Marketing professionals are expected to be data-driven, yet over 60% of marketers say they do not have quality data to work with.

Gartner, SalesForce, HubSpot and all other big players in the market report between 70 – 91% of customer data is incomplete, inaccurate, and unreliable. Without quality data, marketers cannot create marketing strategies nor track the effectiveness of a campaign. Worse, poor-quality data in marketing can lead to legal risks, reputational damage, angry customers, loss of revenue – among many other micro and macro consequences.

So what can a marketer do to get quality data?

This guide will help you through the process of identifying poor data, getting help, and ensuring you have quality data to work with.

Reading and Understanding the Flaws in Your Customer Data

You’ve been using the data for emails, marketing campaigns, lead analysis etc. But have you ever really *looked* at the data.

Let’s do an exercise.

Open your CRM contact list.

Scan your first 100 rows.

Can you spot:

  • typos in customer first name and last names
  • names are a mix of upper and lower case
  • phone number fields are incomplete
  • address data is incoherent and messy
  • multiple records for more than one person

If 10% of your records show these problems, you have a data quality challenge.

See the example of a poor data below.

data matching table

Why You Cannot Ignore Poor Customer Data Quality in Marketing?

If 90% of your records are fine, why should you be bothered with the other 10% of poor records? Because over time, poor records multiply.

For example, another set of customers can have up to 20% of poor records. If you ignore these too, then you now have 30/200 poor records. While this may not seem a big amount, in the long run, you’ll struggle with poor insights, misleading information (imagine telling your CEO you’ve acquired 20% new customers, but in reality, it’s just 5% new customers!), and campaigns that do not deliver expected ROI because you’re targeting the wrong people using the wrong metrics.

Therefore, you must prioritize data quality as a core marketing process, especially if your organization is “data-driven.” Sadly, most marketers are too busy on running, measuring, and monitoring a campaign than focusing on the data itself.

Examples of Poor Quality Data in Marketing and its Impact on Business

Duplication, inconsistent standards, and messy data cause serious problems in business operations.

Here are some examples of how bad customer data in marketing can affect a business.

Error TypeExampleImpact
Duplicate recordsA customer uses three different emails to sign up to a formCan lead to wasted marketing spend and inaccurate reporting
Missing dataA customer does not provide a complete phone number with country codeCan lead to inaccurate targeting and poor customer experience
Inaccurate dataA customer’s name/email/or personal information is misspelledCan lead to bounced emails, angry customers, and return mails
Inconsistent dataSome names are in UPPERCASE, some are UPperlower, some are lowercaseDirectly impacts customer experience and causes inefficiencies in processes

To avoid these negative consequences, it is important for marketers to take steps to ensure that their data is consistent.

The question is how?

Read on.

How Can Marketers Improve Customer Data Quality?

Ask any marketer how they handle customer data quality and they’ll most likely mention Excel.

Yes. Marketers still manually import data from a CRM, fix them using Excel formulas, and believe the data is good to go.

Excel is a powerful tool. Sure it can help you standardize data and remove duplicate records. But it does not use algorithms to solve complex data challenges.

For example, you have two records:

‍ Mary Anne | Female | 38 | Work Email -> marieanne@work.com

‍ Marie Anne | Female | 38 | Personal email -> marieanne85@gmail.com

Would Excel catch this record? Would you be able to catch this record? 

Probably not.

So although you have a pristine data set in terms of presentation, it is still flawed.

Therefore, Excel, or manually fixing this data is not the solution.

What you need is a no-code fuzzy matching solution that can help you clean your data.

What is a No-Code Data Cleaning Solution?

A no-code data cleaning solution is a software application that allows users to clean and prepare data without having to write any code. This can be beneficial for marketers because it can help them to improve the quality of their data and make it more useful for marketing campaigns.

WATCH: No-Code solution in action. See how our marketing manager uses WinPure to fix data quality problems in minutes!

Why No-Code Data Cleaning?

Because cleaning data isn’t as simple as using Excel. Data specialists use scripts to clean data. But what if you don’t have a data engineer on board? What if you’re a small business and can’t afford a data specialist in your team?

That’s when you need a no-code solution so you can take charge of your departmental data without having to rely on third party sources or hiring a data analyst costing you hundreds of thousands annually.

No-code data cleaning solutions typically offer a variety of features that can be used to clean data, such as:

✅Matching Non-Exact Data: The example of Marie Ann above is a classic non-exact match situation. A typo, a different interpretation of the spelling has caused a person’s record to be duplicated. In the dataspeak language, this is called a fuzzy duplicate, which is a record that is similar to another record, but not the same. To resolve these duplicates, you would need software that uses fuzzy algorithms to match fuzzy data.

✅Performing Data Cleaning Tasks: You only clean what you can see with Excel. What about hidden errors or issues you can’t see. For example, odd characters in an email address are hard to identify. Or extra characters in a phone number can be difficult to see. Furthermore, solving for these errors would also require you to be exceptionally good at Excel. What if you aren’t? That’s when no-code tools can help you save the day.

