How Quantzig’s lead scoring model helped improve the conversion rates in the sales pipeline of a telecom major
Highlights of the Case Study:
Predictive Lead Scoring Model and Machine Learning-based Lead Scoring Model for the Telecom Industry
With the advent of advanced analytical solutions and automation, organizations, including telecom service providers all over the world, have shown a willingness to upgrade their processes for identifying potential leads from a wide and diverse range of customers. These customers sign up every minute for the services/products that a business has to offer. However, the conversion rate of these potential leads to actual paying customers is very low. This is especially true in the highly competitive environment of the telecom industry, where the churn rate is high. This is where the idea of lead scoring gains importance. Lead scoring is an automated process of analyzing attributes of the incoming leads by scoring them based on various data points. Lead scoring allows companies, including those operating in the telecom space, to obtain insights into consumer readiness to buy a product and/or avail of a service.
The Challenges of the Client
Our client is an Indian telecom giant with a global presence. However, the clients monthly conversion rate fell from 9% to 4% even as the monthly investment in lead generation, lead reach-out program, and lead analysis doubled. It was found that 70% of the clients lead generation budget was spent on leads that never got converted and that 30% of the leads fell into the non-reachable category.
The client approached Quantzig to implement its full end-to-end custom-built lead scoring model, including extracting the data, developing the model, implementing the model, and finally testing the accuracy of the model.
With Quantzig’s lead scoring model, the client wanted to improve the conversion rates.
Quantzig’s Predictive Lead Scoring Model and Machine Learning-based Lead Scoring Model for the Telecom Industry
Quantzigs data analytics team and industry experts curated to get a granular view of the situation. They found that 70% of the clients lead generation budget was spent on the leads that were never converted and that 40% of the leads were poor-quality leads. Therefore, the Quantzig team concluded that there exists a considerable deviation in the volume of customer sign-ups and the size and ability of the sales and marketing team to categorize the potential leads and reach out to those leads. The Quantzig team defined the clients lead data into insights and came up with a two-phase solution; in the first phase, they developed an ML-based lead scoring model. This allowed them to access the historical data of all the leads signed up on the clients website using Quantzigs web-based computing platform. Then the Quantzig team identified the variables, including if the lead would be converted into an actual subscribing customer. Finally, the team created a database with multiple such response variables and supporting features.
The second phase of Quantzigs lead scoring model involved testing the model for computing the lead score of the customers. After the database was created, the Quantzig team conducted an analysis that resulted in output in terms of leads being categorized under three quality zones, namely high-quality, medium-quality, and poor-quality, with a detailed demographic analysis of each lead. After months of testing this model in the real-life scenario of the telecom giant, the Quantzig team created a system that not only gave insights into the probability of the existing lead base but also ran a background analysis on every new sign-up based on Quantzigs ML-based algorithms.
Impact Analysis of Quantzig’s Predictive Lead Scoring Model and Machine Learning-based Lead Scoring Model
Quantzigs predictive lead scoring model and ML-based lead scoring model help organizations marketing and sales teams reduce 50% of their efforts and lead generation budget by eliminating too hard-to-reach leads and identifying leads that are most likely to get converted. Quantzigs custom-build lead scoring model could identify the potential leads from the sales pipeline that got converted with 92% accuracy. Our model helped the clients sales and marketing team unlock the following benefits:
- Monthly lead conversion increased by 10%
- The turnover rate increased by 20%
- Higher profitability resultant from an increase in sales
- Large customer base
Key Outcomes
Quantzigs predictive lead scoring model and ML-based lead scoring model helped the client identify the potential leads from the sales pipeline that got converted with 92% accuracy. The clients monthly lead conversion went up by 10%, while its turnover rate went up by 20%. After a successful pilot testing of Quantzigs custom-built lead scoring model, the client extended its contract with Quantzig for implementing the model across all its facilities.
Broad Perspective on Lead Scoring in the Telecom Sector
Business organizations, including telecom service providers all over the world, have shown a willingness to upgrade their processes for identifying potential leads from a wide and diverse set of customers. The latter sign up every minute for the services/products a business offers. However, the conversion rate of these potential leads to actual paying customers is very low. This is especially true in the highly competitive environment of the telecom industry, where the churn rate is high. This is where the idea of lead scoring gains importance. Lead scoring is an automated process of analyzing attributes of the incoming leads by scoring them based on various data points. A lead scoring modelranks prospects according to the perceived value they represent to a company in terms of the revenue opportunity and the likelihood of a purchase.
Key Takeaways
Quantzigs predictive lead scoring model and ML-based lead scoring model helped the client achieve the following:
- The monthly lead conversion went up by 10%
- The turnover rate went up by 20%
- The increase in sales translated into high profitability
- It led to a large customer base