, then Activate it!

Now sit back and let everyone marvel at how clean Account and Contact owners are in the org.

Get more solutions like this from Sercante

Need help figuring it all out? Reach out to the team at Sercante to get a conversation going.

Original article: Automatically Update Contact Owners to Match Account Owners in Salesforce

The post Automatically Update Contact Owners to Match Account Owners in Salesforce appeared first on The Spot.

By Erin Duncan|2023-11-14T16:48:18+00:00November 14th, 2023|Categories: Data Management, Marketing Automations, Pro Tips, revive|

A Campaign Member’s First Associated Date records the date a Lead/Contact became a member of a Salesforce Campaign, and it’s a great metric to use in your reporting. First Associated Date can be used to show how many Leads/Contacts a Campaign touched in a given time period, how long the Lead/Contact was in the campaign before they moved to a “Responded” status, or how long the Lead/Contact was in the campaign before an associated Opportunity was opened. However, sometimes the Campaign Member’s first associated date gets skewed.

Common causes of this are:

Lead/Contact should have been added to the campaign on September 15th, but was stuck in the Account Engagement sync queue until October 1st
New Leads from an event we’re not uploaded into Salesforce until a few weeks after the event
Campaign Members were brought over from another Salesforce org during a migration
Sales didn’t enter a new Lead they were working with until after the Opportunity was created

If you are relying on Campaign Member First Associated Date for your reporting, any of the above causes can really throw off your data and make a Campaign, or a time period, look less successful than it actually was. Luckily, you can backdate this field with a few system permissions and the help of Data Loader!

You can insert, but not update!

Before we get into the nitty-gritty of how to do this, it’s important to note that you can’t update the Campaign Member First Associated Date of existing Campaign members. You can only insert new Campaign Members with a backdated first associated date. However, you can use Data Loader to export Campaign Members, their Campaign Status, their dates, etc. from a Campaign, delete the Campaign Members, then re-add them to the Campaign with new dates.

Permissions needed

The first step to updating First Associated Date is enabling “Set Audit Fields Upon Record Creation”.

Navigate to Setup > User Interface

Ensure the “Enable “Set Audit Fields upon Record Creation” and “Update Records with Inactive Owners” User Permissions” option is selected

Select Save

Next, create a Permission Set for “Set Audit Fields Upon Record Creation” and assign this Permission Set to the user(s) who will handle the Data Loader imports.

Navigate to Setup > Permission Sets
Select New
Name your Permission Set “Set Audit Fields Upon Creation”
Select Save
Within your new permission set, type “Set Audit” into the “Find Settings” box
Select Set Audit Fields Upon Creation

Select Edit on the resulting page and select the Set Audit Fields Upon Creation checkbox
Select Save
Select Manage Assignments
Select Add Assignments
Select any users who will be handling the Data Loader imports of Campaign Members, then select Next and Assign

Import your data

Finally, get your Data ready for import! At a minimum, you’ll want to make sure your file includes:

Campaign ID
Lead ID and/or Contact ID
- If you are importing both Leads and Contacts into the Campaign, I recommend splitting the import into 2 files.
Campaign Member Status (if different from the Campaign’s default Status)
Campaign Member first associated date
- Ensure the column is formatted using one of the options below, otherwise you will get an error.
  - MM/DD/YYYY (example: 04/23/2012)
  - DD/MM/YYYY (example: 23/04/2012)
  - YYYY-MM-DD (example: 2012-03-25)

To import the data

Open Data Loader and login
- Note: Updating Campaign Member First Associated Date is not possible with the Data Import Wizard, only Data Loader.
Select Insert
- Note: The ability to map to Campaign Member First Associated Date will not be available if you select Update or Upsert.
Check the Show all Salesforce Objects checkbox and search for CampaignMember

Select your CSV file and click Next
Select Create or Edit a Map and map your fields
- CreatedDate is the field you’ll need to map to the Member First Associated Date column

Select OK > Next > Finish

And Voila, beautiful, accurate Campaign Member data!

Original article: Fix Your Reports! How to Backdate Campaign “Member First Associated Date”

The post Fix Your Reports! How to Backdate Campaign “Member First Associated Date” appeared first on The Spot.

By Erin Duncan|2023-10-12T18:49:12+00:00October 12th, 2023|Categories: Analytics & Reporting, Data Management, Pro Tips, revive|

Marketing Cloud Intelligence (previously known as Datorama) is a tool that offers many potential uses. But with those uses comes the uncertainty that you are using the tool to its maximum potential or even correctly. That’s where a Marketing Cloud Intelligence audit can help.

In this blog post, we’ll cover the reasons you should audit your Marketing Cloud Intelligence instance, the steps to take during your audit, and what you should do with the information you gain.

Why would I need a Marketing Cloud Intelligence audit?

There are several reasons you could need an audit, including but not limited to the following topics.

Reason #1. You want to validate the effectiveness of your work within the platform

Having worked with this tool for years, we have seen it all. An Intelligence audit serves as a second set of eyes to ensure your performance is not being stretched or that you are governing your field usage effectively. It can make a serious difference.

Reason #2. You want to explore if you are missing value adds in the platform

Suppose you are already using Marketing Cloud Intelligence for one set use case and not the full suite of features. In that case, an Intelligence audit will review options based on your needs and ask the right questions to ensure you are maximizing value. As a constantly evolving tool, there is always a new data connector, app, or feature to utilize and build value for your team from a few clicks.

Reason #3. API connectors show inaccurate data in reporting/dashboards

It can be discouraging to set up a data flow into Marketing Cloud Intelligence only to find your output from the platform, whether it be reports or visualizations, look off. An audit can guide you on everything from filtering your data to managing redundancies in setup.

Reason #4. Your Marketing Cloud Intelligence instance has mostly sat idle

You can do so much with Marketing Cloud Intelligence, and even automate processes you may not expect. But that is not of help if the platform is sitting empty or unused. An audit will take what you currently have and guide you toward possible uses you may not have explored.

