From Mess to Success: The Ultimate Guide to Data Cleaning

But that only applies when the data you’re using is accurate – and for most businesses, that’s not always the case.

A study by MarketingSherpa found that 25-30% of contact data is deemed inaccurate each year, leading to ineffective marketing campaigns and missed revenue that can run into the millions.

If you’re looking to invest in true, thorough data cleansing and validation, good news: You’re already lightyears ahead of everybody else. A solid data cleansing process will save your team hours of labor and reignite your data quality to generate a new-look marketing ROI.

In this article, we’ll explore what data cleansing is, the advantages of quality data hygiene and how you can achieve it.

Data cleaning is the process of evaluating your existing customer or prospect database and identifying, correcting and removing errors or inconsistencies. It’s an initiative used to modify or purge inaccurate attributes in data profiles to improve marketability.

The goal of cleansing is to make your database as accurate as possible, and ensure that it is valid for use in personalization and campaign delivery. This means more than fixing spelling or syntax errors. Data cleansing typically involves:

An unclean database might fall at the bottom of a long list of business concerns, but it has endless potential to damage your profits and brand. Marketing teams who don’t have accurate contact data at their fingertips risk sending targeted messaging to unmarketable records – or worse: random, untailored outreach with the wrong contact name or erroneous order details.

In fact, recent reports by Forrester and Gartner found that:

• $1.2 million is the average annual loss caused by poor data quality at a midsize firm.
• $16.5 million is the average annual loss caused by poor data quality at an enterprise-size firm.
• 21% of media budgets are wasted annually due to poor data quality.

With low-quality data, sales and marketing teams can also spend tedious hours identifying and targeting new opportunities – all in vain. Not only does this waste resources and dull your competitive edge, it can also have a significant impact on team morale, and therefore, attrition. 

Not to mention, the inability to organize, identify and protect customer data can lead to issues with PCI, HIPAA and GDPR compliance and other regulatory breaches – damaging brand reputation and provoking legal sanctions.

92% of leading marketers believe using first-party data in marketing activities is critical to growth.

But only 24% of B2B marketers would describe their organization’s data as “high quality” according to a recent survey by DemandLab. 

Why?

It’s easy to see why more businesses are waking up to the power of data quality. Those that already use data-driven strategies drive five to eight times as much ROI as businesses that don’t.

So where does this improved return on investment come from?

Nothing frustrates buyers and prospects more than brands reaching out with inaccurate data. Being able to reach and communicate to people with the correct title, address and contact details is not a luxury – it’s imperative to your ROI and results.

Excluding or remedying inaccurate profiles in your database can heavily reduce wasted expense by preventing communications from being sent to redundant contacts. This is particularly true for direct mail initiatives, which can cost businesses millions due to misplacement. In 2016, IBM estimated the global cost of inaccurate data to be $3 trillion each year.

Modern marketing, in theory, should be about marketing to an individual. The only way you can truly achieve that is by understanding who they are, their value, previous interactions, the products and services they care about and their known behaviors.

Showing that you’ve put time, effort and care into connecting with your customers has a far more measured impact on brand loyalty than spray and pray outreach. By unifying contact data, you can send targeted communications and offers that are personalized and relevant to segmented customers, making each feel seen and heard by your brand.

Collecting, organizing and cleaning marketing data from different sources takes an average of 3.55 hours a week, according to HubSpot. With a rigorous database cleansing strategy in place, businesses can reduce labor and routine admin for their marketers, freeing up time and resources to invest in actual revenue-generating work.

The benefits can be felt far beyond just marketing teams. By verifying data accuracy, businesses can greatly reduce bottlenecks for support staff facing urgent customer or vendor demands. Requesting customer details multiple times, not being able to provide the right insights for a specific query, or struggling to issue refunds quickly due to disparate payment details can be serious detriments to a brand’s reputation.

Cleanliness creates customer visibility. With accurate, validated customer profiles, you enjoy more measurable performance analytics and business intelligence insights that can help you improve marketing execution. 

Clean data helps you to understand customer trends in real time – creating visibility into buying behaviors, customer intent and the way your market is moving. It’s no secret how important that is when you’re faced with increasing saturation and fierce competition. These insights can also provide a sophisticated foundation for strategists to optimize campaigns and initiate more advanced targeting or customer segmentation, based on genuine human response to your brand’s activities.

With the deprivation of cookies, many businesses nowadays are trying to embed third-party enrichment data into their strategy as an alternative. They can spend millions of dollars building clean profiles from scratch with the attributes and behavioral variables needed for their propensity model or scoring algorithm.

The challenge is that much of the data they choose to invest in already exists within their database – they just can’t see it yet. By cleaning their data prior to buying more, businesses can minimize spend and improve their ROI, ensuring that they only invest in the data they need and validating their foundations before layering with more complex insights.

The techniques used for data cleaning may vary according to the types of contact data businesses collect and the systems they use. While there is no one-size-fits-all for achieving full data cleanliness, we follow a seven step process.

There are many unique approaches you can apply to data cleaning, and an ever-growing list of tools that can help you do it – or at least claim to. 

It is not economically viable for most businesses to clean their data from scratch. AI is pioneering a new way for businesses to trawl and sanitize their databases at scale, automating entry and preempting decay; but it requires some serious time and investment to implement, a luxury you may or may not have.

Here are some key considerations for every business looking to cleanse their customer or prospect database:

At first glance, data cleaning seems like a heavy lift – but it doesn’t have to be. 

As more detailed customer data emerges at breakneck pace – and in far greater quantities than legacy databases can process – having the right data management solution in play is more vital than ever to stay ahead.

With our proprietary Global Customer Data initiatives, we help clients to reap the benefit of relentless cleansing and data verification – against seven unique third-party sources. 

Our solutions can be tailored to suit your specific needs – and can be implemented natively into any internal or legacy system. We can transform a mass of disparate records into a single, cohesive view that enables segmentation, true modeling, campaign delivery and marketing success.