What Is Data Quality For Lead Scoring?

DEFINITION
Data quality for lead scoring involves ensuring the data input into lead scoring models is clean, standardized, and scorable, resulting in more reliable lead scoring models and a smoother lead handoff process.

💡Understanding Data Quality For Lead Scoring

Lead scoring is the process of ranking leads collected using a numeric system, usually assigning point values, such as 1-100 points.

In many cases, a company will assign point values to data elements gathered from its data profiling and other external sources. This level of information management determines who the qualified leads are to prioritize customers who need follow-up.

In this context, the quality of the data you input to create lead scoring models is crucial.

Having quality data helps companies determine where prospects are in the customer journey and let the sales team know if they are ready to buy and the right time for the lead handoff.

Some of the best practices to evaluate the quality of the data you’re feeding your lead scoring models include cleaning your data, standardizing your data capture, and scoring what you have data on.

🖋 Takeaway

High-quality data and data accuracy are the keys to successful lead scoring.

It is not ideal for sales teams to follow up with poor data quality, and it wastes time when there is no consistent data to use. 

Poor quality data collected about the leads often causes the sales team to focus on clients who have not shown not to be the sales opportunities they initially thought they might be.

Although having a high number of leads generated may appear suitable on quarterly review slides, you don’t want to go in with fuzzy logic. Here, quality is better than quantity.

If you have many leads, but they’re qualified inconsistently (such as the quality of the data you input is poor) then the lead is all for nothing.

To start applying data quality for your lead scoring initiative, you need to ensure clean data, standardize your lead capture and score what you have data on.

What Is Data Quality For Lead Scoring?

The concept of data quality for lead scoring involves the application of standard scoring criteria to quality data collected from marketing campaigns and third-party data sources. Assessing data quality for lead scoring enhances marketing and sales efforts by assigning point values to customer data and identifying where a potential customer is in the sales process.

This data quality improvement process drives scoring models also at the early stages of the funnel, even before transferring them to sales and marketing teams, translating into effective marketing campaigns.

What does this process look like?

Some of the essential best practices to ensure you have quality data for your lead scoring endeavors are:

1. Clean Your Data

When building your lead scoring system, one of the issues that frequently arises is the quality of your input data, as data quality is a crucial factor in accurately identifying and qualifying leads.

But how to ensure you have clean data?

While the reality is that there is no perfect data, the good news is that you really need clean, relevant, and up-to-date data to implement lead scoring.

However, most companies still struggle to understand the quality of the data in their CRM. (Which fields are populated or incomplete, what sort of data they have, which data points correlate with revenue, and their impact).  

A tool like Breadcrumbs Reveal dives deep into a company’s CRM or marketing automation software to quickly surface the state of the underlying data infrastructure, identify gaps in the data collection strategy, and highlight strong correlations between contact fields and their likelihood of becoming paying customers.

2. Standardize Data Quality Management

There are three primary ways that a company can gather high-quality data.

• Form Submissions–forms on a website (i.e., Contact forms) or landing pages that support your lead generation efforts.
• Inbound CRM Integration–data is automatically imported into the MAP from a company’s CRM.
• List Uploads–offline lists are often received from events, trade shows, or sales reps. 

Form submissions often capture basic information on the customer, i.e., name and location data, but can also be applied to find more personal-based information, such as title and industry. A uniform system is always recommended to capture data in the collection of form submissions and data collected from incoming CRM integrations.

Every vendor will likely provide new data in a format that has not precisely standardized the same way from each list. It may be helpful to set up programs in your MAP to “standardize” the data sets, which helps to limit any manual data cleaning you may need to do when data quality issues arise through inconsistent data.

3. Score What You Have Data On

It is often overlooked to check which fields you have data from, but it is essential to determine how you weigh your lead scores. You need to ensure that you have enough records that have the data you want to score against.

Time-to-revenue is extremely important for lead management; you want to predict how long it will take for a prospect to become a customer–from their first interaction and all along the path to closing the deal.

Getting Started With Data Quality For Lead Scoring

A sound lead scoring system requires good data management as much as it requires good data quality. Nearly 40% of businesses and their sales teams report higher opportunity-to-sell conversion rates simply by implementing a standardized data quality management system.

Begin enhancing the quality of the data you input in your lead scoring process. Create your free Breadcrumbs Reveal account today and get a comprehensive view of your data, its value, and where collection and enrichment gaps exist to improve fill rates.