Role of Data Cleansing in Reviving Marketing Efforts

Have you heard about Samsung Securities’ infamous “Fat Finger” error? It’s a classic example of how a simple data entry error cost the company $105 billion and severely damaged its reputation.

The error happened when an employee mistakenly typed “shares” instead of “won” while inputting dividend payments. As a part of its stock ownership program, the company was supposed to give 1000 South Korean Won (worth about $1) to each of its employees (a total of 2000 employees participated). However, owing to this error, the company had to issue 2.8 billion shares (worth $105 billion) instead of 2.8 billion won to its employees. Though the mistake was identified within 37 minutes, it caused an 11% drop in stock prices, resulting in a $300 million loss and mistrust among key customers.

While the Samsung case is extreme, the web contains thousands of examples of organizations suffering significant losses due to poor data quality. In this blog, we will uncover more real-world instances to highlight how bad data can impact business operations, marketing performance, and overall ROI, along with discussing strategies to prevent these costly errors.

Prevalent Data Quality Issues in Organizations and Root Causes

The most common data errors in business databases include incomplete information, duplicate records, typing mistakes, obsolete or missing details, and inconsistent formatting. But what are the root causes behind such data quality issues? To explain this, renowned data quality management company SunTec India surveyed its 50 clients from diverse industries, including healthcare, finance, real estate, and eCommerce. Their report highlighted several key factors affecting data hygiene in organizational datasets.

1. Lack of Defined Data Governance Protocols

According to 82% of survey respondents, their organizations lack formal guidelines for data collection, processing, storage, and retrieval. This absence of pre-established protocols often leads to problems such as data silos, duplicate records, unstructured information, and discrepancies within business databases.

2. Human Mistakes during Manual Data Entry

Approximately 75% of respondents reported that they leverage manual data entry methods without adequate data validation or verification processes. This often leads to errors such as typos, incomplete entries, and missed information, primarily due to insufficient data entry instructions or lack of subject matter expertise, ultimately compromising the quality of the data. Data validation is crucial to ensure consistency, completeness, and accuracy in the data to avoid such discrepancies.

3. System Integration Errors

68% of participants highlighted legacy systems with incompatible formats, improper data mapping prior to integration, and inconsistent taxonomy across systems as key contributors to errors and inconsistencies in their business data.

4. Inadequate Processes for Routine Database Maintenance

The survey found that only 25% of companies have established protocols for regular database audits, updates, and maintenance. As customer and business data quickly becomes obsolete, it’s crucial to continuously monitor and refresh this information. Neglecting to update or enrich data can result in misguided insights, inaccurate forecasting, and missed growth opportunities.

How Poor Data Affect Prospect and Customer Journey

From initial interaction to follow-up and remarketing, accurate and complete data is required to nurture every stage of the customer journey in a sales funnel. However, bad data can affect your marketing efforts at various levels:

1. Inaccurate Targeting

Whether you want to target prospects for initial outreach or to follow up with an existing customer, you need pertinent data to make an impactful pitch. Without precise and updated information on their preferences and buying history, you may end up targeting the wrong audience segments or wasting your marketing efforts.

For instance, if a company has outdated information about a prospect’s industry or job title, they might send marketing materials that don’t resonate with the prospect’s current role or needs, resulting in low engagement.

2. Ineffective Personalization

Personalized marketing can help businesses generate 40% more revenue than industry averages. 

Image Source: McKinsey

However, its success hinges on how well you have segmented customer data into relevant categories. Over time, customer preferences change, and thus, their data needs to be updated regularly. If you don’t have correct or accurate user data for customer segmentation, chances are high that you end up categorizing them in the wrong segments and undermining personalization efforts (especially in sectors like eCommerce).

A notable example of poor personalization is Amazon’s mishap, where an infertile woman received baby registry emails due to incorrect data segmentation.

3. Lost Growth Opportunities

If the customer data (including purchase history, browsing behavior, and buying preference details) is inaccurate, incomplete, or not properly analyzed, you can miss out on upselling or cross-selling opportunities. 

For instance, if your data analytics system doesn’t correctly attribute all purchases to a customer (e.g., due to guest checkouts or in-store purchases not being linked to online profiles), you’ll have a partial view of customer behavior. This incomplete data can prevent you from effectively identifying and capitalizing on opportunities for upselling or cross-selling, ultimately stalling potential revenue growth.

4. Improper Lead Scoring

Lead scoring systems rely on data to evaluate and rank leads based on their potential lifetime value. If the information fed into these models is not correct or relevant, they can misclassify leads (high-potential prospects are undervalued, and low-potential leads are prioritized). As a result, the sales team may not prioritize some of the high-potential leads, missing a valuable opportunity.

