Via HR Dive : Planning employees’ life cycles: A blueprint for effective talent management
Establishing a thoughtful strategy from day one will help you maximize each individual’s tenure with your company, writes Greg Shepard, chief strategy officer and chief technology officer at Pepperjam
The days when people would spend decades at one company are long gone. These days, the average time a worker stays at one organization is four years, according to the U.S. Bureau of Labor Statistics — and it is even shorter in the tech sector. Though high turnover rates are becoming the norm, it is costing companies a pretty penny: Employee Benefit News reports that, when a worker leaves, it costs employers 33% of a worker’s annual salary to hire a replacement.
This type of hit can make or break a business — and it is especially damaging for startups. Equally detrimental is when companies lose great employees by hiring them at starting salaries that are too high, levels that don’t leave enough room for regular increases. Either the organizations wind up paying too much for one person’s output, or the employee leaves because there’s no room to advance, regardless of performance.
As a veteran of building and running growth businesses, I have seen these situations many times. Fortunately, there are several ways to mitigate the problems, including offering reasonable salaries with room for incremental, merit-based raises.
Planning for each person’s full “employee life cycle” means coming up with the right starting package; setting and clearly communicating the goals that employees are expected to meet; and, ultimately, preparing well in advance for the person’s eventual exit.
Starting off on the right foot
When hiring new staff, employers frequently try to offer attractive starting salaries to attract the best and the brightest. But hiring managers often mistakenly focus on recruiting a person in the short-term without considering a given employee’s longer-term prospects at the company and whether the initial salary offers room and incentive to grow.
Let’s say a software company offers a programmer $90,000 per year to start, but the maximum it can afford to pay someone in the position is $100,000. This gives the company only two long-term options: offering the employee a disappointing raise each year, to the point where the person moves on, or bumping up the person’s salary until the company can no longer afford it.
I have personally made this mistake multiple times. I’ve witnessed employees hit their salary cap early in their tenures and leave in frustration, or I eventually had to let them go because I couldn’t keep paying them more. It was not only embarrassing, but my poor planning cost the company.
Hiring managers should determine in advance the maximum they can pay someone in a given role, based on the candidate’s experience and geographic location. A starting package should leave room to increase with regular raises during the life cycle of the employee’s estimated time in the role — be it two years, three years, or five. And any increases should be tied to reaching the goals necessary to make the employee a profitable contributor.
Align employees’ performance with company metrics
Workers tend to be more engaged when they understand what’s required of them to advance. This is especially true for people aged 20 to 35, according to a report by strategy firm Department 26 about Millennials’ workplace satisfaction.
Millennials may have a stronger desire for more specific goals, but all employees benefit from managers taking the time to communicate key performance indicators (KPIs) they are expected to meet or exceed. For example, a social media manager might be rated on the number of new Facebook followers or retweets on Twitter.
Spelling out those goals — and updating them regularly — can help companies track their efficiency relative to employees’ output. In today’s fast-paced world, meeting once or twice a year to discuss an employee’s performance is no longer sufficient. Managers should have regular check-ins about KPIs and measure progress in milestones.
With regular raises, clear expectations and the potential to advance if an employee keeps hitting or exceeding KPIs, there’s a good chance that you’ll be able to retain good workers longer than the average four years. Nonetheless, companies still need to prepare for the possibility of employees’ eventual departures.
Ideally, by the time any employee has been in a role for a while, the company should begin thinking about how to prepare for when the person moves on, either because of a promotion or because the person has found another opportunity.
Cross-training staff gives everyone a better understanding of how a department works, and it can also help employees pick up the slack if someone leaves unexpectedly. One strategy is to have mid-level employees take new people on as protegees, both to train them and to help prepare them to take on more responsibilities as they advance.
At a minimum, managers should encourage all employees to document their work and best practices so that others can fill in for them during vacations, unexpected medical leaves, or if a person resigns. Having employees write training manuals for their jobs on an ongoing basis saves everyone time and money in the long run.
