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Featured
11 min read
Josh Lemoine
Measuring New Hire Failure Rate in an actionable way and acting on the data will save your company money. In this blog, I'm taking a look at how your organisation can save significant sums of money and minimise workforce continuity risks by measuring and understanding your new hire failure rate. Since recruiting and onboarding new employees is expensive, retaining new employees past their earliest phase of employment is critical. When you reduce new employee turnover you save money. A powerful tool for enabling this change in your organization is measuring New Hire Failure Rate. What is New Hire Failure Rate? New Hire Failure Rate is the percentage of a group of hires that leave the company within a set period of time. More specifically, it's people hired during a specified time period who leave the company within a certain number of months divided by all of the hires from that specified time period. The time to termination is a lever that can be adjusted but generally ranges from 90 days to 2 years. It's a powerful measure because it spans recruiting, onboarding, and employment. A lot of data is captured during each of these phases, lending to a large number of factors available to analyze. Measures similar to New Hire Failure Rate include New Hire Retention Rate and New Hire Turnover Rate. Either one could be substituted for New Hire Failure Rate with a similar value proposition. New Hire Retention Rate is the same thing but the inverse and has a more positive name 🙂. It puts the focus on those who stay rather than those who leave. The New Hire Turnover Rate calculation is a bit easier to perform but the measure can be more difficult to interpret due to it being based on headcount rather than hires. Why is it costly? New hire failure is almost universally a negative thing. Even if you're losing hires who are not a good fit for your company, it's costly. Situations like seasonal holiday hiring at a retailer might be an exception in some cases but can be excluded from your analysis if necessary. Some specific reasons that losing employees early in their tenure is costly include the following: The rate is surprisingly high at many if not most companies. A quick internet search yields numbers in the 20% to 80% range. This article isn't going to cite specific numbers since plenty of other articles already do that and your company is unique. If you were informed though that half of your new hires leave in the first year would you believe it? If I were a leader in the Talent Acquisition or Human Resources areas, I would certainly want to know the rate at my company. Hiring and onboarding costs a lot of money. New hire failure increases the amount of both processes that need to happen. Monetary costs include the following. Talent Acquisition employee salaries Paid sources Training resources Time spent by hiring managers interviewing and onboarding people Companies get little productivity from employees who are not yet up to speed. Employees leaving early in their tenure are leaving before they're productive. People leaving teams is bad for morale of those teams. People in senior leadership leaving can be bad for morale of the entire company. Brand reputation can suffer. Why don't all companies measure New Hire Failure Rate? You'd be hard-pressed to think of a People Analytics metric that's more powerful and actionable than New Hire Failure Rate. So why isn't it usually a key performance indicator for Human Resources and Talent Acquisition teams? Calculating New Hire Failure Rate is surprisingly tricky Hires from a specified time period that terminated within a certain number of months divided by all of the hires from that specified time period sounds easy enough. But you have to ensure that both the numerator and denominator come from the same group of hires. So you need to know the hire date but also the termination date at the same time. And you need the differences between those dates bucketed so that you can adjust the "Time to Termination" between 3 months, 6 months, a year, etc. to find the sweet spot. You also have to offset the group of hires back from the current date to allow enough time to know whether the hire terminated or not. By this, I mean that if you're looking at New Hire Failure Rate within 6 months, you don't want to include hires from the past 6 months since you don't yet know whether they'll terminate within 6 months. New Hire Failure Rate Example: My colleague Phil Schrader, One Model's Solutions Architect, performed this new hire failure rate analysis from scratch in less than 5 minutes. Could you do that with your existing HR analytics today? Take the People Analytics Challenge today! The measure itself isn't actionable unless you know other things about the hire Knowing that your company has a high New Hire Failure Rate highlights that a problem exists but does not help you solve it. In order to improve retention, you need to know as much as possible about the hires who are leaving (and the ones that are staying for that matter). Luckily, companies leveraging modern applicant tracking, onboarding, and HRIS systems have a lot of useful data available. Unluckily, this data is often not available in a useful way. To improve your New Hire Failure Rate, you need to be able to slice it every which way to find the attributes and areas to focus on. Unfortunately.... The hiring process spans two separate teams and often two or more separate systems The Talent Acquisition and Human Resources functions both involve hiring but in most companies, they're two separate teams. Not only that but they often leverage two separate systems (ATS and HRIS) to manage their processes. Even companies who use one system such as Workday to manage both Recruiting and HR suffer from the data from the two functions not being cleanly linked together for analysis. On top of this, there's often data related to onboarding such as survey data. This is extremely valuable data when tied to outcomes like early tenure terminations. Unfortunately, many companies use a survey vendor separate from their ATS and HRIS vendors and obtaining survey results comes with its own set of challenges. How can companies measure it in an actionable way and save money? The first thing you need is a People Analytics team. A People Analytics team services both the Talent Acquisition and Human Resources functions. Since New Hire