Archive for CFO

Using advance Tech for predictive analytics in employee retention

Using advance Tech for predictive analytics in employee retention

This technique can help managers reduce attrition costs.

The future of human resources is changing. Like the rest of the business world, chief human resource officers (CHROs) and their teams are beginning to find that they need to focus on building a robust analytics capability to best prepare for the data-driven world.

“CHROs have said that they feel [pressured] as the only ones not bringing data to the table. The business is expecting HR to have similar numbers to marketing, though maybe not finance or operations,” observed Andrew Marritt, CEO of OrganizationView, a people analytics practice based in St Moritz, Switzerland. According to Marritt, the data-centric modern HR leader needs to know not only what has happened, but what is likely to happen.

A key HR concern for businesses is employee retention. There are significant financial and intangible costs associated with losing loyal and high-performing employees. Investments need to be made to find, hire, and train their replacements. There could also be a negative impact on the stakeholders they worked with regularly such as suppliers, colleagues, and customers. Some companies are starting to look to predictive analytics to increase their ability to mitigate the risk of employee turnover and increase retention.

Investment in building a people analytics capability need not be big at first, and businesses can benefit greatly from it. “Our research shows that the financial costs associated with attrition can range anywhere between 13% and 23% of annual compensation depending on the function/level of the employees under the scope of the study. In our experience, a focused attrition analytics predictive model can help lower this risk by 5% to 8% annually,” said Neeraj Tandon, director for workforce analytics and planning, Asia-Pacific, at Willis Towers Watson, in Gurgaon, India.

WHAT’S NEW

Traditional HR analytics are descriptive in nature and examine employee data across different dimensions such as department and demographics to identify past patterns within metrics like turnover and retention. Conclusions are then used to formulate talent policies. Descriptive analytics, however, cannot predict future outcomes at an individual employee level.

Predictive analytics does this by going a step further and using the evidence from descriptive analytics as inputs for advanced techniques like statistical modelling and machine learning. These methodologies provide forward-looking measures such as flight risk, which quantifies the likelihood of an employee’s leaving the organisation within a certain period of time.

Predictive analytics also identifies hidden connections between key factors contributing to employee turnover. The main predictor variables normally studied include pay, promotion, performance reviews, time spent at work, commute distance, and relationship with a manager. (See the chart, “Factors Contributing to Voluntary Turnover”, for a breakdown of key reasons for attrition at a sample organisation.) Organisations also use external data such as labour market indicators and the current economic scenario as causative variables while formulating hypotheses and building models for retention. HR teams and managers use the findings from the modelling to better design timely interventions to help retain employees.

Factors contributing to voluntary turnover

An ADP Research Institute white paper examined the factors leading to voluntary turnover at a sample company. The graphic below breaks down the reasons cited. By collecting and analysing the factors that contribute to turnover, companies can institute policies and procedures to address concerns.

In this example, management may want to focus its retention efforts on industry veterans who have not been with the company for very long or look at implementing more lenient telecommuting rules to ease attrition.

Source: ADP Research Institute white paper, Revelations From Workforce Turnover Study.

Deloitte estimates that about 8% of global businesses leverage predictive analytics for talent management, and the ones that do tend to be larger. According to Brian Kropp, group vice president at Gartner, organisations that develop this capability tend to be in sectors that are intellectual property dependent such as financial services, healthcare, and fast-moving consumer goods. Globally, businesses in all major economies are working towards acquiring this competence.

COST VERSUS BENEFITS

Organisations looking to develop competence in predictive analytics have several options. Consulting organisations offer expertise towards building this capability. For businesses looking to set up internal capabilities for smaller capital outlay, many choose to employ or train in-house data scientists who may turn to inexpensive software such as IBM SPSS or free open-source software known as R for their initial modelling.

External vendors that set up human capital management systems with predictive analytics capabilities are also available at different price points. However, experts warn that internal teams should make sure that the human capital management systems offered integrate with data systems within the organisation. The systems should not overpromise and underdeliver in terms of features and tools, and vendors should provide the guidance to use them insightfully.