It’s impossible to build a business without recruiting the right people. To make sure you attract and retain the best talent, it’s critical to compensate correctly without breaking the bank. You also need to make sure workers are challenged and motivated throughout their employment and still ensure that you are not left scrambling if and when they decide to exit. It’s a delicate balance, but establishing a thoughtful strategy from day one will help you maximize each individual’s tenure with your company.
Via FastCompany : Moneyball for business: How AI is changing talent management
Fifteen years after Billy Beane disrupted Major League Baseball by applying analytics to scouting, corporations are rewriting the rules of recruiting.
The online games were easy–until I got to challenge number six. I was applying for a job at Unilever, the consumer-goods behemoth behind Axe Body Spray and Hellmann’s Real Mayonnaise. I was halfway through a series of puzzles designed to test 90 cognitive and emotional traits, everything from my memory and planning speed to my focus and appetite for risk. A machine had already scrutinized my application to determine whether I was fit to reach even this test-taking stage. Now, as I sat at my laptop, scratching my head over a probability game that involved wagering varying amounts of virtual money on whether I could hit my space bar five times within three seconds or 60 times within 12 seconds, an algorithm custom-built for Unilever analyzed my every click. With a timer ticking down on the screen . . . 12 . . . 11 . . . 10 . . . I furiously stabbed at my keyboard, my chances of joining one of the world’s largest employers literally at my fingertips.
More than a million job seekers have already undergone this kind of testing experience, developed by Pymetrics, a five-year-old startup cofounded by Frida Polli. An MIT-trained neuroscientist with an MBA from Harvard, Polli is pioneering new ways of assessing talent for brands such as Burger King and Unilever, based on decades of neuroscience research she says can predict behaviors common among high performers. “We realized this combination of data and machine learning would be hugely powerful, bringing recruiting from this super-antiquated, paper-and-pencil [process] into the future,” explains Polli, sitting barefoot on a couch at her spartan office near New York’s Flatiron District on a humid May morning, where about four dozen engineers, data scientists, and industrial-organizational psychologists sit behind glowing iMacs.
Pymetrics is part of a legion of buzzy startups leveraging artificial intelligence, big data, and other tech tools to disrupt the hiring space. Research firm CB Insights expects VC investments in HR-tech startups to hit $2.9 billion in 2018, up 138% year over year. When Polli and I meet, she’s in the midst of raising a Series B round.
What’s driving all this investment? A January 2018 survey of 1,000-plus C-suite executives found that attracting and retaining talent is their number-one concern, outranking anxiety over the threat of a global recession, trade war, and even competitive disruption. Yet human resources, Polli complains, is still an “archaic system” that relies on “biased” evaluations of “irrelevant” staples such as résumés and cover letters. Polli spouts off numbers like a lab-coat-wearing scientist in a disaster movie whom the townspeople perilously ignore: Recruiters review each résumé on average for a mere six seconds; three-fourths of candidates are cut at this phase, often arbitrarily; and surviving new hires ultimately fail in their positions 30% to 50% of the time. With the unemployment rate at an 18-year low and a historic scarcity of mission-critical skills plaguing every industry (there are now more open jobs in the U.S. than there are active job seekers to fill them), talent acquisition has reached a breaking point. “This system is fundamentally not working for companies, candidates, or the economy,” Polli says.
Companies are therefore turning to new technologies to help inform increasingly granular employment decisions, from hiring to productivity. “It’s Moneyball for HR,” says Polli, referencing the best-selling Michael Lewis book about the 2002 Oakland Athletics who, led by forward-thinking general manager Billy Beane, upended the game of baseball by applying statistical analysis to build a roster that could compete against its deeper-pocketed rivals. (The 2011 movie adaptation featured Brad Pitt as Beane and earned six Oscar nominations.) Beane’s numbers-based approach valued RBIs and on-base percentages over traditional metrics such as batting average and anecdotal assessments like the look of a player’s swing. The corporate world has been eager to adapt that recruiting model ever since, but until fairly recently lacked the tools to fully assess employees. Now, as companies adopt more productivity software and enterprise tools like Slack and Workday, management has access to reams of data on employee activity. So just as the Oakland A’s used data analysis to determine that the potbellied catcher who almost never struck out was a better bet than the golden-armed Adonis who made old-school scouts salivate, the Unilevers and Burger Kings of the world are increasingly exploiting data to examine performance and predict potential.
