Whats Missing in Your Cost Per Hire Metric?

One of the most common metrics used in recruitment is cost per hire. Generally used to bring together all the costs associated with filling an open position, cost per hire is probably the most widely used metrics across all types of recruitment. It is a close to a universal metric as we have. However, most of us are not using it correctly.

First make sure your calculation includes all factors related to filling the positon that have an external cost like marketing, advertising, job fairs, job board fees, travel time to events, remote interviewing, etc. Any and everything you can think of that happens outside the office that adds to your total cost.

Now do the same for factors that are internal to the business. Salaries, bonuses, reimbursement expenses, application tracking systems, copy and printing costs, etc. Make a list and notate the expense for any and everything you can think of that happens inside the office.

In both cases, also include data for shared costs from expenses that cover more then one opening. In many cases we don’t include things like rental expenses, association fees, government requirements, really anything that your organization spends money on that directly supports your recruitment efforts. In many cases, you can divide the total amount by open positions to come up with some kind of weighted amount assigned to each open req.

Now one more piece to your cost per hire metric, that most of us miss. Expenses related to not filling the position. How much is lost in productivity? What revenue forecasts come up short? How much is spent on overtime and other compensation for staff covering for the open position? When you factor these items in you can get a much deeper understanding of the cost per hire to the business.

If you are doing all of these things and feel you have a very solid cost per hire metric, then you are in the minority. In an ideal world, recruitment teams can better allocate resources based on what positions cost the most to fill. Better understanding all the data points that are added into the cost per hire calculation can also uncover opportunities for savings that you might not otherwise see.

On the other hand, if you are looking for some guidance on assessing your cost per hire metric to make sure its optimized to capture all of the relevant data points to your business then connect with me. I can help you get a true read on the cost per hire in your business.

HR & Recruitment Analytics – The recruitment and retention of top talent is the biggest challenge facing just about every organization. DMAIPH is a leading expert in empowering HR & Recruitment teams with analytics techniques to optimize their talent acquisition and management processes. Contact DMAIPH now at analytics@dmaiph.com or connect with me directly to learn how to get more analytics in your HR & Recruitment process so you can rise to the top in the ever quickening demand for top talent.

Aligning Strategic Business Insights Through HR Analytics

I recently gave a talk about Aligning Strategic Business Insights Through HR Analytics to a group of HR and Recruitment Managers and Senior Professionals.

Learning Session Description
From sourcing, through hiring, beyond training and towards retention, the best HR teams know their data and use it to stay focused on the overall strategy of the organization. This learning session is designed for HR and Recruitment professionals to identify key data points and be exposed to analytic techniques that are paramount to successfully aligning HR to a strategic business plan.

Learning Session Objectives
The specific objectives of this unique fun-filled learning experience were;

Be exposed to cutting edge analytic techniques being used by successful HR and Recruitment Team in the Philippines and abroad.
Gain a deeper understanding of what key metrics and data points add value to HR teams as they use data to align business strategy.
Be empowered to produce actionable reports that give decision-makers the right data at the right time to ensure a more solid business strategy.

Key Benefits from Attending this Learning Session
In this session, your organization was able to:

Define the most important data points to the organization’s strategic plan.
Develop an analytics strategy around how to better use data in decision-makin
Deliver new analytics techniques to the rest of their team to better align HR and Recruitment with the core business strategy.

In this session, your participants were able to:

Identify key data points within their HR and Recruitment business data.
Learn how to bring these data points into an inventory that allows quicker and more powerful analysis.
Integrate these data points and analysis into management reports full of actionable insights.

Who Should Attend

This session is suitable to a wide range of professionals but will greatly benefit:

Executives, Managers and Business Leaders who are looking to empower their HR and Recruitment teams to use more data analysis in their strategic planning.
HR and Recruitment Managers who use data and analytics as well as employee analysts to help in strategic planning and business optimization.
HR and Recruitment Supervisors and Team Leaders who use data and analysis to manage their teams and implement strategy.
Analysts working with HR and recruitment data who add value to the overall HR strategy though their reporting and analysis.

Learning Session Outline

This session was broken into 4 key areas:

Cutting Edge HR Analytics
Finding the Right Data
Key Analytic Techniques
Actionable Reporting

Teams that are successful in each of these 4 areas, will be ahead of the game when it comes to keeping HR at the forefront of defining, aligning and implementing business strategy.

