The Marketing Operations Strategist - Copy these templates for your work: data enrichment, intent data

Hey! 👋

Hope you’re doing well — this month has felt like a year for me. 😅 Very busy, very chaotic!

Today I want to tell you more about data enrichment, cleansing, and intent data. You’ll leave with an understanding of the categories and use cases you can use to brainstorm, as well as templates full of data you can copy + adjust to your liking!

Let me tell you more about it after a quick note from our wonderful sponsors 💖

Have leftover professional development funds and looking to invest in your MOPs career? Check out my new course, The Marketing Operations Master Playbook and learn from yours truly! Save years of mistakes and hard lessons learned, as I share what I’ve learned through my 10+ year marketing career.

Okay, back to the templates! 🗒️

Data Enrichment, Cleansing, Normalization, and Validation Use Cases 🧼

Maintaining clean, accurate, and enriched data is arguably the most important thing in marketing operations. Poor data leads to inefficient targeting, wasted ad spend, and inaccurate reporting. If you have clean data, it makes everything else within marketing easier.

This quick data 101 guide provides insights into the four pillars of data management: enrichment, cleansing, normalization, and validation.

Data Enrichment

Data enrichment ensures your marketing database is filled with complete and up-to-date information about your leads, contacts, and accounts. By appending additional details like firmographics (e.g., company size, industry), technographics (e.g., tech stack), or behavioral data (e.g., last interaction), you gain a more complete view of your audience.

For example, enriching your lead data with job seniority allows you to customize outreach: sending C-suite executives strategic insights while focusing on tactical value for mid-level managers. Enrichment helps address incomplete records, so you can maximize the potential of your existing database instead of wasting time and resources acquiring new leads at a more costly expense.

Data Cleansing

Data cleansing is the process of removing duplicates, inaccuracies, and inconsistencies from your database; essentially eliminating the clutter. Without regular cleansing, errors in your database can accumulate, leading to poor segmentation, bounced emails, and even skewed performance reports.

Imagine running a campaign where 15% of your emails bounce because of outdated or invalid email addresses…that’s not just wasted effort, it’s a missed revenue opportunity. By conducting routine data cleansing to remove or correct bad records, you ensure that every contact in your database is actionable and reliable. This is especially critical for maintaining trust with sales teams, who rely on marketing to deliver quality leads.

Normalization

Data normalization is all about standardizing formats across your database. For example, ensuring dates follow a consistent format (i.e., MM/DD/YYYY vs. DD/MM/YYYY), phone numbers are standardized to international formats, and job titles are unified (e.g., “CEO” and “Chief Executive Officer” are treated as the same role).

Normalization plays a critical role in enabling seamless integrations between your CRM, marketing automation platform, and other tools in your tech stack. A disorganized database leads to misaligned systems, reporting errors, etc. Everything is just much harder and more time-intensive.

Validation

Data validation ensures that the information entering your database is accurate from the start, reducing the need for annoying manual fixes or mistakes later. For example, validation tools can check whether an email address is deliverable or a phone number is in the correct format before you reach out to the person.

By combining these four pillars of data management: enrichment, cleansing, normalization, and validation…you can build a database that fuels every aspect of your marketing strategy, from better segmentation and personalization to more accurate reporting.

Ready to dive into the details of the use cases? Download + make your own copy of the Data Enrichment, Cleansing, Normalization, and Validate Use Cases sheet here 👈️ 

Please do share any feedback you have! If this is a helpful resource, I’ll continue to build upon it for the community. 🙂 

Here’s a quick win to get started:

Focus on deduplication and validation of your most active records (basically, leads in your current campaign or high-priority accounts). Here's how:

1. Run a Deduplication Audit:

   • Use your CRM or marketing automation tool (e.g., Salesforce, HubSpot) to identify duplicate records.

   • Prioritize deduplicating active leads or contacts tied to open opportunities or recent interactions.

   Pro Tip: Many CRMs have built-in deduplication features. Tools like DemandTools and Ringlead can also help automate the process.

2. Validate Email Addresses for Key Campaigns:

   • Before sending your next email campaign, run your contact list through an email validation tool like NeverBounce or ZeroBounce. This will reduce bounce rates and improve sender reputation instantly. This is especially important if your list could be stale or is very large or very small.

3. Normalize Key Fields:

   • Pick 1–2 fields, such as job titles or phone numbers, and standardize them across a small sample of records. For example, unify titles like "Marketing Manager" and "Mgr. of Marketing" into a single format to clean up segmentation.

4. Enrich 10 High-Priority Accounts:

   • Use an enrichment tool like Clearbit/HubSpot Breeze or ZoomInfo to fill in missing data (e.g., industry, company size) for your top 10 accounts. This enables personalized outreach immediately.

