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Playbook
Data Cleansing for Accurate Account Ranking
Role:
KPIs:
Target Market:
GTM Motion:
ABM, Inbound/Outbound, Segmentation
The Problem
Revenue teams often struggle with inaccurate or incomplete data in their CRMs, leading to ineffective account prioritization, wasted sales efforts, and missed opportunities. Additionally, implementing automated account ranking systems without clean data causes poor categorization and misaligned sales focus.
This is for GTM functions that:
- Want to focus their resources on their priority accounts.
- Have huge databases (more than 100k contacts) that need to be cleaned, verified, and enriched.
- Require accurate and complete data to improve account prioritization and drive more effective sales strategies.
- Face challenges with unreliable or incomplete data, making it difficult to optimize CRM systems and focus on high-value opportunities.
What KPIs will this impact?
- Enhanced accuracy of account categorization and ranking systems.
- 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 account strategy for better outcomes.
Data Cleansing for Accurate Account Ranking
This play is designed to help RevOps teams optimize their account categorization and ranking systems by cleaning, verifying, and enriching the data that drives those systems. Our data technicians work within the CRM, ensuring that account data is accurate and complete. This allows teams to categorize and prioritize accounts correctly, enabling sales teams to focus on high-value opportunities.
The Solution & Process
- The client provides us with access to their CRM, which contains outdated or incomplete data that hampers account ranking and prioritization.
- We thoroughly review the CRM, identifying missing, inaccurate, or outdated data points that affect account categorization and prioritization.
- We enrich the CRM data by verifying niche-specific fields.
- Our team of researchers sources and verifies any missing or incomplete data, ensuring that every account is complete and properly categorized within the CRM.
- We remove outdated or irrelevant accounts, streamlining the CRM to focus solely on high-potential opportunities that drive sales efforts.
- The enriched and clean data is seamlessly integrated with the client’s automated account ranking system, ensuring accurate categorization and prioritization based on the most up-to-date information.
Real-world use case:
"Improving account ranking through data cleansing."
Our client, a software solutions company, struggled with their automated account ranking system, which was 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.
- Account ranking systems operate smoothly, driven by accurate data that helps categorize accounts effectively.
- Sales resources are optimized by targeting relevant accounts and minimizing wasted outreach.
This play is particularly effective for:
- Organizations implementing automated account ranking systems.
- Teams struggling with cluttered or outdated CRMs that hinder account ranking 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
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