Learning Session Process

This session utilized a variety of proven adult learning techniques to ensure maximum understanding, comprehension and retention of the information presented. This includes thought provoking discussions and analytics solutions presentations.

I can do the same thing for an in-house training for your business. You can either connect with me directly or get in touch with Ariva Events Management for a free consultation on how to get started.

Only Data Dinosaurs Promise “One Day” or “On the Spot” Hiring

In the ever quickening world of BPO companies in the Philippines, the latest recruitment slogans promise One Day or On the Spot Hiring. I even saw one today that promised a coaching session on how to pass the interview before the interview. Crazy. Like the last dinosaurs, these big companies are making a futile effort to avoid extinction.

Anyone ever involved in recruitment will tell you, that its hard enough to find good candidates, but trying to find rock stars with a one day, end to end, recruitment process is ludicrous. Not using more data and analytics in their process, will lead them to walk with the dinosaurs.

When it comes to trying to compress the recruitment process cutting edge companies look to technology and data analysis to help them narrow the field and make quicker hires. While dinosaurs just through more manpower at the problem. They ramp up with staffing staff and take shortcuts in skills assessment, candidate fit and potential success to meet the ever increasing demand for talent.

When you see the headlines promising one day hires, you look at the companies and see they are doing things like its still the Jurassic Era. Resume screening in mass, scaled down skills tests and group final interviews. No demographic profiling, no analysis of the candidates distance to work or difficulty of commute, no predictive modeling based on candidate data to show likelihood of employee success. Its like watching a bad sci-fi movie about dinosaurs taking over the planet. It will not end well.

In today’s hyper competitive job market, only the companies who evolve to using more intelligent recruitment methods will prevail. The rest will someday take their place in a museum of failed BPO companies from the early 21st century.

And its not that expensive, not is it really that hard to invest in analytics solutions. The cost of ramping up and hiring more people to do hiring is always more costly over time then a good business intelligence tool.

Instead of shortening your process to stave off eventual extinction, evolve your business to get with the times. Don’t end up like everyone else offering the promise of expedient hiring to fill seats, that in the end just need to be filled again and again. Hire a recruitment analytics expert, have them dig into your data and come up with a smarter solution.

I can show you how. I have helped dozens of BPO companies come up with analytics solutions that help them avoid the trap of one day hiring. Connect with me if you want to survive.

Using Data Analytics to Assess Work Ethic

When you oversee the growth of a team from 6 to 100 employees in just over a year like I have, one of the biggest challenges you face is keeping up with recruitment requirements. When in rapid expansion mode, it is easy to lower standards and fall into a “just fill the seat” mentality. When this happens, high attrition generally follows.

One way to try and curb high attrition rates is to get better at measuring candidate work ethic. For most people assessing the work ethic of candidates is something that seems very subjective and not something that is east to apply metrics too. And in with that assumption, you are missing some very easy data points to capture and use in being more analytical in your recruitment process. Let me highlight three data points to capture in the recruitment process that have a strong correlation to work ethic.

Timeliness
Resume Quality
Preparation for Interview

We all make note of these items during the process, and often include them in the overall evaluation of the candidate. But rarely is anyone capturing these items as data and using it to help measure work ethic and use it to predict work ethic once employed.

Timeliness is simple. Where they early, on-time, late, really late or a no show for any of the interviews in the process. If people are early or on-time it’s a positive and can show a general behavior once employed. On the other hand if a pattern of being late or not showing up is already evident before being hired, why would you expect that to change once they are part of your team?

One thing that is common here in the Philippines is dramatic excuses for being late or missing interviews. If you are or ever have done recruiting here, I am sure you can rattle of a long list of excuses; family emergency, death of a loved one, getting sick, and stuck in traffic being the ones I hear the most. Its easy to dismiss the excuse as a valid reason to be late or not show, but time and time again when we hire people who started like this, they don’t stick around. Putting a weight behind timeliness is extremely important. Over time you can track the attendance patterns of people you hired with low timeliness scores and I guarantee you that you will see a strong correlation between the two.

