Skip to main content

Lowering Costs Through the Power of Clean Data

Published Date: Thursday, Oct 05, 2023
Last Updated on: Thursday, Oct 05, 2023
Lady leveraging clean data to make marketing and sales decisions.

Dirty data is expensive and the demand for quality customer and prospect data is at an all-time high.

Gartner estimates that businesses lose an average of $15m per year due to their unclean databases. This is a challenge that is felt globally – one that shows no signs of slowing down. As businesses look to enrich their campaigns with complex, niche individual attributes – often across multiple regions or industries (B2B) – they inevitably face more severe issues with consistency and quality.

Still, it’s no secret that data is critical for marketers who want to succeed. Businesses who use data-driven strategies drive five to eight times as much revenue as those who don’t. The question is: Are you doing enough to prevent your “bad data” from taking a major stake in your bottom line?

In this blog, we’ll explore the impact dirty data can have on your marketing initiatives and its role in reducing your profitability.

What is “bad data”?

Bad data, or “dirty data” as it is commonly coined, refers to datasets that contain issues with accuracy, standardization, formatting or duplication. Examples can include misspelled contact names, outdated home or business addresses, missing field values, old phone numbers, duplicate customer profiles and many more.

When left untreated, dirty data has the potential to jeopardize decision-making and marketing outreach. From addressing customers with the wrong name to sending targeted print materials to irrelevant addresses, the impact of dirty data can be chaotic – and eye-wateringly expensive. In fact, 21% of media budgets are wasted annually due to poor quality data.

The presence of bad data is a serious concern for most businesses. Its consequences can include:

Increased operational costs due to time being spent fixing errors
Loss of customer loyalty due to mistakes
Decision-making based on erroneous information and data
Regulatory penalties for non-compliance
Image of individual who is concerned by the quality of their data.

What causes bad data?

Some analysts will tell you: There’s no such thing as “bad data,” just bad analysis.” This is fundamentally untrue. Customer data is prone to change – it doesn’t stand still. While data collected from prospects may be correct at the point of entry, there’s no guarantee that their ageing profile remains marketable today. No dataset is beyond this risk; it affects everyone from major international firms to burgeoning startups.

Still, decay is only a single factor. There’s a real variety of influences that can contribute to poor data quality:

Incompletion: Customer profiles can be littered with blank fields caused by process and qualifying changes, poor service quality or simply forgetfulness.
Duplicates: Modern marketing solutions are incredibly siloed. Many automation and customer tracking platforms can create duplicates due to a lack of native integration.
Inaccuracies: Human error stirs up endless potential for inaccuracies. Whether by customers or agents, there’s always a risk of error with manual data entry.
Inconsistencies: The same data can be stored in multiple different datasets – causing siloes and inconsistencies between departments.
Training: Errors can get worse when those inputting data have limited knowledge of the technology itself. When ignored, these mistakes can slowly breed across your team.
Training session to enhance the quality of data acquisition.

The cost of poor data quality

According to recent research from Treasure Data, while the majority of marketers say they have access to customer data, around one-fifth said their data wasn’t accurate or high-quality. This low quality resulted in inaccurate targeting (30%), lost customers (29%), lost leads (28%), reduced productivity (27%) and wasted marketing spend (28%).

An unclean database also limits your understanding of market opportunities. With “dirty data” you’re not only wasting your spend targeting ill-fitting prospects, but also missing opportunities to target better-suited customers for your product or services. So how else can “dirty data” interfere with your bottom line?

Accuracy and misplacement:

Misplacement can drum up some of the largest wasted spend in modern marketing. When data quality is low, businesses are destined to miss their target – resulting in shipping errors, billing disputes and tarnished brand reputation.

By cleaning and verifying data in advance, marketers stand a better chance of reaching the right customer with the right focus. Not only does this increase the chance of conversion, it also protects team productivity, marketing spend and reduces wasted communications.


When decision-makers rely on inaccurate data, it can lead to flawed strategic planning, forecasting and capacity modeling. Without any reliable data, leaders and marketers can make decisions based on gut feeling or unproven trends, leading to missed opportunities and reduced results.

Poor data can also cause leaders to allocate resources ineffectively. Investment, headcount and time can be assigned to target key accounts based on suspected intent or perceived opportunity. These misguided decisions can take focus away from core revenue areas, leading to a high-risk combination of wasted spend and shortfalls in attributed revenue.

