The first rule of Marketing Cloud? Every journey begins with a rock-solid data model. The data model is what gives the Marketing Cloud its “fuel” to build super-customized onboarding journeys and launch dynamic campaigns that speak to audiences with surgical precision.

But if you’re a marketer, building a data model is easier said than done. Marketers are skilled at reading their audiences, building dynamic campaigns and creating strategies. Asking marketers to build a data model so they can reap the full benefits of Marketing Cloud is a big ask. Building data models is not their specialty.

Here’s an analogy: Would you insist that your kitchen designer install the plumbing system for the sink and dishwasher? Sure, maybe they could get it done without a plumber. But without the training and expertise, it will not only take far longer to make the kitchen functional, there’s a high potential for errors — which show themselves as soon as you turn on the spigot.

The same is true in building data models for Marketing Cloud. Without a solid data model, the result is a string of unintended consequences. In the best-case scenario, marketing doesn’t have the details to build the data extension to create a journey to go after a specific audience, leaving them to send mass emails to your contacts. What you’re left with is an expensive marketing tool that doesn’t work.

Worst-case scenario? Fines and other costly headaches, which we’ll explain below.

Here’s a rundown of what can go wrong when marketing doesn’t have a strong data model to power Marketing Cloud.

Pitfall #1: Inability to Build the Targeted Send List

This one is pretty straightforward. If your data model can’t pull in key details, the campaign stalls out. As an example, let’s say a bank wants to create an offer for creditworthy homeowners to refinance. But with errors in the data model, the team can’t build the data extensions they need to target these customers.

Pitfall #2: Wrong Offer, Wrong Subscriber

Does that sound like another name for a blanket marketing email? It’s actually worse. Let’s say the marketing team built a campaign to refinance auto loans to 1.99%, and created a data extension around these parameters:

• California customers
• Auto loans
• Paying interest rates of 7% APR or higher
• Have 12 consecutive on-time payments
• Holds credit score of 750 and greater

With a badly built data model, that data extension can send that carefully targeted campaign into the wrong inboxes.

The results can be costly. Especially if it goes to people who do not meet the credit or payment threshold. That’s a problem, because now the financial institution is legally obligated to make good on it, even if the recipient doesn’t qualify.

These costly and embarrassing errors can be avoided if the data model is correct to begin with.

Pitfall #3: Campaigns Become Mailbox Clutter

When you don’t have that sturdy data model, marketing misses out on the key benefit of using Marketing Cloud: Customers receive only the campaigns that are relevant to them.

Less “junk” and less “noise” increases engagement and improves satisfaction with a brand. Marketing campaigns that resonate start with good, reliable data. Without the ability to build the data extensions to create and execute these campaigns, the team will have to revert to general campaigns that speak to everyone, cluttering inboxes with unopened, irrelevant emails. When that happens, the organization loses subscribers.

Pitfall #4: CAN-SPAM creeps in

No one wants to spam their own customers. A badly built data model can lead to that thanks to the duplicate subscribers on your list. The outcome? A recipient who unsubscribed would continue getting offers from the financial organization. If that has the customer seeing red, they can report this to the Federal Trade Commission, resulting in a fine for each instance of CAN-SPAM received. If a data model is properly built to begin with, organizations can avoid unwanted fines for CAN-SPAM.

Poor Results

Without a proper data model, marketing can’t send targeted campaigns. When they can’t send campaigns directly to the customers you want to go after, there’s no good data to analyze the results. Marketing Cloud’s ability to report on the activities of a campaign is a key advantage of this resource. If you don’t have good data in the system to start with, it’s going to be very difficult to gain any value from using Marketing Cloud.

Gaining the tools to build and send targeted offers to your financial customers is a great way to reach them. But as you’ll soon discover, your campaigns will only be as good as the data you have in Salesforce. Building a campaign on incomplete data creates costly mistakes and reflects badly on the bank or financial institution.

Bottom line, without a foundation of a solid data model, Marketing Cloud won’t reach full functionality.

Let EMS Consulting build your data model in Marketing Cloud

EMS Consulting can help you quickly build a data model that can truly help your organization achieve results that drive more return on your Salesforce investment. Talk to us today about any Marketing Cloud journey or development work you may have in mind.