Using dirty data to fuel your business initiatives is like putting the wrong fuel type in your car. You think you’re doing everything right to keep your vehicle (or business) running smoothly when, in reality, you’re doing serious damage.
Want a well-oiled, revenue-blasting business machine? Think clean data.
In today’s blog post, we tackle some urgent dirty data questions:
- What is dirty data?
- What are examples of dirty data?
- What are the consequences of not cleaning dirty data?
- How do you prevent dirty data?
Let’s jump in.
What is dirty data?
Dirty data are faulty bits of information that can present problems in businesses databases.
For B2B businesses using data to steer successful sales and marketing efforts, having access to clean data is imperative.
What are examples of dirty data?
Common dirty data that damage your sales and marketing results include the following main types:
In your CRM, duplicates are the doubling of information (i.e., IT Director Rob Smith showing up twice under different companies). They can show up in your prospect lists, contact data, and sales accounts.
How do duplicates happen? Generally, you’ll muddy your data with copies during data migrations and manual inputs.
Outdated data is hard to track. Prospects change jobs, c-suites get shuffled, and companies merge. That means your emails will bounce due to no-longer-in-service addresses, or front desk operators will rebuff your calls.
If there’s one type of dirty data you don’t want, it’s data. With data and security laws more stringent than ever, cleaning your data regularly is critical for staying compliant.
Have you got data gaps? Your incomplete data will poke holes in your outreach efforts. Without information such as industry type, title, or last names, you risk excluding valuable leads in your campaigns.
Imagine trying to sell geolocation software to a lead that’s located in “N/A”.
Incomplete data can hurt your sales team’s call-to-connection rate. Use our interactive calculator to track ROI against improved connect rates.
If your data is plain wrong, you can run into all sorts of problems — from missteps on cold calls to inaccurate reporting and decision-making.
Consider these statistics
- 43% of sales and marketing teams say a lack of accurate data remains a challenge for them.
- 54% of B2B businesses say their biggest challenge to achieving success is the absence of data quality
These numbers may catch your attention — but statistics alone don’t explain how and why dirty data hurts your bottom line. It’s far cheaper to verify and cleanse data regularly than doing nothing at all.
What are the consequences of not cleaning dirty data?
1. Ineffective marketing campaigns
Dirty data creates an inaccurate idea of your ideal customers and throws off your marketing efforts to target the right people.
Your marketing team likely uses many channels and tactics to identify, target, and engage with prospects and customers.
Inaccurate data skews your understanding of your target audience, which has a domino effect as it negatively influences your approach to each campaign.
Let’s use email marketing as an example.
Say you want to promote a new product by sending a free trial offer to your audience’s specific segment.
Your goal is to target prospects who are most likely to purchase the product — a conclusion you reach by examining online behavior, purchase history, job function, and other data points.
Many of these data points are outdated or entirely inaccurate — resulting in low open rates and abysmal engagement.
Additionally, when you send emails to invalid or outdated addresses, you increase bounce rates and spam complaints.
Look up any email! See who owns it and search for more information on the owner. Take the Reverse Email Finder for a spin.
As a result, internet service providers (ISPs) penalize your sender score and damage your email sender reputation. This affects the deliverability of future messages, resulting in less successful campaigns and less revenue from marketing.
The effectiveness of your marketing tactics — in particular email campaigns — depend on the accurate data.
2. Poor customer experience.
When bad data results in poor customer experience, you’ll lose out on valuable prospects and fail to retain current customers.
The modern customer has more control over their buying journey than ever before. When they’re interested in buying from your company, they want seamless interactions.
Yet, something as simple as calling a prospect by the wrong name can be the difference between closing a deal and losing a potential customer to your competition.
Whether your reps are talking to a prospect for the first time or the account executive follows up on an existing customer, these interactions’ success depends on clean data.
3. Damaged brand reputation.
Unclean data can hurt your company’s reputation in more ways than simply encouraging negative customer feedback.
In today’s hyper-connected world, customers don’t just abandon your business when they have a poor experience. They tell their friends, family and colleagues about it.
Think about it:
How many people will share a bad experience they have with a company? Their negative reaction may be posted on social media, popular review sites, and other channels that facilitate customer feedback.
Another thing to consider: There are customers who won’t say anything. And they won’t contact your customer support team. Come renewal time, they cancel.
This is a pandora’s box you do not want to open. Instead, build your brand on reliable data and keep hard-earned customers coming back.
Get excellent tips for building your brand: 5 Ways To Develop Your B2B Brand
4. Misinformed decision-making.
When bad data contaminates your sales and marketing metrics and reporting, it can hurt your business on a massive scale.
In the past, executives and key stakeholders relied on instinct and intuition to make important long-term business decisions.
Now, data is so readily available that there’s no longer a need for guesswork when it comes to crucial decision-making.
Here’s the problem:
If your executive team relies on inaccurate data, it can lead them to make misinformed and potentially damaging long-term decisions.
The good news is that clean data provide decision-makers with the tools they need for accurate and comprehensive reporting.
5. Misaligned sales and marketing teams.
Dirty data makes marketing and sales alignment difficult. And, lead generation is one of the first initiatives to suffer from low-quality data.
This means your marketing team will end up sending low-quality leads to sales. Your sales teams will ignore them. Over time, the relationship between the two departments fractures, leading to a decreased lead flow and fewer conversions.
On the ZoomInfo blog, we often stress the importance of sales and marketing alignment. We do so because these departments’ alignment has a major effect on your business’s bottom line.
To ensure that marketing sends the most qualified, ready-to-close leads to sales, your teams need data they both trust.
6. Decreased ROI from Sales and Marketing Technologies.
Bad data prevents your technology stack from operating at its full potential. When you invest in marketing automation and CRM platforms, you do so to improve your sales and marketing initiatives’ efficiency and effectiveness.
Therefore, having access to a constant stream of new data is critical for meeting your sales and marketing goals.
Arming your sales and marketing teams with confidence in your CRM is key to unlocking the value the CRM was intended to create” — Henry Schuck, Founder, and Ceo, ZoomInfo.
7. A Slower Sales Cycle.
Your dirty data will create roadblocks throughout the sales cycle. That includes poor lead management, with reps contacting high-quality leads too late, or sometimes, not at all.
This slows the rate at which leads move through the sales process. And, as a result, good leads go bad, and you lose out on potentially lucrative opportunities.
Here’s the thing, customers have many options at their fingertips. They’re in a position to demand a quick and seamless buying experience.
With ongoing data hygiene, your teams develop faster, more efficient sales processes to ensure every lead touchpoint is phenomenal — and time-to-close is shorter.
How Do You Prevent Dirty Data?
To prevent data decay, you need a fresh stream of data feeding into your CRM. Where do you start?
- Begin with regular CRM health assessments. You can do this manually or partner with your data provider.
- Use a good mix of data sources — first party, third party, and intent data.
- Cleanse your data regularly and fill in any gaps.
- Practice ongoing data management.
Get a visual on preventing dirty data with our infographic: 7 Simple Steps to Improve Your CRM’s Data Quality
Wrapping up Dirty Data
Just about every modern business initiative can suffer at the hands of inaccurate data.
The key is to identify the types of bad data in your CRM, clear them out, and replenish it with a stream of high-quality, actionable data. That sets you up to attract and convert more customers.
This post was updated January, 29, 2021.