DATA-BASED CHALLENGES

According to Bersin by Deloitte, an HR research organisation, setting up clean and accurate data streams is, and will remain, a challenge for people analytics. As the research indicates, most big organisations have five to seven systems of record for their human resources data. This means that information often used in predictive modelling is inaccurate or unavailable, a serious stumbling block.

“As statisticians, we do deploy multiple data treatments to improve the quality of data. However, often data on some important variable are incomplete, and as a result we ignore these variables. Some of these variables could be important to predict the outputs. Hence, it’s important that organisations continuously focus on data quality improvement,” Tandon said.

Companies should run specific data quality programs to make the data fit for modelling. These programs would be of greater effectiveness if they were directed at key variables that predict output variables such as attrition rather than across the entire dataset, he added.

BUILDING A GOOD MODEL

Besides clean, accurate data streams, a few further steps can be taken to ensure that predictive retention models are a robust tool for decision-making. For one, studying the workforce in clusters of employees with similar characteristics and reasons for leaving the organisation is essential for building models that lead to targeted and effective retention strategies, according to Tandon.

Model building also goes through multiple iterations to ensure it fits the data optimally, which includes choosing or eliminating causative variables scientifically, and testing the model on an existing dataset to gauge how accurately it predicts actual outcomes. With the acknowledgement that numbers do not tell the entire story, intuition is also factored into models. “There is a good reason people are intuitive; they have got experience,” explained Marritt on how this contributes to the model’s effectiveness.

However, a degree of inaccuracy is associated with predictive modelling, and this is where HR and managers play an important role. “Data should just be another voice at the table. Decisions have to be made by humans,” said Marritt, on how these tools can influence employees’ working lives. It is always better to roll up the data and use them at an aggregated level such as teams, rather than at an individual level, because the implications of making an incorrect decision are considerable, he added.

Last but not least, as with any new initiative, organisations must recognise that adequate coaching and oversight mechanisms should be in place to help users leverage the technique correctly and thoughtfully. According to Tandon, managers are being trained on the key objectives of developing attrition models and coached on how to use the information to prevent high-performing employees from leaving, without creating a bias against the identified individuals.

Central governing teams (often comprising business and HR team members) monitor and track interventions taken by line managers to reduce attrition risk for employees identified as a high flight risk. This also helps organisations bring some level of consistency in interventions to control attrition, Tandon added.

TARGETED APPROACHES

Once these checks and balances are in place, a data-driven approach that includes predictive analytics is seen to bring greater transparency and balance to decision-making. “There have been instances where decisions were made by those who were the most vocal. This will be harder in a world where data is needed to support decisions,” observed Marritt.

The key causative variables that emerge during modelling will also help organisations craft more effective retention strategies. If commute distance emerges as a major driver, for example, greater efforts can be directed towards options such as remote working. If a limited training budget is available, it can be used to provide inputs for those employee segments that have a high flight risk. While HR and managers have always designed these interventions, a forward-looking, rigorous technique enables them to direct time and money towards these efforts with greater precision and with greater confidence in the outcome.

Furthermore, finding unexpected patterns in the data can help design retention strategies that make strong business sense. Marritt’s team at OrganizationView, for instance, found that high work pressure was a key cause for attrition at a certain financial services organisation. However, it was more so for low to midlevel performers while top performers actually thrived under high pressure and were more likely to leave in its absence. Since high-performer attrition had a greater financial impact, the organisation focused on this rather than overall attrition.

THE NEAR HORIZON

Companies are experiencing a massive change in the data they have about customers, and the same change is coming to what they know about employees, according to Kropp. Organisations that figure this out and get there faster will retain a higher-quality workforce. It will be the single most successful differentiating factor on that front, and a must-have for businesses that cross a thousand employees, he added.

