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StrategyDriven Organizational Performance Measures Article

Predictive Analytics Tools Can Create a Better Workforce

Understanding What Predictive Analytics Is

Predictive analytics (PA) is well known in many business arenas but has never entered Human Resources until recently. PA is a form of technology that learns from other existing data. This process results in predictive results. It is most frequently used to predict very specific individual behaviors in Human Resources.

Predictive analytics examines data or content to answer the question “What is likely to happen?’’ This is important in any business area but is critical to Human Resources who previously depended on intuition to determine future needs of both the company and employees.

With the support of predictive analytics, it’s no longer necessary to make decisions because of intuitive feelings, or ‘’gut’’ reactions to some issue or plan. Data is gathered, analyzed and presented quickly, without stumbling or bumbling on the data or statistics. Intuition or ‘’gut’’ feelings are often unsuccessful, while data mining information that predicts uncertain outcomes is much more reliable and trustworthy.

How Predictive Analytics Affects Recruiting

Applying predictive analytics in Recruiting and Staffing helps companies foresee and enhance several areas, including:

  • Potential top talent is easier to identify with predictive analytics. This makes a recruiter’s job much easier and accurate. Predictive analytics can easily identify the candidates with the most potential, better understand when these talents can be contacted and understand if and why a job opening may be attractive to candidates.
  • Predictive analytics helps companies optimize the responses to their job openings. The analysis can help companies understand how duration, location, occupation and industry will likely affect recruiting results.

Why Companies Utilize Predictive Analytics in HR

73% of companies surveyed said the primary reason they used analytical data was to make the workforce planning process more efficient. 69% believe the main reason to utilize predictive analytics is to more accurately plan for the future and also, create plans to eliminate skills gaps in their organizations. 65% of companies credit predictive analytics as identifying high potential employees.

Other companies indicated that predictive analytics provided better analysis of company needs to align people and company strategy; while others indicated that analysis provided the needed links between performance and compensation; the remaining companies surveyed stated that predictive analytics provided more in-depth knowledge of external talent pools.

Predictive Analytics Helps HR Look Forward

HR has historically been responsible for forecasting the right amount of talent and knowing when to hire additional talent. Unfortunately, before predictive analytics was utilized in HR, the forecasting was more backward-looking than forward. For example, one or more employees terminate, and HR suddenly decides there is talent disproportion.

Unfortunately, few companies have implemented predictive analytics for their HR groups. According to Deloitte, in 2015, only about 8% of global organizations have adopted PA for their Human Resources groups.

The few companies that utilize predictive analytics have had great success and freely share their efforts and results. For example:

  • Google: This company is a strong advocate of statistics and freely admits that it is the most critical tool in their Human Resources group. All interview questions in their hiring process are computerized and perfected to ensure that the best candidates are hired. Additionally, Google’s predictive analysis can estimate the likelihood of future terminations, why they are terminating, and what could have been done to circumvent the termination.
  • Hewlett Packard (HP): HP is also a leader in the HR predictive analytics arena. Recently, HP shared with news media that ‘’when their attrition rates started moving upwards, they utilized predictive analytics to predict which employees were likely to leave by developing a ‘Flight Risk’ score.” This analysis was able to define both the ‘who’ and ‘why’ of their 300,000+ employees who would potentially terminate. For example, higher pay, promotions and better performance ratings were negatively related to flight risk, but the analysis proved that there were strong relationships between these findings. For instance, they were able to analyze that a promotion without a substantial pay increase would likely result in a termination.

“HP’s Flight Risk scores helped managers make better decisions since the early warning signal from Flight Risk allowed time for managers to intervene or if the loss was unavoidable, it prepped the manager to react accordingly. The predictive analysis in Flight Risk allowed HP to save an estimated $300 million.”

The extensive time previously spent on creating charts, reports, quotients, etc., will soon be history because predictive analytics easily allows organizations to analyze the past and predict upcoming trends for both positive and negative analytics.

The end result of having predictive analytics in Human Resources is the ability to predict future needs that are accurate and verifiable. This becomes the organization value to business that Human Resources has long struggled to create. HR has for years attempted to get the infamous ‘’seat at the table’’ and predictive analytics will ensure this happens.

StrategyDriven Organizational Performance Measures Article

Ways Data Analytics Can Boost Your Business’ Growth

Big data is here to stay, and although that can present a few challenges to business (storing it, keeping it safe, etc.), for the most part, it is a boon, which if used correctly can easily boost business growth.

If you’re still skeptical about the use of data analytics, take a look at these very real ways it they can boost business. They’ll have you convinced in no time:

It Can Improve Ordering

If you collect and analyze past sales data, you can identify trends in your company’s sales so that you can order exactly the right amount of stock for your needs. This will help to ensure that you don’t run out of anything, thus helping to keep the customer happy, and it will ensure that you don’t end up ordering too much and spending money on something that will just go to waste.

