Examples of Companies Using Analytic Scorecards

This is the continuation of the transcript of a Webinar hosted by InetSoft on the topic of "Business Intelligence Agility" The speaker is Mark Flaherty, CMO at InetSoft.

Another doctor might be one that won’t even give you an appointment, and you’re still just communicating through emails and phone calls, and so you have the beginnings of a relationship there, but maybe they don’t trust your pharmaceutical company, and they’ve had bad relationships with you before, and so that would be an example of somebody you’re trying to get a first date with whereas the other one would be like a marriage.

Another example is a company that’s in the textile business. They call this gauge their matrimonial index, and that’s something everybody understands. The bottom of the scale is a lunch date and the top of the scale is married and they will love each other eternally.

So that to me is an example of an index. Another client, a bank, has an employee satisfaction index. One of the measures in there is how much do employees work on a regular basis. How many hours do they work? They can track when they go home at night but still spend two hours at night going through all their emails, they are still working.

Then they do a weekly how was your week survey; red, yellow, green, and you do it when you turn in your timesheet. If you had a green week you feel guilty getting paid, you had such a great week. Yellow is it’s pretty typical, but that’s why you get a paycheck, and the red is you’re thinking about putting your resume out there.

So everybody put this in there on a weekly basis and it gives them a way to measure the overall level of stress on a weekly basis. That goes into the index and they do focus groups once a month with a random selection of eight or ten employees and ask them ten questions and then they track turnover as a measure. They track absenteeism as a measure, and they track unused vacation time, people who don’t take their vacation because they are too stressed out or too overworked. So all these roll up into an index that says what level of employee satisfaction do we have in our company. So those are just a couple of examples indexes for complicated things that are hard to measure.

Case Study of a Bank Using an Analytic Scorecard to Track Employee Satisfaction

Prime Financial Bank (PFB) is a mid-sized, regional bank with over 5,000 employees, known for offering a wide range of financial products and services. In a competitive market, PFB understands that its success is directly tied to the satisfaction and well-being of its employees. Acknowledging that employee satisfaction is not only key to reducing turnover but also to improving customer service and overall productivity, the bank decided to implement a more data-driven approach to track and enhance its employee experience.

In 2022, PFB introduced an Analytic Scorecard designed to track employee satisfaction across multiple branches and departments. This scorecard integrated data from employee feedback surveys, HR metrics, and productivity reports to create a comprehensive view of how satisfied employees were with their work environment, management, benefits, and overall job satisfaction. The goal was to use this data to inform policies, reduce attrition, and foster a better work culture.

Challenges Faced by PFB

Before implementing the Analytic Scorecard, PFB faced several challenges regarding employee satisfaction tracking:

1. High Employee Turnover: PFB's turnover rate was climbing, especially among customer-facing roles such as tellers and customer service representatives.
2. Low Employee Engagement: Employee engagement surveys revealed that many workers felt disconnected from management and were not motivated to give their best effort.
3. Fragmented Data Collection: Employee satisfaction data was collected through periodic surveys, but the bank lacked a cohesive, real-time system to track and analyze employee feedback over time. Survey results were often generalized, with limited granularity to specific branches or teams.
4. Lack of Predictive Insights: Without advanced analytics, PFB found it difficult to predict which teams or branches were most at risk of employee dissatisfaction and turnover.

Given these challenges, PFB needed a holistic solution that could not only measure employee satisfaction more accurately but also provide actionable insights to management.

Solution: Analytic Scorecard Implementation

PFB decided to adopt a data-driven approach by implementing an Analytic Scorecard. The scorecard was designed to consolidate various data sources, analyze trends, and give management real-time visibility into employee satisfaction across departments and locations. Here's how it was built and integrated:

1. Data Sources:
   • Employee Surveys: Monthly, targeted surveys focusing on work-life balance, job satisfaction, management effectiveness, opportunities for growth, and the working environment.
   • HR Metrics: Data such as absenteeism rates, overtime hours, promotion rates, and turnover statistics were integrated into the scorecard.
   • Performance Data: Metrics related to employee productivity, customer service ratings, and individual performance reviews were included to correlate job satisfaction with output.
   • Sentiment Analysis: Text analysis tools were used to scan open-ended survey responses and internal communications to extract employees' sentiment towards their work environment.
2. Scorecard Design:
   • KPIs (Key Performance Indicators): PFB identified key metrics, including the Employee Satisfaction Index (ESI), engagement score, likelihood of recommending the bank as a place to work (eNPS), and turnover risk score.
   • Dashboards: Custom dashboards were designed for different management levels. Senior executives could view overall trends, while branch managers had access to detailed data specific to their teams, allowing them to track satisfaction down to individual employees.
   • Visualizations: The scorecard included visual representations such as heatmaps showing satisfaction levels across branches, trend lines for tracking changes over time, and bar charts for comparing departments or demographics.
   • Predictive Analytics: Machine learning algorithms were implemented to identify patterns and predict which teams or branches were most at risk of dissatisfaction or attrition, based on historical data.
3. Feedback Loops:
   • Real-Time Alerts: If satisfaction scores dropped below a certain threshold, or if employee turnover risk spiked, managers received real-time alerts with suggested interventions, such as conducting one-on-one meetings or team engagement activities.
   • Action Plans: Based on the insights provided by the scorecard, PFB designed customized action plans for improving satisfaction at the branch or team level. These plans were tracked and evaluated in subsequent months to measure effectiveness.