Failure Rate spans both teams, it's best to have a neutral third party reporting it. This should help prevent false assumptions about the causes of high rates stemming from the other team. There's also the word "Analytics" in " People Analytics", and some analytical prowess will be useful in tracking down the causes. Tracking New Hire Failure Rate is only valuable to a company if they act on the findings. The function of a People Analytics team is to provide actionable insights, so they're well-positioned to maximize the impact of the measure. A People Analytics team needs the right tools in order to be successful. The best tool to measure New Hire Failure Rate is a People Analytics platform. A People Analytics platform provides: All of the data in one place and joined together in one data model (subliminal hint) Core HR data such as Business Unit, Job Level, Location, and Manager Recruiting data such as Application Source, Time to Hire, and Recruiter Candidate Survey results Onboarding Survey results A complex yet intuitive way to deal with time All of the attributes structured into dimensions for grouping and filtering the data A compelling visualization layer for distributing the insights to the people who can act on them Watch my colleague Phil Schrader perform a similar analysis in One Model At this point, it should be clear that performing a one-off analysis of the drivers of New Hire Failure Rate would be very difficult. How can companies achieve even more success? Saving your company money was mentioned in the introduction to this article. In this article, Phil describes how you can leverage One Model to calculate source costs and cost per hire. If you know how much it costs to hire someone, you know how much money you’re losing when they leave the company right away. Being able to go to leadership with dollar figures, even if they’re estimates, can be a very powerful driver of change in your organization. Last but certainly not least, companies can maximize success in measuring New Hire Failure Rate by leveraging Machine Learning. This is a great use case for a causal analysis highlighting drivers of new hire failure. An advantage of performing this type of analysis using machine learning is that it’s far more efficient than doing it manually. A tool like One Model’s One AI is able to take all of the attributes from all of the data sources described in this article and run them through a classification algorithm, returning the most predictive of both new hire failure and retention. It can do this in an intuitive way that doesn’t require Data Science skills. If that sound too tricky, embedded insights in One Model powered by One AI can deliver various onboarding retention statistics right within storyboards. Most things that save you money in the long run require some up-front investment. Measuring New Hire Failure Rate is no exception. Like installing solar panels save you more in the long run than installing water barrels, leveraging a People Analytics team and platform to measure New Hire Failure Rate will be much more impactful than a one-off analysis. This is an opportunity to achieve quantifiable results and further cement the value proposition of People Analytics teams. The answers are closer than you think. Let us show you. Request a Demo
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Featured
8 min read
Josh Lemoine
With the introduction of One AI Recipes, One Model has created an intuitive interface for no-code machine learning in people analytics. One AI Recipes (Recipes) are a question-and-answer approach to framing your people data for predictive models that answer specific people analytics questions. Adding this capability to the existing One AI automated machine learning (autoML) platform results in a more accessible end-to-end no-code solution for delivering AI from right within your people analytics platform. We call them Recipes because they walk you through each of the steps necessary to create a delicious dish; a predictive model. Simply select the ingredients from your data pantry in One Model then follow the steps in the Recipe to be guided through the process of creating a successful model. Recipes democratize the production and reproduction of AI models with consistency, accuracy, and speed. Understanding some of the terminology used above and how it relates to One AI will be useful in explaining why Recipes are so useful. What is a no-code machine learning platform? “No-code machine learning platform” is somewhat of a vague term. The definition is pretty straightforward. A no-code machine learning platform is a tool that enables you to apply artificial intelligence without writing any code. It provides a guided user experience that takes business context as an input and produces predictions and/or causal inferences as output. Where it becomes vague is in the range of complexity and flexibility of these platforms. On one end of the spectrum, there are simple-to-use AI builders where the user answers a few questions and is presented with predictions. These tend to only be useful in very standardized use cases. There is often very little transparency into what the machine learning model is actually doing. On the other end are the complex and powerful platforms like Azure ML. Azure doesn’t require writing code and is also very powerful and flexible, but it is also complex. Anyone without a working knowledge of data science would be hard-pressed to create trustworthy models on platforms like this. One AI is aiming at the sweet (no dessert Recipe pun intended) spot on the spectrum. Being designed specifically for people analytics, it allows us to leverage the question-and-answer approach of Recipes. Experienced Chefs can still toss the Recipe aside and cook from scratch though. The One AI kitchen is well stocked with machine learning tools and appliances at its disposal. What is autoML? AutoML is a series of processes or “pipeline” that performs data cleaning and preparation, algorithm selection, and parameter optimization for machine learning. Performing these tasks manually can be labor intensive and time-consuming and requires expertise in data science techniques. AutoML automates these tasks while still delivering the benefits of machine learning. One AI has always provided an autoML pipeline, albeit one where any default setting can be overridden. Even so, there were two areas where we knew we could improve: 1. The data structure for analytic purposes is not the same as the data structure necessary for machine learning. Performing machine learning on data in One Model at times required additional data modeling, a task performed by an expert. 