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Accurately Predict Personnel Performance

Talent is arguably the last big differentiator a business has

The difference in talent is what separates better companies from average. The best-performing companies harness the greater potential of well constructed motivated teams.

Every business is concerned with the data relating to finances and profit, often not realizing that there's equally important workforce and related workplace data that can be leveraged to deliver quantifiable value.  So why would we not extend the use of evidence-based knowledge to make optimal personnel decisions.

Hire clones of your top performers and know with 85% accuracy exactly how much revenue they would generate BEFORE you hire them

COFFE unlocks sales potential in ways not previously possible, changing the way C-suite, Sales and Human Resource leaders can strategically think about building and sustaining a competitive advantage, elevating the relative value within their teams.

We develop bespoke personnel prediction models for clients to leverage greater people productivity. A people intelligence platform extracts meaningful insights from a rich variety of proprietary talent and workforce data. Machine learning-enabled systems are engineered to produce predictive Selection -Development- Retention intelligence for client teams and key job roles.

A convergence of technology applied in a unique way

Lets go invent tomorrow rather than worrying about what happened yesterday

Steve Jobs

"I had my GPS in my car, my camera, video camera, my day planner...but the iPhone blended all of the sensory things in a new way that unlocked entirely new ways of working and thinking. A convergence of technologies applied in a unique way” led Jobs and apple creating the the smart phone.

Although not apparent to all at the time.

There's no chance the iPhone is going to get any significant market share. No chance

Steve Ballmer - 2007 Microsoft CEO

Reimagining sales team performance

A unique application of convergent technologies enables a new dimension of insight that reveals what drives top performance, the results of which can be practically applied to increase talent density.

Leaders crave accurate talent evaluation to inform resource allocation decisions that generate greater, more predictable productivity

Lavan helps sales teams systematically increase talent density. Our talent and data science platform helps sales organizations to only hire people with the probability of being top performers, while concurrently optimizing the current team.

Deployment ensures resource allocations harness the potential of the best fit talent. Clients fix performance metrics and close accountability gaps. They say :

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In the early days, some like Mr Ballmer, shrug their shoulders, while others see the advantage, embrace it and act to capture previously unseen value. 

It ain't what you don't know that gets you in trouble it's what you know that ain't so

A profoundly wise person

People and data science applied in new ways

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

Deployed companies significantly improve almost every metric on the sales team: # of high-performers versus average, Speed to ramp, Turnover, COS, training efficacy, revenue and profit.

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An international team of data scientists, psychology professionals, IO consultants and leading experts on personnel performance underpins this unique approach. The team is dedicated to the mission of predicting actual operating performance pre-hire while also using this capability to remove bias from the employment process and create opportunities for rewarding careers with a broader segment of people.  

Workforce Challenges

According to CSO Insights, around 50% of sales reps are meeting their sales quotas, up to 40% underperform, leaving only a small number of top-performers.

Poor hiring, coaching and career pathing-decisions create high early- tenure attrition, slow ramp, lost leads and revenue opportunities, lost customers and uninspiring customer experiences. Leaders naturally want more top performers, and to limit the expense of bad hires, where tangible and intangible costs are multiples of a salary.

Improving workforce performance maybe highest ROI opportunity available to all businesses, particularly if there’s a repeatable formula to drive desirable and predictable outcomes. What drives performance in the top performers?

Metrics that matter

Developing and fostering high performing teams means consistently overcoming great challenges.

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Capturing unseen value dramatically improves the economics of a sales organization.

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Reframing people assessments - A novel approach

This solution differs from and test-publisher methodologies. The approach is overtly designed around predictive modeling. While the talent science adheres to well established principles of psychology, test-development and psychometrics when building the suite of talent evaluation measures, this solution is firmly planted in data-science.

The most common solutions in the hiring space are assessment-based. They are based on simple bivariate correlations which are used to create loose job-fit buckets (e.g., pass/fail; low fit vs. high fit), and simply are descriptive not predictive analytics.

From our perspective, the purpose of pre-employment talent evaluation is not about the psychometric soundness of a simple assessment, but about the ability to accurately, precisely and consistently predict whether a candidate can and will perform in a job role for which they’re being considered. In short, while many talent evaluation tools exhibit excellent psychometric properties because of their specificity of measurement, they are inherently limited for the same reason. They are, quite frankly, too narrow, and therefore unable to measure the vast number of potential traits and attributes that capture human complexity and explain, more comprehensively, the variance in predicted job performance.

