Data Cleaning in CRM – How We Recovered 12% of Lost Leads
Many IT companies lose money because their CRM systems are full of junk data and duplicates. In March 2024, at Silesia Performance Lab, we proved that simply organizing the data transfer from website forms allows recovering real sales opportunities that previously ended up in the trash.
Database Mess is a Real Loss of Money
We started work with our software house client with a simple test. We reviewed 1,420 records in their Pipedrive system entered between January and March 2024. It turned out that as many as 168 of them were duplicates, and another 94 inquiries never reached the sales department due to API integration errors. These aren't just numbers. These are real people who wanted to buy a service but no one called them back because the system considered them an 'error' or 'spam'.
The problem was that the forms on the home page and service subpages sent data in different formats. One form required a phone number with a country code, another without. When the same customer filled out both, CRM created two separate cards instead of one. The salespeople saw chaos, and we saw that the data speaks for itself – the company was losing control over who was a new client and who was a returning user.
At Silesia Performance Lab, we don't believe in magic solutions. We check facts and look for technical gaps. In this case, the gap was in the JavaScript code handling the data submission. Fixing this error took us exactly 14 hours of programming work, but the effect was immediate. Before we started, the average lead response time was 26 hours. After organizing the database, it dropped to 4 hours because salespeople stopped wasting time manually merging contacts.
The data speaks for itself – a CRM mess is not an aesthetic problem; it's a hole in the company budget.
Technical Causes of Disappearing Leads
The most common mistake we detected during the audit in Katowice was the lack of server-side validation. A customer would type an email address with a typo, for example, 'john.doe@gmial.com'. The system accepted this without blinking, but the automatic welcome message never reached the recipient. In April 2024 alone, we counted 23 such cases. These are 23 people who thought the company was ignoring them.
Another issue is the so-called 'over-mapping' of fields. The company tried to collect too much information at once: from budget and team size to preferred technology. Every additional field in the form is a risk that the script sending data to CRM will crash. During our load tests, we determined that with 5 fields, the delivery success rate is 99.8%, but with 12 fields, it dropped to 84.2%. Simplicity wins over excess data.
We do technical SEO that works, but SEO ends where the form begins. If your analytics doesn't show exactly which button sent the data to CRM, you don't know which campaign is earning. For our client, a poorly configured GTM (Google Tag Manager) was overwriting traffic sources. As a result, 42% of leads were marked as 'direct entry', even though they came from paid ads on LinkedIn.
How We Recovered 12.4% of Lost Opportunities
We divided the recovery process into three stages. First, we introduced a unified data entry standard. Every form on the site must now pass through the same cleaning script that removes unnecessary spaces, formats phone numbers to the E.164 standard, and checks email syntax in real-time. This eliminated the creation of new junk in the database almost entirely.
Next, we searched through the 'historical trash'. Thanks to a Python script we wrote, we compared the CRM database with web server logs from the last 6 months. We found 47 quote inquiries that got stuck at the data transfer stage. Salespeople contacted these people in May 2024. The result? 6 closed contracts with a total value of 112,400 PLN net. This is real profit from clean analytics.
The final step was a specific audit of the sales funnel. We introduced automatic Slack notifications for the sales team the moment a new, correctly verified lead comes in. We eliminated the mediation of Excel spreadsheets where data could sit for 2-3 days. Now information goes from the customer's browser to the salesperson's phone in an average of 47 seconds. Such speed builds trust with an IT client.
We recovered 47 leads that were 'stuck' in the system. 6 of them turned into contracts worth over 100,000 PLN.
Analytics That Supports Sales
At Silesia Performance Lab, we know that reports are not just charts in Google Looker Studio. They are a tool for the head of sales. That's why we added a unique session ID to every lead in CRM. Thanks to this, the client now sees that Mr. Mark, who signed a contract for 50,000, first read three articles about Cloud technology and then spent 12 minutes on the pricing page.
We also introduced a 'Lead Scoring' mechanism based on facts, not hunches. Points are awarded for specific interactions on the site. If someone downloads a case study, they get 10 points. If they just visit the 'About Us' page, they get 1 point. CRM automatically ranks contacts so that salespeople call the most interested ones first in the morning. This saves about 3.2 hours per week for each sales department employee.
Summarizing our actions: we improved data cleanliness by 87%, shortened response time by 22 hours, and brought in an additional 112,000 PLN in revenue without spending a single zloty more on advertising. Specific audit, specific profits. If your CRM resembles a landfill, it means you're throwing money away every day. It's time to end this and bet on hard data.
