What Causes Low Employee Retention Rates [+ How to Fix!]

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To create an effective and long-lasting team of dedicated workers, you must first understand what causes low employee retention rates.

Retaining employees is vital to the success of any organisation. Efficiency comes from an experienced quality team familiar with both the business and one another.

When employees decide to make their careers with you, they bring their knowledge of your audience and processes with them on their climb up the proverbial ladder.

When you keep the employee retention rate high, you save on hiring costs and precious training time. You create a workforce that rarely has to slow down to let a new person catch up.

But when you’re constantly losing and replacing team members, you’re stuck paying out the nose to recruiters and hiring agencies repeatedly.

The average cost of hiring can add up to a considerable amount. This expense ultimately eats into profits and moves the goalposts for success.

So, how can you make sure your employees want to stick around?

The first step is identifying what causes low retention rates. Then, you must learn how to avoid and fix those mistakes within your organisation.

In this article, we’ll explore several common mistakes businesses like yours make that cause employees to abandon ship. Then we’ll give you actionable advice on how to fix them.

Low compensation

Compensation is a huge sticking point for a lot of full-time employees. Earning a liveable wage is one of the driving forces behind every career. These people must provide for their families and can only do so when paid fairly.

Paying employees below the industry average for their positions can make them feel as though they’re undervalued. It tells the team member your business and you, by extension, don’t appreciate their work.

Low or unfair wages will cause your employees to look for opportunities elsewhere. If one of your major competitors happens to be paying at or above the industry standard, that’s where their ongoing job search will start.

By failing to meet this basic need, you’re‌ helping competitors build the long-term efficient team you want. You’re working against yourself when you fail to pay a liveable wage.

The best way to combat this issue would be to pay your employees more.

This initiative might mean having a smaller team than you initially hoped for. But a smaller team of well-paid, long-lasting professionals will be more beneficial to your business than a huge team of cheap labour new hires.

When people earn more, they’re more invested in keeping their positions. They do better work, there’s less turnover, and company morale and culture better maintains its peak.

If you can’t offer higher wages, consider offering perks with some value.

For example, an extra week of vacation time or using a company expense card could sweeten the deal for unhappy team members.

You could also offer to let team members work remotely for a few days every week. Make sure you know how to manage remote teams to keep your company productive.

Poor employee benefits

After wages, the next most crucial factor for quality employee retention is benefit packages. These benefits include health insurance, retirement plans, life insurance, pet insurance, and more.

A failure to offer these benefits is a huge mistake driving your retention rate into the mud. It’s especially true if you’re operating in the United States, where government-funded healthcare doesn’t exist.

With the rising cost of healthcare and pharmaceuticals, employees need coverage from their employers. If you don’t offer it, they’ll go to a company that will.

Another major mistake leading to low employee retention is providing costly benefits or healthcare plans that don’t offer quality care.

To fix this issue, put some time and effort into choosing the right benefits options for your employees. There should be several different plans to choose from. You must also make sure the plans you present are affordable for employees.

Don’t jump at the first benefits package you see. Do your homework and slowly gather information. Compare offers from many insurance firms.

Put time and effort into this decision. It could be the deciding factor for an employee to stick with you.

Lack of employee engagement

Low employee retention rates can come from ineffective communication and employee engagement within the company culture.

Employees may feel unheard or disengaged without a platform to voice their concerns and provide regular feedback.

However, with platforms like Workday Peakon Employee Voice, organisations can address this issue head-on.

By leveraging the power of real-time employee feedback and forecasting employee turnover, companies can gain valuable insights to identify and rectify areas of concern. To fully harness the benefits of this integrated solution, companies should partner with reliable Workday consulting services.

These consulting services provide expert guidance and support in implementing and optimising Workday solutions. That guarantees organisations maximise their employee engagement efforts and ultimately improve retention rates.

Talk to the members of your team. Let them know they have a voice and employee satisfaction matters to you. Ask for feedback, and give employees the credit they deserve when you implement a suggestion. The result is team members who feel engaged and appreciated.

Those are the kinds of employees with high job satisfaction who’ll stick by a company for years.

Toxic management styles

If you notice your employee turnover rate is rising, there may be an issue in the chain of command. Never tolerate toxic management styles. They can drive valuable team members away and into the arms of a competitor.

What do we mean by toxic management styles?

Toxic management typically speaks to managers who are overly negative or hostile. It could also mean there are some ethical issues in the workplace. Toxic management is terrible for employee morale.

Toxicity could include;

Yelling;
Having unrealistic expectations;
Sexual harassment;
Inappropriate jokes;
Spreading gossip;
Bullying;
Lack of accountability, and;
Micromanaging.

You can avoid this issue by making sure to train your managers the right way. If you want them to perform to your expectations, you’ll need to clarify those expectations.

