Expanding our vision of what is possible AI can serve as a strategy to fulfill a critical company mission.
Netflix recommendation engine doesn’t make the recommendation based on the movie description. They have complex Ai algorithms that watch the movies and break them into millions of data point: emotion, characters, theme, actors, races, language, cadence etc. They also “watch”/ingest your viewing habits and through pattern recognition across millions of people they are able to make very accurate recommendations. In the end you make the choice…but you have better info. Just like our product.
Uber: Complex mapping Ai’s to connect us with the most likely matched driver in seconds.
AI uses mathematical models that have existed for decades, many driven from 'probability theorem' discovered by 'Bayes' 300 years ago. Like regression analysis, applied to many algorithms to drive accurate predictions, and greater insight for better and faster decisions.
Business is arguably the ultimate team sport, where high-performing teams are essential to compete and win.
Business leaders place tremendous importance on data relating to finances and profit for insight to aid critical business decisions. Typically, they don't leverage equally important, untapped workforce and workplace data to help improve personnel investment decisions that have arguably the largest impact on overall performance.
Leaders who prioritize talent and business strategy develop resilient companies, through higher-performing, adaptable and more agile workforces. They recognize performance is about fit. Survival, or 'thrival 'of the the fit not fittest.
Many leaders believe they leverage people data, but really don't to the extent thats possible. Not even those who embrace and practice talent optimization. Why is that?
Talent is arguably the last big differentiator a business has. It’s what separates average companies from more successful resilient, innovative companies who recognize the importance of their people and prioritize talent, making it an integral part of strategy. Talent optimization requires an intentional people strategy, aligned to a business strategy to dynamically improve business results.
Poor personnel, hiring, coaching and career pathing-decisions create high early- tenure attrition, slow ramp, lost leads and revenue opportunities, lost customers and uninspiring customer experiences. Reducing miss hires, and increasing top performers, maybe the highest ROI opportunity available to all businesses, particularly if there’s a repeatable formula to drive desirable and predictable outcomes.
More top producers, less mis-hires. Hire only people with the highest probability of becoming top performers, but do so while helping to ensuring DEI policy. To aid this effort we want to Remove unconscious bias, to mitigate high liability legal indefensibility risks and creating employment opportunities for a broader group of people.
Reducing miss hires, and increasing top performers, may be the highest ROI opportunity available to all businesses, particularly if there’s a repeatable formula to drive desirable and predictable outcomes.
COFFE© - Cognitive Operational Forecasting for Enterprises - our decision engine, delivers the ability to predict performance both pre-hire and for existing personnel. The resulting white box selection algorithm and analytics are compelling; satisfying widespread challenges, improved scalable hiring selection, personnel development while uniquely eliminating bias opportunity. Blind recruitment is the best practice to remove unconscious bias, and ensure equal employment opportunity and workplace diversity, equity, and inclusion.
Talent optimization is about getting the right people in the right seats more effectively and fully engaged. People who have a proven propensity to perform, who can be identified based on validated data. People are more likely to be fulfilled and enriched if they are naturally wired to fit and do well in a role, team and organizational culture. If we can measure key drivers of performance we have a basis on which to improve.
Arbitrage is the simultaneous buying and selling of securities, currency, or commodities in different markets or in derivative forms in order to take advantage of differing prices for the same asset.
Applied to performance: Fixed COS (Cost-of-Sales), largely captured in the payroll & benefits of the sales team. The difference in a person with 170K renumeration package delivering at 50-60% of plan, versus 100% of plan? Multiplied by number of under-performers? How do we capture that undelivered, unrealized value?
What’s the difference inside your org between top and bottom performers?
Intuition, and subjectivity are powerful qualities, which can be difficult to reason with even in the face of science.
"People trust that the complex characteristics of applicants can be best assessed by a sensitive, equally complex human being. This does not stand up to scientific scrutiny" The same can be said for an existing team. How does training help a team member, or team collective? How do we know what the ROI is on training? How can we improve coaching?
A great deal of scientific research in this area concludes that intuition and subjectivity results in very poor selection decisions. Also results in bias related to race, gender, pedigree
"The traditional unstructured interview has remained the most popular and widely used selection procedure for over 100 years(Buckley, Norris, & Wiese, 2000). This is despite the fact that, during this same period, there have been significant advancements in the development of selection decision aids"
When we look at key data, the numbers tell the story. Hiring and developing top performers, even at companies with best practices in talent optimization can be a widespread economically inefficient pursuit, and very costly.
A bespoke algorithm, leveraging multivariate regression analysis, applied across granular company data enables a business to confidently improve team performance - Hiring better, Improving incumbent teams, and deep analytics enabling more effective management.
Perhaps the greatest ROI opportunity for all businesses is the ability to optimize talent.
If the goal of employee selection is to select people with a high probability of performing well in a specific job, then the selection 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
Transparent, Explainable and Provable. Through a blind process, assessment results identify what human qualities drive 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.
Below is an example of the qualities driving productivity taken from a company sales role performance fingerprint.
Traits, attributes and overall performance drivers are role specific, as proprietary to each business as their environment, and they often challenge the intuition of leaders.
Performance is situational and contextual; Role fit, manager effectiveness, team dynamics and culture are all drivers of performance being measured at a snapshot in time. Forensic analysis of performance qualities of team members can reveal why disparity occurs across similar teams with similar prediction numbers such as Insight into manager effectiveness.
In addition to being job attainment data driven, 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, such as changing applicant socio-demographics, economy, market, products, customers, job task etc, 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, and continuously learns 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 (ie. correlated with its performance) to check its predictive robustness with actual performance outputs.
The role fit is paramount. Untapped data provides insight that shifts reliance on subjectivity, and gut instinct responsible for many mis-hires
Knowing the qualities that drive performance - through validated data -is critical
Out of the 400+ Human Centered attributes we measure, if you were asked about how “cognitive intelligence” would effect the ability to set sales appointments as an example what would you say?
In this company's case, there is no correlation
We find many Sales VPs rank candidates higher if they have the ability to speak well, and in an articulate way
Again, in this case, with this company there is no correlation
Something that we might intuitively know but that is rarely part of the conscious decision making process.
Our clients usually are delighted as they gain a deep forensic understanding of what is contributing uniquely 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.
Ineffective managers have a high impact on performance. Knowing the qualities that work for high performing managers is crucial
Budgets set aside for training and coaching, can be tailored to individuals' precise needs with more granular data
The slide below speaks to the issues faced by C-Level or VP level leaders who have other sales manager’s reporting to them NOT salespeople.
How do they evaluate the effectiveness of leaders across different regions when they are one-two levels removed?
In the past, they simply rely on numbers. What about the core capability of the team? Are the team’s deployed operating at the top of potential? Which manager’s are most effective at raising the performance capacity and narrowing the gap between projected and actual?
We answer these questions on a forensic level, removing disparity in the raw talent DNA.
The following are some further examples of the 400+ qualities that can correlate to specific companies performance KPIs
Pressed for time
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.
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
Candidates interact with our 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.
An interesting example below shows the correlation of some traditional more subjective, decision criteria. Correlation between performance and a coin toss is obviously 0 - Education 0.10, Experience 0.20 ...Not great predictors of performance!
The pricing model has two primary components:
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. Setting up branded portals. This is one time fee.
The second component is a 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.
Framing what the typical 'get started' process looks like