“When you have a 4% unemployment rate and a skills gap in every sense for the talent we’re competing for today, you have to use data to win,” says Travis Kessel, senior director of recruiting for Walmart’s Jet.com, which also utilizes gamified testing like Pymetrics. “The war for talent is so hot right now that you can’t afford not to.”
After I survived my Pymetrics-designed Unilever test, my results were instantly calculated, determining that I’m a cautious risk-taker, yet 72% more likely to “use trial and error to formulate a plan” than more deliberate strategizing. Pymetrics’s games collect troves of this kind of data, which, once fed to its algorithm, can determine where each applicant might fit within an organization (if at all). For Unilever, Pymetrics matches prospects to seven internal divisions: a person suited for Unilever’s finance department, for example, might not have any trouble solving the probability puzzle that flummoxed me. “It’s like Netflix or Spotify matching you with exactly what [content] you’re looking for,” Polli says.
That’s more than the 2002 Oakland A’s could boast: Billy Beane didn’t have AI to augment his data crunching. Nor did he have computer vision, another tool in Unilever’s kit, which now enables companies to automate initial interviews, using webcams to analyze facial expressions and voice tonality. “We capture tens of thousands of data points–emotions, words you use, active versus passive verbs, how often you say ‘um’–and automatically score [candidates] based on [qualifications] Unilever gave us,” says Loren Larsen, CTO of HireVue, the startup behind the service. “If you never smile, you’re probably not right for a retail position.”
Unilever’s digitized approach enabled it to expand on-campus recruiting in recent years from around 20 colleges to more than 2,500, surfacing a dramatically more diverse selection of candidates. “The best student at New Mexico State is probably as good as the average student at Harvard,” says Larsen. “Businesses are trying to get good talent fast–if Facebook and Google get there first, they’re toast.”
Mike Clementi, HR head for Unilever’s North American operations, says return on investment of its new hiring methodology is substantial: When the company first tested the system for certain entry-level jobs and internships in late 2016, applicant volume shot up 100%, to 275,000 candidates globally. At each step of the application process, algorithms (such as the ones Pymetrics and HireVue developed) sharply narrowed that talent pool, eventually to just 300 final-round candidates in the U.S. and Canada–before applicants interviewed with humans. “We bet pretty big on this,” says Clementi, touting that the company “moved from a process that took four months to four weeks.”
Unilever’s HR costs also went down, Clementi says. And though it’s too early to say whether recent hires will thrive at the company in the long run (they only began their careers last year), the fact that they even received offers is a strong indication of the recruiting machine’s accuracy: Human HR managers extended job offers to eight out of every 10 of the robots’ final-round picks. “In my mind, there’s no question that data and digital automation is the way we’re going,” Clementi says.
Several weeks after my visit to Pymetrics, I’m in New Brunswick, New Jersey, visiting Johnson & Johnson’s stately headquarters, where Sjoerd Gehring, the company’s VP of talent acquisition, is showing off the inner workings of Shine, a sleek new platform that’s profoundly altering the 132-year-old healthcare giant’s approach to hiring.
Shine is a web and mobile product developed, as the name suggests, to illuminate the historically opaque process of getting a job. Analytics power most interactions between employer and job seeker today, but traditionally only one side has any visibility into the data. J&J wants to rectify that to improve the “guest” experience. Now, J&J job applicants can log in to the service to track every step of their journey via a bright, clean interface. Gehring, whose tidy office is surrounded by colorful printouts of product road-map deadlines, compares the experience to monitoring a package delivery on Amazon. “Our vision is to bring a much more consumer-like approach to recruiting,” he says.
