May 21, 2025

Why one marketing ops team finally turned to Cloudingo for help

Sarah didn’t plan to spend her entire week cleaning data.

As the Marketing Operations Manager at a fast-growing SaaS company, she was juggling campaign launches, reporting requests, and fielding last-minute asks from sales. But instead of focusing on strategy, she was buried in Marketo exports, trying to fix a growing mess of duplicate records.

The issue had been bubbling up for months. Reports looked off. Campaign performance was dipping. Automation rules were misfiring. And when Sarah dug into the data, she realized the root cause: duplicate and inconsistent lead records were clogging up Marketo.

The Hidden Cost of Dirty Marketo Data

Marketo was synced with Salesforce, but the integration was far from perfect. Some records existed only in Marketo. Others were partially synced, with conflicting values. And some were exact duplicates with minor differences that Marketo’s native tools could not catch.

Sarah faced issues like:

• Leads with slight variations in name or company, creating false duplicates
• Multiple versions of the same contact split across different regions
• Conflicting values for key fields like lifecycle stage or lead source
• Incomplete records that broke lead scoring and nurtures

Her team tried manually cleaning the data. It took over a week across multiple people. Even then, mistakes happened. Some important records were merged incorrectly. Others were missed entirely. They also tried outsourcing the work, but the offshore team didn’t have the context to make the right decisions.

Behind the scenes, marketing automation was suffering. Leads were being removed from nurtures, scoring models weren’t firing, and personalization efforts were losing their effectiveness. Every campaign launch came with a risk that dirty data would derail it.

Where Marketo’s Native Deduplication Falls Short

Marketo’s built-in deduplication tools only check for matches based on email address. That works in some cases, but not in real-world scenarios where people use multiple emails, switch domains, or get entered into the system with typos or variations.

There was no way to match based on name, company, or custom fields. There was no fuzzy logic. For Sarah, that meant the data was always slightly behind. And it was costing them.

What Changed with Cloudingo

A colleague in RevOps recommended trying Cloudingo. Within days, Sarah’s team had a better handle on their data.

Here’s what helped:

• Custom merge logic allowed matching across multiple fields, not just email
• Marketo-only records could be cleaned independently without touching Salesforce
• Scheduled automation ran daily, catching new duplicates before they caused issues
• CSV imports were pre-cleaned, reducing manual work and preventing errors
• Undo and audit capabilities gave the team confidence to clean more aggressively

Cloudingo also provided full transparency. Every action was logged. Every decision could be reversed. And filters made it easy to flag problematic records without the guesswork.

What Happened Next

With Cloudingo in place, Sarah’s team no longer had to block off days for cleanup. Campaigns ran smoother. Reports were more accurate. And Sales stopped complaining about bad data.

The operations team could finally shift its focus from damage control to optimization. Instead of reacting to problems, they were proactively maintaining data quality.

A Familiar Story for Marketo Teams

If you work in Marketing Ops or RevOps, Sarah’s story might sound familiar. Duplicates are easy to overlook until they start affecting performance. And when your systems are fragmented, your automation suffers.

Cloudingo was built for these exact challenges. Whether you need to clean up Marketo, Salesforce, or both, it gives you control over your data and peace of mind that the job was done right.