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Using data to solve the staff retention challenge

WORKFORCE ANALYTICS

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Solana Workforce Analytics Case Study

You wake in the middle of the night in a cold sweat. Tossing and turning as the same refrain plays over in your mind. As much as you try to put the work thoughts aside, you know that staff turnover is too high. Yet, you are not sure how to retain staff, improve staff engagement, and reduce the costs of constantly recruiting and training new hires. These thoughts interrupt your much-needed sleep night after night. You remember reading an article about how “Big Data” can help solve agen-cy problems. You mull the topic in your mind. The skeptic in you doesn’t believe the answers could really be buried in the information you store in your software system. The idea of sifting through and analyzing all that data seems daunting. Still, you make a mental note to yourself to look into the concept. Could data analytics really predict the future you wonder as you finally nod off?

Sound familiar? You are not alone. Nearly every human services agency, home health care firm, and mental health treatment center across the country is having the same problem. STNA’s, MHT’s, DSP’s are churning at an ever-increasing rate. Many reasons point to the cause of high turnover – a low unemployment rate, slow growth of industry wages, low levels of engagement, etc. However, what can you do to mitigate those factors and retain the staff you need to give the individuals you serve the best possible service. Can analyzing your data really point to the solutions? Actually, it can.

Just the Facts, PleaseYour current software system holds an immense amount of information that Solana can use for data modeling. In fact, we recently did a study of an agency that employs more than 900 employ-ees and serves over 1,500 individuals in the community. We assessed nearly 100,000 records during this project.

After reviewing 10 years’ worth of quantitative data we found the agency has an average need to replace over 20 staff every month. When adding up the cost of recruiting, the effort it takes to interview, screen and fill the position; along with the time to train new staff to a level of solid productivity, the organization was looking at upwards of $5,200 per replacement hire! Their goal was to reduce the number of replacement hires by 20%. This equated to a reduction of nearly five replacement hires per month or a savings of about $21,000 each month!

Figure 1: Monthly replacement staff for terminated employees, voluntary and involuntary

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By looking at both quantitative data elements and qualitative staffing interview information, we found some key areas where the agency could focus their efforts to affect the improvements they sought.can.

The Qualitative View Builds a Hypotheses FrameworkIt is very important to hear from current staff members, so we conducted interviews asking ques-tions such as:

• How long have you worked in the industry?

• Tell me about your future at this agency.

• Tell me about the skills of your supervisor/manager.

• What is your overall satisfaction on a scale of 1 to 10?

• Are wages and benefits competitive?

• Do you sense fairness on your team?

• Do you like your co-workers?

• Do you believe your job has a positive impact on the people you serve?

The interviews ascertained some very good news at this agency as we found that the majority of employees mostly liked their job and their manager. Additionally, they really liked their co-workers and believed they have a positive impact on the individuals they serve. However, while they found their job rewarding, they also felt their job was stressful. They thought that they needed more train-ing and development opportunities. The interviewees also shared that alternative local jobs paid higher wages. In addition, they asked for more feedback from their managers to better understand their performance. Finally, the main complaints surrounded work schedule changes and unreliable technologies.

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The Numbers Can Also Tell a StoryNext, we looked at the one-year retention rate of new hires to determine the median baseline for the agency. After reviewing the data, we found that only 78% remained employed after a year with the biggest drop occurring during the first 90 days.

The questions we can ask around the timing of when employees quit can help us make improve-ments. Why did they quit within 90 days? Did they find a better, higher-paying job? Was the job too stressful? Were the last-minute scheduling changes making it too hard for staff to make plans? Was the job different than what they expected? Did they not have a positive onboarding experi-ence? The story starts to develop further and solutions begin to be formed as we compared the median against the following factors to determine their impact on turnover rates in the first year and during the first 90 days of employment.

• Experience at time of hire

• Wages

• Education level

• Impact of their supervisor or manager

• And many additional data elements…

Figure 2: Median one-year survival curve, includes all staff and a 12-year history

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Experience Helps Improve RetentionAs the amount of work experience a new hire has increases, so does the one-year retention rate. This rate dips to almost 60% for staff with little to no work experience. Why? Is it because it’s their first job? Or is something else at play? The numbers improve for new hires that previously had work experience at one to two different jobs when they started. For those, the one-year retention rate hovers between 70 to 80%. Perhaps these hires know what to expect when working with the individuals you serve. The most experienced new hires showed the best tenure results with almost 85% retention after one year. These employees have a career in the human services industry and most likely enjoy the work they do. So, what can we infer from this? Your data can help you know.

More Pay Is Better…To a Point As you might surmise, those staff paid the lowest rate had the highest turnover with only a 44% one-year retention rate. Those that were paid slightly better did see an increase in retention, but were still below the agency’s median at a 67% one-year retention rate. The retention rate contin-ued to increase at both the one-year and 90-day mark as the wages increased. However, more interesting was that the turnover rate for those paid the highest wage was not that much higher than those paid a mid-level rate. The one-year retention rate for both camps was between 88% and 94% at one year, which is well above the median. The numbers look just as good with 95 to 97% still employed after 90 days. When overlaying the wage information with the experience the new hire had at the time of hire, we see that those paid the least with the least amount of experience have these least longevity, while those with more experience that were paid a slightly higher wage tend to stick around much longer. There are times when paying a slightly higher starting wage can SAVE your agency a lot of money.

Figure 3: Before and after look at the impact of slight wage increases

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Managers Have a Substantial Impact on RetentionWe analyzed the turnover rates of the agency’s current managers that supervised 30 or more staff. These numbers take into consideration those staff that left both voluntarily and involuntarily. When looking at one-year retention, the top supervisors had a 93% one-year retention rate while the bottom had a 72% rate.

What contributes to the manager’s turnover rates? Does the manager with the highest turnover also manage the least experienced and lowest paid workers? Does the manager with the lowest turnover manage more experienced workers? What else might contribute to a manager’s turnover rate? The location where the staff work? Does it have the oldest technology? The most last-minute schedule changes? What about the individuals that staff are serving? Do they have higher needs than the individuals at other locations with a lower turnover rate? The answers lie in your data and Solana can help you find them!

The ResultsResults are outstanding with Solana’s Workforce Analytics service!

Figure 4: Impact of supervisor quality on staff retention

Figure 5: Results of 30-day and 90-day retention following improvement efforts triggered by analytics

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Ask the right questions When you look more deeply into your data to find those challenging answers, you can start to implement positive changes. You can influence your retention rates and watch turnover fall - along with the high costs. A data analytics project does not need to be overwhelming. We know you have a lot going on at your agency and analyzing your data to find answers can drop lower on your priority list compared to the day-to-day activities of filling open shifts, making sure everyone is trained and the individuals in your care are satisfied and happy. We understand. Solana is here to help your agency find the answers you need. You do not need to be a Solana software customer to take advantage of this service and our fees are based on how much improvement you find.

So please, make a risk-free call to our CEO to learn more.

Doug NafzigerChief Executive Office419-330-3425