Create a management guide laying out the management style you expect from your team leaders and make sure they stick to it. You should let employees know they can come to higher-ups about issues with management without fear of reprisal.

If you find you have managers driving team members away with toxic leadership, dismiss them immediately. Let the team know you’re taking steps to guarantee nothing like this ever happens again.

Do your best to reassure team members and keep them from seeking employment elsewhere.

Lack of growth opportunity

If you want employees to remain with your organisation for a long period of time, then you need to make sure there are incentives in place. You should have a promotion track employees can set themselves on.

This creates growth opportunities for them. Advancement is a huge selling point for new hires. It gives them a goal to strive toward. The lure of more money, authority, and responsibility can keep team members with your company.

When you need to hire someone for a management position, promote from within. If you go for an outside hire over someone from within the organisation, employees will start to feel a glass ceiling over their heads.

Additionally, promoting from within gives you a manager who knows your company. They start already knowing your team and can hit the ground running with new responsibilities. It’s a win-win situation.

Businesses that don’t prioritise professional development are a shortstop for most employees. It’s not a place to lay roots down. Once they identify advancement opportunities elsewhere, you’ll lose the team member. It’s even likely they’ll work for one of your direct competitors.

Little to no employee appreciation

Employees need to know they’re appreciated if they’re going to stick around. When there’s no acknowledgment from management, it’s easy to feel like you’re getting taken advantage of.

That’s why recognition and appreciation for solid work are crucial. It could be something like an employee of the month award with their portrait photo placed in a pre-designed custom frame and their name engraved below. These awards go well together with special perks for that month like a small bonus, free lunch or extra day offs.

It could also be special public recognition for going above and beyond the call of duty in front of the entire team. Even an email going out company-wide praising the efforts of team members who have gone the extra mile can make someone feel appreciated.

Take your team out for lunch regularly. This generous act gives the workers time to bond with one another while also helping them feel appreciated by management.

These steps ultimately inspire loyalty, which leads to higher employee retention. A loyal employee might even turn down more money elsewhere to stay with your company for a prolonged time period.

Improper training

Another common reason for low employee retention rates is the organisation’s need for clearer roles and responsibilities.

Employees shouldn’t need clarification about what to do, who they report to, and their goals. They may feel frustrated, demotivated, and unappreciated.

For example, in some companies, there may be confusion between the roles of product owner vs. product manager. These roles have different scopes, skills, and deliverables.

They also may overlap or conflict in some confusing areas.

To avoid this problem, it’s important to clearly and regularly define and communicate each team member’s roles and responsibilities.

You should cover this in the initial employee training period. Create a training guidebook guaranteeing everyone trains ‌the same way. You won’t lose employee productivity or create confusion with a lack of preparation or knowledge.

When trying to figure out what causes low employee retention rates, one can often point to frustration in the workplace. Improper training can lead to dangerous frustration.

Hiring the wrong people

If you’re not hiring the right people, the quality employees you do have will become disillusioned and leave.

When you bring on a large percentage of team members unqualified for their positions, your quality employees will get frustrated.

Never force outstanding employees to pick up the slack from underperformers. This mistake breeds resentment. Those quality employees will start to look for gameful employment elsewhere.

Then you’ll only have subpar team members, and the entire organisation will suffer.

You can avoid this costly mistake by sticking to a well-thought-out hiring process. This document should guide your hiring manager, helping them select the type of person needed for every position.

For example, if you’re looking for a Website designer, you’ll want someone with at least a bachelor’s degree in web design. Hiring someone with no employee experience or degree will only place more of a burden on everyone else and drive them away in droves.

Overworking employees

Your team must be able to maintain a work-life balance. When expectations are too high, and workloads are too heavy, you risk alienating quality workers.

What’s more, overworking is bad for employee health. They’ll search for a job with more time off and reasonable deadlines.

It can be easy to overwork your employees. You have your eye on profit margins and deliverables. But you need to keep your expectations reasonable to improve employee satisfaction.

That means avoiding unreasonable deadlines. It also speaks to not expecting employees to answer emails and phone calls during their off hours. Dissuade them from coming in early or staying late to meet expectations.

Not knowing what causes low employee retention

Of course, one of the major issues impacting your retention rate is not knowing what causes low employee retention. Thankfully, this article has given you a solid idea of what employees expect and how to keep a solid and efficient team from leaving. But there could be other unforeseen issues to watch out for.

Keep your eyes and ears open and interview employees who resign. Find out why they felt the need to go elsewhere and use those insights to implement changes to your employee retention strategies.

In Summary

Employee retention can improve your profits and efficiency‌. But to avoid involuntary turnover and keep your employees for a long period of time, you must avoid the issues we’ve highlighted above.

