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What are some successful WFM data analysis projects and how were they implemented?

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Data analysis is a powerful tool for workforce management (WFM), as it can help optimize staffing, scheduling, performance, and customer satisfaction. However, data analysis projects can also be challenging, as they require clear objectives, reliable data sources, appropriate methods, and effective communication. In this article, we will explore some examples of successful WFM data analysis projects and how they were implemented in different contexts.

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Vinay Vasudevan
Maaz Ahmad

Business Systems Analyst | Business Strategy & Operations Analyst

1 Demand forecasting

One of the most common and important WFM data analysis projects is demand forecasting, which involves predicting the volume and patterns of customer interactions and workload. Demand forecasting can help WFM teams plan ahead, allocate resources, and adjust schedules according to changing needs. For example, a retail company used historical sales data, seasonal trends, and external factors to forecast the demand for each store and department. They then used this information to create optimal staffing plans and schedules for each location, reducing overtime costs and improving customer service.

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Vinay Vasudevan
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In WFM, statistical forecasting is the best implementation of data analysis. With models like Holt-Winter, ARIMA, ARIMAX which can be excel based, forecasting can be made more accurate which leads to saving cost. With the introduction of R & Python which is free, models become more robust and less time consuming. Currently research is taking place on Neural Network based model and on ML and AI

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Maaz Ahmad

Business Systems Analyst | Business Strategy & Operations Analyst
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In this era of data driven decision making all the WFM tasks and the decision pipelines are made automated backed by statistical data models making the most ofecise decisions based on years of historical data. I have been and currently am part of such multiple projects which are focused on shifting towards automation by introducing model training. The amount of time and human effort saved is tremendous along with really effective business insights on a very high frequency.

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Ted Lango

Founder @ WFM Labs | SVP Intradiem | Customer Experience, Contact Center Operations, Workforce Management & Artificial Intelligence
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Successful WFM data analysis projects involve establishing a structured process leveraging three critical types of information: 1) Raw statistical data generated by contact centers. 2) Expertise & knowledge that live outside the data. 3) The judgment of the forecasters. Gather the information, conduct preliminary data analysis, select the appropriate model with the best fit, and test and evaluate the model fit. The key is not relying solely on the raw statistical data and the statistical models, regardless of how sophisticated they are. The real world is full of known and unknown events not incorporated in your base trends. The expertise, knowledge, and judgment of humans combined with clean data and tested models yield the best results.

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2 Employee engagement

Another WFM data analysis project that can have a significant impact on the organization is employee engagement, which measures how satisfied, motivated, and committed the workers are. Employee engagement can affect productivity, quality, retention, and customer loyalty. For example, a call center company used surveys, feedback, and performance data to assess the engagement level of their agents. They then used this data to identify the drivers and barriers of engagement, and to design interventions such as recognition programs, training opportunities, and career development plans.

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Ted Lango

Founder @ WFM Labs | SVP Intradiem | Customer Experience, Contact Center Operations, Workforce Management & Artificial Intelligence
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Measuring your employee's engagement is often thought of by conducting annual or semi-annual employee surveys. This method must be updated for organizations that recognize the value associated with the service-profit chain. Data analysis projects that collect the pulse of employees through non-invasive means outside the standard employee survey can leverage AI models to understand and predict employee well-being risk factors. Well-being and employee engagement are complex models with hundreds of fluid elements that directly and indirectly contribute to an employee's outlook. Whether employees are satisfied or stressed can now be modeled with the power of machine learning models.

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3 Process improvement

A third WFM data analysis project that can enhance efficiency and effectiveness is process improvement, which involves analyzing and optimizing the workflows, procedures, and systems that the workers use. Process improvement can help WFM teams eliminate waste, reduce errors, and streamline operations. For example, a manufacturing company used data from sensors, machines, and quality checks to analyze the production process. They then used this data to identify and eliminate bottlenecks, improve maintenance, and optimize inventory management.

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4 Customer satisfaction

A fourth WFM data analysis project that can boost revenue and reputation is customer satisfaction, which evaluates how happy and loyal the customers are with the products or services. Customer satisfaction can help WFM teams understand the needs, expectations, and preferences of the customers, and to improve the quality and delivery of the products or services. For example, a hotel chain used data from reviews, ratings, and surveys to measure the satisfaction level of their guests. They then used this data to identify the key factors and areas of improvement, and to implement changes such as room upgrades, loyalty programs, and personalized offers.

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5 Data visualization

A fifth WFM data analysis project that can facilitate decision making and communication is data visualization, which involves presenting and displaying the data in a clear and engaging way. Data visualization can help WFM teams convey the insights, trends, and patterns of the data, and to persuade and influence the stakeholders. For example, a healthcare organization used data from electronic records, surveys, and reports to visualize the performance and outcomes of their services. They then used this data to create dashboards, charts, and maps that showed the key metrics, benchmarks, and goals of their services.

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6 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?

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What are some successful WFM data analysis projects and how were they implemented?

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2

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5

6

1 Demand forecasting

2 Employee engagement

3 Process improvement

4 Customer satisfaction

5 Data visualization

6 Here’s what else to consider

Workforce Management

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What are some successful WFM data analysis projects and how were they implemented?

1

2

3

4

5

6

1 Demand forecasting

2 Employee engagement

3 Process improvement

4 Customer satisfaction

5 Data visualization

6 Here’s what else to consider

Workforce Management

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