The case for predictive lead scoring in recruitment

How predictive lead scoring can optimize the recruitment process

by Miro Maraz March, 2018

Predictive Lead Scoring Recruitment Optimization Machine Learning in Recruitment

A core function and a primary challenge many talent agencies and recruiters face today is the pressure to generate more qualified leads (job requisitions), prioritize them and convert them into successful placements. But when good lead generation strategy feeds new jobs into the pipeline and it greatly exceeds the capacity to fulfill those jobs – a new approach to prioritization and segmentation is needed to maximize conversion rates. This is where predictive lead scoring comes in as a modern, analytical and automated solution designed to predict the likelihood of a conversion or the potential value of a open job requisition.

What predictive lead scoring can do

Predictive lead scoring provides talent agencies with a more precise, quantitative view of which jobs are most likely to convert. This view can consist of multiple outcomes – quantitative values aimed at capturing various perspectives of a lead value. This can be likelihood to convert (interview/placement), revenue potential, hourly rate or risk of cancellation or loss to competition.

Predictive lead scoring is an automated system that will assign scores based on knowledge it systematically extracted from historical data. Behind the scenes, machine learning algorithms crunch vast amounts of relevant data and learn from past events to extract patterns and rules that are used to score new incoming jobs in real-time to determine their potential value. It presents itself as a set of scores attached to each new lead for aimed to help decision makers prioritize, segment and fine-tune their recruitment process. It is consistent, automated way to qualify leads in real-time based on your own data – turning your historical data into a valuable asset providing insights into the future.

How to get started

Machine learning is still a pipe dream for many organizations with Gartner estimating that fewer than 15 percent of enterprises successfully get machine learning into production. High tech, telecom, and financial services are the leading adopters of machine learning and AI. These industries are known for their willingness to invest in new technologies to gain competitive and internal efficiencies. Even so, companies need to start experimenting now with machine learning applications to get it into their DNA to better refine techniques and use cases and to stay competitive before a new disruptive force will can threaten their business models. This is true for the recruitment space as well. Recruitment space has seen its share of modern AI: from AI-powered chatbots to automation of job advertisements to screening and matching candidates from a large applicant pool. Data and algorithms are becoming important components in a fast-changing and the increasingly competitive world.

Predictive lead scoring is a machine learning driven process and as such it requires a lot of data. This data comes from both internal systems and external ones. Internal systems include that ATS, CRM, financial systems, time-entry but can also include data from activities such as email and communications and more. External data can come from public data sources, external clients and partners. The prerequisite for a truly effective model is to analyze and stitch together relevant and meaningful data that can provide both specific information about the given organization as well as capture external factors in pay such as broader economic factors, market situation and more.  Companies attempting to build predictive lead scoring models often encounter limitations in their own data, low fill rates, missing attributes or simply not enough relevant history. Similarly assembling rich datasets from multiple disparate sources has its own set of challenges, starting with a single source of truth to duplication, incorrect entries and more.

However hard and complicated it may sound, the time to act is now. You will not know if you have enough good data to build an effective model unless you try. It can start with a simple assessment project that maps out potential data sources and evaluates the fitness to build predictive models. From there a series of prototype models can be built that will provide indispensable information into understanding the quality of your data, the holes that need to be filled and ultimately the potential to your bottom line. There are no shortcuts here, no magic – building effective predictive models is best done in a collaboration with business folks, domain experts, data scientist and engineers.  

Why lead scoring matters

So why should you care or consider predictive lead scoring? Predictive lead scoring is a new tool in a modern recruiter’s toolkit – it will analyze and learn from your historical events using cutting-edge machine learning and AI technologies to determine exactly which lead has the biggest potential value for your organization. From a strategic standpoint, predictive lead scoring will provide your organization insights into what makes a good lead, will help you understand your pipeline and uncover hidden opportunities for additional revenue and efficiencies. It’s a step towards a smarter, more data-driven & analytical organization. Finally, you can stop guessing if your leads are worthy, have a richer perspective backed by facts and numbers and deploy prioritization strategies to invest time and money into leads that will produce the greatest ROI.

Want to score your job requsition leads?

We make this lead scoring process available via a platform called ReQue, you can learn more about it here.

If you're interested in how ReQue can help you, we'd love to talk to you - please schedule a brief call below.

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