In contrast, our personnel productivity solution is designed from the ground up to generate predictability.  While we start with a rigorous talent science foundation including the use of reliable construct valid psychological measures and criterion-validation, our end product is based on the use of advanced multivariate regression machine learning algorithms to identify models that maximize the prediction of actual on-job performance.  

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Our prediction engine can assess individual differences between people at a ‘forensic level’.  That is because the system built is capable of assessing talent using over 400 broad trait and facet level measures that cover personality, abilities, attitudes, soft-skills, interests and more.  We then configure each talent evaluation differently based on a single job role that’s being analyzed.  This results in our ability to distinguish the unique combination and elaborate interaction between psychological measures including non-linear relationships that predict job performance with a high degree of accuracy.  Accordingly, we do not create exhaustive psychometric evaluations for all psychological measures because we do not use the same items repeatedly in our prediction models.  Not only would it be unfeasible to analyze and document the psychometrics of 400+ measures, it would also essentially be meaningless for our use case. Ultimately, our methodology is about what is predictive and forward looking.

We offer a repeatable process that builds highly customized prediction models, not a repeatable assessment designed around explainable psychometrics.  We have taken a different approach, a more predictive approach that is about helping clients hire and develop the best talent in an unbiased way.  

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The platform continuously displays both predicted performance together with client verified performance results. This allows users to ascertain with greater certainty, the validity of the performance predictions being made.  We believe this local validation offers the strongest correlation to efficacy available by being able to unequivocally answer the question: “Does the unique blend of psychographic measures combined with business intelligence data give us the performance outcomes we’re seeking from our evaluation of the existing employee cohort?”

Why this reframed approach?

We believe the current approach to talent selection used by industry has great room for improvement:

1.         Companies over-rely on resume screens and interviews when making hiring decisions. Neither of these methods has predictive validity and each of them introduce significant bias into the hiring process.

2.         All hiring solutions using assessments utilize a one-size fits approach, none of which can predict actual on-job performance because they measure only a small handful of the characteristics that make us unique (e.g., traits, abilities, etc.).

3.         Most assessments create generic hiring recommendations that lack context around the job role a candidate is being assessed for.  Consequently, people are being denied jobs based on generalized benchmarks rather than client and role-specific validation data.

4.         Too many talent evaluation tools oversimplify human behavior.  These tools are built around assumptions such as more is always better, that good ‘traits’ are always good, and that all ‘traits’ are equal predictors - none of which hold up when validated with criterion data. People and their contextual performance are far more complex than that.

Our mission is to improve upon the status quo.  To offer clients a contextually predictive, customized, bias-free solution that understands the complexities of human behavior and delivers talent intelligence to help enterprises optimize human capital. 

Foundations

The Platform is designed as a decision support engine that ingests client business intelligence and employee talent data with the goal of hiring and promoting people with the highest probability of being top performers. This is achieved through the development of Performance Fingerprints.

Performance Fingerprints are a bespoke configuration of psychographic characteristics that predict actual on-job performance with a high degree of accuracy in a specific job role.

Performance Fingerprints are continually updated (recalibrated) by adding new performance data over time.  The Platform offers forensic level talent science utilizing over 400 available psychographic measures covering personality traits, interests, attitudes, soft-skills, and abilities.

Performance Fingerprints use a ‘Scientist-in-the-loop’ talent and data science approach. First, the Science Team conducts a job analysis to understand the role, the traits that help or hinder successful performance in the role, and the intricacies of a client’s performance data for that role. Next, incumbent employees complete a custom-configured psychographic questionnaire in parallel with the collection of performance data for those same employees. Finally, the talent data and the performance data are analyzed using a multivariate regression machine-learning algorithm to distinguish the unique combination of psychographic characteristics that best predict actual performance in the role.

Because a Performance Fingerprint is locally validated through this analytics process, it is generally Equal Employment Opportunity Commission (EEOC) compliant from the outset. Unlike traditional assessments that focus on simple linear relationships between traits and performance, a Performance Fingerprint truly represents the intricate interplay between specific psychographics and performance outcomes, capturing the complexities of human behavior ‘at work’ in a defined context.