When the Dutch-born Gehring arrived at J&J from Accenture’s talent innovation lab three years ago, he discovered that the company’s recruiting process wasn’t so much a black box as a black hole. The company was taking what Gehring calls a “post and pray” approach: Its job listings were filled with jargon, and its hiring process was “broken,” rarely informed by data. “We were flying blind,” he says. J&J had been receiving 1.2 million applications per year for 15,000 job openings, each requiring multiple interviews. It wasn’t uncommon for candidates to endure months of silence only to receive a rejection letter. “It was the definition of a slow-moving, traditional recruiting organization,” recalls Gehring. At J&J’s scale, this not only threatened the caliber of its in-house talent, but also risked tainting the company’s brand with the million-plus people it rejected annually–people who, Gehring says, are sick of companies “treat[ing] them like crap.”
Gehring’s boss, chief human resources officer Peter Fasolo, tells me that the company desperately needed to move away from the “myths” that long had driven its approach to hiring. “Don’t give me anecdotal, ‘I feel’ this does or doesn’t work–go in and study it,” he says. Gehring set about “reimagining recruiting,” bringing aboard designers and data scientists who scoured for pain points in J&J’s process. The key was building out a cohesive ecosystem that prioritized the user–not simply injecting myriad new technology tools now at their disposal. “A lot of my peers see a new AI tool or a chatbot and say, ‘Oh, let me add that so I can show I’m doing something with AI,’ ” Gehring says. “When you do that year after year, you end up with 40 to 50 tools that are super disconnected and don’t deliver results.”
The Shine team, which Gehring formed in mid-2016, needed to create a personalized experience because, Gehring explains, “candidates are now assessing organizations more than organizations are assessing talent.” He and his colleagues expanded the tracking features so that users can see not only which leg of the journey they’ve reached (e.g., “You’re at Stage 3: Recruiter Screening”) but also receive estimated response times and insights on progress (“15% of candidates get this far”) as well as feedback on what’s coming next. To guide people through the process, J&J partnered with career website the Muse to create 80 original advice-filled videos and articles that are sprinkled throughout Shine.
Another major focus for J&J has been diversity. For its career listings, the company teamed up with Textio, an AI startup that analyzes job descriptions to remove gender-biased language while also measuring which terms are most effective in attracting talent. “If you’re trying to hire competitively against companies like Google and Apple, you really need to find innovative ways to succeed,” says Textio CEO Kieran Snyder. “It’s not an accident that Amazon uses the word maniacal in their job posts 11 times more often than the rest of the industry.”
When J&J rolled out this system last October, response rates to recruiting write-ups rocketed 24%, and the Shine system’s Net Promoter Score–an industry-standard metric of how likely users are to recommend it–quintupled within four months. Most compellingly, J&J’s Textio-enhanced job listings, dissected to remove gender bias, resulted in a 14% increase in qualified female applicants for STEM roles and a 7% uptick in hires.
This is exactly the type of tectonic shift that AI startups are promising in every field today. But Porter Braswell, cofounder of Jopwell, a career platform designed for minority job seekers, says companies shouldn’t see technology as a panacea to their inclusion problems. Not only is there the risk of machine bias, when algorithms are inadvertently engineered to favor one demographic over another, but diversity is a cultural issue that runs deeper than anything a machine alone can address. Companies still need to have the “awkward and uncomfortable conversations about what diversity actually means and where [they’re] struggling with it,” he says. (A J&J spokesperson says its approach to diversity is multifaceted, including investing in unconscious bias training and STEM education programs for underrepresented minorities.)
Parag Kothari, 22, was just out of college when he applied for a finance position through Shine. He tells me that he found its transparent tracking refreshing and the overall process enjoyable–despite not having landed the job. “It felt like they were looking after candidates as if they were already employees,” he says.