Reason #5. A key admin has recently left your company or organization

Want to understand what your admin was working on and how data flowed before disaster strikes (or perhaps after)? An audit can help put it into clean process flows and documentation that you may be missing, or even help break down existing documentation into usable guidance.

What does our audit look like in practical steps?

After going through lots of Intelligence audits, we’ve come up with a straightforward process that works in most cases.

Every audit will be a bit different (a series of 3,000+ data streams is more complex than a workspace with five streams). But these are the core processes we review during an Intelligence audit.

Step 1. Having a conversation to discover your goals with marketing analytics

With minimal dialogue, we help clients route to what steps are needed to get the most out of Marketing Cloud Intelligence and their larger tech stack.

Step 2. Combining your priorities and our standard template

We center our solutions around clients’ needs, using our standard process as a springboard to ensure there is always something to explore.

Step 3. We share a detailed breakdown of the usage of platform features

We recommend various features to explore such as Einstein Marketing Insights, Reporting, and Dashboards, and how you can maximize their functionalities for the client’s needs.

Step 4. Reviewing premium features, such as Sandbox and Granular Data Center

When you buy into the more complex and pricier features of Marketing Cloud Intelligence, it may be frustrating to find new learning accompanying these tools. We break it all down so those learnings are succinct and easy to follow.

Step 5. Breaking down the impact and effort of platform features

We showcase what tasks are high impact and low effort (and of course other levels of impact and effort) to make sure you get the most out of the platform in a swiftly actionable manner.

What will a Marketing Cloud Intelligence audit provide?

We know that an audit can unlock a powerful set of tools for you, such as the following.

Recommended platform features to utilize

We tailor our audit to your specific needs and make high-level and in-the-weeds recommendations that are centric to your business needs.

A clearer sense of data challenges to explore and recommended fixes

We showcase any glaring issues for you to skip the puzzle-solving and instead work with our tailored guidance to have a steady QA process.

Reducing redundancies for simpler data flow

We make it easy for clients to organize their data streams and remove reporting duplications so they have a clear roadmap to avoid data duplication and increase ease of navigation.

A path forward for using the platform to its full potential

At the end of our audit, you have a simple must-hit checklist based on your needs and a whole set of status updates on platform features and guidance on how to maximize their use when time allows, making a complex journey into a series of steps to explore.

How can I explore an audit with the Sercante team?

We are here to help. Our team includes Marketing Cloud Intelligence system administrator experts and readiness to explore your data to maximum effect.

You can contact our team to explore what your audit could look like and how we can best work together!

Original article: Why You Should Get a Marketing Cloud Intelligence Audit

The post Why You Should Get a Marketing Cloud Intelligence Audit appeared first on The Spot.

By Aaron Stayman|2023-10-09T14:35:52+00:00October 9th, 2023|Categories: Analytics & Reporting, Data Management, Pro Tips, revive, Strategy|

It’s very common for sales and marketing teams to leverage title-based “personas” to influence their activities. Knowing who you are speaking to can radically alter the message content, type, and frequency needed to progress the buying process. In this post, we’ll address why and how to update marketing persona fields in Salesforce using Flow to assist sales and marketing.

Why Use Flow to Update Marketing Personas?

Let’s start with a very simple question. Why flow? The answer is really based on where your data lives and who needs access to it. I’ve used Engagement Studio in Account Engagement to update persona values in the past, but what happens if the prospect is not in Account Engagement? That’s right — no persona will be updated.

This solution accounts for the fact that all Salesforce data might not be syncing to Account Engagement (or Marketing Cloud Engagement) and that sales still needs persona values.

Step 1 – Understand Your Buyers and Influencers

Before we can classify records, we first need to understand who is buying from us, who is influential in the purchase decision, and who is not (this is just as important). This is best achieved by analyzing data and speaking to your sales team.

Analyze the data

Create reports based on closed won opportunities and look at the contact roles for job titles that stand out. Odds are there will be clear winners – titles that appear with greater frequency. It’s also likely that you’ll see a mix of the people who actually use your product and a level above them (based on the purchasing authority needed to complete the transaction).

Talk to sales

Chat with some of the top sales representatives to find out where they are having success. Are there certain leads that they cherry-pick based on job titles? Are there certain leads that they deprioritize based on the same criteria?

Step 2 – Group your data

Now that we know what titles we should be going after (and those that we should avoid), we need to group them into “Persona” (think of these as containers that hold records with similar/related titles). These are the values that we will be populating from our flow and will be used in future segmentation.

It’s important to create values for those that you want to target and those that you do not. An exclusion persona can be just as valuable as a target persona.

Target Personas

Records that are buying from you or are key influencers in the purchase process.

Exclusion Personas

Records that are in your system that do not buy from you and should not be included in campaigns.

Examples could include: Marketing, Sales, Students, and Human Resources to name a few.

Once you have your target and exclusion persona values defined, create custom “Persona” fields (picklist) on the lead and contact objects. I like using global picklists when creating picklists with the same values between objects. Global picklists speed the setup, are great for ensuring consistency, and make maintenance a breeze (should more values need to be added in the future).

Don’t forget to:

Use the same API name on both objects when creating custom fields (this is critical if you want to map the fields back to Account Engagement).
Map the lead field to the contact field on conversion.

Example: Global Picklist Value Set

Step 3 – Determine Keywords

Now that we know what titles we should be going after (and those that we should avoid), and we’ve defined the groups that we would like to use for categorization, we need to identify keywords that can be used to query the records (actually – we’ll be using them in formulas). It would be great if titles were standardized, but they are not. Based on this, we are going to look for common factors.

Example: Marketing

Here are some common marketing titles. It would be great if “marketing” was included in all of them, but it’s not. Therefore, we’re going to use keywords like: marketing, brand manager, campaign, content, media relations, product research, SEM, and SEO in our formula to make sure that we properly tag our records.

