Augmented People Analytics For Enterprise

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Smart Pattern Discovery from People Data

By unearthing non-obvious patterns from people data, you can understand employee data and its impact on business performance.

HeartCount, the number of employees who are culturally fit, highly engaged, and high performing, is what counts for the sustained success of any organization. HeartCount solution will enable you to outsmart your own biases through data-driven talent decisions.

FEATURE

Describe, Discover, Predict People Data

By simply uploading an excel file, you can immediately access descriptive, exploratory, and predictive analysis. There are so many patterns hidden in people data that are too complex to be recognized by human mind alone.

Drill-down

Slice and dice HR KPI to provide contextual information to HR KPI/Metrics. By breaking down KPI according to various dimensions, informaion suddenly gains new meaning.

Driver / Difference

Driver: Quantitatively understand major drivers/factors associated with KPI. Difference: Compare statistical differences between any combination of two employee groups.

Micro-Segmentation

Find the micro-segments of employees who have shown the same characteristics or behaviors. You can improve KPI by explaining what constitutes the most homogeneous employee groups.

Smart Discovery

Smart Discovery feature can algorithmically discover non-obvious pattern(insight) from People Data. Our hypothesis-free approach searches for meaningful patterns until it finds some.

Visual Discovery

You can have liberating experience with the visualization tool at the speed of your thought. With SmartPlot and SmartSearch, you can visually understand relationship between variables and search employees who are most similar to the given conditions.

Predictive Analytics

Predictive analytics in HR today are in their infancy — they have simply not been used for long enough by enough people. However, once you have reliable explanatory model then you can build predictive model for retention/performance. Also, you can collect data using our survey tool.

Our Approach

Unearth the hidden talent and stop being blindsided by the unpredictability of employee.

Problem Definition

Data change the way we think about managing people. Far too many important people decisions are still made on the basis of gut feel or anecdotal experience. You should think hard about what you do not know to make better, more informed decisions and ask the well thought-out questions to the data. Asking the good questions is the first step of data-driven talent management.

Data Gathering

One major misconception in HR predictive analytics is that big data is necessary for analytics to provide big value. not only is this false, it obscures the fact that the economic value of analytics projects often has as much to do with de-biasing decisions by analyzing the pattern in available data sets as with the volumes and varieties of data involved. All you do is to hand over currently available HR data in excel/csv format then HeartCount will apply the right machine learning algorithms in secure manners complying with the necessary regulations.

Predictive Modeling

Using Machine Learning algorithms and Data Science disciplines, HeartCount builds customized predictive talent models (flight risk model, high-potential model) that could predict specific behaviors (attrition, high-performance) for specific organizations. HeartCount also transparently presents the predictive models to help you understand the causal relationships between predictors and performance.

Problem Fixing

Understanding the personality traits(mind sets) and/or skill sets of specific job families (call center, salespeople) that could predict high performance (higher customer satisfaction andl sales achievements) can provide critical insight to a business – both in terms of hiring decisions and as a route towards understanding skill development needs within the job familly. HeartCount’s predictive models could help organization in fixing unwanted employee attrition and under-performance.

Model Improvement

Both people and machine make decisions based on on their models. Difference is that people seldom change the model, the existent new conflicting information notwithstanding, but machine can improve the model continually learning from new information. HeartCount’s model accuracy can be continuously improved by learning from new, relevant data creating positive feedback loop around the learning, explanation/prediction, validation process.

Values to Customers

Maximize Human (and Enterprise) Potential

Our intuitions frequently lead us astray. Relying on your gut, decision making becomes faster and simpler, but quality often suffers. To “de-bias” our employee decisions, it’s essential to broaden our perspective by scientifically unearthing new and, sometimes, counter-intuitive insights about employee. HeartCount algorithms will help you quantitatively articulate what truly drives employee performance through science and evidence. Data analysis is not a destination, but a journey. HeartCount will stay with you.

Hiring Success
Which candidates will perform the given job better, longer, more happily.

Unwanted Turnover Prevention
Which (high-performing) employee are at risk of leaving the organization and why?

Business KPI
What are main drivers for increase/decrease of people-related business KPI/Metrics?

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