The Hidden Mess in CRMs

The issue with CRMs is that everyone can create their own little mess inside:

• Connect the mailbox you use for cold emailing, and it’ll create thousands of misplaced contacts.
• Use Lusha’s LinkedIn extension to create contacts, and you’ll end up with countless duplicate companies.
• Realize that 25% of your contacts change jobs every year.
• Import your TAM (Total Addressable Market), and it’s chaos.

Having a tidy and up-to-date CRM might not directly help sales hit their quarterly targets or marketing achieve its OKRs. However, letting your CRM data rot will surely render it useless over time.

So, How Do We Get It Cleaned ?

To clean your CRM, you need to:

1. Identify duplicate records.
2. Remove useless contacts and companies.

Step 1: Identifying Duplicates

To identify duplicates, you need to enrich all records to have matching points such as:

• Domain
• LinkedIn ID
• LinkedIn URL
• Company name
• Full name + company name
• Email
• Phone number

Step 2: Enriching Your Records

The best data source is LinkedIn. Here’s why:

• It’s crowd-sourced, allowing a global community of professionals to share and update data collaboratively in real-time.
• Most companies are registered on it, making it a unique and standardized database that works across countries.
• There are few duplicate records and increasingly fewer fake accounts.
• It’s the unacknowledged source for all other corporate database companies.

What Solution Should You Use to Enrich Records?

I strongly recommend using a solution that performs real-time requests to LinkedIn to get the most up-to-date information.

There are several tools available, such as captaindata.com, phantombuster.com, and rapidapi.com. However, my favorite is Enrich-CRM. It’s plug-and-play, with unique logic to:

• Maximize the percentage of retrieved contacts and companies.
• Avoid enriching records with homonyms through built-in safeguards.
• Process hundreds of records per second without being limited by your LinkedIn session cookie.

If you already have subscriptions to services like Apollo or Zoominfo, you can use them for the initial cleaning. They’ll more or less get the job done, but I wouldn’t recommend purchasing them specifically for this task.

Once all your records are enriched, use Dedupely to merge duplicates.

Step 3: Cleaning Up Useless Records

In my past companies, we often changed ICPs. Each time, we ended up with plenty of useless contacts that cost us money (e.g., marketing contacts in HubSpot) and decreased team clarity.

To clean up, you need a scoring logic. This can be done using your CRM’s built-in scoring system or manually in Excel/Google Sheets via import/export.

Steps to Implement Scoring Logic:

1. Identify What Your ICP Is Not: Even if you’re unsure of your ideal ICP and personas, you can identify characteristics that clearly don’t fit.
2. Score Records Based on Fit:
   
   • Use common characteristics to assign low, average, or high scores depending on their alignment with your ICP.

For example, at Enrich-CRM.com:

• Companies with characteristics like “Marketing and CRM agencies,” sales teams >5 people, or companies older than 3 years get higher scores.
• Companies with CRMs containing fewer than 50k records, early-stage startups, or those using CRMs not covered by our integrations get lower scores.

Example Scoring:

• If a company is in “Marketing services” or “Publicity services,” assign +10.
• If a company is less than 3 years old, assign +1.

Add up all scores for each characteristic. The results should represent how well companies and personas align with your ICP. Based on these scores:

• Delete records with very low scores.
• Classify others as Tier 1 or Tier 2 and take appropriate actions for each label.

Wrapping It Up

Cleaning your CRM doesn’t have to be daunting if you have the right tools and methodology. Once done, your team will have a clear, actionable database that supports data-driven decisions and enhances productivity.