Brand manager
Campaign manager
Channel marketing director
Chief marketing officer
Content marketing manager
Content specialist
Digital marketing manager
Director of email marketing
Internet marketing specialist
Media relations coordinator
Product research analyst
SEM manager
SEO specialist
Web marketing manager

Step 4 – Create the Flow (In Sandbox)

We’re going to use a record-triggered flow to update our persona values. The flow will automatically update the persona value when the title field is updated. Since contacts and leads are distinct objects, a flow will need to be created for each object.

Here’s an example of what a very basic flow would look like. This flow is just updating the value to be Marketing, Human Resources, or Other. A full version of this flow would contain many more paths.

Configure Start

This flow is based on the lead object and is triggered when a record is created or updated. Since we don’t want to trigger the flow whenever a lead is updated, we’re using a formula to set the entry conditions. We want the flow to run only when new leads are created (and the title is not blank) or the title field of existing leads is updated to a non-blank value.

Finally, the flow will be optimized for Fast Field Updates, since we are updating fields on the same object.

Create Persona Formulas

This is probably the hardest part of this process. We are going to need to create formulas for each of our persona groups using the keywords that we’ve already defined. It’s important to note that formulas are case-sensitive by default. This is good in some cases but could cause records to be missed in other situations. Fortunately, we can address this as well.

Sample Formula 1

This formula selects the marketing keywords that we identified, but it’s case-sensitive. It would evaluate “True” for a lead with the title “digital marketing manager”, but would not for the title “Digital Marketing Manager”.

OR(

/* Title contains any of these title strings */

CONTAINS({!$Record.Title}, “marketing”),

CONTAINS({!$Record.Title}, “brand manager”),

CONTAINS({!$Record.Title}, “campaign”),

CONTAINS({!$Record.Title}, “content”),

CONTAINS({!$Record.Title}, “Content marketing manager”),

CONTAINS({!$Record.Title}, “media relations”),

CONTAINS({!$Record.Title}, “product research”),

CONTAINS({!$Record.Title}, “SEM”),

CONTAINS({!$Record.Title}, “SEO”)

)

Sample Formula 2

This updated formula evaluates the same keywords that were identified but addresses the case sensitivity issue. Here, we’ve used a function to convert the titles to lowercase and then compared them to a lowercase value. This formula would evaluate “True” for the titles “digital marketing manager”, “Digital Marketing Manager”, or “DIGITAL MARKETING MANAGER”.

OR(

/* Title contains any of these title strings */

CONTAINS(LOWER({!$Record.Title}), “marketing”),

CONTAINS(LOWER({!$Record.Title}), “brand manager”),

CONTAINS(LOWER({!$Record.Title}), “campaign”),

CONTAINS(LOWER({!$Record.Title}), “content”),

CONTAINS(LOWER({!$Record.Title}), “content marketing manager”),

CONTAINS(LOWER({!$Record.Title}), “media relations”),

CONTAINS(LOWER({!$Record.Title}), “product research”),

CONTAINS(LOWER({!$Record.Title}), “sem”),

CONTAINS(LOWER({!$Record.Title}), “seo”)

)

Sample Formula 3

Sometimes, you are going to need a mix of case sensitivity and case insensitivity. As an example, we would not want to update any job title that contains “hr” to Human Resources. This could lead to a lot of false matches. In this case, only titles that contain “HR” in all capitals will evaluate “True”.

OR(

/* Title contains any of these title strings */

CONTAINS(LOWER({!$Record.Title}), “human resources”),

CONTAINS({!$Record.Title}, “HR”)

)

Sample Formula 4

There are also going to be times when you need to look for a specific value, like CEO, and also look for title strings. We can do that too!

OR(

/* Title is any of these values */

{!$Record.Title} = “CEO”,

/* Title contains any of these title strings */

CONTAINS(LOWER($Record.Title), “chief executive”),

CONTAINS(LOWER($Record.Title), “president”)

)

As you can see, there’s a fair bit of work involved in creating and testing the formulas. That’s why working in a sandbox is critical. If you can get all the formulas to update all the values exactly as you would like on the first try, I encourage you to check out our careers page!

Configure Flow Elements

Each path includes a Decision and an Update Records element (learn more about Flow Elements). We’ll walk through the marketing paths and the same logic can be applied to additional paths. The only difference is that the “No” outcome for the final decision should update the persona value to “Other”. We want to add a value to leads that don’t match any of our formulas for two reasons.

We want to verify that they were processed by the flow.
We want to be able to identify the leads that were not matched by our formulas so we can evaluate and improve. This is VERY important.

Decision Element

The element is pretty straightforward. The “True” outcome looks for leads where the marketing formula evaluates to “True”. Leads that do not evaluate true progress down the “False” outcome and move to the next decision element.

Update Records

Leads that match the “True” outcome conditions then proceed to the Update Records element. This is where the magic happens and the record is updated in Salesforce.

Debug

The final step before activating your flow is to do some debugging. Test by updating the titles of a few leads to make sure that they progress down the correct path, Be sure to vary the case of the titles to make sure that upper, lower, and mixed cases work as expected.

Step 5 – Rinse and Repeat

Once deployed into production, your flow is not going to be perfect. There are going to be some records that are classified as “Other” that should fall into other categories. That’s OK!

The final step is to do regular reviews and updates of the records that have the “Other” persona. It’s possible that we missed a keyword on our first pass or that a new hot title has emerged. I compare this a lot to scores in Account Engagement. You don’t quit once you define your scoring model, you evaluate and refine it. The same process applies here.

Give it a Shot!

We’ve done a lot in a short post. I encourage you to give this a shot in your sandbox. You’ll be surprised by the number of records that you’ll be able to update and the value that it will bring to your sales and marketing teams. If you get stuck, let us know. That’s why we are here!

Shout out to Heather Rinke and Jason Ventura for their collaboration in building this process!

Original article: How to Update Marketing Personas Using Salesforce Flow

The post How to Update Marketing Personas Using Salesforce Flow appeared first on The Spot.