As Gehring and his team get ready to complete Shine’s worldwide rollout by October (the service will be integrated with WeChat in China to appeal to local tastes), they’re paying specific attention to the rejection page so that it can surface additional opportunities. This way, J&J won’t lose candidates like Kothari simply because they didn’t get the first job they applied for.
On a recent rainy Tuesday, around 70 developers are gathered for a hackathon at IBM’s Mass Lab, the tech giant’s largest software hub in North America, in Littleton, Massachusetts. A handful of youthful engineers stand at the front of a large conference room, explaining how they’re trying to give a voice to Watson Career Coach, an AI-assisted talent adviser. The bot is designed to help employees navigate their careers at IBM, providing feedback via a mobile app on everything from reskilling opportunities to job advancement. Employees set specific career goals on the app, and Watson will guide them through possible job paths, explain the training required, estimate times to promotions, and provide automated answers to questions.
The team presenting today hopes that speech will make Watson’s chat experience more accessible. Their prototype is very rough–and Watson is having trouble with the local dialect. “Intense accents can be tougher than a foreign language,” says cognitive-software engineer Cameron MacArthur, who jokes that the system is “built to take in Spanish or Mandarin–but not built for Bostonians.”
Watson Career Coach is currently a pilot with 12,000 employees but will be available to IBM’s global workforce of 366,000 later this year as part of the company’s commitment to retaining and retraining talent, especially as millennials–a job-hopping generation nearly twice as likely to defect than older colleagues, according to a 2016 Gallup report–become the largest part of the U.S. labor force this year. “Things have changed dramatically with the availability of big data and AI,” says chief HR officer Diane Gherson. “There’s a real skills shortage in this new era.”
Yes, even a company that receives 2.5 million résumés per year for tens of thousands of positions still faces a talent deficit, and innovations like Watson Career Coach are helping IBM recalibrate for the future. After all, the company boasts more than 1,500 blockchain employees alone, a field that didn’t exist a decade ago. How better to hire for those positions than by home-growing talent? The company invests $500 million annually in employee learning and retraining. It runs an internal developer academy and partners with Coursera and Udacity on a portfolio of online courses. Watson keeps track of employee skill advances via a digital badging system: IBMers have earned 40,000 AI certifications for completing courses in areas such as architecture foundations and conversational services, for example. “We know what your skills are and will tell you if your skills are in decline so you can pivot,” says Gherson. “If you’re a Java programmer, and the demand for that skill is going down, [Watson] will say, ‘Here are six blockchain programmers. They took these courses and had these [work] experiences, and they’ve since been promoted three times.’ ”
Gherson recognizes that these training efforts are only likely to make employees more enticing to competitors. It’s one reason why the company has also invested seriously in workforce analytics. Through Watson, executives and supervisors can also analyze employee performance and growth–a “Fitbit for managers,” as Gherson has called it–that will automatically alert them if a team member, say, isn’t earning enough based on his or her proficiency. Through patented proactive retention algorithms, the system is getting to a point where IBM is starting to anticipate employee departures. By comparing data trends of departed workers with current ones, the company is able to identify patterns of flight risk.
Last year, the company says this warning system resulted in roughly $100 million in net savings based on how much it would have cost to replace that lost talent.
On June 14, Billy Beane delivered the opening keynote at the Recruiting Automation Summit in San Francisco. Now the Oakland A’s executive VP of operations, he has become a fixture at hiring and data conferences in recent years, and on this bright Thursday morning, Beane once again sings the gospel of Moneyball. “Moneyball was about paying for the right skills that would help us win–not who looked good in a baseball uniform,” Beane told the audience of 400, stressing that data–not myths–are key to hiring the right talent.
Given that the Oakland A’s haven’t won a World Series since 1989, has the analogy gone too far? Wharton School professor J. Scott Armstrong has applied Beane’s statistical analysis to his research on recruiting–one paper he coauthored was called “The ‘Moneyball’ Approach to Hiring CEOs”–but even with the rise of artificial intelligence Armstrong hasn’t seen enough scientific evidence to support its widespread adoption in HR. “It’s mass hysteria: ‘Everyone else believes it, so we believe it too,’ ” he says of the industry.