Over the last three years, Gartner has also seen a significant increase in the number of organisations that collect employee data in unconventional ways, such as social media activity, speed of keystrokes, mood recognition, email text and frequency, and wearable microphones. Organisations are attempting to understand employee behaviour and experience through these experiments, and some of them will be input into models, which will increasingly graduate from predicting flight risk and quality of hire, which are relatively easy to measure, to hard-to-define variables such as employee engagement and performance, Kropp said.

On the maturity front, while only a small percentage of organisations surveyed by Deloitte currently have the capability for people analytics, in a more recent survey 69% of businesses say they are integrating data to build a people analytics database. The analytics function will also grow into a multidisciplinary team that will solve business-critical problems to drive business results.

Source : FM UK

High demand for board positions for CFO’s

High demand for board positions for CFO’s

CFOs to participate on corporate boards is increasing.

Seventy-nine per cent of CFOs are experiencing increased demand for their expertise on corporate boards, according to an Ernst & Young survey of 800 global finance chiefs. CFO and Beyond: The Possibilities and Pathways Outside Finance communicated the results of the survey and a study of 347 companies worldwide with annual revenue over $5 billion.

Current or former CFOs make up 14% of board members of the companies studied, up from 8% in 2002. And 41% of audit committee chairs are current or former CFOs, up from 19% in 2002.

The desire on the part of CEOs to have finance professionals look beyond their functional silo to collaborate effectively on strategic decisions was revealed in the CGMA report Rebooting Business: Valuing the Human Dimension. Those same skills are sought by corporate boards, and CFOs are supplying them.

Jim Ladd, CPA, CGMA, senior vice president of finance and operations at the Institute for Systems Biology in Seattle, estimated that he has served on about 18 boards during his career. His current board responsibilities include an audit committee role for a New York Stock Exchange-listed company, a lead independent director position with a privately owned company in Seattle, and participation on two not-for-profit boards.

He said finance executives can contribute a lot to boards.

“They’re generally sought out initially because of finance background and a knowledge of financial reporting and audit risks and that sort of thing,” Ladd said. “But CPAs have a broader background than that. And people discover that.”

Audit committee a good fit

Finance skills make CFOs ideal candidates for audit committee positions. In many jurisdictions, regulatory requirements demand that at least one audit committee member have financial expertise to keep abreast of evolving accounting standards, risks and regulations.

Public companies listed in the United States, for example, must disclose whether they have at least one financial expert independent of management on their audit committee. The United Kingdom’s Corporate Governance Code says a board should satisfy itself that at least one audit committee member has recent, relevant financial experience.

This can be a benefit and a frustration to CFOs. Eighty-one per cent of them say finance leaders are good choices for audit committee jobs because of their finance acumen. But CFOs want to make sure their skills in strategic development and other areas are recognised, too.

“Some of them can be a little insulted that the breadth of their experience as CFO is not necessarily recognised,” Gerard Dalbosco, an E&Y managing partner, said in the report.

Opportunity to branch out

Although CFOs already have busy jobs, about two-thirds of them reported that they have taken on, or would be willing to accept, more part-time, voluntary or non-executive roles. Twenty-seven per cent said they already have taken such a role, and 40% said they haven’t yet, but would be interested in doing so.

Scott Lampe, vice president and CFO of Hendrick Motorsports in North Carolina, serves on a few community and government boards and said he is willing to consider working on boards of companies that don’t have a lot of risk and are looking to grow organically. “I want to work with companies who share my philosophy about how a business should be run and what kind of contribution it can make in improving the communities is operates in,” Lampe said.

What do CFOs reap from serving on boards? Three-quarters of survey respondents said gaining general management or board level experience is a benefit. Other top benefits included gaining exposure to another company or industry (65%) and getting a different perspective on running an organisation (62%).

“You get to look beyond the purely financial and think more strategically about a different organisation,” Qatar Foundation CFO Faisal Al-Hajri said in the report. “You can also use these roles to play a broader role in society or the community.”

