ScoreFast™ helped increase conversions of job applicants by optimizing candidate-recruiter matching

by Jan 23, 2019Contact Center, Success Stories

Success Story

Staffing: How ScoreFast™ helped increase conversions of job applicants by optimizing candidate-recruiter matching

Our client, a large staffing organization with more than $100M in yearly revenue, has been in the business of providing services to recent graduates looking to launch their new careers. Our client’s overall mission is to enable companies to attract the best new talent and also maximize the opportunities available for those talented individuals to express their values and use their skills from early on in their careers. Agents (known as operators) from this staffing organization approach prospective companies to get job listings on their site.

While our client has a list of 1.5 million companies to pursue to get new job listings, the current conversion rate is only 1.5%. The lack of knowledge around which companies to target for the best possible outcomes, and the best operators to assign to handle each company has handicapped the supervisors and forced them to use a brute force method for outreach. The client wants to achieve a higher conversion rate without adding any new operators.

Offline Analysis:

For our deployment, ScoreFast™ ingested three sources of data; company data, call detail data, and agent or operator data. After exploratory analysis, data audit, feature engineering, and augmenting with additional external datasets, multiple models were built on the ScoreFast platform. After building the model using train and test data, we scored the datasets for the entire company with the built model and got a ranking of how likely a company is to list with the client. The client then focused more on the higher ranked companies, and less on the lower ranked companies. Here were our key findings:

  • 96% of the listings could be attributed to the top 6% of the companies
  • By focusing on the top 10% of companies, we increased conversion using significantly fewer resources
  • We showed a 60+% increase in conversion by focusing on the higher ranked companies and matching operators with the right companies.

 

 

A/B Testing:

After demonstrating the result of faster conversion and better use of resources to the client, we moved to A/B testing stage. The goal of the A/B testing phase, was to prove that our ranking of the companies, as well as our operator matching, performed better than the client’s original method.

Overall, the ScoreData platform outperformed the client’s existing methodology by 66% significantly exceeding the client’s expectations!

About ScoreData

ScoreData Corporation was founded in 2014 to help businesses leverage their data to transform customer experiences with artificial intelligence and machine learning.  ScoreData delivers cloud-native inference augmented AI/ML customer engagement applications for enhancing efficiency and increasing revenues for global companies operating in agent-intermediated markets.  ScoreData has built the ScoreFast scoring platform which is an end to end data ingestion to model development and deployment platform.

ScoreData is the only predictive analytics company that combines patent-pending dynamic machine learning, robust algorithms & econometrics, driving business results that are consistently profitable.   ScoreFast deployments have demonstrated significant lift, and performance improvements in legacy systems for several use-cases in risk analytics, fraud detection, churn and cross-sell, in a number of Fortune 1000 customer engagement applications in intermediated markets such as banking, insurance, staffing, and healthcare industries.

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