Why Data Quality is Essential in Marketing

By Mark Stephens, 360pro.ai

Professionally, I support many companies with their marketing outreach strategies and I am often faced with questions about the use of their own existing data.

Cutting to the chase; there is no problem in using your own data, if it is well-maintained and demonstrates better-than-average metrics.

But if it doesn't meet proven high standards, why would you even consider using it?

Why Data Quality is Essential in Marketing

Today, we live in data-driven world, and marketing efforts rely heavily on accurate and actionable data.

Companies invest millions in data acquisition and marketing campaigns to maximize their return on investment (ROI), yet one pervasive issue—bad data—undermines these efforts.

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A staggering 54% of companies report that poor data quality is their biggest hurdle in achieving data-driven marketing success. The consequences of ignoring this problem can range from wasted marketing budgets to missed opportunities and ineffective campaigns. In fact, I have personal experience of seeing results literally double when the old data is replaced with quality data from a reliable source, and that can result in a significant multiple increase in meetings and revenues, that should take the data question off the table.

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This article explores why data quality matters so much in marketing, the impact of bad data, and the benefits of generating new data, or at least validating it properly, for each marketing campaign.

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Why Bad Data is Harmful to Marketing

Bad data creates a ripple effect of inefficiencies across marketing efforts. It leads to inaccurate audience targeting, misinformed decision-making, and flawed campaign analytics. When data is incomplete, outdated, or incorrectly formatted, marketers often fail to reach their intended audience effectively. Consequently, marketing budgets are spent with minimal returns, and the company’s overall strategic goals may be jeopardised.

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For example, poor-quality data can result in duplicate customer entries or outdated contact details, rendering segmentation ineffective. Messages reach the wrong audience, or worse, customers receive multiple versions of the same communication, leading to dissatisfaction and diminished brand reputation. In many cases, companies don’t immediately recognize that their data is flawed, leading them to invest even more in refining campaigns based on faulty insights. As a result, these marketing efforts, instead of yielding value, become a costly drain on resources.

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In addition to these negative effects, there can also be longer-term more damaging and far-reaching impacts, where poor data quality achieves low delivery rates, gets tagged as spam, and damages domain reputations.

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Why 50% of Internal Data Sets are Problematic

One of the main reasons for widespread bad data is the complexity of modern data environments. Data often comes from multiple sources—websites, social media, CRMs, surveys, and other platforms—which are not always aligned in terms of format, accuracy, relevance and compliance. Internal data is prone to duplication, inconsistencies, and outdated information due to various departments entering or updating records with differing standards.

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Studies indicate that over 50% of internal data sets contain inaccuracies or errors, partly due to the high volume and rapid pace at which data is generated. Another contributing factor is the lack of a unified data governance framework within many organisations. Without consistent standards for data entry, validation, and maintenance, errors quickly accumulate, leading to a situation where the data can no longer be trusted.

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According to research conducted by Demandscience this year, the average company sees a natural deterioration and depreciation of their email and contact data, that is in excess of 5% per month, and when annualised that comes to around 25%. Even website giants, like LinkedIn admit that around 15-20% of their registered user data and job titles are always out of date.

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Benefits of Generating New Data Sets for Each Marketing Campaign

Creating new, purpose-specific data sets for each marketing campaign offers several advantages. Instead of relying on historical data, which may be outdated or irrelevant, generating fresh data allows marketers to align insights directly with the objectives of the campaign. New data is more likely to reflect current customer behavior, trends, and buying intent, enhancing the accuracy of audience targeting and message personalisation.

Each marketing campaign has unique goals and may target different demographics, behaviors, or purchasing patterns. By gathering new data, companies can adapt their approach to better match the audience’s current needs and preferences, resulting in more effective and engaging campaigns. Furthermore, this approach helps minimize the chances of working with obsolete data, which can lead to inaccurate assumptions about customer behavior and preferences.

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What Constitutes High-Quality Data?

High-quality data is the foundation of successful marketing campaigns. Key attributes of high-quality data include:

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1. Accuracy: Data must be free from errors, redundancies, and outdated information.
2. Completeness: Each record should be fully populated without missing values or blank fields.
3. Relevance: The data should align with the campaign’s specific goals and objectives.
4. Validity: All information should be verified and validated, ensuring that it is trustworthy.
5. Consistency: Data should be standardised across formats, fields, and sources.
6. Real-Time Availability: Data should be regularly updated to reflect current customer information and behavior.

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High-quality data enables companies to fine-tune their marketing strategies, ensuring that each interaction with customers is meaningful and personalised. With complete and reliable data, marketers can create campaigns that not only attract attention but also drive real engagement and conversions.

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Business Case for Maintaining High-Quality Data

We live in an era where customers expect personalised experiences and precision-targeted messages, and maintaining high-quality data is essential for competitive success.

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Organisations with access to accurate, up-to-date data are better equipped to make strategic decisions that align with customer buying intent. Clean, actionable data enables precise audience targeting, supports machine learning and artificial intelligence initiatives, and leads to more reliable analytics.

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The cost of poor data quality extends beyond wasted marketing spend; it also impacts brand reputation, customer satisfaction, and overall business performance.

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By investing in regular data cleansing, consistent data governance, and purpose-specific data collection, companies can ensure that their marketing efforts are built on a foundation of trustworthy information. Ultimately, maintaining high-quality data is not just a best practice; it’s a business imperative that aligns marketing with strategic goals and empowers organizations to make smarter, data-driven decisions that drive growth.

This article was originally published: https://360pro.ai/news/marketing-data

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