FINANCIAL SERVICES TEAM
Beginning Environment: This study included 59 incumbents in one position who had over one year of recorded performance against four objective key performance indicators (KPIs). All incumbents completed a whole-person assessment measuring 20 individual characteristics.Initial Actions: We first combined the assessment results with their performance scores and used regression analysis to (statistically) identify that they had 9 Top Performers, 40 Middle Performers, and 10 Bottom Performers. We then used the Top Performers results create the high-performer benchmark.
Analysis Process: After confirming the benchmark was predictive for all four KPIs, we added machine learning algorithms to prove which specific characteristics (out of the 20) were most important in predicting performance. We then built a Predictive Scorecard that accurately predicted which group each incumbent would end up in (Top, Middle, or Bottom) – 59 out of 59 times, or with 100% accuracy! And we showed the client how to use this scorecard to focus their training and development efforts to reap the highest benefits.
Results: Top Performers Overall job match score was 17 points higher and they completed 80 more transactions/year than the Bottom Performers. They also were more accurate so there was less rework required.
Benefits: Historically, 20% of this team’s hires were Top Performers and 20% were Bottom Performers, and this team plans to add 10 new people in the coming year. If they use this information to hire 2 more Top Performers and prevent them from hiring 2 Bottom Performers, it would result in 160 additional closed transactions per year. At an average profit of $1,500/transaction, this would be $240,000 additional profit.
ROI: ($240,000 – $26,500) / $26,500 = 806%
This Case Study is an example of data science support for Assessment Specialist Mike McCormack from PeopleRight in Addison, Texas.
Download the PDF here: ROI – Financial Services