Serving on charitable and community service boards also gives CFOs an opportunity to give back to the community. Mick Armstrong, CPA, CGMA, recently agreed to serve as treasurer on the board of directors of the chamber of commerce in Meridian, Idaho, where he is employed as CFO of Micro 100 Tool Corp.

“We as a company are committed to the community and realise that just our business environment, the quality of life for our employees, all is wrapped up together,” Armstrong said. “So we choose to be involved in the community.”

Protection from liability

Ladd said a key question any potential board member should ask before considering a seat on a board is whether the organisation carries liability insurance for its directors and officers. He said risk exists even at not-for-profit organisations, so board members should make sure they are protected.

In addition, Ladd said, it is important to make sure you are working for an organisation that supports your involvement on an external board. And you need to have the time and energy to fulfil your board duties in addition to your regular job.

Armstrong, for example, said his duties as chamber of commerce treasurer are made easier by Micro 100’s recent hiring of an accounting manager with a public accounting background. As Armstrong moves toward more of an executive leadership role with his company, this distancing from Micro 100’s daily accounting activity also has helped him find more time – early in the morning, at lunchtime and on weekends – to devote to his board duties.

Ladd said he does a lot of his board work during evenings and weekends.

“I sometimes joke with my wife when I come home at night that I’m starting my second job,” Ladd said. “…But most of the meetings are during the day, so you do have to have an understanding employer. That puts some strain and requires extra time in your life. There is no doubt about that.”

Source :GCMA

Strategies to use analytics for competitive advantage

Strategies to use analytics for competitive advantage

Organisations are building momentum for the use of Big Data by integrating data analytics into their strategy in small projects that deliver substantial results, according a new report.

Almost all respondents – 96% – said that analytics will become more important to their organisations in the next three years, according to a Deloitte report based on a mix of 100 online surveys and 35 interviews conducted with senior executives at 35 companies in North America, the UK and Asia.

Although analytics already is an important resource for many companies, analytical technology remains immature and data under-utilised, according to the report. Getting buy-in for further projects is essential, so analytics leaders are starting small.

“Projects that demonstrate analytics’ ability to improve competitive positioning help these initiatives gain traction across the enterprise,” Deloitte Touche Tohmatsu Limited’s Global Analytics Leader Tim Phillipps wrote in the report.

Companies can prepare themselves to use analytics for competitive advantage, according to the report, by using the following strategies:

  • Acquire the right talent now. Talent for analytics and Big Data is in high demand. Talent shortages may become more of a barrier to analytics implementation as more companies use data to drive more processes and decisions.
  • Tie analytics to decision-making. Better data and analysis don’t necessarily result in better decisions. Specific initiatives to improve decision cultures and processes, along with changing the understanding and behaviours of front-line workers, lead to better decisions, the report says.
  • Apply analytics to marketing and customers. Finance operations are the most frequent area of analytics investment, with implementation by 79% of respondents. Marketing and sales groups, at 55%, are the second-most frequent analytics users, and the report says the best financial returns from analytics often come from marketing and customer-oriented applications.
  • Coordinate and align analytics. There is little consistency among companies with regard to who oversees analytics initiatives. Business units or division heads (23%), no single executive (20%), CFOs (18%) and CIOs (15%) were most commonly cited. More co-ordination may be needed to realise the full benefits of data throughout the organisation.
  • Create a long-term strategy for analytics. While current analytical processes are being implemented, a multi-year plan for the growth of analytical capabilities – linked to strategy development – will help organisations better use data over time, the report says.

The problem is the solution

The problem is the solution

The four-step process for better problem solving

If you strip any project down to its essence, you’ll find there are two fundamental tasks. The first is defining the problem that you’re trying to solve, and the second is actually setting out to solve it.

It sounds pretty intuitive, but I think that first step usually receives short shrift. In my experience, people are so geared up to get in there, roll up their sleeves, and come up with ideas, that they forget to really set the stage and understand why a client even needs their help in the first place. What is their marketplace situation like? How is their business performing? What are they setting out to achieve, and what’s getting in their way?