✅Helping You Create Master Records: Build a single source of truth of your customer data with master records. For example, you retrieve social media data from Facebook forms, website data from website forms, and third-party data from partners to create a consolidated record of your customer. This view gives you a complete overview of your customer journey.

customer 360 process

Marketing data is complex. You don’t want to wait on another team or an outside resource to help you achieve these goals. Marketing must take ownership of its data.

Why Do Marketers Need to Take Charge of Customer Data?

Because no one knows your data better than you.

Marketers cannot be isolated from their data. Yet, in some organizations, marketing data is handled by IT users.

There are a number of problems that marketers can expect if IT teams handle marketing data.

These problems can include:

❌Lack of understanding of marketing needs: IT teams may not have a deep understanding of the specific needs of marketers, which can lead to problems with data collection, storage, and analysis.

❌Data silos: If IT teams are not integrated with the marketing team, it can lead to data silos, which can make it difficult to access and use data for marketing purposes.

❌Data security and privacy concerns: Marketers need to be concerned about the security and privacy of their data, and if IT teams are not properly trained in data security, it can lead to problems.

❌Lack of communication: If there is a lack of communication between the marketing team and IT team, it can lead to problems with data access, interpretation, and use.

To avoid these problems, marketers need to work closely with IT teams to develop a shared understanding of the needs of the marketing department and the capabilities of the IT department. Additionally, it is important to have clear policies and procedures in place for data collection, storage, and analysis. By working together, marketers and IT teams can ensure that marketing data is used effectively and efficiently to achieve marketing goals.

If you’re a small business however, your marketing department must have a solution that can allow you to manage your data efficiently and effectively.

How Does WinPure Help Marketers with Data Quality?

WinPure is a data quality software that can help marketers improve the quality of their data by providing them with a no-code solution for managing their data.

Anyone on your team can be trained to use the software, requiring no technical or coding skills.

Additionally, WinPure can help with:

WinPure is a data quality software that can help marketers improve the quality of their data by providing them with data profiling, data cleaning, data matching, and data deduplication in a no-code solution.

✅Data profiling is the process of understanding the data by collecting information about its structure, content, and quality. WinPure’s data profiling tool can help marketers to identify potential problems with their data, such as missing values, duplicate records, and inconsistent data formats.

✅Data cleaning is the process of identifying and correcting errors in data. WinPure’s data cleaning tool can help marketers to identify and correct errors in their data, such as missing values, duplicate records, and inconsistent data formats.

Data matching is the process of identifying records that refer to the same real-world entity. WinPure’s data matching tool can help marketers to identify duplicate records, but it can also be used to identify records that are related to each other, even if they do not have identical values.

✅Data deduplication is the process of identifying and removing duplicate records from a dataset. WinPure’s data deduplication tool can help marketers to identify and remove duplicate records from their data.

WinPure’s data quality tools are all available in a no-code solution, which means that marketers do not need to have any coding experience to use them. This makes it easy for marketers to improve the quality of their data without having to hire a data scientist or engineer.

Here are some of the benefits of using WinPure for data quality:

✅Improved data quality: WinPure can help marketers to improve the quality of their data by identifying and correcting errors. This can lead to more effective marketing campaigns.

✅Increased efficiency: WinPure can help marketers to increase their efficiency by automating the data cleaning process. This frees up marketers to focus on other tasks, such as developing marketing campaigns.

✅Reduced costs: WinPure can help marketers to reduce their costs by eliminating the need to hire a data scientist or engineer to clean their data.

✅Improved decision-making: WinPure can help marketers to improve their decision-making by providing them with access to clean and accurate data. This can help marketers to make better decisions about where to allocate their marketing resources.

Take charge of your customer data quality with winpure

Marketers need to have access to accurate and up-to-date customer data in order to be successful. However, many marketers struggle with poor data quality, which can lead to a number of problems, such as:

  • Ineffective marketing campaigns: When marketers don’t have access to accurate data, they can’t target their campaigns effectively, which can lead to wasted marketing dollars and lost sales.
  • Customer churn: When customers receive irrelevant or inaccurate marketing messages, they’re more likely to churn.
  • Compliance issues: Marketers who don’t have a handle on their data quality may be at risk of violating data privacy regulations.

WinPure is a powerful data quality software that can help marketers improve the quality of their customer data. If you’d like to know how we can help, get in touch by hitting the chat button!

Written by Farah Kim

Farah Kim is a human-centric product marketer and specializes in simplifying complex information into actionable insights for the WinPure audience. She holds a BS degree in Computer Science, followed by two post-grad degrees specializing in Linguistics and Media Communications. She works with the WinPure team to create awareness on a no-code solution for solving complex tasks like data matching, entity resolution and Master Data Management.

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