TL;DR Version (under 20 seconds to read)

Your B2B marketing database decays at around 2% per month (20-30% yearly). Bad data costs companies 10-25% of their marketing budget through wasted ad spend, wrong targeting, and broken personalization. The fix isn’t technical, but it’s organizational. You need ongoing validation, deduplication, enrichment, and clear ownership. Clean data is the foundation of effective digital marketing. Without it, your campaigns, segmentation, and full-funnel strategy all fail. Start with a baseline audit and build data hygiene into your operations, not as a one-time project.

The Long Version Now

As much as we would like for this amazing scenario to happen, for the sake of everybody’s mental sanity, your CRM database is not a set-it-and-forget-it asset. Yes, you collect leads. And this is amazing. You put them into your CRM Even better. Sometimes, for the first couple of touchpoints you even update the information and do some enrichment. Holy grail.

But you might assume this data will stay fresh. The bad new is IT WON’T. Sorry for this. Hate to burst this bubble.

Usually, data decays at around 2% per month, give or take some.

That’s not great as it is around 20 to 30% per year. In some industries, decay rates hit 70%. People change jobs. They switch emails. Companies get acquired. Phone numbers go kaput. Positions in the company change. Sometimes even the country they live in changes in 12 months, as so many people now want to take advantage of the freedom to work from anywhere.

And while your CRM data decays, you keep spending money on it.

At Milk & Cookies Studio, we’ve worked with B2B tech companies for over 12 years. We’ve seen clean data make B2B digital marketing & lead generation campaigns succeed and dirty data burn through budgets like a hungry engine with a broken turbo.

The difference between a digital marketing ecosystem that converts and one that looses too much money comes down to one thing: data hygiene. Sure, there are so many other factors we should always take into consideration, but every strategy, campaign concept, tactic, channel, you name it starts with data on which you create your first assumptions. Only after this you should get to the next levels: research, tactics, concepts, messaging, channel mix, etc.

And when we say this it is not about perfection and having the best data out there. It’s more about recognizing that your sales and marketing data(base) is a living thing.

And if you don’t maintain it, your targeting breaks, your personalization fails, and, ultimately, your marketing suffers.

Now, because we like to have our numbers straight, here are the numbers that prove it.

What Data Hygiene Actually Means

Ok, ok, we will get to the numbers in a bit. But let’s look at the foundation first, so we make sure we are talking the same things and we are aligned.

Data hygiene is the ongoing process of maintaining the data quality. You identify errors or outdated information, fix them, and prevent new ones from getting into your system, as much as you possibly can. It is important to keep in mind that having 100% accurate data might not be entirely possible, in order for us to set the correct expectations.

This matters a lot because people change. They move. They get promoted. They leave companies. Email addresses bounce. Phone numbers disconnect. Just as a simple example. if you’re running a demand generation campaigns on 15-month-old data for retargeting the people in your CRM, you’re spending (or at least theoretically) money to reach people who are no longer there.

The difference between collecting as much data as possible and curating, cleaning, and enriching data is the difference between having a database (cool stuff) and having a real marketing asset that you can actually use in your campaigns (epic stuff).

Companies keep adding more contacts without cleaning what they have. They optimize campaigns and funnels but ignore the foundation: their data.

The Numbers: How Bad Data Damages B2B Marketing

General Data Quality Problems

According to CMO Council, 62% of marketers are only moderately confident in their data, analytics, and insights systems. That means almost two-thirds don’t fully trust their own numbers and data. In B2B marketing, this messes with segmentation big time. You build your marketing strategies & tactics on shaky data. You might even allocate budget to the wrong channels. You make decisions based on information you don’t fully trust.

When trust is low, execution slows almost as much as the results.

Leadspace confirms that B2B data decays at about 2.1% per month.

That’s roughly 20 to 30% per year. Do the math. If you built a database of 10,000 contacts last year, 2,000 to 3,000 of those records are already outdated to a certain extent. You’re running ABM campaigns to people who left their jobs. You’re sending nurture emails or conversion sequences to “bouncy” email addresses. You’re burning your so precious marketing budget on touchpoints on contacts that will never convert because they’re not even there anymore.