It’s likely, though, that companies will only grow more dependent upon algorithms for hiring talent. For some job seekers already at the mercy of the machine, it’s a scary prospect, as evinced in the many online forums, such as Reddit’s popular “Recruiting Hell” section, where applicants share horror stories as well as tips on how to game new-age systems like Pymetrics and HireVue. “Make sure to look at the camera like it’s a person’s eyes, [because] it captures that,” a user cautioned of HireVue’s automated video interviews. Others talk of the unsettling experience of completing Pymetrics’s online assessment–only to receive a rejection email from an employer almost immediately after the computer tabulates the results. “I spent two hours filling out [the company’s] application and playing 12 of their fucking games,” one user wrote. “I got an email about 10 minutes after finishing the games telling me I had automatically been turned down from the position and will be unable to apply to any job in the company for 12 months . . . What the fuck?” (A spokesperson for Pymetrics clarifies that rejected candidates are allowed to apply to different jobs at the same company and are encouraged to do so in rejection notices.)
Laszlo Bock, the cofounder of data-science startup Humu and former HR guru at Google, is wary of this increasingly automated future. He advises companies to proceed with caution when implementing AI systems, lest they inadvertently punish the very people they’re trying to attract. “There’s risk of tremendous harm,” Bock says. “If you’re building a new sales system and you mess up, you don’t get the sale, but if you’re building a people-related system and you mess up, you have ruined someone’s life. You have made someone feel terrible. You have just shut someone out of an opportunity they deserve.”
Companies must strike the right balance between human judgment and machine learning, says Dane Holmes, head of human capital management at Goldman Sachs, which now uses HireVue and is exploring the use of virtual reality in recruiting. Otherwise, this tale of modern Moneyball may start to become more like an episode of Black Mirror. “We have no interest in creating a dystopian society here,” Holmes says. “We don’t ever view this as a process where you turn on an algorithm and wait for it to spit out [the right hire] in an app.”
Reid Hoffman, the venture capitalist and LinkedIn cofounder, agrees. His new book on building organizational efficiency, called Blitzscaling, details the novel ways startups are growing fast, but even he advises against completely succumbing to modern hiring techniques. “Look, I don’t think Billy Beane said, ‘I’m just going to hire whoever the stats say [to hire].’ He said, ‘The stats are telling me this player is a lot more valuable than the market thinks, so I’m going to take a look,’ ” Hoffman says.
Hoffman’s words reminded me of something I heard during my visit to IBM’s campus in Massachusetts. After the hackathon, I had a chance to catch up with a small set of developers, all fresh out of school in their early twenties, including Cameron MacArthur, the cognitive-software engineer helping to build the company’s career-analytics platform. Does he ever worry about eventually engineering himself out of a career?
With senior IBM executives listening, MacArthur, with a sunny smile, told me he’s not concerned. After all, this is just a learning system that gives you career choices. “That’s the crux of AI: The whole special sauce is that you can say, ‘No, you’re totally wrong. I hated that job.’ And then the computer can learn, ‘Oh, that’s really interesting: If you hated that job, then maybe you’ll like this one?’ ” the young engineer says. “The approach of, ‘The computer is deciding where you belong’–I don’t think any company wants that.”
I shook MacArthur’s hand and wished him the best of luck in his future career. In this data-driven era of employment, he probably won’t need it.
Via People Matters : Fundamental ways to manage talent in the dynamic world- Alexander DiLeonardo
The senior expert pointed out the importance of change and if companies can innovate on a regular interval.
Analytics is the backbone when it comes to revenue measurement and human resources outcome also revolve around analytics. Alexander DiLeonardo, Senior Expert, Associate Partner, McKinsey & Company shared his thought on ‘Trends Shaping the global economy, Impact on business and talent.’
