Lead scoring with machine learning
Lead scoring ranks each contact by its chance of closing, so sales can focus on the best enquiries first.
Background
Sales time is limited, and when every enquiry gets the same treatment, good leads wait. The score also sets a clear threshold for when marketing hands a lead to sales.
Rules or machine learning
Classic scoring awards points under fixed rules, for example for company size or a visit to the pricing page. That is transparent but coarse. A machine learning model learns from your won and lost deals which attributes really matter. Without enough history, we start with rules.
Data sources
CRM data such as industry, company size and previous contacts
Pages visited and downloads, where consent has been given
Responses to newsletters and campaigns
CRM data quality is often the biggest lever, so we review and clean it first.
Explainable scores
Each score shows the main factors behind it. It prepares decisions and does not make them, which matters under Article 22 of the GDPR on purely automated individual decisions.
Implementation
After analysing your data, we train and validate the model. The score goes straight into your CRM, such as HubSpot or Salesforce. Regular checks show when retraining is due.
Cases where the score misses are collected together with your sales team.