Starting small with focused efforts on a specific dataset, like your active leads or campaign list, delivers quick wins without overwhelming your team. From there, you can expand these processes incrementally to tackle your entire database.

Intro + Template for Intent Data 🕵️‍♀️

Intent data can be a powerful resource for demand generation, but only if you know how to use it effectively. 🤔

Here are the different types of intent data:

1. First-Party Intent Data

Data collected directly from your owned properties, such as your website and email engagement.

• Examples: Website visits, form fills, product page views, email opens.

• Why it’s useful: Highly accurate, privacy-compliant, and signals direct interest in your brand.

2. Third-Party Intent Data

Data aggregated from external sources like publisher networks and review sites.

• Examples: G2 buyer intent signals, Bombora’s topic-based surges.

• Why it’s useful: Helps identify accounts researching your category but not yet engaging with your brand.

3. Engagement-Based Intent Data

Behavioral interactions with your marketing campaigns and digital assets.

• Examples: Webinar attendance, ad clicks, social media engagement.

• Why it’s useful: Indicates early-stage interest and is great for nurturing leads.

4. Competitor Intent Data

Signals that a prospect is researching or engaging with your competitors.

• Examples: Visits to competitor pricing pages, G2 comparisons.

• Why it’s useful: Helps with win-back campaigns and competitive positioning.

5. Technographic Intent Data

Data about the technologies a company is using or evaluating.

• Examples: A company using Marketo researching HubSpot alternatives.

• Why it’s useful: Ideal for competitive targeting and solution-based selling.

6. Buying Group Intent Data

Tracks multiple stakeholders from the same company engaging with intent signals.

• Examples: Multiple employees from a company researching the same product.

• Why it’s useful: Helps prioritize high-value accounts in an ABM strategy.

I’ve put together a table outlining key capabilities of intent data, including how it can be applied, recommended vendors, real-world case studies, and whether the data is anonymous or known.

How to Use This Table

• Find your maturity level → Are you just starting with intent data, or ready for predictive insights?

• Match intent data types to your needs → First-party vs. third-party, known vs. anonymous, and which best aligns with your marketing stack and use cases.

• Experiment → There’s an experimentation column where you can start to develop your own company-relevant test strategies like retargeting high-intent visitors or using G2 data for competitive insights.

Download + make your own copy of the Intent Data Template here 👈️ 

What I’m up to/what I’m studying

I’m kicking off on a deep dive for Clay soon, hoping to skill up on advanced use cases ASAP. 🙂 I’ve also been back on my search for the best restaurants in San Diego and recently went to Communion in Mission Hills — it was delicious! Do you have any food recommendations in your neck of the woods? 👀

Dear Sara ✍️

New to marketing operations? On a team of one at your company? Shy/introverted? Wish you could ask a question to an experienced marketing operations professional, without them knowing who you are? Here’s your chance! Submit an anonymous question to me here and I’ll answer a new question in every issue.

Here’s my answer to a question from last week:

Oh no, not post-it notes! 😆😭

Make sure you’re thinking of your audience first when you think about change. If you push too much change too quickly, you risk the chance of losing the trust or interest of your audience. If you take too long, you risk harming the business (if it’s a compliance update, or something to help grow the business).

A lot of companies tend to downplay the impact of change, but the typical journey looks like this (and people can be at all different types of stages at different times):

What I like to do is:

1. Create personas for each type of employee the change will impact (think: executive, middle manager, individual contributor) and map names of employees to each persona.

2. Anticipate where I think each persona will go on this path above — will they run up into denial or disillusionment? Some predictors of this would be: being a legacy employee (having been at the company for a long time, you’re likely bought in to the existing process), how much is changing for the type of role, etc.

3. Create a plan or playbook for each persona, based on this information. Some personas will need more regular check-ins and support than others, and the more prepared you are, the more likely you are to be successful.

4. Think about areas where you can sprinkle joy throughout the process — if they have to file a new Jira ticket for a request, can you show off how much more quickly they can get a response? Can you have Change Champions who are bought into the process and can help hype up the change and get people excited?

Now, this may seem like a lot, but stick with me — if you have a small team, you could map out the personas in a matter of minutes. You may only need 1-3 playbooks. But you’re much more likely to be successful because you are meeting people where they are.

For the initial Dear Sara asker, my advice to you is to think about the change more gradually — are there small changes that will be highly impactful for the team, where you can show a quick win and get people excited for larger changes? Can you get leadership aligned with you and get them to help cheerlead the cause? I know it can be painful to wait longer, but if you go too quickly it may, ironically, end up taking longer than if you go on a slower, more consistent path. I hope this helps and gets you out of post-it note hell soon!

Want more Dear Sara? I probably don’t advertise this enough…if you want to read previous editions of the newsletter, you can check them out here (just scroll down to the archive).

Thanks for reading,

❤️ Sara