- Getting a good lead for a sales team is hard work, especially when a company has a lot of potential customers.
- Most of these potential customers does not turn into actual buyers. This is why prioritizing leads is important. It helps a sales team identify which potential customers are most likely to turn into buyers.
To develop a Lead Scoring system that uses Predictive Machine Learning mechanism to analyse the various attributes of a lead to determine which ones are most likely to convert into a customer.
It then ranks all of the potential customers according to their likelihood of becoming a paying customer. The sales team can then allocate their time to the leads that are most likely to turn into paying customers.
Machine Learning Setup
- A supervised algorithm is required to for training the model.
- The customer profile data collected by the system includes various demographic details such as the name, address, and birthdate of the potential customers.
- The customer engagement data collected by the system includes the various online behaviours of the potential customers. These include the number of times the page was viewed, the number of times they signed up for a free trial, and the number of times they downloaded.
- The data collected by the system and the sales campaign help the sales team identify which parts of the potential customers are most likely to convert into buyers.
- A database for e.g., Snowflake where the data sits.
- A workflow mechanism to retrieve the data from the clients CRMs for e.g. Salesforce
- A MLOPs engine like Predera’s proprietary tool AIQ which hosts the algorithms designed by the data scientists and has model retraining aspects embedded into it.
- Finally, a dashboarding platform which talks about insights and other data attributes.
- A study conducted by Marketing Sherpa revealed that lead scoring systems can increase a company's ROI by 77%. A later study also noted that 68% of marketers consider lead scoring processes to be highly effective.