Bad Data is the Plastic of the Marketing Ecosystem

 

This session, led by fractional CMO Skip Federer, argues that bad data is a hidden, costly problem similar to environmental plastic pollution. The presentation details how to quantify the financial impact of bad data and outlines a practical, four-pronged strategy for improving data quality at the point of capture.

Key Takeaways

 

• Bad Data Cost is Hidden: Like the submerged part of an iceberg, most organizations underestimate the total cost of bad data, which includes wasted ad spend, wasted sales time, and lost revenue.

• The Financial Impact: For one example B2B company, the cost of bad data was calculated to be over £1.4 million annually, effectively increasing the cost of the sales team by 131%.

• Fixing the Top of the Funnel Works: By focusing on improving data quality at the point of capture, the company not only reduced waste but also unexpectedly doubled its lead-to-sale conversion rate.

• Four-Pronged Strategy: The most critical fixes involve Validation, Verification, Uniqueness (Deduplication), and Intention (Routing).

Quantifying the Cost of Bad Data

 

Bad data cripples the bottom line by introducing waste throughout the funnel. Using a B2B tech company aiming for 10,000 new leads annually as an example, the costs break down into three areas:

1. Wasted Spend (Acquisition): Assuming a 20% bad data rate (below the industry average) and a blended Cost Per Lead (CPL) of £80, the initial cost is calculated as 2,000 bad leads, or £160,000 lost in ad spend immediately.

2. Wasted Time (Sales): According to research, 27.3% of a sales team’s time is wasted by bad data (e.g., ringing disconnected numbers, chasing inaccurate emails). For a team of 10, this resulted in a wasted annual salary cost of £295,000.

3. Lost Revenue (Churn): Bad data reduces turnover by an average of 6% because bad records lead to lost potential initial revenue and lost repeat revenue when renewal contacts are unreachable. For a company with £15 million turnover, this resulted in £960,000 left on the table.

Total Annual Cost of Bad Data: Over £1.4 million.

The Nine Ways Data Goes Bad

 

Marketers must focus on these nine aspects of data quality:

1. Completeness: Does the data contain all required parts (e.g., the @ symbol, a valid TLD in an email address)?

2. Accuracy: Is the data correct (e.g., the real person’s name and contact details)?

3. Consistency: Is the data the same across all systems (CRM, Pardot, spreadsheets)?

4. Conformity: Does the data make sense and align with standardized formats (e.g., no random legacy codes)?

5. Uniqueness: Is a single thing in the real world represented by one entity in your data (no duplicates)?

6. Integrity: Is the data uncorrupted by the database itself (e.g., are all lookups valid)?

7. Intention: Is the lead the right person you intend to market to (e.g., the correct job title and geography)?

8. Accessibility: Do you have real-time access to the data to react to digital body language?

9. Freshness: Data degrades rapidly (25-30% per year).

The Four-Pronged Strategy for Data Quality

 

The company prioritized fixing the four most critical data issues at the point of capture.

1. Completeness (Validation)

 

• Action: Added field-level validation (using simple development resources) to check for required parts of critical fields (e.g., presence of @ and a valid TLD in the email, a valid country code for phone numbers).

• Goal: Ensure the minimum viable record is fully actionable.

2. Accuracy (Verification)

 

• Action: Integrated with third-party verification services via API to check email addresses (valid and accepting emails) and phone numbers (assigned and active) before the user left the form.

• Process: When an error was found, the submit button was disabled, prompting the user to correct the problem instantly.

• Trust Building: Sales received a weekly report of records corrected at capture, which built trust and allowed the team to develop new fraud-catch rules as needed.

3. Uniqueness (Deduplication / Pre-Dupe)

 

• Action: To avoid expensive duplicate capture from content syndication partners, the company implemented a pre-duping solution. This involved using a clean room solution to compare a hashed version of their existing database against the media owner’s list.

• Goal: Stop existing customers, former customers, and unsubscribers from being marketed to with top-of-funnel content, preventing unnecessary cost and poor CX.

4. Intention (Smart Routing)

 

• Action: Used smart data routing to filter out leads that did not meet the ideal customer profile (e.g., a junior developer instead of a Head of DevOps).

• Goal: Ensure salespeople only waste time on leads that have the potential to buy. (The non-ideal leads were still paid for, but not accepted into the sales database).

The Financial Reward: Revenue Doubled

 

The results of the data cleanup initiative were far beyond expectations:

• Data Quality: Improved from $80\%$ to $98\%$.

• Expected Revenue Increase: Revenue increased by £1.5 million (23% over budget) due to fewer bad leads.

• Unexpected Conversion Increase: The cleaner data at the top of the funnel caused the lead-to-sale conversion rate to almost double because salespeople were happier and having better conversations.

• Budgetary Impact: Instead of the £1.4 million waste, the company saved £420,000 in the first year alone, resulting in a £2 million swing in potential financial impact.