46% of business leaders say access to relevant customer data is hit or miss or worse.

Translation: half the time, teams can’t get the information they need to do their jobs. Let’s close our eyes (no, no, we’re joking – you cannot continue to read this article with your eyes closed) and let that sink in.

Bad data costs the U.S. economy over $3 trillion per year. The data source for this varies (oh, the irony!!!) and we would take this with a grain of salt. We found similar numbers from Gartner, IBM, or Harvard Business Review, in articles from 2016, 2020, and 2024. The main point is, bad data costs the U.S. economy in the range of trillions of dollars per year. Not great.

In terms of B2B marketing, the cost shows up as wasted ad spend, lost opportunities, and hours spent targeting leads that were never real.

According to Experian, 3 out of 4 businesses leaders believe poor data quality undermines their customer experience efforts. It’s no secret that, especially in B2B, customer experience starts with knowing who your customers are. If your data is wrong, you create the wrong content. You reach out to the wrong person. You make mistakes that erode trust before the relationship even starts. It’s the TOFU decay.

Just to put things into perspective, in enterprise data, from our discussions with our clients (it’s rather empirical, but based on real data from real clients), approximately 15% consists of duplicate records.

Unfortunately, duplicates cause confusion. We can imagine a scenario in which the sales people reaches out to the same contact twice. Marketing sends duplicate emails (grrr, not great). Your metrics get distorted because one person counts as two leads. And cleaning duplicates takes time most teams don’t have.

Some more context from CDP Institute: 83% of B2B companies report having poor product or customer data. This is not just a marketing problem. Poor data touches every department.

Sales and Marketing Impact of Bad Data

Well… 50% of sales team’s time is wasted on unproductive prospecting. If half of a rep’s day is spent chasing dead ends, where does that waste come from? Bad leads. Outdated contact info. Incorrect firmographic data. Your sales team spends hours each month calling disconnected numbers and emailing bounced addresses.

High-quality B2B data is expected to grow. The companies investing in clean marketing data will pull ahead, faster.

Companies estimate that 10 to 25% of their marketing budget is wasted due to poor data quality. For a startup spending $100,000 per year on marketing, that’s $10,000 to $25,000 marketing budget burned on bad data.

Many organizations do not have a formal data quality management program in place. And it’s not only about small startups, but also for bigger companies. Although marketing leaders know data matters, usually they don’t have a system to manage it, or ownership, or processes.

This seems to contradict the earlier stat about too much data: marketers say they don’t have enough good quality data to make good decisions. But both are true. Teams have massive databases full of incomplete records. Usually they have volume but not quality. The data exists, but it’s missing key fields and attributes.

Emails with personalized subject lines are now seeing up to 35% higher open rates. In outreach personalization works really good. But personalization requires accurate data. If you use the wrong name, wrong company, or wrong pain point, your emails don’t just fail. They damage your brand.

13% of U.S. consumers unsubscribe from marketing emails because the content doesn’t match their interests or past purchases (according to an article in Digital Information World citing an Optimove survey conducted on 329 consumers – not necessarily the most accurate sample size, but still a good start).

In B2B, this is worse. If you send software engineers content meant for CFOs, they disengage faster than the speed of light. If you promote features they don’t use, they tune out.

The Challenge of Fixing It

Here’s the problem with data hygiene: the damage is visible but the solution feels impossible to start.

Marketing teams watch their campaigns underperform and trace the issue back to data quality, but addressing it requires answering questions nobody wants to tackle. How much of the database needs cleaning? Who should own the work? What gets prioritized when resources are limited?

The natural first question is scope. Cleaning an entire database of 50,000 contacts could take weeks or months and cost a lot. Focusing only on recent or active contacts feels like ignoring a larger problem that will resurface later, but also missing some opportunities with leads that were not ready to buy 12 months ago, but now they might.

Building automated validation rules requires technical resources that the dev team can’t always spare. Manual review is thorough but doesn’t scale and creates a bottleneck.