To increase your employee retention rate, make sure you’re;

Paying a fair annual salary;
Providing benefits;
Engaging your employees;
Avoiding toxic management styles;
Creating growth opportunities;
Showing appreciation for team members;
Training employees the right way;
Hiring the right people;
Promoting a healthy work-life balance, and;
Understanding why employees are resigning and making changes.

If you follow the instructions in this guide, you can keep employees happy and create a loyal, longstanding, and highly proficient team.

About the Author

Vikas Kalwani is a product-led growth hacker and B2B Marketing Specialist skilled in SEO, Content Marketing, and Social Media Marketing. He works at uSERP and is a mentor at 500 Startups.

The post What Causes Low Employee Retention Rates [+ How to Fix!] appeared first on The 6Q Blog.

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Distilling step-by-step: Outperforming larger language models with less training data and smaller model sizes

Posted by Cheng-Yu Hsieh, Student Researcher, and Chen-Yu Lee, Research Scientist, Cloud AI Team

Large language models (LLMs) have enabled a new data-efficient learning paradigm wherein they can be used to solve unseen new tasks via zero-shot or few-shot prompting. However, LLMs are challenging to deploy for real-world applications due to their sheer size. For instance, serving a single 175 billion LLM requires at least 350GB of GPU memory using specialized infrastructure, not to mention that today’s state-of-the-art LLMs are composed of over 500 billion parameters. Such computational requirements are inaccessible for many research teams, especially for applications that require low latency performance.

To circumvent these deployment challenges, practitioners often choose to deploy smaller specialized models instead. These smaller models are trained using one of two common paradigms: fine-tuning or distillation. Fine-tuning updates a pre-trained smaller model (e.g., BERT or T5) using downstream manually-annotated data. Distillation trains the same smaller models with labels generated by a larger LLM. Unfortunately, to achieve comparable performance to LLMs, fine-tuning methods require human-generated labels, which are expensive and tedious to obtain, while distillation requires large amounts of unlabeled data, which can also be hard to collect.

In “Distilling Step-by-Step! Outperforming Larger Language Models with Less Training Data and Smaller Model Sizes”, presented at ACL2023, we set out to tackle this trade-off between model size and training data collection cost. We introduce distilling step-by-step, a new simple mechanism that allows us to train smaller task-specific models with much less training data than required by standard fine-tuning or distillation approaches that outperform few-shot prompted LLMs’ performance. We demonstrate that the distilling step-by-step mechanism enables a 770M parameter T5 model to outperform the few-shot prompted 540B PaLM model using only 80% of examples in a benchmark dataset, which demonstrates a more than 700x model size reduction with much less training data required by standard approaches.

While LLMs offer strong zero and few-shot performance, they are challenging to serve in practice. On the other hand, traditional ways of training small task-specific models require a large amount of training data. Distilling step-by-step provides a new paradigm that reduces both the deployed model size as well as the number of data required for training.

Distilling step-by-step

The key idea of distilling step-by-step is to extract informative natural language rationales (i.e., intermediate reasoning steps) from LLMs, which can in turn be used to train small models in a more data-efficient way. Specifically, natural language rationales explain the connections between the input questions and their corresponding outputs. For example, when asked, “Jesse’s room is 11 feet long and 15 feet wide. If she already has 16 square feet of carpet, how much more carpet does she need to cover the whole floor?”, an LLM can be prompted by the few-shot chain-of-thought (CoT) prompting technique to provide intermediate rationales, such as, “Area = length * width. Jesse’s room has 11 * 15 square feet.” That better explains the connection from the input to the final answer, “(11 * 15 ) – 16”. These rationales can contain relevant task knowledge, such as “Area = length * width”, that may originally require many data for small models to learn. We utilize these extracted rationales as additional, richer supervision to train small models, in addition to the standard task labels.

Overview on distilling step-by-step: First, we utilize CoT prompting to extract rationales from an LLM. We then use the generated rationales to train small task-specific models within a multi-task learning framework, where we prepend task prefixes to the input examples and train the model to output differently based on the given task prefix.

Distilling step-by-step consists of two main stages. In the first stage, we leverage few-shot CoT prompting to extract rationales from LLMs. Specifically, given a task, we prepare few-shot exemplars in the LLM input prompt where each example is composed of a triplet containing: (1) input, (2) rationale, and (3) output. Given the prompt, an LLM is able to mimic the triplet demonstration to generate the rationale for any new input. For instance, in a commonsense question answering task, given the input question “Sammy wanted to go to where the people are. Where might he go? Answer Choices: (a) populated areas, (b) race track, (c) desert, (d) apartment, (e) roadblock”, distilling step-by-step provides the correct answer to the question, “(a) populated areas”, paired with the rationale that provides better connection from the question to the answer, “The answer must be a place with a lot of people. Of the above choices, only populated areas have a lot of people.” By providing CoT examples paired with rationales in the prompt, the in-context learning ability allows LLMs to output corresponding rationales for future unseen inputs.