The senior expert pointed out the importance of change and if companies can innovate on a regular interval, it can stay relevant for a very long period. The entire talent paradigm is changing and people are switching jobs across sectors. The workforce is going with the pace and looking for jobs where they can accommodate. “No one would have imagined that GE will compete with YouTube for talent and vice versa,” said, DiLeonardo.
He mentioned a few key trends shaping the way organizations manage talent in this dynamic world.
Breaking the perception about millennials he shared that it’s a myth that millennials change jobs very quickly. “They do not change jobs frequently and they work similarly in the same way the previous generation did.” The only change is their approach towards the job. Millennials today want to align with the vision and mission of the company and evaluate their position accordingly.
He emphasized the fact that one needs to change the thought that millennials are lazy, they are not lazy, but they work differently. They want to have open dialogues and are eager to understand the value creation and connected to the front line.
Technology will reduce the HR cost and by using the technology we can break down the barriers for gig economy. The new way of communication is essential for gig economy and it will lead to disruption in this economy.
Automation will not reduce jobs, but it’ll enhance it. Automation will bring a lot of opportunity in the workforce and it will reskill the people and brings change. It will have a profound impact and bring change.
Via Entrepreneur : Building Tomorrow’s Workforce: Why You Should Think About Talent The Way You Think About Sales
Talk to founders of any growing business and you will find one common pain point- lack of access to top talent with the right skill sets. Even the most successful companies in the Middle East are not immune to the region’s skills gap, a widening mismatch between the demand for skills, and the supply of talent that has them. This, of course, isn’t a problem unique to the region. But with one of the youngest populations in the world, it’s more important now than ever that we see significant investments in education and learning to prepare our workforce for tomorrow. But what can business leaders do today to prepare for tomorrow? It starts by rethinking talent management.
Everyone’s looking for senior developers, digital marketing managers, and seasoned sales professionals- but where do they all come from? There’s a culture of talent poaching that’s becoming exponentially more expensive, rather than building sustainable talent structures. 69% of employers say that their inability to attract and retain middle-skills talent frequently affects their company’s performance. There’s another way to solve that problem.
Using sales strategy for talent management
Sales strategy typically works in funnels, with teams helping prospective clients move across key milestones in a sales process, thereby building a pipeline of revenue based on the likelihood of deals closing. This way we can manage sales processes, predict revenues and make plans for the future. More companies need to start thinking the same way in terms of managing talent and that starts with building talent pipelines. Adopt a funnel approach where revenue is the jobs you’re looking to fill, milestones are skills that need to be developed, and leads are potential candidates. Using talent pipelines, companies can solve major challenges for mid-to-senior level requirements by developing junior employees’ skills relevant to their business.
Crafting your talent road map
In most companies, revenue targets are typically set on a quarterly or monthly basis, and with younger companies, even weekly, to align the overall company strategy and goals. Yet most companies don’t have set plans for talent. Talent projections can be mapped out in a similar way- what should your team look like in 6, 12, 18, months? It’s imperative that those plans are aligned with revenue targets, road maps of your products and where you see your competitive advantage materialize.
Building talent pipelines
Start setting up talent funnels with milestones that structure early-stage careers at your company. This starts with building frameworks across your hiring process defined job descriptions, candidate experience, hiring KPIs and insights, career pathways and people analytics. These milestones should be varied depending on the career paths outlined- for a developer, it might look like shipping a product feature end-to-end, for sales, it might be leading new revenue streams, or for marketing, it might be setting up growth engines, and so on.
Predicting leaders and champions
There’s no reason to map out milestones if they aren’t going to be tracked. Use milestones and the pace in reaching them to identify star performers, similar to using probabilities on deals in your revenue pipelines. Create structured programs that watch your star performers and support them to accelerate their growth, whether it might be for skill or leadership development.
Planning supply based on company goals
Once you’ve set up talent pipelines and have a reasonable idea on what your team might look like down the line, you can start mapping data points against your talent road maps. Are you on track? Where are you still seeing mismatches between demand and supply of skills needed? How can you pivot your talent pipeline strategy to correct course? It will need the investment of your time to answer these questions to make sure your business is ready for tomorrow.