By Mike Morris|2023-10-04T15:25:39+00:00October 4th, 2023|Categories: Analytics & Reporting, Data Management, Marketing Automations, Pardot, Pro Tips, revive|

One of the great things about Account Engagement is that the same fields can be used over and over again for different interactions while keeping the full history of the field recorded in the Prospect’s Audit Table. This ensures you don’t need an endless amount of fields and you don’t have to create repeat fields such as “contact reason 1”, “contact reason 2”, etc.

However, sometimes you need a specific interaction frozen in time. For example, say every time someone fills out your “request a quote” form you want the details the Prospect inputs collected into one record that you can reference later, regardless of whether they fill out the form again.

This is a problem I recently solved for our marketing team and I am finding that the solution can be used for many different scenarios. In fact, at the Dreamforce Genius Bar, I recommended this solution for many of the questions that were brought to me by attendees.

So, let’s dive in!

Why don’t we just use Tasks?

You may be asking, if you want an interaction frozen in time, why not just create a Salesforce task as a completion action? This would create a record at the time of the interaction that could later be referenced and followed up on.

While this is true, Salesforce tasks are limited in the information you can provide (example below). You also cannot use Account Engagement Merge tags within the tasks to stamp field values, and you can’t easily create custom task fields to store additional needed information.

Original Scenario

I’ll use the original scenario I solved for as my example for this blog post. Our marketing team is serious about event marketing strategies and wanted to record what they refer to as “Event Interactions.”

These interactions would be created via form fills or via imports and needed to include:

The name of the event
Details about the event
Which team member led the event
What topics were covered
What type of opportunity this is
Lead/Contact the Event Interaction is associated with

What we built

To store these Event Interactions, we created a Custom Object and fields for each data point the team wanted to record. We also created two lookup fields, one for Leads and one for Contacts, so we could associate the Event Interactions with the Lead/Contact records.

Next, we created fields 1-5 on the Lead and Contact Object and in Account Engagement so the data can move between the two systems. Finally, we created a Flow that will fire and create the Event Interaction record when these fields have data.

To recap:

Create a new Custom Object
Create the custom object fields
Create the fields on Leads and Contacts
Create and sync the fields in Account Engagement
Create the flow

Creating the Flow

First, we want to create a flow for Leads. This flow will fire whenever a record is created or updated. The Entry Criteria are that the Event Name is not blank and is not “N/A”.

Next, use a Create Records element to create the new Custom Object record and copy the data from the Lead fields. Don’t forget to copy the Lead ID into the Lead Lookup field!

Finally, use an Update Records element to change the Lead’s Event Interaction fields to “N/A” or “Other” so that they are ready to be used for the next Event Interaction. We’re using “N/A” and “Other” rather than clearing out the fields completely to avoid Account Engagement syncing the previous data back into the fields.

Now you just need to test and activate the flow, and Voila! Well, that and also create the same flow for Contacts.

That’s It?

Yep, that’s it! This isn’t a groundbreaking process, it’s simply taking this process and using it in a new and inventive way. Since creating this process, we’ve found many uses for it in our org and client orgs, such as

Automatically creating an opportunity when specific forms are completed.
Creating custom objects that Account Engagement doesn’t typically interface with, such as Service Tickets or Cases.
Capturing form fills and their current field values at a moment in time for future reference/action.
Creating “Sales Inquiry” records rather than Leads and assigning these records to Sales.
Creating custom UTM Touchpoint records.

What other uses can you imagine for this process? Let us know in the comments!

Original article: Creating Custom Object Records with Account Engagement and Flow

The post Creating Custom Object Records with Account Engagement and Flow appeared first on The Spot.

By Erin Duncan|2023-10-03T14:55:16+00:00October 3rd, 2023|Categories: Data Management, Marketing Automations, Pro Tips, revive, Strategy|

At 8 p.m. ET on Wednesday 9/13, the second full day of Dreamforce, I was sitting in front of my computer curious and a little anxious. I was using my usual evening streaming service catch up hour to tune into the live Salesforce+ broadcast of Dreamforce session, Empowering Nonprofits in Times of Change with Data + AI + CRM + Trust.

As a former boots-on-the-ground nonprofit employee and current consultant primarily working with nonprofit clients, I am acutely aware of the ever-increasing demands on nonprofits. I understand and empathize with the daunting task nonprofits face to tell their story and convey their impact in novel ways and through a multichannel approach.

I also am acutely aware that nonprofits are frequently hindered by low ROI on donor dollars due to the immense staff effort needed to cultivate donations. I was especially eager to hear what Salesforce has to say about the future of nonprofits on their platform and how they might help solve some of these big issues for the sector.

graphic from empowering nonprofits in times of change dreamforce session

A Year of Change for Nonprofits on Salesforce

This has been a year of big changes for nonprofits and Salesforce. A few headlines were the sunset of Elevate and the rollout of the new Nonprofit Cloud platform.

This session added a few more items to the list of highlights, namely the announcement of Einstein, Salesforce’s proprietary AI, for Nonprofit Cloud*. This promises to allow nonprofits to tap into predictive and generative AI to go further and dive deeper with their donor data, with their programmatic metrics and outcomes, and with their tenacious and often time-strapped staff.

Having the ability to leverage data more robustly than ever before is further extended by the offerings of Data Cloud — Salesforce’s data management and harmonization tool. At the core of Salesforce’s Einstein for Nonprofit Cloud + Data Cloud message, they vow to help you do more with less.

* Currently, the Einstein AI features advertised in the session Empowering Nonprofits in Times of Change with Data + AI + CRM + Trust are only available as a part of the Nonprofit Cloud curated solution package.

Einstein for Nonprofit Cloud + Data Cloud Promise to Make Annual Reports Easier and Better than Ever

I am going to use the example of building an annual report (a thought that may give former or current nonprofit employees shudders of anxiety) to illustrate the key promises of Einstein for Nonprofit Cloud + Data Cloud.