Asking yourself “What solution should I recommend?” is the worst first step. Before you can answer that question, you need to do four things.

1. DEFINING THE PROBLEM

All effective problem solving starts with effective problem defining. Too often, people jump right to solving without knowing exactly what they are solving. The big challenge here is figuring out how to separate the symptom from the disease. Many of us address the symptom only to find the solution to be a temporary fix.

A great way to uncover the root cause of any problem is to go through the “Five Whys” exercise. “Five Whys” is a technique that was developed by Toyota to identify manufacturing issues and solve them in the most effective and efficient way possible. The way you start is to articulate the problem you’re facing. In terms of corporate strategy, that’s typically a surface level issue like losing market share or declining sales. With the “Five Whys” technique, the goal is to ask “why” five times to help you dig deeper and deeper to uncover the root cause of the problem.

For instance, say you’re working with a local, downtown restaurant that has seen revenue decline. Ask yourself, “Why is revenue declining?” The answer might be that the average ticket is lower than it used to be. Why is that? Maybe because fewer tickets include an alcoholic beverage? Ask yourself why that is. Maybe it’s because traffic on Friday and Saturday nights is down, which is bringing overall alcohol sales down. Why is traffic down on Fridays and Saturdays? Perhaps it’s because the performing arts center around the corner recently closed down.

By going through the “Five Whys” exercise, you’re able to better define the problem. Rather than a food problem or a bar problem, what you might really need to fix is the entertainment problem.

2. REFRAMING THE PROBLEM

The next step is to create a few different reframes of the problem. Each reframe of the problem statement could lead to a number of potential solutions. The way we do this is by creating “How might we…” statements.

Going back to the restaurant example, a few reframes of the problem statement might be:

How might we get more people to add alcohol to weekday tickets (to counterbalance the dip in weekend sales)?

How might we get more people to spend a Saturday night downtown?

How might we get more happy hour visits from downtown professionals before they leave downtown for the weekend?

Sharp and varied reframe statements can help unlock some new, surprising solutions.

3. COMING UP WITH SOLUTIONS

These reframed problem statements are great fodder for a brainstorming process. Actually, in many situations we prefer brainwriting as opposed to brainstorming. Brainwriting is where a group of individuals is tasked with a problem to solve and each individual is required to think and ideate on their own.

You can do these brainwriting sessions in person with a group of people, or do them remotely and over the course of a few days. Simply asking team members to come up with three ideas for each “How might we…” statement can give you dozens of potential solutions to consider.

Remember, when it comes to new ideas, quantity is quality. The more ideas you generate, the more likely you are to have a few gems in the bunch.

4. EVALUATING OPTIONS

You can’t solve every problem or implement every solution. Resources and time are limited. To narrow in on the best opportunities, evaluate and score each potential solution for 1.) ease of implementation and 2.) its potential size of impact if implemented. This scoring can be done as a group or be the responsibility of a few key decision makers.

Sometimes it’s helpful to even map these out on a two-by-two matrix, with the ideas that are easiest and most impactful populating the top right quadrant.

Getting Bruno Mars to play a few sets at our restaurant every other Saturday might be impactful, but not all that easy to pull off. And a standing karaoke night might be easy to implement but perhaps not all that impactful. The goal is to identify the ideas that check both boxes, and then assign the appropriate resources to them.

Keep in mind, there isn’t a framework or methodology in the world that will get you the results you’re looking for if you’re solving for the wrong problem. Spend as much time (if not more) diagnosing the problem as solving it, and you’re well on your way to generating truly valuable solutions for your clients.

Source : GCMA

10 ways to generate and deliver great insights

10 ways to generate and deliver great insights

A model helps organisations deal with the data deluge and provide insights that support robust decision-making.