We use the few-shot CoT prompting, which contains both an example rationale (highlighted in green) and a label (highlighted in blue), to elicit rationales from an LLM on new input examples. The example is from a commonsense question answering task.

After the rationales are extracted, in the second stage, we incorporate the rationales in training small models by framing the training process as a multi-task problem. Specifically, we train the small model with a novel rationale generation task in addition to the standard label prediction task. The rationale generation task enables the model to learn to generate the intermediate reasoning steps for the prediction, and guides the model to better predict the resultant label. We prepend task prefixes (i.e., [label] and [rationale] for label prediction and rationale generation, respectively) to the input examples for the model to differentiate the two tasks.

Experimental setup

In the experiments, we consider a 540B PaLM model as the LLM. For task-specific downstream models, we use T5 models. For CoT prompting, we use the original CoT prompts when available and curate our own examples for new datasets. We conduct the experiments on four benchmark datasets across three different NLP tasks: e-SNLI and ANLI for natural language inference; CQA for commonsense question answering; and SVAMP for arithmetic math word problems. We include two sets of baseline methods. For comparison to few-shot prompted LLMs, we compare to few-shot CoT prompting with a 540B PaLM model. In the paper, we also compare standard task-specific model training to both standard fine-tuning and standard distillation. In this blogpost, we will focus on the comparisons to standard fine-tuning for illustration purposes.

Less training data

Compared to standard fine-tuning, the distilling step-by-step method achieves better performance using much less training data. For instance, on the e-SNLI dataset, we achieve better performance than standard fine-tuning when using only 12.5% of the full dataset (shown in the upper left quadrant below). Similarly, we achieve a dataset size reduction of 75%, 25% and 20% on ANLI, CQA, and SVAMP.

Distilling step-by-step compared to standard fine-tuning using 220M T5 models on varying sizes of human-labeled datasets. On all datasets, distilling step-by-step is able to outperform standard fine-tuning, trained on the full dataset, by using much less training examples.

Smaller deployed model size

Compared to few-shot CoT prompted LLMs, distilling step-by-step achieves better performance using much smaller model sizes. For instance, on the e-SNLI dataset, we achieve better performance than 540B PaLM by using a 220M T5 model. On ANLI, we achieve better performance than 540B PaLM by using a 770M T5 model, which is over 700X smaller. Note that on ANLI, the same 770M T5 model struggles to match PaLM’s performance using standard fine-tuning.

We perform distilling step-by-step and standard fine-tuning on varying sizes of T5 models and compare their performance to LLM baselines, i.e., Few-shot CoT and PINTO Tuning. Distilling step-by-step is able to outperform LLM baselines by using much smaller models, e.g., over 700× smaller models on ANLI. Standard fine-tuning fails to match LLM’s performance using the same model size.

Distilling step-by-step outperforms few-shot LLMs with smaller models using less data

Finally, we explore the smallest model sizes and the least amount of data for distilling step-by-step to outperform PaLM’s few-shot performance. For instance, on ANLI, we surpass the performance of the 540B PaLM using a 770M T5 model. This smaller model only uses 80% of the full dataset. Meanwhile, we observe that standard fine-tuning cannot catch up with PaLM’s performance even using 100% of the full dataset. This suggests that distilling step-by-step simultaneously reduces the model size as well as the amount of data required to outperform LLMs.

We show the minimum size of T5 models and the least amount of human-labeled examples required for distilling step-by-step to outperform LLM’s few-shot CoT by a coarse-grained search. Distilling step-by-step is able to outperform few-shot CoT using not only much smaller models, but it also achieves so with much less training examples compared to standard fine-tuning.

Conclusion

We propose distilling step-by-step, a novel mechanism that extracts rationales from LLMs as informative supervision in training small, task-specific models. We show that distilling step-by-step reduces both the training dataset required to curate task-specific smaller models and the model size required to achieve, and even surpass, a few-shot prompted LLM’s performance. Overall, distilling step-by-step presents a resource-efficient paradigm that tackles the trade-off between model size and training data required.

Availability on Google Cloud Platform

Distilling step-by-step is available for private preview on Vertex AI. If you are interested in trying it out, please contact vertex-llm-tuning-preview@google.com with your Google Cloud Project number and a summary of your use case.

Acknowledgements

This research was conducted by Cheng-Yu Hsieh, Chun-Liang Li, Chih-Kuan Yeh, Hootan Nakhost, Yasuhisa Fujii, Alexander Ratner, Ranjay Krishna, Chen-Yu Lee, and Tomas Pfister. Thanks to Xiang Zhang and Sergey Ioffe for their valuable feedback.