For the last seven years, I’ve been enamored by a marketing analytics tool (who among us hasn’t, I’m sure), Datorama, or Salesforce Marketing Cloud Intelligence. MCI, as we’ve come to call it, is the most seamless way my customers have found to join their data across multiple sources, built by marketers and for marketers. MCI allows users to join data together and easily create reports and dashboards using plain language from the pre-built data models. This allows for easy-to-populate smart lens dashboards, or deeply complex automated reporting triggered by specific events. 

To my delight, and to the happiness of marketers, Salesforce just announced on March 18, 2025, a new version of the tool, Marketing Intelligence (MI),  built on the Salesforce Platform. This new version takes the best features of existing MCI and layers them into the functionality of Data Cloud’s unified platform, while taking users to the future with agentic features that will provide down-to-earth insights with conversational agents. So without any further ado, let’s dive into this new tool, Marketing Intelligence! 

What is Marketing Intelligence? 

Marketing Intelligence (or MI) is a new application on the Salesforce platform designed to simplify marketing data management, deliver trustworthy, out-of-the-box insights that marketers can instantly act on, and deliver better return on investment for marketing spend. Being built on Data Cloud and connected to the Salesforce platform allows it to be fully extensible, with a toolbox for marketers at the ready- it’s everything you need to build robust, effective, and meaningful dashboards with minimal lift. 

Data Cloud for Marketers Made Easy 

One of the processes I have been spoiled by in Marketing Cloud Intelligence is data mapping that auto-populates based on past usage and logical guesses by the platform’s artificial intelligence. Additionally, certain APIs come with prebuilt models and mapping to build off of rather than user-defined settings. These features have helped get marketers streamlined into the world of data models and dashboarding with less lift than throwing them into a database or asking them to join various tables. 

All of that, to my delight, is back in full form with Marketing Intelligence. You have the option to upload a TotalConnect file (a non-standard API, flat file of your choosing), or to use an existing API connection, with some rolled out at launch and more coming in the year ahead. Choosing a connection like Google Ads allows you to seamlessly grab that data, formatted and ready for quick mapping, and load the data you need into a dashboard in just 3 clicks. 

Clean and Easy Dashboards 

The dashboards look sharp and load with ease. These dashboards come prebuilt, with options to customize, and also have a key new feature compared to the existing Marketing Cloud Intelligence: generative AI summaries of your campaigns, including what’s working and what might not be. This elevates, to me, the future of dashboarding—being able not only to look at quick and easy data points and trends but also being told in plain language for what to take away or dig into. This can help marketers ask questions and dive in further, and even ask their agent to take action on what recommendations surface.

The idea of clear and plain insights especially comes up in implementations of the current dashboard tools I work in. Users looking at a dashboard want to know different information, and for dozens of users looking at a single page, the questions they’re asking are going to be different depending on needs. The option to ask your agent to recommend optimizations, and then act on it will save marketers a lot of time and headache. With Marketing Intelligence,  you just need the data ready for an agent to help your end users get what they need from the data you’ve put in place. 

Tidied Data Across Channels 

Of course, the core goal of any marketer looking for a tool like Data Cloud, Marketing Cloud Intelligence, or this new version of Marketing Intelligence, is to tie data together across channels to tell meaningful stories they can act on. In addition to the standardized API mapping, MI creates value by uniformly harmonizing these fields across datasets and allows for a semantic model to be used on the backend to tie data together in ways that are common sense (campaign name ties across all of your channels, for instance), such as tying your campaign from paid media to your campaigns from your CRM or other tools, even when names are not exactly aligned (more on this in a moment). 

I’m an existing Datorama/MCI User: What’s Worthwhile Here? 

If you have been reading up to this point, you know what was probably on my mind when I first saw this tool: can I love a changed version of my favorite software of all time? (And yes, I have a favorite software of all time.) Put simply, I’m ready to love. Let’s dissect the butterflies in my stomach. (And if your heart skips a beat when you hear about normalization, semantic modeling, and ROI, there’s enough of this platform for us to share). 

One Word: Normalization 

When I lead implementations with clients on MCI, we talk about the ways in which their data joins. Sometimes it’s super straightforward, sometimes it’s messy. More often than not, we can devise ways to join the messy and the clean together, such as by breaking out parts of campaigns to equal full campaign names in other channels, or by using the numerous formulas Marketing Cloud Intelligence offers out of the box.