In a world where uncertainty is the new norm, where technology is getting smarter, where robots are automating and simulating human activity, and where big data is getting bigger, the pace of winning and losing is getting even faster. The margin for error for organisations is now even smaller, meaning high-quality decisions grounded in insight have never been more important. 

It’s true: Technology is capable of automating a lot of what we used to do when it comes to analysing data. It can even take this a step further and simulate some of our thought processes. That said, technology has one shortfall: It is not human, and generating insights is an inherently human process that needs human traits to interpret what is happening.

Faced with a deluge of data, finding a way to combine these human qualities with the tools on offer will provide organisations with more opportunities to make high-quality decisions grounded in great insights.

I propose a ten-step approach to accelerate the process of generating and delivering insights, which forms the basis of the Define-Determine-Deliver model. The model draws on a number of sources. First and foremost, it is based on my experiences of working with some of the largest insight-driven companies in the UK and US. (Deloitte defines an insight-driven organisation as “one which has succeeded in embedding analysis, data, and reasoning into its decision-making processes”.) I was able to observe best practice in the way these companies collected and organised huge amounts of diverse data, and I gained a profound understanding of performance and how they were able to engage their people to take the right next steps, which led to stronger performance.

Second, the model takes up the themes being debated by practitioners, experts, and authors, in terms of how to organise and interpret the huge, diverse data sets organisations are now collecting. And the more diverse and complex the data, the greater the challenge of communicating insights.

The model consists of three stages. The define stage will help you clarify what you need to do and why. The determine stage offers a set of principles to help you generate insights, and the final stage looks at how to deliver your message to achieve the level of impact and influence your insights deserve.

DEFINE: PLANNING YOUR ANALYSIS

1. Be clear on the value of your insights. The beginning of the insight process involves being clear about what you are being asked to analyse. Over the years of working for a number of insight-led companies I quickly came to appreciate that the significant first question was not “what?”, but “so what?” Understanding the value (the “so what”) that your insights will add helps you engage with what the person requesting the information is trying to do. When you are informed and engaged, you build a more relevant and more focused analysis plan.

Tip: If the person making the request hasn’t already outlined the “so what”, asking them “How will the analysis help?” is a good way to understand what they are hoping to gain from the insight.

2. Partner with an expert. In my experience, those who seek help from someone who knows the particular area of operations well deliver the best insights. They could be a call-centre agent or warehouse manager, for example. Share what you are trying to do with them and ask their opinion. Their support can come in many forms. They may share their experiences of the topic being analysed, may highlight obvious pitfalls, or simply confirm that what you are doing is on the right track.

Tip: Ask the person making the request to recommend the right contact. Once you have a partner, be curious, ask good questions, and listen well to what they have to say.

3. Create a hypothesis. It is important that when you are doing your analysis, you don’t try to analyse all the data available because this could take too long. The process of forming a hypothesis will help you think about the relationships between your data, which should end with your forming an opinion (your hypothesis) on the answer you might find once you have done your analysis. A clear hypothesis, therefore, provides you with an indicator of what to look out for when doing your analysis, helping you to stay focused, whilst reducing any wasted effort.

Always create a hypothesis statement that captures this belief before you start analysing your data (eg, “product availability has decreased because supplier “˜out of stocks’ have grown as the cost of raw materials has increased”).

Tip: Take time to run through your hypothesis with your expert (from tip 2) or any other relevant people. This will help ensure you have a reasonable and balanced hypothesis, and help to avoid confirmation bias.

4. Visualise your analysis. It is all too easy to just dive in and start analysing data. Before you begin, be specific about what you need to analyse. This involves visualising what your analysis will look like once it is finished.

Tip: Get a sheet of paper and sketch out what your data will look like once you have collected it all, listing the rows of data down the left-hand side and the column headings across the top. Then sketch out the analysis you will carry out or the techniques you will apply. For example, do you plan to create a column of data that looks at the difference between two data points or a graph of certain variables? Be as specific as you can, as this will really pressure test what you are planning to do and whether it will add value.