5. Poor Lead Nurturing

Effective lead nurturing requires delivering personalized content at the optimal time to guide prospects through the sales funnel. If content is shared too early or too late, it can fail to engage or alienate potential customers. To fine-tune this timing, it’s essential to analyze the customer’s buying cycle—how long they take to move from initial interest to final decision. This analysis is only possible when you have updated and accurate data on customers. Without it, you risk making misguided decisions about content delivery, leading to suboptimal lead nurturing outcomes.

Cost of Bad Data in Marketing and Other Business Operations

We have seen how data quality issues can negatively affect outreach efforts and customer experience. However, if we talk in numbers, the impact of poor data on business ROI can be worse. Let’s understand this via the popular 1x10x100 Rule.

In simple words, the rule states that it costs $1 to prevent a data entry error initially, $10 to fix it once identified, and $100 to correct it after it has been integrated into operations.

For instance, if a company has 500,000 records and 30% are inaccurate, the expense to rectify these issues could amount to $15 million, compared to just $150,000 to prevent them in the first place.

Several surveys have highlighted the cost of poor data for businesses. According to one such study by Gartner, every year, businesses across diverse sectors lose, on average, $12.9 million due to bad data. This cost includes both – the direct cost of correcting data entry errors and the indirect cost of poor data on marketing operations, wasted resources, and missed opportunities.

1. Real-world Example: Unity Technologies Faced $110Mn Loss Due to Poor Data

In Q1 2022, Unity Technologies experienced a 37% drop in their stock value, attributed to data quality problems in their audience targeting tool. The platform designed to help gamers place ads and monetize their games, ingested faulty data from a major client. This led to inaccurate predictions by the machine learning algorithms, resulting in ineffective ad targeting. The incident not only harmed the company’s brand reputation but also incurred approximately $110 million in costs, including model retraining & redevelopment, and direct revenue loss.

Practical and Efficient Ways to Maintain Data Hygiene

To minimize or avoid the cost of bad data, implement these best practices in your organization:

1. Identify the Source of Bad Data

Conduct comprehensive audits to trace data inaccuracies back to their source, whether it’s due to manual entry errors, system integration flaws, or outdated records. Addressing these root causes ensures accurate data collection and enhances overall data quality. You can utilize data profiling tools to track data quality metrics over time. It helps pinpoint when and where data quality began to deteriorate.

2. Regularly Audit, Cleanse, and Validate Business Data

Establish a systematic workflow to regularly audit and manage your data. Verify information against reliable sources, identify inconsistencies and outdated entries, and update missing details. This proactive strategy prevents data deterioration and conserves time and resources that would otherwise be spent addressing data issues at a later stage.

3. Establish Data Governance Policies

For secure data collection, storage, retrieval, and usage, it is crucial to implement a robust governance framework. Define clear guidelines for data access, usage, and security to safeguard against unauthorized access or changes and ensure compliance with regulatory standards. Assign specific roles for data stewardship, including data owners, custodians, and users for secure data handling practices.

4. Implement Multi-Level Quality Checks

• Initial Data Entry Validation: Use validation rules and checks during data entry to prevent errors from occurring. These rules might include format checks, range validations, and required field validations.
• Intermediate Quality Reviews: Perform regular spot-checks or batch reviews of data to identify and correct issues not caught during initial entry. This can be done manually or using automated data quality tools.
• Final Quality Assurance: Conduct a comprehensive review of data before it is used for critical decision-making or reporting. This includes ensuring that data meets all quality standards and is free from errors.

5. Leverage Both Subject Matter Experts and Automated Tools for Data Cleansing and Validation

Adopt a strategic human-in-the-loop approach to enhance data management. Employ automated tools for routine tasks like deduplication, standardization, and error detection. For complex data challenges, information enrichment, and thorough validation, take assistance from subject matter experts.

6. Outsource Data Cleansing Services

If you are short on data experts or want to augment in-house capabilities, outsourcing data cleansing services can be a viable solution. By partnering with a reliable data quality management company and leveraging their expertise, you can streamline data processing at scale, implement best practices, and utilize advanced technologies to improve overall data quality efficiently.

End Note

In this constantly evolving business landscape, data quality is a non-negotiable factor to stay competitive. Whether you leverage cutting-edge AI to automate tasks and tailor your outreach or prefer a more hands-on approach, having reliable and complete data is crucial for driving success. To achieve this data-driven vision, focus on three key elements: implementing comprehensive data quality rules across your dataset, utilizing automated tools to seamlessly integrate, refine, and cleanse your data, and fostering a culture of data stewardship that promotes collaboration among all departments and teams. By prioritizing these elements, you can ensure that your data remains a powerful asset in your competitive strategy.