Resume Quality is also something that generally has a direct reflection on the candidate’s level of professionalism. If you are expecting someone to treat your business with respect and hard work, yet their resume is out of date, incomplete and/or full of typos, once again you are fooling yourself. Im sure we all think at some point the resume is just a resume and bad candidates can have good resumes and vice versa. Well if you do think that, then don’t you owe it to yourself to start tracking data to validate that. When you find you are mistaken, and bad resumes general equal bad employees, you can thank me. Come up with simple scoring system. Like an English teacher would grade a paper. Grade the resume and add the data to both your decision-making and your data analysis.

One of the deal breakers for me when I interview is how prepared is the candidate. When I ask them how did the hear about the job, and they say a friend told me to apply I get concerned. My follow up being did you research the company before coming here. When they say I didn’t. Its pretty close to an automatic fail. If a friend told them about the job, but they didn’t do anything to learn about the company it’s a clear sign they are not taking this serious. So why would I expect them to take their job serious once they start. Again come up with a simple scoring system to indicate how did they hear about the job, what kind of research did they do about the company and how much knowledge do they come in with about the job they are applying for.

So there, you go. That’s how you can add some powerful analytics to your recruitment process. Come up with you own measurements for timeliness, resume quality and interview preparation. Use them along side the tests and assessments and interviews, to build a more complete candidate profile. All track these data points over time to compare to data once they are an employee like schedule adherence, productivity and quality of work. I promise you, you will see strong correlations between the pre hire and post hire data.

Why Analytics Projects Fail – #13: Over Reliance on External Help

The final reason I will articulate in this series of why analytics projects fail is an over reliance on external help. Historically the over reliance would happen when a team is “too busy” to learn the ins and outs of the analytics software they using.

An example would no one internally has the training to maintain or update the software themselves. Any fixes, patches or enhancements have to be done with the help of someone not on the company payroll. This has obvious limitations like not being top priority or made to wait longer the necessary, as well the potential slowdown caused be internal review and QA processes. Not having someone on the insides trained to handle external products is a major risk to an analytics project.

Another examples is when internal analyst don’t have the initiative to own the software. Meaning they just do the minimums, never really learn all the things the software can do and do not offer any new idea of solutions. Being totally dependent on a vendor to keep you up to date on all the new possibilities for use of the software is extremely short sighted. This often causes going the long way on a project instead of knowing about short cuts.

A third example is that your team is not empowered to work independently and their schedule is dictated by the availability of the vendor. Important deadlines might be missed or extended because the vendor resource is not available when you need them.

Regardless of the impact, relying too heavily on your analytics software vendor leaves open the risk of what if the external expert leaves. I have seen this happen a number of times, where analytics projects were halted or even cancelled because the expert was outside the company and left the project. The most common outcome of losing your expert is that things stop working and you have to either use workarounds or start over.

The key lesson here, if you are an analyst working with externally supported software, it behooves you to become the expert on it. This will mitigate any the risk of being over reliant on the vendor. It will also assure you of having more control of maintaining, fixing and upgrading your own analytics process yourself, which makes you more valuable to the organization you work for.

Analysts who know why things fail, are proactive, find solutions and become analytics champions are the ones you want to measured by. In the end, the best way to make sure your analytics projects don’t fail is to be awesome at what you do.

Analytics Culture – The key to using analytics in a business is like a secret sauce. It is a unique combination of analytics talent, technology and technique that are brought together to enrich and empower an organization. A successful analytics culture is not easy to create, but DMAIPH can show you how. Contact DMAIPH now at analytics@dmaiph.com or connect with me directly so we can build a strategic plan to turn your company into analytics driven success story.

Why Analytics Projects Fail – #12: New Technology

Occasionally one of the problems that can doom an analytics project is a new technology that emerges and makes the project obsolete before it is even implemented. This happened to me once when we were using an older and heavily modified version of Business Objects and then we got access to Tableau.

At the time, the flexibility of Tableau made our Business Objects business dashboard obsolete before we even completed the design phase of the project. The data visualization and the ease of use of Tableau Desktop at that time was miles ahead of anything our IT team could build around Business Objects. As a result, countless hours and dollars were lost, but in the end at least the business requirements we had established could be done by end users in Tableau.

Another example of how a new technology might impact your project is when a new version of the database you are using comes out. One that requires some much QA and/or testing to meet internal guidelines, that when it is finally approved it is hardly useful any more. This can often be the case with big companies that have long vetting processes to use new version of software. You’d be surprised how many Fortune 500 companies are still running internal version of Windows XP because using 8 or 10 has not been approved yet.