DETERMINE: DOING YOUR ANALYSIS

5. Collect, clean, stay connected. Developing a plan of how and when you will collect your data is important, as this will help to ensure you have everything you need when you are ready to start analysing. Before you start the analysis, you will need to clean your data to ensure it is accurate, complete, and in the right format. There is nothing worse than unclean data undermining the credibility of your insights. Finally, staying in touch with your expert partner from the previous stage will ensure you get the most out of your analysis.

Tip: It is helpful to have a few (but not too many) expert partners. Picking partners with different types of experience is a great way to get a variety of viewpoints, leading to a fuller piece of analysis.

6. Analyse well. In practice, every piece of analysis is different. Therefore, adapt your approach using these key principles:

  • Let the data lead you to the insight. Don’t assume you know the answer before you have done your analysis; this could really bias your analysis. Be open-minded and let the data lead you to the answer.
  • If there is an elephant in the room, say so. Sometimes, when it comes to analysis, we don’t want to accept the most obvious insight; we yearn for something more detailed and more profound. But sometimes the most obvious answer is the right one, and it’s OK to accept it.
  • Correlation doesn’t equal causality. Take care when verifying whether two variables are linked.
  • Focus on what the business needs. If the person asking you for insights needs them in two days to assess an opportunity, then focus on what can be done in that time frame, rather than on the ideal piece of analysis you would produce given more time.

Tip: When analysing data, it is often more useful to focus on trends rather than on single data points. Trends often give you a more reliable view of what is happening. For example, if you are trying to determine which stores are driving low product availability over the year, then focus on the stores that are experiencing consistent decline over the time period (those trending downwards) rather than focusing on one store that had a low score for a small amount of time. (It would be interesting to know why, but don’t miss the big trends contributing to your low product availability.)

7. Bring it all together with a conclusion and indicated actions. Once you have developed some good insights, the next step is explaining what is happening and how the business should respond. This can be a daunting task for finance teams, as the fear of suggesting the wrong thing can create a lot of pressure. Grounding your “indicated actions” in insights will give you confidence in your proposal.

Tip: Seek to ensure your conclusion-indicated actions are correct by writing them out using the following structure: dilemma, insight conclusion, indicated actions:

“I conclude that the reason for ‘the shortfall in sales’ (the dilemma) is because store staff are struggling to get the stock out onto the shelves as the increase in customer numbers means they do not have enough time to restock (the insight conclusion). I propose a pilot project to increase staff in the stores with the biggest declines in sales. If this is successful, I propose a wider review of resourcing in our stores (the indicated actions).”

DELIVER: COMMUNICATING YOUR INSIGHTS

8. Prepare a clear insight message for your audience. The previous step, in which you generate conclusion-indicated actions, is based on what is happening and what you need to do next. The critical difference in this step is that you need to build an insight message to convey to your audience. The insight message is often the only part of your process that the audience sees, and if you want to achieve the right impact and influence, the message needs to be clear and engaging.

Tip: Do the “elevator test” to see if you are ready to deliver your insight message. If you were in the elevator with your manager, could you convey your message (the dilemma, the insights, your recommendation) clearly and succinctly in the time it takes to reach the right floor, all in a way that will resonate and inspire the audience to act on your findings?

9. Craft an engaging message. If you want to deliver an engaging message, then logic alone will not be enough. Engagement requires you to connect to people’s emotions. Your message may well have a good structure, clear visuals, clear arguments, and recommendations grounded in your insight findings. But you also need to build an emotional connection by finding the right tone, forming a connection based on shared aspirations, or focusing on how the proposal will directly benefit the insight requestor and their teams.

