Implementing the DEEP-C Framework: A Hypothetical Case Study

Let us take the example of "ABC Corp", a mid-sized technology company experiencing high turnover in its software development team. ABC Corp aims to use the DEEP-C framework to analyse the problem and create data-driven solutions. Follow the steps I proposed last week: Define, Explore, Experiment, Predict and Communicate.

1. Define: Setting Objectives and Identifying Problems

Key Activities:

• Collaborate with stakeholders to identify the core problem or business question.
• Align objectives with organisational goals and HR strategies.
• Define KPIs and success metrics for the analysis.

Guiding Questions:

• What specific problem are we trying to solve?
• Which HR or business objectives does this analysis support?
• What metrics will indicate success?

Deliverables:

• A clear problem statement.
• List of defined metrics and KPIs.
• Project scope document.

Scenario:

ABC Corp has noticed that software developers were leaving the organisation within the first year, leading to increased recruitment costs and project delays.

Problem Statement:

"Why is the turnover rate highest among first-year software developers, and what can be done to reduce it?"

Objective:

"Reduce turnover among first-year software developers by 20% within the next year."

KPIs:

• Turnover rate.
• Time to productivity for new hires.
• Employee engagement scores.

Stakeholders: HR Director, Software Development Managers, and the COO.

2. Explore: Data Collection and Initial Analysis

Key Activities:

• Identify and collect data from relevant sources.
• Clean, normalise, and validate the data.
• Conduct exploratory analysis to uncover initial patterns.

Guiding Questions:

• What data sources are available and relevant?
• Are there any gaps or inconsistencies in the data?
• What patterns or trends are emerging?

Deliverables:

• A high-quality dataset.
• Preliminary insights from exploratory analysis.

Scenario:

The HR team gathers data from the HRIS, exit surveys, and employee engagement scores.

The dataset includes 100 software developers over two years, with columns:

• Employee ID
• Tenure (months)
• Engagement Score (0-100)
• Exit Status (0 = Stayed, 1 = Left)

Exploratory Analysis:

• Turnover rate = Number of leavers/Total employees×100: Turnover rate=40/100×100=40%
• Average engagement score of leavers = Sum of engagement scores of leavers/Number of leavers: Example calculation: 1500/40=37.5
• Distribution of leavers by tenure: 80% leave within the first 6 months.

Preliminary Insight:

Engagement scores are lower among leavers, and most exits occur within the first 6 months.

Deliverable Example:

Metric Value

Turnover Rate 40%

Avg. Engagement Score (Leavers) 37.5

% Leavers in First 6 Months 80%

3. Experiment: Hypothesis Testing and Model Building

Key Activities:

• Develop hypotheses based on the defined problem and exploratory analysis.
• Test hypotheses using statistical or machine-learning models.
• Identify key drivers of workforce outcomes.

Guiding Questions:

• What relationships do we expect to find in the data?
• What statistical or modelling methods are most appropriate?
• What are the key drivers of the outcomes we are analysing?

Deliverables:

• Tested hypotheses.
• Models identifying drivers and correlations

Scenario:

Hypotheses:

• H0: Engagement scores do not affect turnover.
• H1: Lower engagement scores are associated with higher turnover.

Analysis:

• Logistic Regression to predict turnover based on engagement scores.
• Model equation: logit(p)=β0+β1(Engagement Score)

Results:

• β1=?0.05 (negative relationship).
• Odds Ratio: e?0.05≈0.95 indicating that for every 1-point increase in engagement score, the odds of leaving decrease by 5%.

Key Finding:

Low engagement scores significantly predict turnover.

4. Predict: Forecasting and Scenario Planning

Key Activities:

• Develop predictive models to forecast future outcomes.
• Simulate various scenarios to evaluate potential actions.
• Validate model accuracy and performance.

Guiding Questions:

• What workforce trends can we forecast?
• How reliable are our predictions?
• What “what-if” scenarios should we explore?

Deliverables:

• Predictive models.
• Scenario analysis reports.

Scenario:

Using the regression model, predict turnover for different engagement scores.

Prediction Example:

P(Turnover) =1/1+e?(1.2?0.05×Engagement Score)

For engagement score = 50:

P(Turnover)=1/1+e?(1.2?0.05×50) ≈ 0.57 or 57%.

Scenario Planning:

• If average engagement scores increase from 37.5 to 50, predicted turnover decreases from 70% to 57%.
• Simulating retention initiatives: A mentorship program estimated to raise engagement scores by 10 points could reduce turnover further.

Deliverable Example:

Engagement Score---------Predicted Turnover

30 -------------75%

50 --------------57%

70 ---------------35%

5. Communicate: Visualisation and Decision Support

Key Activities:

• Create clear, compelling visualisations and reports.
• Present findings to stakeholders, highlighting actionable insights.
• Facilitate discussions to drive data-informed decisions.

Guiding Questions:

• How can we present insights understandably?
• What recommendations can we make based on the findings?
• How will these insights inform decision-making?

Deliverables:

• Dashboards and visualisations.
• Comprehensive report summarising findings and recommendations.
• Stakeholder presentations.

Scenario:

The HR team creates a dashboard and presents findings to stakeholders.

Key Visuals:

• Bar chart showing turnover rates by engagement score quintile.
• Line graph projecting turnover reduction with engagement improvement.
• Table summarising cost savings from reduced turnover.

Recommendations:

1. Implement a mentoring programme for new hires.

2. Conduct quarterly engagement surveys to monitor progress.

3. Train managers to address engagement challenges early.

Deliverable Example:

• Key Insights: Low engagement scores drive turnover; 80% leave within 6 months.
• Recommendations: Mentorship, surveys, and manager training.
• Projected Outcome: 20% reduction in turnover within one year.

Summary

Using the DEEP-C framework, ABC Corp systematically analysed its turnover problem, identified low engagement as a key driver, and predicted that targeted initiatives could significantly reduce turnover. Combining data analysis, predictive modelling, and actionable recommendations, ABC Corp is poised to make evidence-based decisions that enhance workforce stability and organisational effectiveness.

Additional Notes for Implementation

• Tools: For modelling and visualisation, use analytics tools such as Power BI, Tableau, Python, or R.
• Collaboration: Maintain open communication between HR, data analysts, and leadership teams throughout the process.
• Feedback Loop: Periodically review the framework to incorporate lessons learned and adapt to evolving organisational needs.

How to Use This Template (Framework):

This template (framework) is designed to be flexible and adaptable to any organisation, regardless of size or industry. Follow the stages step-by-step, and ensure all key activities and deliverables are completed before moving forward. Regularly involve stakeholders to maintain alignment with business goals and promote a data-driven culture within your organisation.

By implementing the DEEP-C framework using this pragmatic template, your organisation can unlock the full potential of People Analytics, driving smarter decisions and achieving better outcomes for employees and the business alike.