B2B Data Cost: What Is Bad Data Really Costing Your Business?
Databroker Summer Series - Part 3 of 8 – Building Your Best Q4 Starts Now
Over the last couple of weeks, we’ve looked at why your September pipeline starts long before September arrives and why giving your CRM a summer MOT can make a real difference before the autumn rush. This week, I want to look at something businesses rarely put a figure on: the cost of bad data. Not the cost of buying data, but the cost of using poor-quality information every day without really noticing it.
If I asked most Sales Directors how much they spend on prospect data each year, they’d probably have a rough idea. If I asked how much inaccurate data costs their business every month, I suspect far fewer could answer. And the second figure could easily be the more important one.
In 30 Seconds
- Bad data costs far more than marketing budget.
- It wastes sales time, reduces confidence and creates missed opportunities.
- Small inaccuracies become expensive when they’re repeated across an entire sales team.
- Improving data quality should be viewed as an investment, not simply another cost.
Poor Data Makes Good Marketing Look Average
One thing we’ve noticed over the years is that businesses are often quick to blame the campaign when results aren’t where they want them to be. Email response is down, telemarketing isn’t generating enough meetings or sales conversions have slowed, so the immediate conclusion is that the marketing isn’t working.
Sometimes that’s absolutely the case. Quite often though, the problem sits further upstream. It doesn’t matter how good your salesperson is if they’re ringing somebody who left the business eighteen months ago. A brilliantly written email isn’t going to perform if it’s being sent to dead addresses, and clever targeting isn’t much use if your CRM still thinks a company employs 50 people when it now employs 500.
Poor data has a habit of making good marketing look average.
Here’s a Question…
How often does someone in your sales team hear, “They don’t work here anymore” or “You’ve got the wrong number”?
One conversation isn’t a big deal. The salesperson updates the record and moves on. But if it happens repeatedly across an entire sales team, those little interruptions quickly become hours of wasted selling time. Imagine ten salespeople each making twenty outbound calls a day. If just two calls per person are wasted because the contact information is wrong, and each costs around five minutes, that’s roughly 100 minutes of lost sales time every day. Across a 20-day working month, that’s more than 33 hours.
And that’s before you add bounced emails, duplicate records, incorrect job titles, missing telephone numbers and companies that no longer fit your target market. Suddenly, bad data doesn’t look quite so harmless. Learn about how Databroker can supply you with new B2B data lists here.
The Hidden Cost Is Confidence
There’s another cost that’s much harder to put into a spreadsheet. Trust.
If salespeople encounter incorrect information often enough, they stop trusting the CRM. Marketing starts questioning audience selections because previous campaigns haven’t performed. Managers start questioning reports because they’re not convinced the information underneath them is reliable.
That’s when people create their own spreadsheets, prospect lists and versions of the truth. Before long, different departments are working from different information and the CRM that was supposed to bring everything together is doing the opposite.
For me, that’s one of the biggest warning signs of poor data quality.
From the Databroker Desk
We’ve had clients come to us convinced they need thousands of new prospect records because lead generation has slowed down. Sometimes they do. But not always. Once we look more closely, we might find duplicate companies, old decision-makers, missing contact information or businesses that no longer fit the client’s target market. In those circumstances, simply adding another 10,000 records isn’t necessarily going to solve the problem.
Factor this in. If your current data set or CRM has more holes than a Swiss cheese, then adding patching it up by chucking in some new data won’t work. You also need to lift the lid and repair what you already have. Sometimes the quickest route to generating more business isn’t expanding your database. It’s improving the one you’ve already got.
Learn more about our Data cleansing and Data enhancement services by clicking here.
Good Data Pays for Itself
This is why I encourage businesses not to think about data quality purely as a cost.
When the information is right, salespeople spend more time having worthwhile conversations. Marketing can segment audiences with greater confidence. Reporting becomes more reliable and management decisions are based on information people actually trust. It’s difficult to put an exact value on all of that, but the difference between a business that trusts its data and one that doesn’t is easy to spot. One gets on with using it. The other spends far too much time checking it.
Better Data Doesn’t Always Mean More Data
At Databroker, if someone asks us for more prospect data, we’ll always try to understand why first. A fresh audience might be exactly what’s needed if they’re entering a new sector, expanding geographically or targeting a different type of organisation.
But sometimes the opportunity is already sitting inside the CRM.
Cleansing existing records, updating decision-makers, appending missing contact details and improving segmentation can all help a business get more from information it has already invested time and money collecting. That’s an important distinction. More data and better data aren’t the same thing.
What Does Bad Data Actually Look Like?
It’s not always as obvious as a bounced email or disconnected telephone number. It could be a Managing Director who has moved into a different role, a company that’s doubled in size but is still sitting in the wrong employee band, a business that’s relocated, or duplicate accounts that mean two salespeople are unknowingly approaching the same organisation.
None of those issues looks disastrous on its own. That’s exactly why bad data is so easy to ignore. It builds gradually until sales and marketing teams simply get used to working around it. They shouldn’t have to. Good quality data should make sales and marketing easier, not harder.
Five-Minute Challenge
Ask the people using your CRM every day one simple question:
“What’s the biggest frustration with our data?”
If the answers are outdated contacts, missing information, duplicates or incorrect phone numbers, you’ve probably just identified where to start. You don’t necessarily need a bigger database. You might just need a better one.
Looking Ahead
So far in our Summer Series we’ve looked at building your pipeline, improving your CRM and understanding what poor-quality data can really cost. Next, we’ll tackle another question that’s fundamental to successful B2B lead generation:
Are you actually targeting the right businesses?
We’ll look at your ideal customer profile and why better targeting nearly always beats simply building a bigger prospect list.
Frequently Asked Questions
What is bad data in a CRM?
Bad data is inaccurate, incomplete, duplicated or outdated information, including old job titles, invalid email addresses, duplicate company records and incorrect contact details.
How does poor-quality data affect sales?
It wastes selling time, reduces confidence in the CRM and can result in salespeople pursuing contacts or businesses that are no longer relevant.
Does bad data affect marketing performance?
Yes. Inaccurate information can increase email bounces, weaken campaign targeting, distort reporting and ultimately reduce campaign performance.
Is buying new data always the answer?
No. Sometimes new prospect data is exactly what’s needed, but cleansing and enhancing existing CRM data can often uncover opportunities that are already there.
How can businesses improve data quality?
Regular data cleansing, validation, enhancement and CRM reviews help keep business and contact information accurate and useful.