Modifications done in house to off the shelf solutions can also make new versions incompatible. I have seen this happen with both Cisco and Teradata databases, where internal development of data flows and data structures to be so rigid, it was impossible to use updated versions of the same databases.

You can also come across situations where developers and IT teams are ordered to use something else because changes in a vendor relationships or a new strategy from the CTO. In the end you have to adapt and either sacrifice, lose, or give up on what you have put into the project so far.

As the number of data collection and storage options grow, the complexity of data models surge and the types of business intelligence solutions increase, the likelihood of a big analytics projects being impacted by new technology. A good analyst has to stay up to date on what’s hot and new, in order to not advocate the use of something that is on its way to being a dinosaur.

To help me stay current, I follow several blogs and belong to a dozen analytics themed LinkedIn groups. I also try and attend at least one big industry conference a year as an attendee as well. And finally I read a lot. I end up going through 3-4 analytics themed books a month. If you are facing a situation where you are worried your project might fall victim to a new technology, let’s talk about it. I can help you figure out a solution to keep you and your project on the cutting edge.

The key to using analytics in a business is like a secret sauce. It is a unique combination of analytics talent, technology and technique that are brought together to enrich and empower an organization. A successful analytics culture is not easy to create, but DMAIPH can show you how. Contact DMAIPH now at analytics@dmaiph.com or connect with me directly so we can build a strategic plan to turn your company into analytics driven success story.

Why Analytics Projects Fail – #11: No End User Participation

One the most overlooked and under appreciated parts of assuring a successful analytics implementation is getting the participation of end users. End users being defined as the consumers of either the data, the analysis and/or the reports that come out of the project.

I can tell you countless horror stories about stacks of reports that go unread, email summaries that are never opened and business dashboards that are rarely clicked on. In most cases, all because the end user was not involved in the project or its development process.

One example of this is when the ones who need the reports are not asked what they need in the report. This is more common than you might think. Requirements, no matter how well thought out, will always overlook something someone needs. Another reason is not finding out how the end users want it to look. They often are omitted from the design phase and just left to use it. Worse it’s possible that what is delivered in not even compatible with other things they do. This leads to failure by not being useful, a complete waste of time and resources, especially yours.

The best way to assure end user use is to have them involved at the earliest stages of the project. If you are selecting the project team or have influence on the team makeup, make sure you get an end user who can speak for that audience. It might be more than one person.

Another way is to keep the end-users informed and allow for feedback. Finding ways to work feedback into your project is another place you would be surprised by how often it is not done.

And finally make sure you build in a testing period before your project goes into production. In some cases this might include the feedback phase, but in big projects there is often a need for end user testing. If you don’t shepherd this effort, who will?

If you are no sure how to go about involving the end users and/or are not sure of who all the end users might be, then you should really answer those questions as early as possible. No one wants to see their hard earned work just end up in the trash bin because it does not fit the need it was designed for.

Analytics Culture – The key to using analytics in a business is like a secret sauce that fuels Data-Driven Decison-Making. It is a unique combination of analytics talent, technology and technique that are brought together to enrich and empower an organization.

A successful analytics culture is not easy to create, but DMAIPH can show you how. Contact DMAIPH now at analytics@dmaiph.com or connect with me directly so we can build a strategic plan to turn your company into analytics driven success story.

Why Analytics Projects Fail – #10: Key People Leave

One of the toughest analytics challenges to fix is when key people leave. This reason is another people problem, but with a technology bent. Depending on the importance of the person(s) who leaves, you can experience anything from a minor hiccup to a total meltdown of your project.

One example of this is when the one who built the database leaves. Often they take their unique knowledge of the data structure with them. Another example is when the systems architect who knows the ins and outs of where the data flows departs. This can make it difficult to track down errors and bugs. Lastly, the database admin who wrote the code might be the one who quits, taking with them all their coding work. I can even be worse if they leave on bad terms and take a key piece of your development work with them or even destroy it.

In general, the best outcome you can hope for is to is build workarounds that allow you to keep the project going, however sometimes you are better off just starting over or worst case you just live with what you have. So step one is seeing where you are in the process and then determining what it would take to replace that person.