In MI, this is no longer necessary. One of my favorite surprises is seen below: you can classify and normalize data with Einstein AI, so instead of working to modify and standardize all of your data either in the platform of origin or in Intelligence, you can instead have Einstein help you set standardization of your data. This is a fantastic path forward in joining datasets together for synchronized cross-channel reporting.

Two Words: Semantic Modeling 

Though users will have an out-of-the-box paid media data model ready to go, users will have free range beyond the world of pre-defined data models in MCI. In MI, you can set up a semantic model that joins datasets together across multiple objects. While you may miss some of the standardization of having ads, conversion, and web analytics data models, among countless others in MCI, you will get seamless back-end loading of data together, along with seamless joins to standard Salesforce object data. This also means that you can add fields and relationships with full customization as your datasets evolve, or as you update back-end nomenclature to more cleanly join fields from one connection to another.

 Three Words: Return on Investment 

Speaking of Salesforce data, what elevates a good MCI implementation to a great one, in my books, is the joining of cost/engagement data to tangible ROI and meaningful dollar results. With the new integration to the semantic model and the ease of connecting standard objects from Sales Cloud, users can handily create and easily visualize ROI metrics in MI wherever data cleanly intersects, which is now made much easier with Einstein normalization and semantic modeling.

Additionally, attribution is a more straightforward possibility with MI than in current MCI, with the framework to capture website events within Data Cloud data model objects, providing marketers end-to-end visibility into touchpoints where ads have been seen by end users. This will include attribution models for first and last touch for users, and can further be a method to validate ROI and pinpoint specific interactions with customers.

I’ve Never Used MCI or Datorama-Why Should I Explore Marketing Intelligence? 

The Tool for Data Harmonization 

MCI has long been the gold standard for harmonizing marketing data. When clients come to me looking for data solutions, if the core users (front and back-end) are marketers, MCI is always what I recommend. Now with MI, you have the power of what current MCI can do to enable marketers to aggregate and act on data in the same platform as your CRM and marketing data, with the added benefits of generative and agentic AI, 3 click data setup, Data Cloud, and embedded Tableau Next visualizations.

The Tool for Visualization 

Current Marketing Cloud Intelligence has some great visualization options, but the two big enhancements I’ve always wanted and have tried to build guardrails for on my own are: 

1. Faster load optimization for dashboards 

2. Plain language recommendations for end users 

With MI, you get both of those with minimal lifting. Data is smoothly joined together using Data Cloud and back-end semantic modeling, with minimal loading for calculated fields and other computing intensive processes in current MCI. Additionally, with generative AI suggestions and an agent to help you pause underperforming ads, end-users are no longer limited to looking at charts and figuring out what it means,—because actions are ready and available to springboard from on the page.

The Tool for Marketing Customization 

Have you ever wanted to redefine your campaign names in reporting by extracting certain parts of your campaign names from across systems? Have you wanted to group different Google Analytics traffic sources and merge them against ad spending from the respective paid media platforms on an automated basis? What about renaming and grouping a set of campaigns based on criteria only you and your team know and then dynamically filtering for a handful of those campaigns? 

That’s the sort of fun I love to explore with clients, and it’s back in full force in MI with processes like patterns to extract data points from various fields, calculated fields in the semantic layer, and the normalization processes Einstein brings to the table.

A New Era 

Marketing Intelligence launched on March 18th (with Data Cloud and MI licenses required). Talk to your account executive to explore this new product. Marketing Intelligence is going to be the gateway into a new world of dashboarding intelligently (no pun intended) and is sure to streamline data for marketers in ways that have only been hinted at by Marketing Cloud Intelligence previously. I know I’m excited to take this ride, and I hope you’ll join me!

Original article: Salesforce’s New Marketing Intelligence: As Seen by a Datorama Enthusiast

©2025 The Spot. All Rights Reserved.

The post Salesforce’s New Marketing Intelligence: As Seen by a Datorama Enthusiast appeared first on The Spot.

It’s time for part two of our three-part series on getting the most out of your Salesforce Marketing Cloud Intelligence, or Datorama, instance. Part 1 of the series touched on little tricks and shortcuts to success with data ingestion and transformation. Today, I’ll be going through how you can speed up the platform for yourself and your team and boost efficiency in reviewing your data. Read on for Marketing Cloud Intelligence tips that will help you maximize its performance.