First let’s start with a short overview of what an annual report is from a nonprofit standpoint. An annual report usually serves as a physically manifested summation of the prior year for a nonprofit. They typically contain programmatic impact data (frequently with fancy charts), thank yous and recognitions of various groups from the highest dollar donors to volunteers, financial data for the year, and a call to action. They are intended to be compelling, illustrate transparency, and highlight mission, vision and outcomes. They are also an absolute BEAST to create.

Here are some of the ways Einstein for Nonprofit Cloud + Data Cloud can help nonprofits complete the daunting task of creating an annual report:

Compiling Programmatic Data and Communicating Meaning

Einstein for Nonprofit Cloud offers the ability to summarize programmatic data over a specific amount of time with consideration to the intended audience. In the case of an annual report this would look like a summary of your programmatic data for the prior year, intended for an external audience.

Einstein for Nonprofit Cloud also allows you to choose if this summary should be long form (more detailed) or short form (less detailed) increasing flexibility to help you meet the needs of your specific annual report requirements with less manual work.

Segmenting and Standardizing People Data for Acknowledgement Lists

Data Cloud shines in its potential ability to solve the problem of disjointed data for Nonprofits. We all know the pain of manually sleuthing through and deduplicating your database and external systems of that one historical volunteer record for one of your major donors who is also an event attendee and auction lot buyer. This hypothetical generous philanthropist could have upwards of 4 records in your Salesforce CRM and connected systems! Data Cloud to the rescue!

Through the power of the unified profile you can see all of that disorganized and disconnected data in one place. This is especially helpful in the annual report example as it allows you to be absolutely sure you are thanking and acknowledging your kind supporters for ALL the ways they support, financial contributions, event support, volunteerism through a single 360 view.

Crafting a Compelling Donation Appeal

Admit it or not, a key function of an annual report is to compel folks to donate. Einstein for Nonprofit Cloud + Data Cloud have your back in your daunting donor segmentation and compelling storytelling efforts.

Through use of the 360 Constituent view made possible by Data Cloud, you can use Einstein for Nonprofit Cloud to segment your donors and its predictive AI capabilities to draw conclusions based on historical data.

You could put together a segment of those donors identified as having high propensity to give, high affinity for your organization and high capacity and really hit them in the feels with your call to action. Generative AI can then help you craft an irresistible donation appeal and from there the dollars are sure to roll in to support your important work.

Einstein for Nonprofit Cloud + Data Cloud: Getting Nonprofits Closer to their Core Missions

Overall, I am cautiously optimistic that, together, Einstein for Nonprofit Cloud + Data Cloud have the potential to improve the way nonprofits communicate with their donors and the public. By automating time-consuming tasks and providing insights that were previously unavailable, or extremely time or resource-intensive to procure, these tools can help nonprofits focus on their core mission of making a difference in the world.

I encourage nonprofit leadership to explore and invest in these powerful tools. I think they can help support and extend the capabilities and impact of a nonprofit’s greatest strength, its team.

Need help navigating these new announcements and ever growing suite of Salesforce products? Sercante is here to guide you through and help you achieve your nonprofit’s mission by leveraging the power of the Salesforce platform. Reach out!

Original article: Dreamforce 2023 Recap: Salesforce Leans in to AI + Data Cloud as the Future of Nonprofit Success

The post Dreamforce 2023 Recap: Salesforce Leans in to AI + Data Cloud as the Future of Nonprofit Success appeared first on The Spot.

By Abbey Freund|2023-09-28T14:59:12+00:00September 28th, 2023|Categories: Analytics & Reporting, Data Management, Industry News, Pro Tips, Strategy|

If you’re getting ready for a Salesforce Data Cloud implementation, then this post will get you ready for it.

There are so many buzzwords with this particular Salesforce product that it often makes it hard to understand what Data Cloud is and what it can do for your business. If that sounds familiar, this article should help you understand the product’s core capabilities and key considerations.

Having been lucky enough to work on a Data Cloud implementation, I’ll be drawing on both my theoretical knowledge from countless Trailheads and accreditation courses as well as my practical understanding from the challenges I faced when implementing this intricate cloud.

What is Salesforce Data Cloud?

First things first, Data Cloud, like a lot of Salesforce marketing products, has been through a lot of rebranding and was formerly known as CDP. However, it is not to be confused with Salesforce 360, which will leverage the power of Data Cloud across the whole Salesforce Ecosystem.

So what actually is Data Cloud?

Simply put, it is Salesforce’s long-term customer data platform (CDP) solution. The platform allows users to create a unified view of their customers by integrating data from multiple sources, both internal and external. This data can include demographic and behavioral information, purchase and order history, digital and non-digital interactions, and much more. By combining these diverse datasets, users can gain a deeper understanding of their customer’s preferences, behaviors and most importantly, needs.

Source: Salesforce

What are Data Cloud’s Capabilities?

If you’re looking at implementing Data Cloud and wondering whether or not it is right for you, let me talk you through its capabilities in a little more detail:

Data Unification

First and foremost, Data Cloud’s primary function is to unify data from multiple sources into a single, consolidated view of the customer. Data can come directly from Salesforce Core, Salesforce Marketing Cloud or it can come from an external system such as an in-house data warehouse. However, as I’ll go on to explain later on, Data Cloud works best when your data is in a healthy position.

Data Enrichment

With a plethora of connection options ranging from out-of-the-box (OOTB) connectors to FTPs to APIs and more, Data Cloud offers its customers the ability to enrich customer profiles by appending additional information to existing datasets. In other words, imagine using both lifetime value (LTV) metrics and social engagement metrics to truly understand who your most loyal customers are.

Segmentation

There is no point in having all this data if you’re unable to use it. Data Cloud offers users the ability to create Segments using all of the data ingested by Data Cloud. Segments and Calculated Insights — multi-dimensional metrics (i.e. calculate LTV by summing all completed orders) — can then be pushed into external systems such as Marketing Cloud for future use.

Better yet, Segments and Calculated Insights can be created without needing knowledge of SQL, although there are limitations as I’ll go on to explain.

Real-time Data Updates

If you’re in need of up-to-the-minute data and insights, Data Cloud might just be for you. Its streaming insights and real-time Data Streams allow users to work on the most up-to-date data instead of outdated insights and decisions.

