7. How Do We . . .
Improve Quality of Hire
Improve Speed to Performance
Build Leadership Capability
Improve Bench Strength
Improve Employee Engagement
Align Effort to Business Outcomes
14. Levels of Talent Analytics
What
occurred?
Describe
Why did it
occur?
Explain
What will
happen?
Predict
How can we
optimize
results?
Control
1 2 3 4
Push by:
Focus on specific questions
Asking “So what?”
Stop reporting, Start prescribing actions
Monetize key decisions
15. 8 Dimensions which Enable
Sustainable Talent Analytics Capability
Sustainable Analytics must:
Require leadership and governance (1)
Integrate analytics into end-to-end talent
processes (2)
Clarify roles and assign accountabilities (3)
Build skills in measurement and evaluation (4)
Develop standards, common tools (5)
Implement a common technology platform to
enable scalable measurement and reporting
(6)
Ensure results are acted upon (7)
An overarching goal is to build a culture in
which data informs decisions and actions (8)
16. Analytics Embedded into Process
Business
Strategy
Workforce
plan
Recruit
for gaps?
Yes
No
B
A
Recruiting
B
L&D
C
Talent
D
Leadership
E
Engagement
F
Performance
Source
talent
Job Offer
Accept?
Yes
No
Survey
non-hires
Competency/
Leadership
review
Needs
Assessment?
No
Yes
Design,
Develop,
Implement
Evaluate
programs
Impact &
Value?
No
Yes
Continual
review of
business
needs
Review
scrap
learning
factors
Review
workforce
plan
Build
competency
model
Multi-rater
assessment
Report out
gaps/
strengths
A B F
Create
succession
plans
Trained
Leaders?
Yes
No
Collaborate
w/ L&D for
programs
Engagement
Survey
Engaged
Workers?
Yes
Validate
Performance
Gains
No
Implement
Engagement
programs
Establish
Performance
goals
Performance
Appraisal
Exceed
Goals?
No
Yes
Train for
gaps Consider as
High
Potential
D
Performance
Plan
(PIP)
Improved?
No
Employee
turnover
Yes
Exit
Interview
Applicant
Assessment
Identify
High
Potentials
20. Poll
Which of these talent development areas is
most critical for your organization to improve
analytics? (select one)
o Onboarding
o Learning
o Leadership
o Engagement
o Capabilities / Performance
24. Onboarding Analytics Example
Profile:
Large Insurance Company
Over 25% Turnover within 1st 90 Days
Key Metrics:
Onboarding Effectiveness
Quality of Hire
New Hire Satisfaction
Solution:
Automated 30, 60, 90-day
new hire touchpoints
Targeted reporting to managers
& program owners
Identified “flight risks” for
early intervention
Results:
93% of “flight risks”
retained
73% reduction in
measurement costs
25. Learning Analytics Example
Profile:
Large Business Services Company
Multi-Million $ Annual L&D Investment
Key Metrics:
Program Effectiveness
L&D Operation Efficiency
Linkage to Business Outcomes
Solution:
Automated post-class &
on-the-job evaluations
Monthly imports of business
metrics
Operational & executive
reporting
Results:
Strengthened partnership
with business
High ranking on Training
Top 125
26. Leadership Analytics Example
Profile:
Large Technology Company
1000s of new leaders to develop
Key Metrics:
Bench Strength
Leadership Effectiveness
Business Outcomes
Solution:
Semi-Annual 360s of Emerging
& New Leaders
Leadership Program Evaluation
Leadership-to-
Business Outcomes Correlation
Results:
Significant Leadership
Effectiveness increase
Business Outcomes linked
to leadership development
27. Results:
Increased
performance for 70%
of sales force
Increased retention
by 8%
Capabilities Analytics Example
Profile:
Large Biotech Company
Improve sales competencies
Key Metrics:
Certifications
Quota Attainment
Sales Employee Retention
Solution:
Annual sales competency
assessments: 180s & 360s
Global, 15+ languages
Correlated competency gains to
business outcomes
28. Engagement Analytics Example
Profile:
U.S. Government Department
High turnover, little insight into drivers
Key Metrics:
Employee Engagement
Employee Retention
High Performer Retention
Solution:
Automated Exit Interview
surveys
Filtered & ranked by 15
employee demographics
Monthly management reporting
Results:
Identified and
addressed key drivers
Increased retention
by 5.6%
30. Quality of Hire
Correlate to:
Source
Qualifications
Interviewers
Revise Hiring Profiles
Repurpose: Needs Assessment
90-Day New Hire 360 Score
31. Speed to Performance
Assessment varies by role
Milestone achieved
New Hire 360
Ranked by Onboarding Program
Onboarding Assessments
% of Hires Reaching
Competency within X Weeks
34. Quality of Turnover
Rank by Department
Engagement Pulse
Automated Exit Interviews
100% - Recurring Low Performers - Regrettable Losses
35. Poll
Which of these next generation talent
metrics would provide the greatest impact to