It Makes for Better Product Management

Analyzing sales data is also a good way to find out which of the thousands of products that you could be selling are the most popular so that you can stock more of the things that are likely to sell in big numbers and fewer things that are unlikely to make you very much money at all.

It Can Improve Your Marketing Strategy

If you’ve ever spent a small fortune on advertising only for it to fail and not bring in as many customers as you would like, there is a good chance that you simply were not targeting the right people or you had the right audience in mind, but you weren’t targeting them effectively. If you were to do what the digital marketing agency MyOptimind do, or even hire them, and use data to improve your marketing efforts, you would be able to spend less money and get greater returns because you would know exactly what pushes your target audience’s buttons.

It Can Help You Train Your Staff

If you collect data on your staff and how they work, then you can use it to identify undesirable patterns in their work or areas in which they aren’t as productive as their peers. Why is this important? Because once you know what your employees’ strengths and weaknesses are you can tailor future training packages to target them, thus saving you money while improving your business from the inside out.

It Can Help You Cut Costs Everywhere

Of course, if you collect data, with Zoho, for example, on every aspect of your business from ordering to time spent fielding calls, you could work out where your time is being well spent and where it is being wasted, as well as identifying where you’re spending too much money and by analysing this data, you would be able to make changes to save money and boost productivity.

You Can Find Upselling Opportunities

If you can use data to identify when certain products are bought in conjunction with others, you can start offering cross-promotions that are likely to appeal and which could be pretty lucrative over the years.

You see, data really is important – time to start collecting it!

StrategyDriven Organizational Performance Measures Article

How Data Flow and Statistics Are Growing in Importance Across Public and Private Sectors

StrategyDriven Organizational Performance Measures Article
The collection of data has never been more important in both the private and public sectors. With the likes of Amazon’s AWS cloud service now offering their Snowmobile data truck that will pull up to your server room, copy your exabytes of data on request, and then drive away to place it in the cloud (saving potentially years of upload time), the size of the data collection effort often relating to customers past and present is immense.

Big Data & It’s Implications

The era of big data is certainly upon us. Collecting the data isn’t the big problem now. For companies and public services like the healthcare sector, sifting through the data and organizing it into useful records that inform at the right time to make better decisions is the real challenge ahead of us. Big data and its data management are becoming a specialist area in its own right now because of the complexities involved.

In the health field, health informatics is a fairly new area that specializes in the collection and management of computer health files. There’s an online master in health informatics degree at the University of Cincinnati where students learn the fine art of data management, protecting systems from a security breach and what to do for disaster management. Their online MSHI program prepares health staff for the patient data challenges ahead from privacy concerns to merging technology and data record access together to let both doctors and nurses have access to patient records when and where they need it.

Web Analytics

The concept of data analytics for anyone who owned a website was something that often passed them by. Some webmasters in the early days didn’t know who was visiting their website, how long they stayed or what pages they viewed. They might have known how many people visited yesterday, but beyond that, the information was far too limited to be really useful.

With the advent of Google Analytics, a free web analytics SaaS from the search giant, the ability to see how many people were visiting, what they did, which pages were the most popular, the average on-page time and host of other pertinent information were available at your fingertips.

Using Data to Get an Edge Over the Competition

Data is becoming a specialist area now. How to collect it, store it and analyze it for potential advantages. Managers can pose the question whether the company has enough stored information to properly determine whether existing customers will approve of a new product launch, a redesign or simply a new flavor or color choice. Data experts can then determine the best way to go about confirming the information that’s being requested using all the available resources available to them with in-house data, along with public information sources like message boards, Facebook groups, Twitter feeds, and more.

It’s fair to say these days that it’s all in the data. For busy or cash-strapped public and private organizations, not having to guess saves money and time while speeding up implementation of ideas to turn them into reality.

StrategyDriven Organizational Performance Measures Article

Doing Big Business With Big Data? Avoid These Big Dangers

StrategyDriven Organizational Performance Measures Article
Photo courtesy of Pexels

Big Data is that important that businesses are doing more than using it to increase traffic. Today, savvy enterprises realise there is money to make, and they are trying to monetise the information they have. In fact, this blog has a post about why a business needs to start monetising data.

There is no doubt that companies can make a lot of money from information, as seen by Big Data’s rise in popularity. However, just because there is an opportunity for revenue doesn’t mean it is worth taking. Before you attempt to get into the Big Data world, you should understand the big pitfalls.

Here are the ones to watch out for regarding monetising info.