Then there’s the ownership question, which becomes political fast. Marketing believes sales should maintain fresh contact data since they interact with prospects directly.

Sales argues that marketing owns the database and should keep it clean.

RevOps points out that neither team follows the data entry standards that were established two years ago.

IT has the technical capability but won’t allocate engineering time without executive sponsorship and a clear ROI calculation.

While these discussions drag on, the data continues to deteriorate at its natural decay rate.

The good news is the fundamental challenge isn’t technical. Tools for validation, deduplication, and enrichment are readily available and relatively affordable. The processes for maintaining data quality are well documented across the industry.

We saw this firsthand when working with Zemp. They had the right tools in place, but it wasn’t until we helped them define clear ownership and workflows for data hygiene that their campaigns started scaling reliably. Check out the client case study here.

The actual barrier is organizational. Data hygiene requires sustained coordination between marketing, sales, and operations teams that normally work in silos. It needs executive commitment to ongoing investment rather than one-time project funding. Most importantly, it demands operational discipline to maintain standards when everyone is busy and the work itself is tedious.

The lack of visible wins makes this worse. Nobody gets promoted for maintaining clean data. There’s no campaign to showcase at the quarterly business review.

The benefits emerge slowly across every marketing activity rather than showing up dramatically in one place.

Targeting gets slightly more accurate, conversion rates inch upward, budget efficiency improves by a few percentage points each quarter. Nice, but not WOW. Trust builds through the steady absence of mistakes rather than through impressive achievements.

What makes data hygiene particularly hard to justify is that success is almost invisible. The disasters that didn’t happen never appear in reports.

This explains the persistent gap between problem awareness and action. Almost every B2B company acknowledges that data quality affects marketing performance. Far fewer commit the necessary resources to address it systematically. The pain from bad data spreads diffusely across teams and gets blamed on other causes like poor campaign strategy or weak sales follow-up. The solution requires cross-functional collaboration that conflicts with how most companies organize their operations. The payoff arrives gradually and stays hidden in aggregate improvements rather than announcing itself through obvious breakthroughs.

What This Means for B2B Tech Companies

At Milk & Cookies Studio, we build B2B digital marketing strategies & full-funnel marketing systems for B2B tech companies. We’ve seen clients spend six figures on digital marketing only to discover their assumptions were not great at best.

Here’s what happens when data hygiene breaks:

  • Your ICP becomes fiction. You think you’re targeting mid-market software companies, but half your database is outdated.
  • You’re running account-based strategies to companies that don’t exist anymore.
  • Your lead scoring stops working. Points get assigned based on job titles that are wrong, email engagement from addresses that bounced, and account data that’s six months old.
  • Your qualification process becomes guesswork.
  • Your segmentation falls apart. You can’t build nurture tracks if you don’t know what stage the contact is in. You can’t personalize if the firmographic fields are blank.
  • Your automation sends generic emails because there’s no data to trigger anything better.
  • Your conversion rates drop. Marketing generates leads that are unqualified, unreachable, or uninterested.
  • Sales stops trusting the handoff.
  • RevOps spends more time cleaning data than analyzing performance.

And your metrics don’t show you the real story.

Data hygiene is not a one-time project, as much as we would love it to be. In reality it’s marketing operational discipline.

This is not sexy work. But it’s the foundation of data-driven digital marketing. Without clean data, your targeting, personalization, and segmentation all fail.

And your 360 full-funnel strategy collapses.

What to Do About It

Here’s how to fix this.