2. Framing up the problem and interpreting the results often required an expert to be involved to ensure accuracy and coherent insights. Recipes address these challenges. Recipes both re-frame the data in a way that a machine learning model can work with and provide a coherent statement that explains both what the model will be predicting and how it will be doing so. How can you benefit from One AI with Recipes? Resource Savings Recipes lighten the load on the technical resources that are likely in high demand at your organization. People analytics is a key strategic business function, yet most people analytics teams aren’t lucky enough to employ Data Engineers, Data Scientists, and Machine Learning Engineers. These teams often fight for the same technical resources as other teams for people who are very talented but can’t possibly possess a deep understanding of all of the different areas of business in the company. Predicting and planning for outcomes has become a key deliverable of people analytics teams, yet they’re often not well equipped to succeed. Companies are increasingly looking for software for automation in HR. Machine learning tools are making great strides in taking business context as an input and producing useful insights as an output. The full realization of this functionality is no-code machine learning platforms. Time Savings With Recipes, time-to-value for machine learning from your people data is substantially reduced. The difference in time required to manually perform this work versus leveraging a no-code machine learning platform is stark. It’s weeks to months vs. hours to days. Even if you have Data Scientists on staff that have the skills necessary to build custom predictive models, they can save time by prototyping in a no-code environment. Interpretability Having the clear statements that Recipes provide that explain what it is you’re predicting and how you’re going about it makes the results easier to interpret. Contrast this with manual machine learning where details can get lost in translation. This prediction statement is in addition to the exploratory data analysis (EDA), model explanation/performance, and Storyboards that One AI provides. One Model also employs a team of experts in the ML and AI space that are available to assist if uncertainty is encountered. Transparency Since One AI is part of One Model, your model configuration and performance data is available in the same place as the predictive or causal data and your people data (at large). Also, your models are trained on YOUR data. These are not “black box” models. At One Model we emphasize making model performance data easily available anywhere predictive data or causal data is included. Compliance As a One Model customer, your potentially sensitive employee data resides in the same place as your machine learning. You do not need to export this data and move it around. On the flip side, the output from your models can be leveraged in your Storyboards in One Model without exporting or moving sensitive data outside of your people analytics solution. The predictive outputs can even be joined to your employee dimensions to help you identify where risk sits. Control and Flexibility Users have the option of configuring data and settings manually in a very granular way. Just because One AI offers a no-code option for creating machine learning models doesn’t mean you’re tied to it. Want to use a specific estimator? You can do that. Want to modify the default settings for that estimator? You can also do that. Recipes just expand the number of One Model user personas able to leverage AI on their data. In Summary One AI Recipes provide a question-and-answer approach to building predictive models that answer key questions in the people analytics space. The resulting democratization of the production of AI models provides benefits including: Resource Savings Time Savings Interpretability Transparency Compliance Control and Flexibility You can have all of this as part of your people analytics platform by choosing One Model. Since you won’t learn about these Recipes by watching Food Network, schedule a demo here: Request a Demo
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Featured
4 min read
Josh Lemoine
Software companies today aren't exactly selling the idea of "lovingly crafting you some software that's unique and meaningful to you". There's a lot more talk about best practices, consistency, and automation. It's cool for software capabilities to be generated by robots now. And that's cool when it comes to things like making predictions. One Model is a leader in that space with One AI. This post isn't about machine learning though. It's about modeling your company's people data . The people at One Model work with you to produce a people data model that best suits your company's needs. It's like having your own master brewer help guide you through the immense complexity that we see with people data. Why does One Model take this hands-on approach? Because the people employed at your company are unique and your company itself is unique. Organizations differ not only in structure and culture but also in the combinations of systems they use to find and manage their employees. When you consider all of this together, it's a lot of uniqueness. The uniqueness of your company and its employees is also your competitive advantage. Why then would you want the exact same thing as other companies when it comes to your people analytics? The core goal of One Model is to deliver YOUR organization's "one model" for people data. A Data Engineer builds your data model in One Model. The Data Engineer working with you will have actual conversations with you about your business rules and logic and translates that information into data transformation scripting. One Model does not perform this work manually because of technical limitations or an immature product. It's actually kind of the opposite. Affectionately known as "Pipeo", One Model's data modeling framework is a major factor in allowing One Model to scale while still using a hands-on approach. Advantages of Pipeo include the following: It's fast. Templates and the "One Model" standard models are used as the starting point. This gets your data live in One Model