Ownership Rights

Don’t make the mistake of assuming the data is yours to sell in the first place. It is important to remember that the company might not have the rights depending on the chain of events. For example, you might not have asked customers to accept the terms and conditions when they landed on the site. Or, the info might have come from a third party which muddies the waters. Before any data goes on sale, you need to have the right of ownership. Otherwise, they could be a lawsuit in the firm’s near future.

Contracts And Privacy Policies

Understand that the contracts and policies which apply now might be void in the future. It is possible for a stipulation to exist which terminates the security protocols in place and leaves the firm vulnerable. At the very least, a disclosure will be necessary to cover all of the bases. As a rule, take a look at the policies which relate to data and double check the fine print. It is better to be safe than sorry.

Laws And Regulations

The government takes the transference of data seriously, particularly in the day and age of extremist terrorism. Therefore, they pass laws which prevent the sale or transmission of certain pieces of information. If you are in possession of such a file and don’t comply, the consequences will be severe. Depending on who the info goes to, it could be treason. The way to stay on the right side of the law is to research ITAR compliance and EAR compliance. These are the regulations that deal with data transference.

Probably the biggest issue with monetising data is confusion. Because there is a lot to handle, it is easy to mess up the collection and storage processes. Not only does this affect the money side of things, but it is also a security flaw. Big data monetisers, to avoid this problem, form different organisations to specialise in this area. Although it seems like a big move, it is a clever and hassle-free way to cash in on data. Keeping the two sides separate negates confusion and smoothes out the business side of things.

There are lots of opportunities with Big Data, but there are lots of dangers, too.

Rhian Silvestro

Surf your data!

Is your strategy built on received wisdom or analysis of performance data? – management rhetoric or business reality?

Are you building your business strategy on received wisdom or real data? Corporate strategies are often based on assumptions about what drives business performance rather than data from the company itself. J.W. Marriott (founder of Marriott Hotels) is famous for saying “You’ve got to make your employees happy. If the employees are happy, they are going to make the customers happy”. TNT Express promotes the slogan “Take care of your people, let them take care of your customers and the rest will take care of itself”. The implication is that happy employees make happy customers, which drive profits. But does this really happen in your organisation?

The problem is that often some drivers of performance aren’t measured at all; let alone the correlations between them. For example, you may believe that loyal employees create satisfied, loyal customers, but do you have data which demonstrates that your longest serving staff create the highest levels of customer loyalty? Another assumption is that loyal customers are the most profitable; we’re often told ‘it is five times more profitable to serve existing customers than loyal customers’. It makes sense. The better we know our customers the better we are likely to serve them. And because customer spend tends to increase over time, it may well be cheaper to serve long-term customers than keep attracting new ones. But, can you prove this is the case in your organisation?

Performance topology mapping is a tool that can help with this analysis. The first step is making sure that you’re measuring the right thing. So if your business is built on the assumption that employee loyalty is necessary to create loyal customers, collect loyalty data. Identify your key performance indicators, and then measure the correlations between them in order to build a map of business drivers.

The findings can be astonishing. For example, the link between customer loyalty and financial performance is often regarded as a basic principle of retail management. However when they came to explore the data in their own organisation, the management of one home improvement retail chain discovered that there was no such correlation. They could not prove that the stores with the most loyal customers were the most profitable.

Analysis of the performance topology map of one of the UK’s big four grocery superstore chains also revealed counter-intuitive results. Its management bought into the idea that satisfied employees created customer satisfaction which drove store profitability. But the data revealed negative correlations! In fact the stores with the highest levels of employee satisfaction were the least profitable. The explanation for this lay in the value proposition: customers in these stores did not value contact with staff so much as product availability, price and checkout speed. Therefore their shopping experience did not hinge on the quality of their interaction with employees.

In other businesses, of course, the interactions between staff and customers are likely to be much more critical. Take, for example, the professional services of clinicians or lawyers. Their services are based on more sophisticated interactions between staff and clients, and long-term business relationships may well be an essential part of the value proposition. Therefore employee engagement is likely to be a more important driver of profitability in professional services.

Understanding the performance drivers is crucial. Because failing to understand what drives profitability is to fail to understand why your company has succeeded… or indeed failed. The reality is that your business strategy is based on all sorts of assumptions about what investments will yield increased market share, revenue growth or profitability. To get the strategy right, better start testing those assumptions… surf the data wave!

About the Author

Dr. Rhian SilvestroDr. Rhian Silvestro is Associate Professor of Operations Management at Warwick Business School. Rhian has conducted service management research in a number of large, leading edge organisations including retail companies, banks, transport companies, health services and call centres. She has publications in over ten international journals in the fields of service design, performance improvement and supply chain integration.