Our prediction engine is not a one-size-fits-all assessment or a one-time event. Performance Fingerprints are customized for each job role based on a process of local validation and continue to get more accurate and deliver more intelligence as they are re-calibrated at regular intervals.  The Platform can quantify actual performance (e.g., predicted sales revenue) before a hire or promotion is made, establishing a repeatable, bias-free hiring formula that is improved in accuracy and integrity over time. The ideal user for the prediction platform recognizes the importance of people analytics for making evidence-based decisions and the value that on-going validation has on the quality of talent decision-making.  

While the system was designed for hiring and promotion, it has three specific use cases:

1.         Better Incoming Talent:  Quite simply, the system is designed to create a bias-free method for hiring the right talent with the greatest probability of being a top performer in a specific job-role.

2.        Improve Existing Team:  secondly, the talent intelligence from the Platform provides data about who to promote, potential career paths, and how to invest enablement resources (e.g., training and coaching) most effectively, for promotion and career paths. As new Performance Fingerprints are added and new performance data ingested, the system identifies how effective employees will be in other job roles.  As for enablement, the Platform provides clear data about where candidates may struggle, providing clear direction for training and coaching.  And because the predictive models make it possible to identify who is reaching their potential versus who is not, the Platform provides intelligence about where to invest valuable resources.

3.        Deep Learning:  The third use case is for more formal strategic workforce planning and people analytics.  As the Platform continues to get smarter with more data, it can then begin to predict more complex longitudinal outcomes such as tenure, turnover, absenteeism, or low-base rate criteria like safety incidents.  From employee fixed costs (salary, benefits, etc.) versus performance, leaders can rethink their organizational structures and human capital needs. For example, it may be possible to realize the same performance with a smaller but more capable team or for the same cost, bring on more junior but equally effective talent.

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Platform Design

Technically the Platform is an adaptive closed-loop business intelligence platform designed to:

1. Construct highly predictive models of job performance, Performance Fingerprints, based on the statistical relationship between psychographic characteristics of employees and job performance metrics (KPIs) for a specific role.  Performance Fingerprints are developed within the Platform using a proprietary combination of Talent Science and Data Science. The Talent Science component includes the ability to build customized psychographic questionnaires that measure any combination of over 400 psychographic characteristics including personality traits, attitudes, interests, abilities, and soft skills.  The Data Science component is designed for ingesting and analyzing employee-specific business intelligence data including objective metrics (e.g., % of quota met, # of units sold/month), performance ratings, counterproductive performance indicators (e.g. absences) and other KPIs of interest.  A multivariate regression machine learning algorithm is used to identify the statistical relationship between the psychographic characteristics and business intelligence data to arrive at an initial Performance Fingerprint.

2. Function as an enterprise-grade talent evaluation system for screening employees, career pathing, making promotion decisions, evaluating productivity challenges, and developing workforce strategies.  The platform provides assessment administration capabilities (i.e., sending invites to complete a psychographic questionnaire), candidate/employee screening guidance (i.e., the Performance Predictor score), psychographic reporting (i.e., comprehensive explanation of candidate/employee scores), and user management (i.e., different privacy/security settings for various users of the Platform).  In addition, the Platform provides a learning management system designed to provide enablement resources based on candidate/employee psychographics.  Because this document is focused on the science behind the Platform, product use features and technical specifications have not been included.

3. Provide continuously recalibrated Performance Fingerprints based on the inclusion of new business intelligence data.  Because business conditions are constantly changing, the Platform is designed to continuously ingest performance data, re-analyze the psychographic vs performance relationships, and provide a refined Performance Fingerprint for talent decision-making.  Because the algorithm is updated using actual performance data from current employees including those who were hired using the Platform, the robustness of the prediction model continues to evolve and improve over time.

The Platform is currently available in several languages including English, Chinese, Spanish, French and Japanese, and is being used to hire talent in over 50 countries. While the Platform’s technological features are updated on an on-going basis, the talent and data science of the components are well-defined and comprehensive.

Reach out for further information contained in our Technical Manual comprising the following 3 core sections:

A.         Developing Performance Fingerprints

Summarizes the entire approach to the development of client-specific Performance Fingerprints. This section sets the stage for understanding the purpose of the talent science and data science.

B.         Talent Science Overview

Includes information about the job analysis process used to configure the employee-validation version of a psychographic questionnaire, the nature of the psychographic measures, their psychometric properties including validity, reliability and fairness, and information about legal defensibility.

C.        Data Science Overview

Covers the data cleaning process and the in-depth multivariate regression analysis as it applies to the development of Performance Fingerprints.

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