This list is by no means an extensive list of Data Cloud’s capabilities but a list of what I believe are Data Cloud’s most useful tools. For a full list of capabilities, it’s worth checking out the product in more detail.

What are key considerations during Data Cloud implementation?

So at this point, Data Cloud sounds pretty fantastic. And don’t get me wrong, it is!

However, like any software out there, there are key considerations to take during Data Cloud implementation. Here they are.

Data Quality

Data Cloud is only as good as the data it is supplied. I was, and I’m sure among many, one of those Salesforce marketing enthusiasts who thought Data Cloud would solve all my data silo issues.

Data Cloud works on reconciliation rules, it uses these rules to unify data coming from different sources, so if your data sources don’t have commonalities between them, you’ll have a hard time creating your unified profile. Likewise, if your data sources are providing inconsistent data in each run, your Segments and Calculated Insights are only going to be so effective.

Set-up Complexity

Data Cloud is a very flexible platform, and it allows users to consume a variety of Data Streams and utilize a wide range of Data Model Objects. However, this also brings complications as it requires users to have a broad understanding of Data Mapping, APIs and Data Transformation, as well as having a solid understanding of Salesforce.

This is particularly important when Unified Profiles are involved. And furthermore, it requires a deep understanding of the platform’s very intricate nuances.

To list a few of the nuances:

Unified Individual – The Unified Individual Object itself is non-editable. It essentially acts as a carbon copy of the Individual Object and is only created once Reconciliation Rules have been set up.
Activations – Only fields that are mapped to the Individual Object are available as fields in segments pushed from Data Cloud. Related Objects and their fields are pushed as parsed fields, which adds complexity for using tools like Marketing Cloud Engagement.
Profile Explorer – The OTTB Profile object is very limited and will require a lot of Salesforce expertise to build a usable page for viewing Unified Customers.
Learning Curve – Data Cloud is a data-heavy tool and, at least in my experience, usually falls under the MarTech umbrella. Whilst this provides marketers with data-driven insights and segments, it also means a lot of learning is required.

As mentioned above, there are new concepts as well as new terminology such as Data Streams, Data Bundles and Data Lake Objects, but the biggest learning curve will come from the Segmentation and Calculated Insights.

Whilst Data Cloud does offer a ‘Builder,’ creating both insights and segments using the Unified Individual (the main reason for using Data Cloud) is achieved via SQL due to how the Unified Individual reconciles multiple profiles from multiple sources.

Use these tips for a successful Data Cloud implementation

I don’t want to sound like I’m being negative as, in reality, Data Cloud is a fantastic tool and can help drive meaningful engagement. But I do want to stress that understanding the detailed capabilities and key considerations of Data Cloud is the only true way of ensuring your Data Cloud implementation will be successful.

Questions to ask before Data Cloud implementation

If I was procuring Data Cloud for myself, I would consider the following;

Where is my data coming from? If the majority already sits within a Salesforce product, then the chances are I can get a unified customer profile through smart architecture.
What is the state of my data? As mentioned, Data Cloud won’t fix your data health issues. If your data is generally incomplete and lacks consistency, then you’re not going to be ready for Data Cloud yet — it doesn’t mean it won’t be right in the future.
Who is going to own this product? It’s often marketers who will benefit from the segments and insights. But more often than not they don’t have SQL experience. If you’re hoping the segment and insights builders will make up for a lack of SQL knowledge, it might be worth reconsidering.

Need help with your Data Cloud implementation? Reach out to the Sercante team who can walk you through it and get you the results you’re trying to achieve.

Original article: Lessons Learned During Salesforce Data Cloud Implementation

The post Lessons Learned During Salesforce Data Cloud Implementation appeared first on The Spot.

By Ralph Green|2023-09-11T18:55:04+00:00September 11th, 2023|Categories: Analytics & Reporting, Data Management, Pro Tips, revive, Strategy|

The last blog post in this 3-part series may have left you wondering if you should buy or build a customer data platform (CDP). We’ll answer that question in this last installment.

In the first post, we looked at six reasons to implement a CDP. Then in the second blog post, we discussed the five major CDP components.

The five main CDP components are:

Data Ingestion and Storage
Data Modelling and Data Processing
Identity Management and Consent Tracking (for Marketing)
Profile Enrichment and Audience Building
Actions and Insights

We briefly discussed Consent Management as an important part of any CDP marketing use case.

Obtaining a customer data platform can be achieved by purchasing a CDP suite, sometimes called an off-the-shelf CDP solution, or by selecting the various individual component pieces and using them to build your own customer data platform. The latter is known as a composable CDP solution.

Assembling a CDP team

In either case, CDP implementations need people with deep expertise and knowledge of data architecture, data modeling, and data engineering. These skills are needed to achieve data ingestion of internal and external sources into a data storage repository in a well-architected way that is both scalable and cost-effective. For composable CDP solutions, it is critical that the team construct a workable plan that incorporates how each of the pieces will be combined.

Platform Expert

Beyond that, it’s important to have tool or platform-specific knowledge to build out the capabilities of the various components of the selected customer data platform. For example, it would be beneficial to have Salesforce Administrator skills to assist with the Salesforce Data Cloud implementation and administration of the Data Cloud afterward.

Marketing Operations

It’s also important to have marketing domain expertise, especially for use cases that involve audience building for marketing purposes. Not all CDP use cases involve the marketing function, but there are a significant number that do involve marketing use cases.

Advanced Technical Experts

No matter whether you choose to buy or build a CDP, there’s a variety of skills needed to get up and running on a customer data platform, so it’s likely going to take several different people, each with varied skills.

How to fill the skill gaps

Large enterprises that employ a big IT department experienced at building applications or SaaS companies of any size where the core business is building applications, are more likely to consider composable CDPs as an option. And organizations with use cases that require real-time capabilities will need to carefully consider whether an off-the-shelf CDP solution will provide the needed functionality.