your organization? (select one)
o Quality of Hire
o Speed to Performance
o Project Outcomes
o Leadership Effectiveness
o Quality of Turnover
36. 2 Options to Get Started
Option 1: Quick Win
Select 1 talent area to advance analytics
Select an expert partner
Leverage technology
Option 2: Strong Foundation
Select an expert partner
Document 3-year strategy
Build in-house analytics roles & skills
37. Thank You
Jeff Grisenthwaite
VP, Client Success
+1 312.676.4450
jeffg@knowledgeadvisors.com
knowledgeadvisors.com
Contact me to:
Receive whitepaper
Talk to an expert
View a demo
Editor's Notes
In our session today, we’ll take a look at the current state of talent analytics and why it’s important to advance your capabilities in this arena. Then we’ll look ahead to the not too distant future and delve into the promise of talent analytics. We’ll follow that with some tangible case studies of organizations that are leveraging talent analytics to improve decision support in critical areas. And finally, I’ll share with you the top 5 talent metrics that most organizations aren’t measuring well today, but really should. If there’s time, we’ll wrap up with some Q&A. I will send this deck out to you after the session.
Everyone is a buzz about talent analytics these days. When you cut through the noise, you can see the draw, and it’s coming from 3 different perspectives.
First, your company’s leaders are reading and hearing all about how talent analytics can give them a competitive advantage in the knowledge economy. They’re drawn to the siren’s song of using data to solve all their workforce challenges and boost performance. This isn’t just fiction—some companies are doing amazing things in this area and executives in your organization want to get in on the action.
Second, from the perspective of the HR or Talent Management department, this is our last and best shot at getting a seat at the table. We’ve been trying to do it for years, and now we have a real opportunity. By providing the business with a steady stream of actionable, predictive insights about the workforce, we will become a trusted advisor that they depend on for critical decisions.
And lastly, from a personal perspective, there’s probably nothing better that you can do to kick your career into high gear than getting into this field. Talent Analytics is a branch of data science, and the current and future prospects are bright for high-paying, fascinating, influential jobs in this arena.
Are your organizational leaders currently asking for quantitative measures of human capital metrics across all aspects of the HR organization? If not, it’s coming. 4 years ago, less than a third of companies were demanding this, now it’s happening in the majority of organizations. If it hasn’t happened yet for you, I fully expect 1 of 2 things to occur: your leaders will demand comprehensive talent metrics soon, or there will be an unpleasant change for either your department or the company. HR functions that aren’t doing this well will be replaced by people who can, and in the long run, companies that aren’t doing this well are going to lose the war for talent and eventually disappear.
Part of the reason that you are being asked for more talent metrics now or will be soon is that there is more scrutiny at the top. Executives and boards are awakened to the potential gains that strong talent management can offer and want to ensure we have the analytics in place to continually improve.
This Harvard Business Review article from last May had the unfortunate headline of “Talent Management: Boards Give Their Companies an F”. Thanks HBR! When you dig into the data a bit you see that across every industry and in every area of talent management, very few boards strongly agree that the organization is effective.
I attribute this to two things:
1. There are certainly many companies that are not effective at some talent management areas
but the second reason is that almost no companies are effective at demonstrating their effectiveness. HR has never been particularly strong at communicating successes in a way that resonates at the executive level or sharing the kind of predictive, actionable talent insights that showcase robust and effective talent management processes.
But that world is here, and if we’re not ready yet, there’s no time like the present to work on catching up. We have this convergence of 2 major trends: Big Data has a lot of hype around it, but it also is seeing a lot of investment, energy and momentum. Analysis of customer behaviors and intentions is well ahead of where we are with analyzing the workforce, but it’s coming along.