Marketing Cloud Intelligence Performance Boosts

Marketers are competitive by nature. You’re fighting for bids, placements, and user attention. So why wouldn’t you want the best performance out of the tool you’re using to measure your data and give you insights? 

We’re going to walk you through how to ensure you get the speediest and most efficient version of Marketing Cloud Intelligence you can!

Less widgets, more pages – why?

You may be inclined to make a gigantic page of widgets for one humongous dashboard that you use — this is a fine starting point! It’s especially useful for the sake of exporting data in a PDF or PowerPoint in an advanced manner, using the platform’s advanced export feature.

But when it comes to using the actual dashboards in Intelligence, you should try to break your pages out as much as possible. This could be done by channel, campaign, or any other kind of unifying but specific factors you might be thinking about. 

We’ll touch on this more below, but the more compact your view and the fewer widgets you have, the less you’ll wait for pages to load. 

A load by any other name

You may have sat in front of numerous online analytics tools and seen long loading times. You probably used the old answer as to why: slow internet speeds.

Source: https://i.imgur.com/us5fOfG.jpeg

The true answer to why your analytics tool is slow to load though is… it depends:

JavaScript ifs VS. spreadsheet IF formulas

I’ll cite a specific example below, but any calculated fields may slow your load down. I noted some of this in my first blog in this series, but using JavaScript ifs can help a bit by setting quick end criteria (ie. if it’s this thing, this is the output), as opposed to large loops in Excel style IF formulas.

An additional point of clarity: when I note calculated fields, I mean calculated measurements, filtered measurements, calculated dimensions, and patterns.

Data fusions & transitive fusions

Data fusions similarly happen live and can significantly slow down your instance, either with heavy data sets or more than 5-10 fusions existing back end. Transitive fusions (a is fused to b, b is fused to c) can also cause problems in heavy use.

Page reloads

Accessing a page in a way that makes data load anew can cause slow load times.

• I’ll hit some rules of this later, but scrolling down will cause data to load anew 
• Any updates to the data back-end will cause reloads of the data when you open a page with that data
• Clearing your cache either in the platform or in your browser will cause data to recalculate and populate the dashboard page
• Changing filters will also reset the loading of the page

Identifying load problems & getting help

There is not currently a public-facing timer for loading data in Intelligence, but you know a long load when you see it. 

What can help in identifying the root causes of slow loads is that Salesforce support can help diagnose timing issues down to certain widgets. I recommend this (or the next method) to validate that your internet/instance is not the issue. You could also have a colleague access the same dashboard and compare load times with you using a timer to ensure you are capturing the issue correctly.  

Come Together (but also maybe don’t)

One of my favorite functions of the Intelligence platform is using Coalesces and other calculated dimensions whenever I can’t cleanly join data. There are other ways to do this — like using if statements with ISEMPTY formulas, using data fusions, or doing Vlookups to tie entities together, among others (we’ll touch on some of these below, too). 

Coalesce is often one of the cleanest and quickest ways to join data points where you need a data point from one stream or another, depending on what’s available. A good example is something like lead source, where you may have leads and cost data to join across Salesforce data and paid media APIs. In that case, you would check the lead source field or the channel name of the data stream feeding cost data). 

Technical tip sidenote: Coalesces can be incredibly confounding, so here’s a brief walkthrough of this function. In this platform, as documented in Intelligence’s Help Center, they let you return the first non-empty value of a set of data formatted like COALESCE([value1, value2,…]). The really confusing part is that you have to have brackets around those dimensions, so correct syntax would be like COALESCE([[day],[campaign start date]]).

Scroll first, ask questions in a minute

One thing that can be really frustrating, even when things load okay as a page opens, is that as you scroll the content may not be there when you move down. A way to avoid this is to scroll all the way through a page before walking through it. 

Why is this? Two big reasons:

1. Intelligence loads about 15 widgets at a time. Anything more than that queues up, so multiple widgets loading piece by piece can be difficult on the system.
2. The system only loads what you see. There is no secret back-end loading happening wherein data is populating that you don’t see. You’ll still have to reload widgets when you filter, but by scrolling down, any initial data sets will populate and make you appear to be the marketing superstar we know you are.

Filter secrets-greater than 0

• Have you ever gone into a page and waited for a filter to load? 
• Or, have you opened a filter widget’s dropdown to find hundreds of irrelevant campaigns when you know your workspace should only have a handful of campaigns? 