That said, the quickest way to get up and running on a customer data platform is usually by purchasing a CDP suite that includes all the major components ready to go out-of-the-box. In addition to accelerating the time to value, a customer data platform suite is a great choice when your organization lacks the IT support and skills needed to evaluate, select and piece together all the various components of a CDP.

Salesforce as a CDP suite

Choosing Salesforce as the CDP suite to buy is pretty straightforward if you already have Salesforce Sales and Service cloud, or an industry equivalent such as Salesforce Nonprofit Cloud or Health Cloud, and/or Salesforce Commerce Cloud.

Those Salesforce platforms have a direct connection to the Salesforce Data Cloud as well as a connector to the Salesforce Marketing Cloud. This removes the need to build pipelines between these systems as would be needed for a composable CDP solution. As a result, security is ensured by Salesforce because all data is contained within the Salesforce system.

Considerations for composable (build-your-own) CDP solutions

Composable CDP solutions are an option to consider if your first-party data doesn’t live in a CRM like Salesforce and your IT team has the skills, experience, and bandwidth to build out a solution for the organization. Composable CDP solutions are a great choice if your organization already has some of the five major CDP components installed and working well.

If your first-party data is already ingested into your data warehouse or data lakehouse, is being processed and transformed, and you have robust machine learning tools in place, then your existing system likely meets several of the CDP requirements already. In that case, it might make sense to just add the missing pieces to your existing platform rather than purchase a CDP suite.

Steps to decide if you should buy or build a customer data platform (CDP)

There is a lot to consider when making a CDP choice. Both CDP suites and composable CDP solutions are viable options but to help you figure out which one might be a better choice for you and your organization, here are four things you can do.

1. Define your CDP use cases and know what problems you need to solve

For what purposes do you want to use unified customer data? Do your use cases need to be solved for only marketing concerns or will the sales and service teams benefit from a unified customer profile? Do any of these use cases require real-time capabilities?

2. Evaluate the current gap in your CDP requirements

Where does your current first-party data reside today? Of the given CDP components you need, how many does your organization already have in place? Are there any digital transformations or architectural upgrades planned in your organization for the next 12-15 months that you are aware of and should consider?

3. Consider your internal teams’ skill sets, experience, and bandwidth

Does your IT team have the skills and experience to select the right pieces and compose a CDP from different vendors? Will your IT team be able to prioritize building a CDP over other internal projects?

4. Review vendors’ track record and consider the likelihood they’ll continue investing in the product

What is the vendor(s) track record in the CDP space? Are they new to the modern marketing tech stack or have they been in the CDP space for quite a while and perhaps considered to be more of a legacy product? How likely are they to continue investing in their product(s).

Consider your long-term plan when making a CDP build or buy decision

Customer data platforms are a long-term investment — you want to know your CDP vendor(s) will continue to improve their product. And you’ll want to make sure you’re setting your team up for success by considering the level of effort needed to administer and support the tool and platform choices for your CDP.

Whether you choose to acquire an off-the-shelf CDP suite or build a composable CDP, there are many reasons why your organization would want a customer data platform.

Remember to reach out to the team at Sercante for guidance when you’re ready to implement a CDP at your company or organization.

Original article: Should You Buy or Build a Customer Data Platform?

The post Should You Buy or Build a Customer Data Platform? appeared first on The Spot.

By Joyce Avila|2023-09-07T01:16:12+00:00September 7th, 2023|Categories: Analytics & Reporting, Data Management, Pro Tips, revive, Strategy|

Thinking about implementing a CDP? Understanding the main components of a customer data platform (CDP) is a good way to make the decision.

A CDP is not a marketing campaign execution tool, but it does provide a solid foundation for marketing personalization. While a CDP is frequently employed to better orchestrate the customer journey, that isn’t the only reason you’d want to consider using a customer data platform. The unified customer profiles built in a CDP can be made available to sales and service teams so that they can close more and bigger deals and provide better customer support.

In the previous blog post, we discussed why your organization would want to consider implementing a customer data platform solution.

There were six main reasons discussed

Increased demand for personalized customer experiences
The customer data problem of siloed data
Need to track multi-touch points
Demise of third-party cookies in 2024
Government regulations regarding privacy
Unified profiles can be used in data clean rooms

It’s not unusual for sales and service teams to work with some of the same technology tools. For example, an organization’s customer relationship management (CRM) system is a commonly shared platform.

CRM systems were designed to collect first-party data about an individual customer, member, patient, or donor, depending on the use case. A CRM can also be used to collect first-party data about companies or organizations. First-party CRM data will likely include name and contact information, at a minimum.

In contrast, there are some tools and platforms used primarily by marketers. One such example is a data management platform (DMP) that can be used to segment audiences and optimize ad spend. A DMP is a cookie-based solution that temporarily stores second and third-party data about audiences and advertising campaigns. As we learned in the previous blog post article, third-party cookies are going away in 2024, which is an important reason why a CDP implementation could be worth considering sooner rather than later.

Five main components of a customer data platform (CDP)

A customer data platform is a repository for large quantities of internal and external customer data. CDP input data sources often include data from an organization’s customer relationship management (CRM) system and data management platform (DMP). Both CDPs and CRMs are persistent, long-term storage solutions, whereas DMPs generally have shorter retention periods around 90 days or so.

Customer data platforms generally include at least five major components which are described next (see figure below). There is one caveat. Consent management is a very important item not always included in the requirements for a customer data platform. If you use a CDP for marketing use cases, however, you’ll need to consider how to manage and track consent.

1. Data Ingestion and Storage

At its core, a CDP must provide a data storage component where all the customer data is securely stored and managed. Additionally, you will need to have a way to bring all the customer data into the storage layer. Data ingestion for external data sources is usually automated by using various connectors. It’s very important to consider data governance as part of this component. Depending on the CDP selected, your organization could be responsible for all data governance requirements.

2. Data Modeling and Processing

Before ingesting data into your CDP, you’ll want to design and create your data models. It’s a good idea to build a data dictionary as part of the data modeling exercise, prior to data ingestion. Creating a data dictionary will help highlight any formula fields to be created and data transformations to be undertaken.