If you are able to continue, then you need to start doing a better job of documenting and making sure information is shared so this won’t happen again. I learned this lesson early in my career. Learn all you can about all aspects of the data environment and document them. A lot of times a clear understanding and documentation will be required by management to assure funding and resources.

If you have to stop the project until you can find a replacement, then you should also learn, document and share everything so that the new person can pick things up as soon as possible.

In this case, the new person will likely be dependent on you to learn the ropes so use that opportunity to change your culture to be more open.

A final point to add, make sure you understand why the person left.

If there are things you can do to make sure the same thing does not happen again then it is on you to do just that. If it is a cultural thing, then you can be a catalyst for change. If its a compensation thing, then you can help define the expected scope of work and help in the compensation planning. If they left because of a personality conflict, then you can help find someone who will fit in better. Analysts have so much power to shape conversations. Use it.

Why Analytics Projects Fail – #9: Bad Data

In my experience, most of the time analytics projects fail its generally traceable back to a purely human problem. However, sometimes you see things fall apart because of technology, the misuse of technology and/or just bad technology. This is the case when projects fail because of bad data.

There are a lot of ways bad data can happen.

One common way you end up with bad data, is the data was not captured correctly. Perhaps the data was manually input with lots of error. Or maybe your data is not consistently collected so it has gaps. Knowing what exactly goes into capturing your data and being able to understand how it is collected is extremely important.

Another cause of bad data is that you are not getting all the data or you are getting data that has been altered. A lot of times when data passes from the collection point to you, it might be being truncated, or blended, filtered or converted. Lots of databases are structured for optimal data storage, not usage. A lot of database admins who don’t really know the data will add data flow shortcuts. Or maybe the fall under the datakeepers category and partition or cut out some of the data you need.

Bad data also comes in the form of old and out of date data. When you are making decision on data that just not recent enough, it can lead to a lot of problems. Keeping data fresh is something some companies just don’t value. If that’s the case, you will likely see your analytics initiatives come up with analysis that points you in the wrong direction.

In all three of these examples, one solution I suggest to mitigate the chance you have bad data is to build a data map. Learn about every point in a data flow that touches your data. Talk to the ones in charge of each touch point to make sure your data is not being impacted in any way that can result in bad data. Even if you cannot fix the problem, understanding it can help you set more realistic expectations of what your analytics project can achieve.

I have found using Visio to build data flow visuals is the best way to explore, document, and report how the data being used in my projects is being impacted by the environment it lives in. Knowing Visio is a valuable skill for an analyst. If you don’t use it, I promise you that once you do you’ll be sending me a thank you.

Why Analytics Projects Fail – #8: Lack of Resources

To start with a lack of resources should probably be called lack of time. Lack of time to design an effective strategy. Lack of time to find the right talent. Lack of time to get everyone on the same page. We are all just too busy and have too much to do. We say lack of resources, but mostly we mean our team doesn’t have time.

A lot of times you hear about failures with analytics projects is because of lack of resources. When I hear about this, I always ask for a better definition of what is meant by lack of resources. Is it lack of leadership support, lack of funding, lack of strategy, lack of focus and vision, lack of talent? They are all often disguised as lack of resources.

In each of the previous seven blogs in this series I talked about a reason why analytics projects fail and since they can all fall under the boarder lack of resources, let’s do a quick recap.

Lack of Focus – People are not on the same page
Lack of Vision – People don’t know where this is going
Lack of Management Support – People don’t know who to follow
Lack of a Champion – People have no one to cheer lead
Lack of Organizational Support – People don’t really care
Lack of Funding – People don’t want to waste money on this
Lack of Talent – People can’t do the job

There are all people driven reasons for why your project may be in danger of failing. They are all fixable using people skills. This is why I often argue a good analyst who can communicate is worth more than a great analyst who cannot. The reasons why analytics projects most often fail is human, not technological.

In the end, for whatever of the reasons above, your project is in jeopardy, it will be up to you to show people why they should invest the time needed to get things back on track.

You have to push for focus, share the vision, educated your managers, become a champion, gain organizational support, secure funding and align the right talent to make things work.

I have been in this situation numerous times. In every situation the one constant variable that changed possible failure into a success was me. Bring a truly great analyst means showing people how your project will be a solution to their problems and is well worth their investment of time.

When you do this, they you won’t be in a place where lack of resources dooms your analytics project.

#IamDMAI

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