Customer Loss

Customer retention is just as valuable as acquisition in today’s market. In fact, a 5% increase in customer retention can boost profits by up to 75% according to Bain & Company. When customers notice discrepancies in your data, it decreases their trust in your brand – leading to lessened advocacy, growing scepticism and increased customer attrition.

It doesn’t take much either. A study by PWC found that 1 in 3 customers will leave a brand they love after just one bad experience. 92% would completely abandon a company after 2-3 poor interactions. The same goes for B2B – 90% of businesses would go to a competitor if they felt their supplier wasn’t able to keep up with their needs. This poses a monumental risk to your profitability – let alone the cost of associated marketing efforts to replace or re-engage the customer.

Dirty data challenges being resolved

Legality and regulations:

In an era of stringent data protection regulations like GDPR and CCPA, poor data quality can expose your organization to legal and regulatory risks. Non-compliance can result in hefty fines and legal consequences. Ensuring data accuracy, privacy and security is essential for maintaining trust with your customers and avoiding legal punishment.

Equally, carelessness around your obligations as a sensitive data carrier can lead to an increased likelihood of being breached. The average database breach costs US businesses approximately $8.19m – $333 per record.

Wasted investment:

Fixing data errors after the fact is often more costly and time-consuming than preventing them in the first place. Businesses spend a significant amount of resources on data cleanup, data migration, and rectifying errors caused by poor data quality. These costs could have been better allocated to strategic initiatives that drive growth.

Plus, with the recent limitation of cookies, many businesses are investing in new third-party data to enrich their contact profiles. This doesn’t come cheap. Businesses have spent millions enriching their data for advanced targeting and multichannel delivery. But, if contact profiles aren’t clean initially, any investment into data enrichment is essentially a wasted exercise.


“Dirty” data can slow down productivity and bottleneck business operations. Employees can spend a major share of their working hours correcting errors, remedying inconsistencies and resolving data-related challenges – resulting in increased costs and less time being spent on actual revenue-generating activities.

In the age of automation, many businesses still fail to move with the times. In 2016, unnecessary admin work was estimated to cost US businesses $687 billion dollars annually according to Kronos Inc. This figure is likely far greater now.

Poor data quality leading to business challenges being discussed

What can you do to resolve your “dirty” data?

Unclean customer databases can feel like an inevitability – but it doesn’t have to be that way. As businesses accelerate their digital efforts, they simply cannot thrive without a robust data cleansing strategy in place. Only then will they be able to create a competitive data advantage that boasts profitability and lower labor costs. Here are some key considerations:

Understand your priorities: Customer databases can be gargantuan. While a CRM-wide cleanup may appear to be the best course of action, it will require some serious time and resource investment – luxuries you may not have. Begin by working with a high-value subset of your data that is critical to key campaign performance and ROI. This will form an effective proof of concept (POC) for data cleansing initiatives moving forward.
Monitor deliverability and other key metrics: Data validation is critical to your campaign success. A simple update of contact profiles doesn’t guarantee accuracy – where has this data been sourced from? Has it been verified? Has it been tested? Cleansing requires continual cycles to reflect the ever-changing nature of customer data. Profile existing data quality, set benchmarks and pay attention to key delivery metrics to understand the success of your initiatives.
Repeat and refresh: Data cleansing is not a “one and done” activity. Businesses looking to improve data quality should invest in long-term, sustained initiatives – not single applications. Data moves incredibly fast. Neil Patel estimates that 20% of all postal addresses change every year. Businesses that aren’t prepared to keep the pace can only expect to fall behind annually.


Addressing and resolving dirty data is not merely a task to be checked off a list; it is a fundamental responsibility for any organization seeking to thrive in today’s data-driven landscape.

The quality of your data directly influences the quality of your decision, the efficiency of your operations and the trust your customers place in your brand.

Data quality is not a one-time fix but an ongoing commitment. It requires vigilance, automation and a cultural shift toward data-conscious practices. By investing in data quality, you not only minimize the risks and costs associated with “dirty” data but also unlock the full potential of your CRM system. Clean and reliable data holds value far beyond just lowered costs – it empowers your business to make smarter decisions, improve CX and sharpen your competitive edge.

Don’t have the time or resources to unlock this potential right now? We can help. Harte Hanks can take the toughest data cleansing initiatives out of your hands. Tapping into over 1,000 competitive and non-traditional customer attributes, our GCDI, merge-purge and NCOA programs can help to achieve full data clarity – no matter the complexity.