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Measuring the Value of Defected Lead Information: A Step-by-Step Guide

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Losing Sales Due to Poor-Quality Lead Data

In a recent Forrester report on the impact of inaccurate data on demand generation, it was found that approximately 25% of customer and prospect data in a company’s database contains significant errors. These inaccuracies lead to serious challenges for marketing and sales professionals, including difficulties in accurately estimating deals and identifying the correct point of contact within a company. Consequently, organizations end up jeopardizing or losing a significant number of sales each year.

High Influx of Lead Data

One of the primary causes of poor lead data quality is the multitude of devices, channels, and platforms that consumers use throughout their buying journey. In fact, it is expected that the number of internet-connected devices used by consumers while interacting with a brand will soon increase from four to thirteen.

As a result, we receive lead data from various channels such as emails, webforms, chatbots, social media platforms, and web cookies. Managing the quality of data for each lead becomes challenging with such a high influx of information. This is where your business can potentially encounter serious data quality issues. Implementing techniques like data cleaning, data standardization, data matching, and data deduplication can help rectify many of these issues.

Prioritizing Lead Quality Over Lead Quantity

While most companies focus on generating more traffic and attracting more leads to their website and social media platforms, only a few actually pay attention to the quality of the data collected. This significantly impacts the lead-to-customer ratio and, by the end of each year, executives are left wondering what went wrong. This is the result of basing the performance key performance indicators (KPIs) of your marketing team on arbitrary variables like lead count instead of more meaningful metrics, such as lead quality.

Measuring the Quality of Lead Database

To assess the quality of their database, organizations typically use a set of ten data quality metrics. These metrics include:

  1. Accuracy: How accurately do data values depict reality?
  2. Lineage: How reliable is the source of the data?
  3. Semantic: Do data values stay true to their intended meaning?
  4. Structure: Do data values conform to the correct pattern and format?
  5. Completeness: Is your data as comprehensive as you need it to be?
  6. Consistency: Do the same data values across different sources match?
  7. Currency: Is your data up-to-date enough?
  8. Timeliness: How quickly can the requested data be accessed?
  9. Reasonableness: Do data values have the correct data type and size?
  10. Identifiability: Does each record represent a unique identity and is not a duplicate?
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Measuring the Cost of Defective Lead Data

While these ten data quality metrics are useful for assessing data quality, companies often want a quick overview of the current state of their data quality. This helps in catching data quality errors in a timely manner and calculating the cost of poor data quality in the lead database. To address this, Tom Redman proposed a method called the Friday Afternoon Measurement (FAM). It powerfully and rapidly answers the question: When should you be concerned about data quality?

FAM is a four-step method that calculates the cost of poor data quality in your lead database on a weekly basis. It also raises red flags before the situation gets out of hand and ensures that your marketing and sales activities are based on accurate data.

Step 1: Collect Recent Lead Data

Start by gathering the most recently created or used data from your customer, prospect, or lead database. Select about 100 records and identify the top 10 or 15 attributes that represent the most significant information about these entities.

Step 2: Label Records as Defected and Defect-Free

Invite two or more individuals from your team who have a good understanding of the data being analyzed. Ask them to highlight any errors they come across in the 100 selected records. These errors can be incomplete, inaccurate, invalid, or missing fields. Additionally, it may be discovered that the same record has been entered into the database multiple times. All such discrepancies should be noted.

Next, add a new column to the data sheet and label each record as “Defected” or “Excellent” based on whether an error was encountered. Finally, calculate the total number of lead records labeled as “Defected.”

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Step 3: Measure Data Quality

Calculate the percentage of records that were labeled as “Excellent” in the last 100 entries in your lead database. For example, if out of the last 100 records, 38 had data quality issues, while the remaining 62 were excellent, then the 38% error rate raises a red flag and indicates serious data quality issues.

Step 4: Consider the Rule of Ten (RoT) to Calculate the Cost of Poor Data Quality

The final step involves calculating the cost of poor data quality in your dataset using the Rule of Ten (RoT), which states that it costs ten times more to complete a unit of work when the data is flawed compared to when it is error-free.

For instance, if it costs $1 to complete a unit of work when the data is error-free, according to RoT, it would cost $10 to complete the same work when the data is flawed. Therefore, the total cost becomes:

Total Cost = (62 $1) + (38 $1 * 10) = $62 + $380 = $442

This clearly shows that your lead dataset cost you about 4 times more than it would have if the data was defect-free.

Using a Self-Service Data Quality Tool for Your Lead Database

While the FAM method provides a quick way to assess data quality and calculate costs, it still requires approximately 2 hours of effort from 3-4 team members on a Friday afternoon. This is where a self-service data quality tool can come in handy. These tools quickly generate data quality reports, automatically label defected and defect-free data, and offer extensive data quality capabilities. They can also act as data deduplication software, which matches lead data to identify duplicates and merges them into one.

Whether done manually or through automated tools and workflows, it has become essential for every company to scan their lead database before labeling it safe for use by the marketing and sales team. This can result in saving the company about 4 times the actual cost of working with a faulty lead database.

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