Meanwhile, in the last 5 years, we have the emergence of true integrated Talent Management Suites. They are huge leaps forward from historical software for recruiting, developing, and engaging employees. When it comes to analytics, they tend to have great metrics around volume: how many people we hired, how many exited, how many training hours. But they lack the kinds of root cause and predictive insights that are needed for true talent analytics.
Often, we talk about talent analytics in the abstract, but when you get down to it, it’s really about answering compelling questions:
How do we improve quality of hire?
How do we onboard employees faster?
How do we build our leadership pipeline?
How do we optimize the contribution to business outcomes with our HR-led initiatives.
You want to be asking the right questions, and then bring together the data in such a way to answer them.
We know what it is and why it’s important, but where do we go from here? I believe we’re on the precipice of something amazing. Imagine the HR or Talent Management function being the most important department in the company. Imagine if we could deliver on the promise of truly optimizing the workforce, of hiring ideal candidates, rapidly developing them to their true potential, and inspiring passion in their daily work.
Traditional HR functions are rapidly going extinct. When they’re not outsourced to BPOs, they’re automated by the latest software. And why is that? Because traditional HR is overhead. It’s a cookie cutter function across every company and dwells in the realm of the tactical and uninspired. But we can transform ourselves with Talent Analytics and become the Data-driven Talent Management function of the future, providing vital, game-changing guidance to our business leaders that drives company strategy and provides a competitive advantage.
What can Talent Analytics do for business executives? A massive portion of the company’s expenses are going toward talent investments—both programs and people. The questions that executives are asking are:
Which of these talent investments are yielding the best results?
Which are performing sub-optimally and should be changed before it’s too late?
And where should we invest to meet the business challenges of tomorrow?
Talent Analytics can provide the answers.
Moving down a level or two from Executives, we can think about how front-line managers can use talent analytics. Here we’re talking about the daily and weekly decisions that managers make to bring out the best performance in their teams. Today, many of those decisions are being made based on gut feel or anecdotal feedback, but Talent Analytics can provide the kind of decision support managers need. Perhaps even more importantly, Talent Analytics can sound the alarm when managers didn’t even know an issue existed, providing action alerts to prompt managers to step in and address emerging problems.
And finally, there are the employees themselves. If Talent Analytics lives up to its promise, the entire employee base benefits. Because it’s not just about squeezing the last drop of productivity out of each employee—that will yield only short-term gains. Long-term, talent analytics puts each employee in the workforce on the path to a better job fit, more engaging work, feedback to improve, and clear growth opportunities.
There are 2 common missteps for talent functions when it comes to talent analytics.
First, it’s the inclination to treat measurement as a project, and not an ongoing process. What do I mean by that? Well, take employee engagement as a prime example. How often does your organization assess employee engagement. For most organizations, if they do it at all, it’s done once every 12-24 months. The problem with this is that it doesn’t provide a continuous feedback loop. After getting the employee engagement results back, there are likely initiatives that are started and changes that are made based on the feedback. How do you know those changes made a difference? Do you need to wait another 1-2 years to find out? Measurement needs to be a process that provides a constant pulse on the talent of the organization.
Second, in HR we often times take a defensive posture and aim to use metrics to try to prove the impact of our programs and our worth to the organization. While I understand where that comes from, it’s self-defeating. Imagine the perspective of the business leaders that you are presenting to. Are they in the meeting for the purpose of deciding whether or not HR adds value to the organization? No, they’re in the meeting to gain insights on how to improve the performance of the workforce. Don’t try to prove your worth directly. Focus on continuously improving your programs and sharing insights, and your value will shine through brightly.
Think about Talent Analytics in these 4 levels:
Describe. Here we’re answering what actually occurred. For example, how many high performers exited the organization last month?
You’re able to Explain why it occurred. The high performers left because they felt there were inadequate opportunities for advancement.
You can Predict what will happen. Looking at the next 12 months, we are particularly vulnerable to exits among our high performing mid-level managers and our sales professionals with 3 or more years of tenure.