  • Set up data validation: Verify email formats before they enter your CRM, check phone number structure, flag invalid addresses, use validation APIs at the point of entry. Simpler put, try to stop bad data from getting in.
  • Run regular deduplication: You can schedule monthly or quarterly deduplication processes or meetings. Aim for under 5%. Audit the results to make sure merges didn’t break anything.
  • Standardize your data formats: Define how country names should appear, decide on job title conventions, create a style guide for how data gets entered, train everyone who touches the database and run periodic audits to catch inconsistencies.
  • Enrich your clean data: Once your base data is accurate, add missing fields such as firmographic info, technographic data if it helps your targeting. Supplement with behavioral data from your marketing automation, but only enrich clean data. Don’t layer new information on top of garbage.
  • Assign ownership. Someone needs to be responsible for data hygiene. This could be marketing ops, RevOps, or a dedicated data manager. Define their role. Give them time to do the work. Make data quality a KPI.
  • Track database health metrics: Measure the percentage of records with missing emails, track duplicate rate, monitor data age, set thresholds for when records need refreshing, build dashboards that show these metrics.
  • Integrate data hygiene into your roadmap: Budget for enrichment tools and make this a recurring part of your operations, not a crisis response.
  • Start with a baseline audit:
    • Month 1: Measure where you are, how many duplicates do you have? What percentage of records are incomplete? How old is your average contact? This gives you a starting point.
    • Month two, implement validation rules. Stop new bad data from entering the system.
    • Month three, run your first real deduplication and clean up existing duplicates.
    • Month four, start enrichment. Fill in missing fields for your most important segments.
    • Month five, build your data health dashboard. Track the metrics that really matter.
    • Month six, review and iterate. Look at what improved and adjust your process accordingly.

This is not a six-month only project. It’s a “always-on” process. But if you start now, you’ll see results fast. Your campaigns will perform better, while your budget will go further.

The Bottom Line

Data hygiene is not optional for B2B digital marketing. The statistics show the scale of the problem and the cost of ignoring it.

For B2B tech companies, treating data as a revenue asset is super-mandatory. Your entire demand generation & lead generation strategy runs on this data foundation. If the foundation is weak, everything built on top of it fails.

Don’t wait until your next campaign underperforms. Start integrating data hygiene into your marketing operations now. The companies that invest in clean, enriched, well-maintained data will outperform their competitors in targeting, personalization, and growth.

Your CRM data is either an asset or a liability.

The choice is yours.

Next Steps

Run a data quality audit. Map your database to identify decay, duplicates, and missing fields. Measure your baseline. Then set up a data hygiene roadmap with clear ownership, processes, and KPIs.

If you need help building a data-driven digital marketing strategy & obviously implementation on top of clean data, reach out to us.

We’ve built full-funnel marketing campaigns for B2B tech companies for over 12 years.

We know how to turn a clean database into results.

FAQ Data Hygiene in B2B Digital Marketing

How often should we clean our database? Run deduplication processes monthly or quarterly. Validate new data at point of entry. Schedule enrichment every 6 months. Data decays at 2% per month, so regular maintenance prevents compounding issues.

Who should own data hygiene in our organization? Assign clear ownership to marketing ops, RevOps, or a dedicated data manager. Make data quality a KPI. Without ownership, the work never gets prioritized and teams blame each other when problems surface.

What’s the ROI of investing in data hygiene? Companies waste 10-25% of their marketing budget on bad data. For a $500k annual budget, that’s $50k-$125k burned. Clean data improves targeting accuracy, conversion rates, and budget efficiency. The ROI shows up gradually across all marketing activities.

Should we clean the entire database or focus on active contacts? Start with active contacts and recent additions to see quick wins. Then expand to the full database systematically. Ignoring inactive contacts leaves problems that resurface when you run retargeting or reactivation campaigns.

What tools do we need for data hygiene? Use validation APIs for email and phone verification, CRM deduplication features, and enrichment services for firmographic data. The tools exist and are affordable. The challenge is process and discipline, not technology.

How do we measure data quality? Track percentage of records with missing key fields, duplicate rate (aim for under 5%), average data age, and bounce rates. Build dashboards that monitor database health alongside campaign metrics.

Why does data decay so fast? People change jobs (2.1% monthly), email addresses get deactivated, companies merge or shut down, phone numbers disconnect, and roles shift. In high-turnover industries like tech startups, decay can hit 70% annually.

Can we automate data hygiene completely? Automation helps with validation and flagging issues, but you need human oversight for deduplication decisions, enrichment strategy, and maintaining standards. Treat it as an operational discipline, not a set-it-and-forget-it system.