What is Data Cleaning?

John O'Shea

Data cleaning or data cleansing is the process of updating or removing inaccurate, irrelevant, duplicate, or incorrectly formatted data to produce a high-quality database or dataset.

Quality data makes sales teams’ lives easier. Instead of pursuing bad-fit or uninterested customers, they can focus on the most valuable leads and importantly, identify opportunities that would otherwise be missed. Volume is important, but, without quality and regular data cleaning, analytics efforts will be lacklustre and many of the key opportunities which data can offer may be missed.

• 50-75% of the success of a B2B marketing campaign is down to the accuracy of the data available.

• ‘Insight-driven’ enterprises will take $1.8 trillion of business annually from their less-informed competitors.

What Is ‘Quality’ Data?

Data covers an expansive range of metrics. Broadly speaking, if it helps your teams locate and hone in on targeted prospects with pinpoint accuracy, it’s quality data. If it delivers insights into your prospects’ pain points and needs, it’s quality data. And if it directs you to the market segments which offer the best opportunities to close deals…. you’ve guessed it. That’s quality data.

Sounds good, right? But hold on a moment because there’s a problem.

Data Erosion and The Importance of Data Cleaning

Data erosion is one of the biggest headaches for sales teams and unfortunately, it’s happening right now, as you’re reading this article. In the last few minutes, the chances are that a proportion of your data has become inaccurate.

Here, the statistics speak for themselves:

• 51% of consumers  expect companies will anticipate their needs and also make relevant suggestions before they make contact.

• Personalised promotional emails produce 29% higher unique open rates and 41% higher click-through rates.

• 72% of consumers only engage with personalised messaging.

And, in case you were wondering, those stats were collected before COVID-19 came along and put a massive spanner in the works. In the middle of a pandemic, with key decision-makers scattered across the country working at home, it’s even harder for sales teams to trust the data they’re being given. Making data cleaning more important than ever.

How Bad Data Can Ruin Domain Reputation

Email bounces are more than just an annoying inconvenience. If the situation isn’t rectified those bounces could have serious consequences.

Here’s why:

Each time an email bounces from a non-existent or inaccurate address – a hard bounce – your domain reputation takes a hit. Collect multiple hard bounces and before long, your domain will be blacklisted.

A blacklisted domain is disastrous – not only for future campaigns, but also for everyday emails to current prospects and clients. Your emails will be automatically flagged as spam.

Managing Data Quality and Cleaning Steps

Data cleaning provides confidence. Not only will it tip the balance away from the costs associated with data erosion and towards greater revenue opportunities, but it will also allow you to interact with prospects and clients more creatively with personalised approaches.

The question is, what data cleaning steps can you take to leverage benefits such enhanced customer insights, improved brand reputation, and opportunities for targeted marketing and sales campaigns?

• Watch out for hard bounces, and make sure those email addresses are corrected.
• If you can, employ a dedicated team for ongoing data enrichment and data cleansing.
• Get subscribers to do the work. Make it easy for customers to update their contact information, unsubscribe or update their preferences.
• If possible, avoid buying lists. They are likely to be riddled with inaccuracies – outdated email addresses, phone numbers, contact names, job titles and the crucial company information that allows you teams to target prospects with precision.

Ensuring effective data collection is challenging, key information is inevitably subject to change as people move jobs and companies relocate, rebrand, merge and liquidate. Indeed, some 94% of B2B companies suspect inaccuracies in their databases.

Adhering to a series of best practices, however, can drastically improve data hygiene. Here are a few tips outlined in our white paper: Dealing with data: How to give your campaigns the best chance of success.

• Make data health checks a regular part of your marketing routine.
• Use data visualisation tools that can give you a bird’s eye view of the rots.
• Remove hard bounces and suppress inactive email subscribers.
• Use a CRM that provides last contact reports to give an indication of likelihood of erosion.
• Verify all data ideally immediately before use.

4. A great sense of humour.

Data cleansing needs to be a regular part of your processes, but it can also be made easier by data enrichment.

Data Enrichment

Data enrichment (the process of verifying internal data against authoritative external data) is one of the best ways to ensure accuracy.

There are two common examples of data enrichment – demographical and geographical.

The former allows you to define users more accurately by specific traits. Marital status, income levels and credit ratings are all examples of demographic data. The latter, meanwhile, helps to enhance data related to the location of your user, and will often include ZIP or postal codes, home addresses, business addresses and similar.

Outsourcing Data Research

Poor quality data is a major pain point for sales teams – but so is the endless task of double verifying each piece of data. Data cleaning and verification is time consuming especially when your sales team should be selling and closing deals.

If you like the idea of saving time and getting a higher return on your database, get in touch with the Growthonics team.