What you’re ultimately aiming for is Control. In order to retain these at-risk high performers, it’s imperative that we reduce the time to promotion and reinforce the opportunities they have. We recommend expanding the number of paths to promotion and initiating a career coaching program for them. We estimate this will cost $2.5 million to institute, but will protect over $300 million in revenue.
If you’re feeling stuck at levels 1 or 2, you can push toward Predict and Control by asking specific questions and then when you get the data back, ask yourself “So what?” Then ask it again. Keep asking “So what?” until you get to an action that you can recommend. Then monetize that action. Make a business case. Show the cost of not doing anything and the value of making that change.
In order to continually advance those levels, it’s vital to invest toward a sustainable talent analytics capability. We help organizations to advance in these 8 dimensions. If you’re missing any of them, you won’t be able to continually advance and reap the rewards of talent analytics. So, you need leadership to set the vision and allocate resources. You need to integrate analytics into your processes. Clarify how measurement fits into various roles (and who owns it) and build the data analytics skills of the team. Leverage standards and technology to establish a scalable, repeatable approach. And ensure that ultimately the results are being acted upon and you’re building a data-driven culture.
When we talk about building analytics into a process, it’s worth mapping out your talent processes and identifying decision points. Ensure that those decision points are supported by data. That data should include trends, benchmarks, alerts, and guidance. Automate it as much as possible. The process won’t likely wait for someone to be manually compiling data.
So hopefully all of what I’ve shared sounds good. Hopefully you want this. As organizations aim to implement this, we see 4 primary barriers to advancing talent analytics:
Organizations will say they have no time or that it’s not a priority. The reality is that if you’re spending 100% of your time just churning through work with no measurement to tell you what’s working, you’re likely wasting a huge percentage of time. Take a step back and aim to devote 5% of your time and resources toward measuring the other 95%.
People are afraid it’s too complex. I say it’s easy to start simple. Crawl walk run. Build your capabilities over time.
Your data is likely in silos and disparate systems. Start to bridge those silos where it makes sense. Take the highest priority talent challenges and focusing on providing decision support around those. You don’t need to connect everything. Just start out with the connections that will provide the most value.
You will need technology to help you to scale those barriers and start on your path toward advancing talent analytics. Metrics that Matter is a talent analytics suite from KnowledgeAdvisors.
The 6 analytics editions of Metrics that Matter provide a good way to think about most pressing talent challenges. Think about which of these areas would be most vital to your organization to get insights for key questions.
Is it Onboarding, where you’re driving retention and speed to performance for new hires?
Is it Learning, where you’re developing the skills and improving workforce performance?
Is it Leadership, where you’re improving the strategic vision and execution of managers and executives?
Is it Capabilities, where you’re focused on bench strength and meeting future talent needs?
Is it Engagement, where you’re focused on bringing out the best productivity, innovation, and loyalty of employees
Or finally is it Performance, where you’re focused on aligning goals and efforts toward organizational priorities?
I’d like to do a quick poll to get a sense of what’s most important to you.
To go from analytics as a project to analytics as a process, we need automation. You need to be able to easily integrate with your Learning and Talent Management Systems. You need to automate data collection of surveys, 360s, and tests. You need to connect this data back to the demographics of employees and the projects, initiatives, and programs that are part of their lives. Then you combine this rich dataset with business efficiency and outcomes data, imported from key business systems, such as the CRM, ERP and Financial applications. Now all of your talent-related data is in one place, which then drives automated role-based reporting, dashboards, and executive summaries that are delivered to key stakeholders both within talent management and in the business.
Let’s look at a few examples of Talent Analytics in practice. We’ll start with a Talent Analytics dashboard. This is the first step toward running talent like a business. Trend your results over time, compare them to goals and benchmarks. Review the high-level performance overview and then drill-down into the details.
But we need to go a step further to make the Dashboards highly actionable, and that is to provide Dashboards unique to each key stakeholder. Imagine if everyone in your department had a Dashboard focused on their area of responsibility, with a balanced view of the efficiency, effectiveness, and outcomes of talent initiatives, and then your clients in the business have their own view into their most relevant talent metrics. This is an essential tool in instilling a culture of accountability and data-driven decision making.