Making tomorrow easier
Bringing in that early stage talent and developing it actually helps solve another challenge looming over businesses today- employee retention. Studies show talent that have gone through a company’s internship program are 34% more likely to stay at the five-year-mark compared to external hires. That means with effective talent pipelines, not only are you developing talent relevant to your business, but are also more likely to retain that talent after those skills are developed. Government and education sector intervention necessary to close in on the skills gap often lags behind, and given the pace of technological innovation rapidly increasing, businesses can no longer afford to rely on market forces for talent, but begin developing the skills needed internally. Or else risk become obsolete, just like the hundreds of jobs at risk.
Via Biz Journals : 5 ways to increase effectiveness in talent management
For 20 years, I’ve been on the forefront of cultural talent management efforts for numerous global projects. As the industry has changed over the years, I’ve learned a few key lessons that always ring true when managing a workforce. Here are some of those lessons I learned to help you and your organization.
1. A human-run HR department is a thing of the past, or is it?
Over the past several years, most companies still had an HR department. This function handles recruiting, hiring, firing, payroll, benefits, job orientations, succession planning, high potential planning and other functions. Yet lately many of these responsibilities have been carved out and segmented across and throughout the entire enterprise. People in this role may now be called digital talent management leaders, enterprise people specialists, chief people officer, sourcing catalysts, life balance coordinators, human capital managers, people social media resources leads and, of course, resources HR managers.
Perhaps we have diluted the HR function from its original definition and it has lost a great deal of discipline for the fundamentals of talent management by becoming 90 percent automated.
While its true you may need recruiting analytics and automation systems for processing resumes, but how well do you know your new recruits and new hires, with this system and how do you match the best resource with your requirements and needs? It makes a difference to do a few things in this process that are personal to bring the “personal” back into HR department.
2. Talent management is more than a trend
Talent management has become a multifaceted job, depended upon how broadly you want to define it. You face an added element when you deal with both full-time employees and contractors. A comprehensive program can include recruiting, on-boarding new hires, digital management, predictive analytics and contractors, training, performance management and tracking (especially important with part-time contractors), competency skills inventories, workforce scheduling and resource planning, succession planning, compensation, benefits and, in some cases, union relations and job profiling.
The concept of having an integrated talent management process makes sense and should be managed as such. View this as similar to process management (as in a manufacturing setting) and process-centric organizations.
To meet the challenge of today’s competitive business climate, it helps organizations to have a talent management and high potential process that is reviewed yearly and revitalized as market conditions change.
3. Knowledge management is a critical element of your talent management program
Many of organizations are building cross-functional, best practice project teams, and informal networks to meet the challenge of streamlining HR processes, compliance and regulations and standardizing performance management systems.
This is being implemented through the establishment of human resources and talent management “Centers of Excellence” that maintain oversight across the entire organization and services organizational designs as well as to serve as advocates for change and monitor standardization and knowledge sharing and quality.
This concept has been extended to joint venture partners, hospitals and suppliers as well. Strategic planning meetings support these activities to realize the HR vision and enhance communications with the business. They also help in standardizing HR roles and ignite teamwork.
Knowledge management directly involves the cross-functional, best practice project teams and is rapidly becoming the key driver that supports talent management programs. Tap into this new model for capturing critical information of your most talented employees (some of which may be leaving soon).
4. Consider company culture in recruiting
While behavior-based interviewing strives to avoid a quality candidate who just doesn’t fit in with the company, sometimes people are just darn good actors and can fool anyone. There are no easy answers here, other than perhaps involving as many people as possible in the interview process to get varying points of view. Engaging others can support finding a perfect fit for your company and steer clear the agony of managing a misfit.
5. Compliments and making people feel confident
The next time your employees or co-workers perform in a special way or makes a value-add suggestion, send them a handwritten note thanking and congratulating them.
Personal notes may feel like a thing of the past, but a note can go a long way.