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Playbook
Data Cleansing for Accurate Lead Scoring
Role:
KPIs:
Target Market:
GTM Motion:
ABM, Inbound/Outbound, Segmentation
The Problem
RevOps teams often struggle with inaccurate or incomplete data in their CRMs, making building effective lead scoring models impossible. Furthermore, teams who ignore this issue and build lead scoring models around inaccurate data are left scratching their heads when the score doesn’t reflect the true quality of the lead. This causes inefficient sales efforts, poorly allocated sales resources and missed opportunities.
This is for GTM functions that:
- Want to focus their resources on their priority accounts.
- Have a CRM that contains data in need of cleaning and enriching.
- Require accurate and complete data to create correct lead scoring models.
What KPIs will this impact?
- Enhanced accuracy of lead scoring model.
- Increased focus on high-value opportunities, improving sales performance.
- Higher CRM data quality leading to more strategic sales decisions.
- Improved efficiency in targeting top-tier accounts.
- Greater alignment between sales and RevOps.
Data Cleansing for Accurate Lead Scoring
This play is designed to help RevOps teams optimize their lead-scoring models by cleaning, verifying, and enriching account and contact data. Our team works within the client's CRM, ensuring that all newly added or existing account data is accurate and complete. This helps RevOps ensure their lead scoring models work as intended and enables sales teams to focus on high-value opportunities.
The Solution & Process
- You book a sample with DataBees. This is where we prove we can locate the ICP data points your lead scoring models would require.
- We thoroughly review your CRM data, identifying the incorrect records breaking your lead scoring model.
- Our team of researchers sources and verifies any missing or incomplete account or contact data.
- We build an ‘always-on’ project, meaning the records that enter your sales pipeline are fully enriched and, therefore, scored correctly.
Real-world use case:
"Improving lead scoring through data cleansing."
Our client, a software provider for warehouses, struggled with their automated lead scoring models, which were misclassifying low-priority accounts as high-value due to inaccurate CRM data.
- Poor account categorization caused by outdated shipment volume and revenue data.
- Misaligned sales focus, with teams targeting irrelevant accounts and wasting time.
- Inefficient sales cycles, slowing down overall revenue growth.
After partnering with DataBees, the organization saw significant improvements:
- Their automated system accurately categorized accounts, leading to an increase in sales efficiency.
- Time spent on irrelevant accounts was reduced, allowing teams to focus on high-value opportunities.
- Their CRM was streamlined, with irrelevant accounts removed and critical data fields updated for maximum efficiency.
This ensures…
- Teams can prioritize high-value opportunities using clean and reliable data.
- Sales teams can focus their efforts on the accounts with the greatest potential, driving higher sales performance.
- Lead scoring models work as intended, driven by accurate data that helps rank and route accounts effectively.
- Sales resources are optimized by targeting relevant accounts and minimizing wasted outreach.
This play is particularly effective for:
- Organizations implementing a lead scoring model.
- Teams struggling with cluttered or outdated CRMs that hinder lead scoring and prioritization.
- Sales and Revenue Operations teams looking to improve efficiency and focus through better data quality.
See how [insert case study] used this exact strategy to achieve x goal
Get started with a sample
We run a free sample for all of our potential customers to ensure that we can find the data that you need. It’s super simple to set up and you'll have the results in 3-5 working days…