This article will guide you through how Andela, a company that connects leading technology companies with talented software developers from tech hubs across Africa, went from hiring 3 senior software developers in 60 days to hiring 10 senior software developers in 30 days.
People Analytics: At Andela we are driven by people analytics**.** We recognize that hiring doesn’t have to be a mystery — you can gain useful insights through well-organised data. A consistent goal of ours is to hire and support the continent’s top technologists. To accomplish this, we need to build systems that let us know how we are doing, and what we need to do to get better at attracting top talent. In this instance, we needed to increase the number of senior developers within the organization in a short period of time in order to support the learning of the rest of our developer community.
Hiring Systems: In order to hire these top software developers in a short amount of time**,** we built a system using Google Sheets that we believe people can leverage from. In a 4-part framework we will walk you through this system:
- Part I: Defining Hiring Probabilities — this section will cover how to use historical data to calculate the likelihood of hiring an applicant within the pipeline.
- Part II: Tracking Role-Specific Candidate Status — this section provides a brief overview of how we integrated Google Sheets with our hiring system.
- Part III: Calculating Expected Values — this section will walk you through how to calculate the probability of hiring active applicants.
- Part IV: Making Time-Bound Decisions — this section provides insights on how to calculate probability by desired attributes.
Part I: Defining Hiring Probabilities
- We used historical data from our Applicant Tracking System (-ATS) to understand what percent of candidates at each stage are likely to be hired.
- We created classes to understand the likelihood that a given individual would be hired. We round these up to the nearest tenths.
- We thought through hiring process changes based on the probability of advancing to the next stage. This step — by — step predictive analysis helped with the goal of increasing efficiency in the hiring process.
Part II: Tracking role-specific candidate status
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We integrated with our ATS using real-time and periodic updates in Google Sheets.Real time updates were done using a Greenhouse add-on — Greenhouse Report Connector — to link Greenhouse reports and Google Sheets.Periodic updates were sent to Google Sheets with Greenhouse candidate exports and formulas. This required manual refreshing/updating of the data.
- We categorized by the most important features, whether role, stack, resumption lead time or any other attribute we wanted to display. We needed to forecast for probability of applicants within different stages in the application pipeline based on their prominent stack and lead time to resume at Andela.
Part III: Calculating Expected Values
- We multiplied the probability by the number of candidates so we could see how many people under a specific stack were in each stage.
- We used that to predict how many people would be hired by stack.
Part IV: Making time-bound decisions
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We showed three dimensions: Time to hire, Role to hire, and Number of people likely to be hired. We used these three metrics because we believe they are the most impactful to growing our business.Google Sheets in three dimensions: Use a data validation-drop down menu to let people toggle the third axis. A three dimensional table is used to display data based on three different variables, in our case by application stage, stack and resumption lead time.
- We included “All Stack” as a dimension: this was included in our drop down menu and we used the “if & countif -formula” to calculate the current numbers.
Result:
- Proven increases in efficiency and outcome: We had hired 3 senior software developers in 60 days prior to these systems. We hired 10 in the 30 days after we implemented these systems. That’s a 233% increase in number of hires, with a 50% decrease in time.
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