The Hidden Costs of Bad Sales and Marketing Data

Dirty data is easy to ignore. And that’s because it rarely causes a single dramatic failure. Instead, it steadily leaks value, often so imperceptibly that very few people notice. And this is where the problems begin.

Dirty data is adding cost to your business:

· Wasted budget: If you’re building your campaigns on incomplete or outdated data, there’s a good chance you’re wasting your hard-won budgets. Your dirty data may mean you’re targeting the same contacts multiple times or sending mail to the wrong addresses. And what about your segmentation - is it based on truth or on unreliable job titles or industries? And then there’s the money that’s just washed down the drain, like in CRMs where you’re paying for duplicate records – ouch!

· Missed revenue: Sales teams feel the impact in even less visible ways, leading to slower results. Perhaps leads are worked twice (or not at all) or opportunities stall because it’s unclear who should own them. And then there are the notes and information for customer accounts split across multiple records, hiding their true value, communication and sales history.

· Reporting confusion: Poor-quality data doesn’t just affect your campaigns and leads – it also leads to low-quality reports. You might find that different teams produce different numbers, leading to a lack of trust. Your leadership starts questioning the data instead of using it and meetings become a frustrating cycle of reconciling reports instead of opportunities to make value-added decisions.

· Time drain: Love wasting your time? No, me neither. But dirty data drags down your team’s efficiency, resulting in time sucks such as manual fixes before every campaign, panicked spreadsheet “clean-ups” whenever you create your board packs and endless DMs asking which number is correct.

What does dirty data look like?

Annoyingly, dirty data isn’t particularly obvious. Rather than jumping out at you whenever you open your database, it hides away in plain sight.

Here are a few real-life examples I’ve come across in sales and marketing systems:

· naming variations resulting in multiple records for the same company

· free-text job titles leading to unreliable segmentation

· lifecycle stages used differently by each team

· lead sources overwritten or incorrectly reused

· old contacts that haven’t been marked as inactive or unengaged

· subtle typos that only the most eagle-eyed would spot.

Get the picture? And the biggest problem is that none of these issues feels dramatic on its own, but together, they’ll undermine nearly everything you do.

How can you keep your sales and marketing data clean?

One of the biggest mistakes teams make with data clean-up is treating it as a one-off exercise. You carry out a big cleanse, things improve for a while and then slowly the mess creeps back in. It’s just like housework!

However, if you use a simple, repeatable structure, it’s much easier to keep your data looking shipshape. And this is where the COAT framework comes in.

COAT stands for Consistent, Organized, Accurate and Trustworthy. It’s a practical way of thinking about whether your sales and marketing data is fit for purpose.

· Consistent data means you apply the same rules everywhere. For example, using the same format for company names and capturing job titles in a usable fashion. Doing this allows segmentation and reporting to work at scale.

· Organized data is structured and categorized so teams can find, use and maintain it. This means clear ownership, sensible field layouts and defined processes. After all, if people don’t know where data lives or who is responsible for it, it won’t stay clean.

· Accurate data reflects reality. For example, up-to-date contacts and lead sources that haven’t been overwritten or guessed. This kind of accuracy comes from fixing issues at the source, not correcting them downstream in spreadsheets.

· Trustworthy data is what you get when your data is consistent, organized and accurate. Your teams stop second-guessing reports, decision-making is faster and energy is focused on action instead of reconciliation.

Using COAT isn’t about adding bureaucracy, but instead making small, intentional choices about how you capture, clean and maintain your data. Start applying it monthly and you’ll turn data clean-ups into a routine part of how your sales and marketing teams operate.

Your regular data clean-up workflow

Data clean-ups are no different to the way we treat our homes, clothes and even our desks – it doesn’t stay clean because you cleaned it last year in one big project. Not at all! Instead, it stays clean when you commit to small, repeatable habits.

To give you an idea of what that looks like, here’s a monthly workflow you and your team can run without any specialist knowledge.

Step 1: Focus on what matters most

Don’t try to clean everything – you’ll get overwhelmed and feel like giving up! Instead, choose:

· one core object (leads, contacts, accounts or opportunities)

· 5–10 fields that directly affect targeting, reporting or revenue.

This kind of focus will deliver the results you need far more quickly.

Step 2: Define what “good” looks like

Before fixing anything, agree on standards:

· naming conventions

· required fields

· accepted values

· formats (capitalization, dates, punctuation).

Doing this means you’ll all be working towards the same outcome, rather than creating new inconsistencies.

Step 3: Identify patterns, not perfection

Look for:

· near-duplicate company names

· records missing critical fields

· fields used inconsistently across teams

You’re looking for repeatable issues, not one-off anomalies.

Step 4: Fix in bulk

Use tools that allow you to batch this work. For instance:

· CRM bulk tools

· spreadsheets with clear rules

· controlled find-and-replace logic

· documented processes

Think of it this way - if a fix can’t be repeated next month, it’s not a real fix.

Step 5: Lock in better behavior

Identify and agree on small governance changes to prevent issues from recurring. These can be as simple as:

· dropdowns instead of free text

· required fields where they genuinely matter

· clear ownership for data decisions

This will help you keep your data clean, reducing future challenges.

Truth bomb: not everything needs to be solved immediately

If you have perfectionist tendencies, you’re in good company. However, I’m going to ask you to restrain yourself and recognize that you don’t need to fix everything at once. Instead, divide your tasks into quick wins and longer-term fixes.

Quick wins: Start with changes that deliver visible impact fast, such as deduplicating core records, standardizing company names and key fields, archiving inactive or unengaged contacts and fixing obvious reporting blockers. These simpler tasks build momentum and confidence and lay the ground for your longer-term changes.

Longer-term fixes: Plan separately for deeper changes, such as redesigning lifecycle stages, improving lead source logic, aligning sales and marketing definitions and reducing manual data entry at source. These take longer but will prevent problems from recurring.

Measuring improvement over time

If you don’t measure progress, your data quality will begin to regress. Simple metrics work best, for example:

· number of duplicate records

· percentage of records with required fields populated

· time spent fixing data before campaigns or reporting

· overall confidence in reports

And remember this: improvement is less about perfection and more about reducing friction and increasing trust.

Sales and marketing teams don’t need perfect systems. They need reliable data they can act on without second-guessing it.

Running a simple clean-up plan every month will save you more time, money and frustration than most new tools ever will. Once your data stops working against you, everything else works better.

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