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Precise job fit evaluation for current and prospect employees

Job Performance Driven Evaluation Algorithms

If the goal of employee evaluation is to engage people with a high probability of performing well in a specific job, then an evaluation algorithm must be built using actual employment outcomes, and which directly reflect organizational goals relevant to the job role. These may include job performance effectiveness and productivity measures such as sales goals attainment %, length of service, promotions, commission increases, probationary survival, disciplinary incidents, absence or any quantitative measure.

These concrete business outcomes then need to be correlated with independent variables, but instead of utilizing resume data, specific keywords or big data, use meaningful and relevant attribute measures that can differentiate individuals such as curiosity, work ethic, accountability, gratitude and optimism, which have no gender differentiation or culture boundaries.

Hence, best practice to prevent bias being fed into an algorithm is not to use any data that could potentially reflect any socioeconomic inequities and only consider traits or attributes that contribute to and or detract from on-the-job performance. Assessing attributes also addresses the deep-rooted biases, prejudices and stereotypes ingrained in corporate cultures, and candidates will be able to verify that the organization has opted to use fair and inclusive selection methods that provide everyone with equal opportunity.

We utilize a unique 'white box' (also known as clear box) approach called performance Fingerprints. The algorithm developed in the construct of a performance Fingerprint is locally validated, meaning that is draws on a company's actual performance outcomes in a specific job role, ie. statistical analysis is conducted between predictor traits and the concrete business outcomes that are derived from existing culture or environment. This is known as criterion or concrete validity and is the most powerful way that a company can demonstrate the job-relatedness and validity of its pre-employment assessment.

Proprietary datasets - not big data sets - are necessary to deliver on the goals of increasing top performers, reducing mis hires and eliminating the opportunity for bias.

Performance Predictors - Forensic inventory of 400+ traits

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Transparent, Explainable and Provable. Through a blind process, assessment results identify what human qualities drive specific on-job company performance.

When human-centric science and data-driven analysis is rigorously applied to the hiring process decisions are faster better and consistently more accurate; with less effort, frustration, and failure than conventional methods.

Why are performance fingerprint possible now?

Below is an example of the latent combination and weighting of traits driving productivity in an AE company sales role.

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Performance is situational and contextual. Each role has unique demands and so each fingerprint will present a unique combination and weighting of traits

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Algorithm Shelf-Life

Job-specific selection algorithms must also be adaptive and continuously learning because prediction results may not be sustainable and/or will decay over time. As conditions change; applicant socio-demographics, economy, market, business model, customers, job task...the selection effectiveness of an algorithm will decrease.

It's therefore important not to 'set and forget' algorithms by continually revalidating them. This is done by continuously ingesting post-hire performance data and recalibrating the algorithm. As the algorithm is refined, it adapts to changing conditions to increase its predictive power and selection effectiveness over time.

Our system provides continuous correlation analysis of employee's KPI performance, learning to replicate the best performers in an organization. Performance Fingerprint algorithms are ingested with new performance data every 6-12 months and the prediction model recalibrated to improve its selection effectiveness. It also continues to be applied back to the existing team to check predictive robustness with actual performance outputs.

Knowing the qualities that drive performance, through validated data is critical

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Out of the 400+ Human Centered attributes able to be measured, if you were asked about how “cognitive intelligence” would impact the ability to set sales appointments as an example what would you say?

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We find many Sales VPs rank candidates higher if they have the ability to speak well, and in an articulate way

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Again, in this case, with this company there is no correlation

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Something that we might intuitively know but that is rarely part of the conscious decision making process.

Clients gain a deep forensic understanding of what is contributing to the success of the people on their team, and what things are early indicators of failure, or lack of fit across a wide variety of human attributes…so that they can make job matches that are more successful, and manage precise coaching training and development for each individual.

The following are some further examples of the 400+ qualities that can correlate to specific companies performance KPIs. All relate to a CAE at a company in the Cybersecurity software industry.

Interpersonal Versatility

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Competitiveness

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As the facet of job involvement presence increases in sellers so does KPI production

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Pressed for time shows a curve peaking at around 60% then tailing off

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In this role and firm Hard-Driving has a more complicated relationship to performance

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Cheerfulness has a positive correlation

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Gregariousness almost flat

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Feedback Seeking

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Psychopathy

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Implementing People Analytics

Performance profiles enable companies to ;

1. better track the impact of poor hiring decisions

2. treat each new hire as an investment decision, with analytical data predicting the sales performance impact pre-hire

3. increase accountability and the consequences for making the right hiring (investment) decision

4. accelerate the hiring process, and make it more agile, by enabling recruiters and hiring managers to prioritize their focus on higher value recruitment prospects

5. stop turnover being viewed as an unavoidable cost of doing business

6. empower sales leadership with data, tools and intelligence to make better hiring decisions

7. develop tailored coaching and training to improve individual weaknesses.

Performance Profiles are not a one-time process - Implementing role-based profiles empowers a company with new tools to engineer performance outcomes.

A look at the overall process.

Our Process - Express Profile Analytics

Delivers a ‘deep’ picture of your sales talent, what to develop and coach, and who you need to recruit. The process is blind eliminating the opportunity for bias elegantly supporting diversity and inclusion policies.

1.  Company Psychographics - measure existing company DNA

2.  Performance Data - add detailed team performance data

3. Performance Profile- run predictive modeling to create a role-specific performance profile

Hiring Candidate Insight

Candidates interact with the system at the start of the process, via a simple link (custom for each role and for your company), with your message and company branding. They spend 20-minutes to complete the assessment, the results of which is provided to you in email summary, and visible in a dashboard. The accuracy of predictions trends in the range of actual performance being within 15-20% of what was predicted.

The creation of the Performance Fingerprint.  This is where we complete the proprietary research and ingest historical performance data and correlate it with the human centered attribute data.   

Subscription access to the Platform. This is an unlimited user annual license to use across your inbound hires for a role but also to recalibrate periodically to ensure elimination of bias as the business evolves, and adapts. Virtual work stretches the qualities that may have worked in a prior process. Another fingerprint will likely emerge.

Engagement Process - First Step - Trial - Results - Deployment

Framing what the typical 'get started' process looks like

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  • Agree and define role(s) on which to create Performance Fingerprint(s)
  • Executing an MSA that broadly outlines an agreement and covers things such as data privacy  
  • Create a TOS that outlines this specific fingerprint 
  • Meeting with stakeholders, the executive sponsors and personnel intimate with of the role - science team.  
  • Initial configuration of the system used to gather the human attribute data 
  • Team invited to participate in a survey online - 45minutes.  In conjunction an assigned admin ascertains performance data to map along with applicable criterion to be might applied to the role(s)
  • Process completion - custom Performance Fingerprint generation
  • Schedule brief/findings meeting with the Executive team and stakeholder to review correlation analysis and personnel prediction data
  • Deployment and integration into existing workflow, ensuring successful implementation and realized value
  • Periodic 6 -12 month recalibration

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