very quickly, allowing for validation and subsequent logic changes to begin early on in the implementation process. It's extremely flexible. Anything you can write in SQL can be achieved in Pipeo. This allows One Model to deliver things outside the realm of creating a standard data model. We've created a data orchestration and integrated development environment with all the flexibility of a solution you may have built internally. It's transparent. You the customer can look at your Pipeo. You can even modify your Pipeo if you're comfortable doing so. The logic does not reside in a black box. It facilitates accuracy. Press a validation button, get a list of errors. Correct, validate, and repeat. The scripting does not need to be run to highlight syntax issues. OMG is it efficient. What used to take us six weeks at our previous companies and roles we can deliver in a matter of hours. Content templates help but when you really need to push the boundaries being able to do so quickly and with expertise at hand lets you do more faster. It's fun to say Pipeo. You can even use it as a verb. Example: I pipeoed up a few new dimensions for you. The role the Data Engineer plays isn't a substitute for working with a dedicated Customer Success person from One Model. It's in addition to it. Customer Success plays a key role in the process as well. The Customer Success people at One Model bring many years of industry experience to the table and they know their way around people data. They play a heavy role in providing guidance and thought leadership as well as making sure everything you're looking for is delivered accurately. Customer Success will support you throughout your time with One Model, not just during implementation. If you'd like to sample some of the "craft people analytics" that One Model has on tap, please reach out for a demo. We'll pour you a pint right from the source, because canned just doesn't taste as good.
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Featured
5 min read
Josh Lemoine
2019 Goals: With it being the dawn of a new year, a lot of us are setting goals for ourselves. This year, I set two goals: To write and publish a blog post To run a marathon As the father of two young children, I'm always looking for ways to maximize time management. As I ran on the treadmill recently, a bizarre idea came to me in between thoughts of "why do I do this to myself?" and "this sucks". I might be able to accomplish the first goal and get a start on the second at the same time. See, on my very first run 6 years ago, I brought my phone and tracked the run using a fitness tracker app. Since then, I never quit running and I never stopped tracking every single run using the same app. I have literally burned 296,827 calories building this data set... ...and this data deserves better than living in an app on my phone. As a Data Engineer, I feel ashamed to have been treating my exciting (to me) and certainly hard-earned data this way. What if I loaded the data into One Model and performed some analysis on it? If it worked, it would provide an excellent use case for just how flexible One Model is. It would also give me a leg up (running pun intended) on marathon training. One Model is flexible! One Model is a People Analytics platform. That said, it's REALLY flexible and very well positioned as the definition of "People Data" becomes more broad. The companies we work with are becoming increasingly creative in the types of data they're loading. And they're increasing their ROI by doing so. One Model is NOT a black box that you load HRIS and/or ATS data into that then spits out some generic reports or dashboards. The flexible technology platform coupled with a team of people with a massive amount of experience working with People Data is a big part of what differentiates One Model from other options. Would One Model be flexible enough to allow for analyzing running data in it? Yes. Not only was it flexible enough, but the data was loaded, modeled, and visualized without using any database tools. Everything you're about to see was done through the One Model front end. One Model has invested substantially over the past year in building a data scripting framework and it's accessible within the UI. This is a really exciting feature that customers will increasingly be able to utilize in the coming year. Years ago, as a customer of a People Analytics provider, I would have given my right arm for something like this. That said, as a One Model customer you also get access to a team of experts to model your data for you. What did I take away and what should you take away from this? Along with gaining a better understanding of my running, this exercise has gotten me more excited about running. Is "excited about running" even a thing? I plan to start capturing and analyzing more complete running data in 2019 with the use of a smart watch. I'll also be posting runs more consistently on social media (Strava). It'll be interesting to watch the changes as I train for a marathon. Aside from running though, it has given me some fresh perspective on what's possible in One Model. This will surely carry over into the work I do on a daily basis. Hopefully you can take something away from it as well. If you're already using One Model you might want to think about whether you have other data sources that can be tied to your more traditional People Data. If you're not using One Model yet but have an interesting use case related to People Analytics, One Model might be just the ticket for you. Without further ado, here's my running data in One Model: "Cool - this is all really exciting. How can I get started?" Did the above excite you? Could One Model help you with your New Year's resolution? I can't guarantee it'll help you burn any calories, but you could be up and running with your own predictive analytics during Q1 of 2019. One Model's Trailblazer quick-start program allows you to get started with predictive analytics now. Want to learn more? About One Model One Model provides a data management platform and comprehensive suite of people analytics directly from various HR technology platforms to measure all aspects of the employee lifecycle. Use our out-of-the-box integrations, metrics, analytics, and dashboards, or create your own as you need to. We provide a full platform for delivering more information, measurement, and accountability from your team.
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