I use a trick that I call “Greater than 0,” which is a calculated measurement that adds all of my measurements in use for a page and filters that on greater than 0 for any relevant filters. My filters load faster and my dropdowns are minimized to just data points I need, not junk data. 

You’ve successfully loaded the end of this article

Thanks for scrolling down here successfully and ingesting our content! Now you have quick wins that can help you maximize Marketing Cloud Intelligence performance.

Check out part one of this Marketing Cloud Intelligence series to get a reasonable foundation on some other data topics. And keep an eye out for part three of this series, which will go in-depth on some data stream tricks to get your best data pulls/updates possible. 

We hope you get some added productivity out of this piece, and if you’re looking for a tailored conversation to your needs, drop us a line.

Original article: Mastering Marketing Cloud Intelligence Part 2: Maximize Platform Performance

©2025 The Spot. All Rights Reserved.

The post Mastering Marketing Cloud Intelligence Part 2: Maximize Platform Performance appeared first on The Spot.

Marketing Cloud Intelligence (formerly known, and forever in my heart, as Datorama) is a deep platform for marketers who have Salesforce in their tech stack. It’s a robust tool for optimizing data across various channels, enabling you to track spend, engagement, and conversion data, among other options. If you’ve landed on this page, I assume that you already have a working knowledge of the Intelligence platform. Now, let’s take your skills to the next level with insights you didn’t even know you needed to unlock greater insights through Intelligence formulas. 

We also recommend you check out this blog post to understand how you can get more from your Intelligence implementation through an audit.

Let’s dive in.

What’s on the Horizon?

In this blog post, we’re going beyond the basics of your standard Trailhead module. Our journey will uncover hidden Intelligence formula secrets in the following areas:

• Formula Syntax with JavaScript
• Parsing Dates
• Referencing CSV/Data Model Fields and When to Use Each Syntax
• Formula Fears – Goodbye!

Intelligence Formula Syntax with JavaScript

Let’s start on some behind-the-scenes basics to give everyone a little bit more working knowledge, and then we’ll hit some deep cuts.

You’ve probably wondered what governs the formulas in the Intelligence platform that somehow you can write Excel-esque statements but also do JavaScript work (more on this in a moment).  Basically, the platform was programmed to contain Excel-like formulas while also allowing for MVEL, a Java-based language, to do some variations of formula work that can work a little differently in processing and possibility than Excel formulas. 

You can read more of an overview here directly from Salesforce on the governance of formulas and the basics to fiddle with, and below you’ll get my deeper cuts that don’t really get elaborated on anywhere I can find, officially. 

The Writer’s Strike did not affect this (Java) Script 

There are two common experiences I’ve had for the last five years when it comes to if statements:

1. Someone implemented the platform and used JavaScript language, and no one still on the team understands how to read or manipulate the formula.
2. Someone made an unruly Excel-like IF(condition, true, false) statement and it’s become unwieldy.

Luckily, I have your fix, and I’ll give a brief why on this too, beyond the notes above — I want to introduce everyone to using lowercase ‘ifs.’ Let’s take the below-calculated dimension (there would be no difference if this was setup in mapping of a data stream, to be clear).

1. We define our first if statement — in a lowercase if context, you just do if() for the first line. Unlike in EXCEL IFs (henceforth capital IFs), you do not need to define a false condition, just a true condition (in this case, if the campaign name contains ‘Facebook’). 
2. If our first condition is true, ‘Facebook’ will be the value returned, as defined by line 2. This is defined by the squiggly brackets {} and the defined value is ended with a semicolon {‘Facebook;’}

*This is an exciting performance element that adds up across large statements, and especially if the value is in a calculated field, which loads in real time, not before a page is loaded. If the statement finds a true match, it doesn’t run through every line of the code, it just stops and computes the next value. An uppercase IF statement, on the other hand, would check through every true/false possibility and then load the next row, which en masse could make a performance difference. 

3. Else if defines some other condition to check for specifically. If you wanted a simple true/false, you could skip straight to the else statement on line five. But for example’s sake, we will assume there’s an else if. This is effectively how you can nest ifs. As noted, the platform stops checking as soon as it hits a true value, so you want to be mindful that you stack this accordingly in your checking if multiple conditions could be true.
4. Once again, you return a value if true on line 4, no variation in formatting to the first true value.
5. Finally, we close by doing a return value with the word “else”, indicating for all other conditions we close off here.