In this first example, we have a large insurance company with an onerous new hire turnover problem. To tackle this, we worked with them to focus on 3 key metrics: effectiveness of the onboarding program, quality of hire, and the satisfaction and engagement of their new hires. To do so, we implemented automated 30, 60, and 90 day touchpoints and delivered targeted reporting to managers in the business and the owners of the different parts of the onboarding program. The most critical piece here were the alerts that trigger early intervention with new hires that were identified as flight risks. It was very tangible and actionable and saw great results.
In this Learning Analytics example, we have a business services company with a multi-million dollar annual L&D spend. They’ve automated post event and on-the-job follow up evaluations to learners and their managers, and they import business metrics on a monthly basis. These roll into operational and executive reporting to provide insights around program effectiveness, operational efficiency, and the linkage to business outcomes.
Our next example showcases what’s probably the most pressing challenge facing business today: the need to rapidly improve leadership effectiveness and build the next generation of leaders. First, we assessed current state of leadership utilizing 360s for current leaders and high potentials. Based on this, leaders were selected to take part in an extensive multi-modal leadership development program, which was measured through program evaluations and benchmarked against similar leadership programs from other technology companies. The leadership effectiveness rating and the changes based on the program were linked and correlated to key business outcomes for the organization, creating a model for future assessment and development.
Our next example features a focus on developing capabilities of a global Sales organization within a large biotech company. Competencies were assessed with 180s and 360s. Product knowledge was assessed and certified through scored tests. Gains in capabilities were then correlated back to quota attainment to identify the capabilities that mattered most and validate the impact of the program.
In this example of engagement analytics, a department of the US Government had a high level of turnover, especially among high performers, and they had little insight into the drivers. With an automated exit interview process, they were able to capture the root causes of turnover and categorize them by 15 different employee demographics. By developing profiles of employees and addressing the causes that were leading them to leave the organization, the department was able to increase retention by over 5%.
I’d like to close out today with the Top 5 Talent Metrics that you’re probably not focused enough on today. Some of these you may not be measuring at all.
Everyone agrees measuring quality of hire is a great thing to do, but most organizations don’t do it, because they find it to be too difficult. My recommendation is to leverage an automated approach to get manager or 360 feedback after the first 90 days on the job. This approach scales well and can apply to any job role.
Then you can correlate those score back to the hiring source, qualifications of candidates, and who interviewed them. This should lead to more effective talent acquisition and selection processes, and ultimately better hires.
For Speed to Performance, you’re looking at the percentage of new hires reaching competency within a set number of weeks. This will differ by role and industry, but you’ll want to keep it as simple as possible within your organization.
This can be assessed by the achievement of specific milestones, such as the first sale for a new sales person. Or it can leverage the same type of new hire 360 we just talked about for assessing quality of hire.
Use this feedback to assess which roles in your organization need revamped onboarding programs.
The majority of work completed in most organizations is in the form of projects. And while most organizations can tell you what % of projects completed on time, almost none of them can tell you which projects achieved the outcomes they set out to achieve. What even more powerful about this is to use project assessments in a predictive manner about 1/3 of the way into project lifecycles to determine which projects need intervention before they fail to deliver desired results.
Most organizations are doing 360s for their leaders, but they don’t do enough with the results. Correlate the feedback to business performance and direct report engagement to determine which leadership traits provide the most value to the organization.
And finally, Quality of Turnover. Most organizations track voluntary vs. involuntary turnover, but that’s not all that helpful to the business. What we really want to know is are we keeping higher performers and are we helping recurring low performers to exit the organization one way or another. If you start to look at attrition in this way, I guarantee you’ll be surprised by certain departments, roles, and levels of tenure.
Let’s take our 2nd and final poll. Which of these 5 next generation talent metrics would provide the greatest impact to your organization?
When you think about getting starting, I can advocate for 2 rather different approaches.
The first is The Quick Win. Select 1 specific question to answer and leverage a partner like KnowledgeAdvisors to help you to do it well.
The second is to build a Strong Foundation. This is for an organization that has a strong commitment to improving in talent analytics and is ready to establish a long-term solution. In this case, we would focus on developing a 3-year strategy and building your team’s analytics skills and capabilities.
I hope that this was a valuable session for you today. I encourage you to reach out to me via email or give me a call to discuss talent analytics in your organization.
At this time, we’ll take any questions from the audience. If we don’t have time to answer all the questions, I’ll be happy to follow up via email.
