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Welcome back to our multi-part series on using data analytics to improve your training programs!
In the previous parts of this series, we discussed setting clear objectives, collecting relevant data and partnering with your operations team. Now, let's dive into step four: analyzing the data.
Data analytics step 4: Analyzing the data 🔎
With data in hand, it's time to dive into the analysis. Analyzing data allows you to transform raw information into actionable insights. Here’s a step-by-step approach to make the most of your data.
Descriptive analytics: Understand the basics ✅
Descriptive analytics is the first step in data analysis. By summarizing data, you can understand what’s happened and view a snapshot of the current situation. Here are its key components:
Average scores: Calculate the average scores on training assessments to gauge overall performance. This gives you a baseline understanding of how well employees grasp the material.
Completion rates: Examine the completion rates of training modules. High completion rates indicate the content is engaging and manageable, while low rates signal issues and opportunities for improvement.
Engagement metrics: Look at metrics such as login frequency and time spent on each module. These metrics provide insights into how engaged employees are with the training.
By starting with descriptive analytics, you establish a foundation of knowledge about the current state of your training programs.
Diagnostic analytics: Uncover the "why" 🤔
Once you have a clear picture of what happened, the next step is understanding why these outcomes occurred. Diagnostic analytics helps you identify the causes behind training trends and patterns observed in the descriptive phase. Here are the key parts of this phase:
Survey feedback: Analyze training survey responses to uncover the reasons behind low engagement or poor performance. For example, if many employees report that the training content needs to be more relevant to their roles, this feedback highlights a key area for improvement.
Correlation analysis: Identify correlations between different data points. For instance, you might find a correlation between time spent on a module and assessment scores, indicating that more time leads to better understanding.
Performance variances: Examine variances in performance across different teams or departments. If one team consistently outperforms others, investigate what they're doing differently and consider replicating their practices.
Diagnostic analytics lets you examine the data more thoroughly, clarifying the underlying factors affecting your training outcomes.
Predictive analytics: Forecast future outcomes 📈
Predictive analytics uses historical data to predict future outcomes. This step helps you anticipate potential challenges and opportunities, allowing you to make proactive decisions.
Trend analysis: Identify trends over time. For example, if you notice a steady improvement in performance after implementing a specific training module, you can predict that continuing this module will yield positive results.
Performance prediction: Use historical data to forecast future performance. If data shows that employees who complete specific modules perform better on the job, prioritize these modules in your training program.
Engagement forecasting: Predict future engagement levels based on past behavior. For example, if engagement tends to drop after a particular module, anticipate this and take steps to maintain interest, such as introducing interactive elements or gamification.
Predictive analytics provides a forward-looking perspective, which helps you prepare for future training needs and optimize your programs accordingly.
Prescriptive analytics: Recommend actions ↪️
The final step in data analysis is prescriptive analytics–suggesting specific actions based on the insights gained from the previous stages. This phase answers, "What should we do about it?”
Content revamp: If diagnostic analytics reveal that certain modules are underperforming, prescriptive analytics might recommend revamping the content to make it more engaging or relevant.
Personalized learning paths: Suggest creating personalized learning pathways based on individual performance and preferences. For example, employees who struggle with specific topics could be given additional resources or one-on-one coaching.
Format adjustments: If predictive analytics indicates a drop in engagement, prescriptive analytics might recommend experimenting with different formats, such as microlearning or interactive eLearning modules.
Continuous feedback loop: Establish a feedback loop to gather real-time employee input. Use this feedback to make ongoing adjustments and improvements to the training program.
Using data analytics and analyzing data is a crucial step in improving employee training programs. By utilizing descriptive, diagnostic, predictive, and prescriptive analytics, you can gain valuable insights into your training initiatives and make informed decisions to drive continuous improvement.
In the next part of this series, we will explore how to implement data-driven improvements and monitor the performance of your training programs. Stay tuned!
For more on how data analytics helped a call center improve their customer service metrics – check out our case study. And for additional information on all things L&D and leadership, follow us on LinkedIn and sign up for our newsletter.
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