This is about as far as you need to know for JavaScript usage in Intelligence. But if you’re making deeply complex conditional formatting, this will hopefully make the process much cleaner and decipherable for you!

Parsing Parsedates

Magic letters you should write down, and yes this probably looks nonsensical before my description, but roll with it: “EEE MMM dd hh:mm:ss zzz yyyy”

This is your fix to one of two likely parsedate situations I have seen regularly. It’s a string that dates frequently get passed into Intelligence as, and you inexplicably get a “cannot parse data” error in your mapping. This is infuriating. The formula in full that you are probably looking for is PARSEDATE(csv[insert field name here], EEE MMM dd hh:mm:ss zzz yyyy”). 

I’ll also note here that this is likely what is being pushed into the platform, even if you see something different in your Excel file/csv file from an Intelligence log (highlighted below from the data streams list in Connect & Mix, in case you need guidance on how to find your log files). You should, anytime you get this error, open your log files using a note program (Sublime Text was my go-to for years as a Windows user for bigger files, though for most people programs like notepad will work just fine). Even if not the above format, you can see definitively (with your columns instead separated and broken out by commas, hence the term csv, comma-separated values) what format your dates come in as (and the magic of a program like Excel to just know how a human reads this data cleanly).

There’s also an error I’ve gotten numerous times in platform and have helped people with but have not been unfortunate enough to encounter recently myself, so I am approximating here with a known fix instead. This happens entirely in calculated dimensions, and it effectively amounts to “Unparseable date: 1234”. This will fully stop you from saving a calculated dimension and it’s infuriating. My fix:

At the top of your formula, set a condition: if([insert field here, likely day but whatever the error tells you] == 1234 ) 

{‘error’}

else if… and continue on with your calculated dimension as planned

*note: if this does not work, you may also want to try “1234” instead of 1234, as a string of text instead of as a number, depending how the platform is reading the problematic value.  

csv verses Dat

Through the course of the above pieces of guidance, you may have noticed something: in my calculated if example, there were yellow highlighted fields simply called “Campaign_Name,” and in my examples referring to mapping, I surrounded fields with the language of “csv[field name]”. Why would I do that to you (the answer is not that I am cruel and seek to confuse you further as I write this blog, I promise)?!?! Well, it’s because there are three variations of referencing fields in Intelligence.

1. When you map data, the csv syntax is needed to establish we expect a column from the inbound file. Even if you use Excel format, tsvs, pdfs, etc., this context will always be referred to as csv in your mapping formulas to establish a recurring column. Commonly this looks like the below, and is probably not something you’ve thought about a lot.
2. So you may be wondering, if this is so commonplace, why even explain it? Surely the easy explanation is that csv appears in mapping, and the highlighted field appears in calculated dimensions. Well, kind of, yes. There’s a twist coming below, but yes in calculated dimensions, because you are operating outside of a data stream and inclusive of your whole workspace, you get the highlighted yellow names, showcased again below, to indicate this is a field in the platform.
3. It’s important to understand those two variations because the third is a marriage of them: referencing data stream fields, not source file columns, in data stream mapping. I have very rarely seen any use of this outside of Vlookups, but for that case alone I’ll highlight what this does: it notes that a value is meant to reference an already existing data point in platform. In the context of a vlookup below, you can see the reason for differentiating these items. 

We have a csv field we reference at every ingestion of data, which we then use to look into first our campaign advertiser data already in the platform, and return us the associated campaign name, also already in the platform. You can see the data model section of the formula editor below, where these existing data model fields can be referenced in mapping.

Else…

Hopefully, these tips have been helpful and can act as an easy cheat sheet as you use Intelligence going forward. Whether it’s using if statements, parsing dates,  or understanding how to reference different types of fields, you hopefully found some new information today to help make you better at the platform (I won’t call you a Dato-dork yet, but I happily will wear that cap and keep trying to take more of you with me)!

Keep an eye out for more in this series. We look forward to growing your Intelligence!

Remember to drop us a line when you’re ready to realize the full potential of your Intelligence implementation and how it fits in with your overall marketing strategy.

Original article: Mastering Marketing Cloud Intelligence Part 1: Unveiling Formula Secrets

©2025 The Spot. All Rights Reserved.

The post Mastering Marketing Cloud Intelligence Part 1: Unveiling Formula Secrets appeared first on The Spot.

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

©2025 The Spot. All Rights Reserved.

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