Results and Outcomes

After a year of using the Analytic Scorecard, PFB saw significant improvements in both employee satisfaction and overall operational performance. Here are the key outcomes:

1. Increased Employee Satisfaction:
   • The Employee Satisfaction Index (ESI) improved by 15% within the first six months. Employees reported feeling more valued, connected, and engaged, largely due to the timely actions taken based on scorecard insights.
   • Feedback from the eNPS (Employee Net Promoter Score) increased, with more employees recommending PFB as a great place to work. This metric rose from 42 to 68, reflecting a growing sense of pride and satisfaction in their work environment.
2. Reduced Employee Turnover:
   • The predictive analytics component helped identify branches and departments with higher turnover risk. PFB was able to intervene proactively, resulting in a 25% reduction in overall turnover, particularly in customer-facing roles.
   • By identifying dissatisfaction early, PFB successfully retained several key employees who had been considering leaving, ultimately saving on recruitment and training costs.
3. Enhanced Employee Engagement:
   • Engagement scores improved by 20%, with employees noting that they felt their voices were being heard more often. The bank implemented several policies, including more flexible working hours and increased training opportunities, directly addressing concerns flagged in the scorecard.
   • Productivity at branches where engagement scores had historically been low saw an uptick, with employees feeling more motivated to meet or exceed their performance targets.
4. Management Accountability:
   • The real-time nature of the scorecard made branch and department managers more accountable for employee well-being. Managers took more initiative in creating positive work environments, supported by HR and upper management.
   • The scorecard helped to foster a culture of continuous feedback, where employees and managers engaged in regular check-ins, rather than waiting for quarterly or annual reviews.
5. Improved Customer Service:
   • PFB observed a direct correlation between improved employee satisfaction and better customer service ratings. Satisfied employees were more engaged and provided better service, leading to higher customer satisfaction scores and more positive customer feedback.

One of our listeners asks, wouldn’t it be necessary for each business unit to have the basic same measures in order to run the business as a whole? How is it done if each group is mutually exclusive?

That’s another great question, and the answer is they do need to have the same measures. That’s why this takes a lot of time. This big pharmaceutical company has various business units, and there is slight differences in how they measure even financial measures of performance, which you’d think would be standardized, but they are not. This tends to happen even more so in companies that have acquired other companies. So when they buy the other company they have their own unique metrics.

All this has to be standardized in order to roll it up to the CEO level. If I’m the CEO, and I’m looking at revenue, everybody has to count revenue the same. Everybody has to measure customer relationships the same and so forth.

You can start out by having an individual business develop their own measures and maybe identify some good practices that way, but then you can’t let everybody count things differently. What that means is that the CEO needs to look at each individual business unit scorecard separately because they are all measuring things somewhat differently. So it does need to be standardized.

Our last question is can you give an example of index and analytic metrics for either customers or employees?

I think I just gave both. I can give you another one for employees. A company has a safety and health gauge for their employees. So, it drills down to two parts; safety and health and under safety, there are leading measures and lagging measures. The leading measures for safety are safety audit scores, safety training test scores and safety behavior measures. They have a behavior based safety program, so observations of inappropriate behavior are tracked. The lagging measures are number of accidents and severity.

The same thing happens under the employee health measure. The leading measure is how many employees have been in for their annual physical. How many employees participate in our wellness programs like our healthy food in the cafeteria or lunch time walking program or quit smoking clinic? Things like that. Lagging measures are the actual health level of their employees. They protect the individual data, but they look at what’s the average cholesterol level of their workforce. What’s the average weight of our workforce compared to norms in the area? So that’s a way for the executives to get an overall view of the health and safety of the workforce without looking at 100 charts. If everything was green, then you don’t need to look any further, and you move on to another metric.

Well I’m afraid we’ve run out of time but I would like to thank everyone for attending. We hope to see you very soon for our next Webinar. Have a great day.

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