3. Identity Management and Consent Tracking

Data matching and identity resolution are the next critical steps to achieving a unified customer profile once data is ingested and securely stored in a CDP. Identity stitching, accomplished by analyzing and resolving data across multiple touchpoints, systems, and attributes, ultimately helps us better understand a customer’s interests and needs. Identity resolution can be achieved using both deterministic matching, best used with first-party data, and probabilistic matching.

4. Profile Enrichment and Audience Building

After reconciling identities, you’ll be able to enrich those identities with external data sources. Once the holistic unified profiles are available, you’ll be able to extract information to be used for analytical purposes. For marketing use cases, you can also use unified profiles to create segments and audiences for marketing campaigns.

5. Actions and Insights

This component makes data in the data layer accessible to machine learning tools or other platforms where the data can be used to achieve actionable insights. With actionable data, organizations can better orchestrate the customer journey. Targeted actions also make it possible to engage with customers in real-time. For example, a customer searching for product installation instructions on the website for a recently purchased item could automatically be sent an email with the needed information.

Explore types of CDP solutions available

Some customer data platform solutions, such as Salesforce Data Cloud can be purchased as a full product suite with all major components included in one platform. Another approach to acquiring a CDP would be to build your own customer data platform.

Most organizations that build a customer data platform opt for a composable CDP which allows individual best-in-breed module selection and combination to satisfy their CDP requirements. Both of these customer data platform acquisition approaches are discussed in more detail in the next blog post.

Remember to reach out to the team at Sercante for guidance when you’re ready to implement a CDP at your company or organization.

Original article: The Five Major Components of a Customer Data Platform

The post The Five Major Components of a Customer Data Platform appeared first on The Spot.

By Joyce Avila|2023-09-07T01:03:21+00:00September 7th, 2023|Categories: Analytics & Reporting, Data Management, Pro Tips, revive, Strategy|

A customer data platform (CDP) is a unified customer database where many different external and internal sources are collected, cleaned, and aggregated to build rich individual customer profiles. These unified customer profiles can then be made available to marketing, sales, and support teams for achieving increased sales, enhanced customer experiences, and improved customer support. If you’re wondering if you should implement a customer data platform, then read on to get six reasons why it’s a good idea.

Building personalized customer experiences

Customers expect more personalized experiences today — they’re demanding more in return for sharing their personal information with an organization. When customers supply their information to one department, they expect the updated information to be available company wide.

However, that is frequently not possible because customer data often exists separately within many different departments. There is rarely a single source of truth. In this situation, a CDP can connect these siloed customer data sources.

CDPs create a complete view of the customer journey

It’s not just that customer data is siloed. The amount of data continues to grow exponentially for many reasons, one is that there are more touch points now in a customer journey.

A customer may start their morning searching for an item on their home computer then continue their search on their mobile device while in transit to work. Later, they may spend their lunch hour browsing on their laptop, ultimately making their final purchasing decision on their tablet in the evening.

A CDP can help stitch together these interactions to provide a more complete view of a customer journey.

Six reasons to implement a customer data platform

Reason #1. Provide better and more personalized customer experiences
Reason #2. Solve problems related to data silos/disconnected databases
Reason #3. Manage more complex customer journeys with multi-touch points
Reason #4. Prepare for the demise of third-party cookies in 2024
Reason #5. Comply with government regulations regarding privacy
Reason #6. Use unified profiles in data clean rooms

These first three reasons why you’d want a customer data platform aren’t necessarily new. It’s a marketer’s goal to deliver the right message at the right time through the right channel by building an understanding of who the customer is and what they want. However, there is a new sense of urgency.

Creating unified customer profiles has become much more important due to recent external driving factors. Those driving factors are the fourth and fifth reasons included in the list below.

Data privacy and the end of third-party cookies

With the availability of information from third-party cookies, it wasn’t a priority for most organizations to expend the resources developing a complete view of their customers. Indeed, it’s expensive to build a complete 360-view of the customer that would allow for more personalized experiences, and it’s been relatively inexpensive for marketers to frequently send communication blasts to a wide audience.

Soon, however, marketers will no longer have a cheap and easy source of consumer information gathered from third-party cookies. Today, third-party cookies are already being blocked by some browsers such as Safari and Mozilla Firefox. In the latter half of 2024, Google plans to completely deprecate all third-party cookies.

The excessive exploitation of technology, including the misuse of third-party cookies, has had the unintended consequence of governments stepping in to create regulations to better protect the privacy of consumers. Obtaining consumer consent and keeping track of this consent, now required, is an important reason marketers should consider using a customer data platform.

Building unified customer profiles within a CDP

A CDP offers organizations a way to bring together customer data to develop a more clear and complete picture. With the unified profiles developed in a CDP, organizations can now develop a first-party data strategy that can be extended with the use of other tools and platforms like a data clean room.

A data clean room, the ultimate in data sharing and data collaboration, provides new opportunities for organizations to process and analyze data more efficiently while still managing the data in a compliant way.

Other departments feel benefits of implementing a CDP

Marketers may initially have the most to gain by using a customer data platform, but unified customer profiles can also result in many benefits for sales and service teams. Importantly, the enterprise must adhere to new regulations and privacy laws.

A customer data platform is often the best way to ensure that the customer profile is complete and accurate. That way, when a customer makes a request related to privacy, the organization can comply with the request.

So, how does a CDP actually work? In the next blog post, we’ll discuss the five major components of a customer data platform.

Remember to reach out to the team at Sercante for guidance when you’re ready to implement a CDP at your company or organization.

Original article: Six Reasons Why You’d Want to Implement a Customer Data Platform

The post Six Reasons Why You’d Want to Implement a Customer Data Platform appeared first on The Spot.

By Joyce Avila|2023-09-07T00:48:02+00:00September 7th, 2023|Categories: Analytics & Reporting, Data Management, Pro Tips, revive, Strategy|

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