Tip: Stories are a good way of helping to deliver a more engaging and memorable message. Stories grab people’s attention, bring messages to life, and help link insights to the big picture. For example, if you are trying to put new customer service metrics into context, you could use statistics. “Customer service scores are at 60%. This is a reduction of 10% versus last year, and we need to do better.” Alternatively, you could tell a story that brings your numbers to life. “Last year we were not at our best for 40,000 customers. That is two out of every five customers that came to us. Here are some of the things our customers said and how we impacted their lives by not being at our best …”

10. Build an insight-led culture. Having a framework is a good way to accelerate the insight process. In the insight-led companies that I have worked for, this framework was embedded into the beliefs of their people, which was demonstrated every day in their behaviours. This level of engagement with the principles of the framework allowed these companies to accelerate insight generation, as well as to adapt those principles to address a particular problem when required.

Tip: Always be a role model for insights, giving your teams or colleagues the confidence and the right to be curious and to always seek out the underlying truth as to what is driving performance.

Source : FM

How To Predict Which Of Your Employees Are About To Quit

How To Predict Which Of Your Employees Are About To Quit

You’ve got more data on how your team members are behaving, thinking, and feeling than you probably realize. Here’s how (and why) to tap into it.

How To Predict Which Of Your Employees Are About To Quit

“People analytics” may sound daunting, expensive, and difficult—something the ordinary manager can’t possibly concern herself with even if she’d like to. But the field isn’t necessarily as high-tech as you might imagine.

There’s more untapped data, of some kind or another, floating around your workplace than you probably think. With a little extra effort to spot behavioral patterns, you may be able to get ahead of some of the more common issues, like employee attrition, that can hurt your workplace and your organization’s bottom line. Here’s how.

PHONING IT IN

Turnover tends to be high at call centers, where many people take jobs temporarily, then quit when once they’ve earned enough to return to school or cover a big expense. Lower attrition means higher performance, so managers are interested in predicting and reducing attrition.

My company helped one call center analyze some basic data that it was already collecting: the length and number of calls operators were taking, and how often those calls got escalated or resolved. At the end of each shift, employees received a “report card” reflecting those data points. Since the call center employees’ compensation was linked directly to that performance data, they were highly incentivized to earn good marks.

But a low overall score wasn’t necessarily a sign that an employee was performing poorly, getting paid less, and therefore planning to bounce. Analysts found two specific factors were much more predictive: increased time spent on calls, and fewer calls ending in resolutions. Those operators were just going through the motions.

So the call center’s managers sent supervisors to meet with each operator within a day of those two indicators popping up. Most, however, hadn’t yet reached a point where they were considering quitting. But they often didreveal job frustrations that were usually easy to address, a like a faulty headset or having to work an undesirable shift. Supervisors were empowered to fix most of these problems, and over the next few months, the call center’s attrition rate fell by half.

FEELINGS AND ACTIONS YOU’RE NOT PICKING UP ON

“Sounds great,” you might be thinking, “but I don’t run a call center.” Even so, you can probably start looking for small, early signs of dissatisfaction that are relatively easy to remedy once you spot them. Here are two:

1. Ask employees how they’re feeling–continuously. Measuring “perceptions” might seem impossible, but it’s not. To collect data on something like this, you can use pulse surveys, run focus groups, or take snap polls using common Slack integrations like Polly.

Some large, physical office spaces even go analog and install those sentiment buttons you might have seen in airports or hotels. They’re simple, inexpensive devices that ask a question like, “How was your day?” and provide red (bad), yellow (okay), and green (good) buttons for people to press quickly as they go about their day. Whatever method you use to gather sentiment data, aim for something easy and anonymous, and watch for trends, not absolute values.

2. Look for dips in hours worked or effort spent. A basic place to start is total login time, but unless your office requires workers to “punch in” or “out,” introducing software to monitor exactly who’s sitting in front of their computers when can feel like surveillance. So start with the data you’ve already got on hand but may not be analyzing fully: How much sick leave is being taken this quarter, compared with last quarter or with the same quarter the prior year? How much annual leave is being requested (regardless of what’s actually granted)?

These are usually good indicators of who may be on their way out. Sick days can be requested to attend interviews or to burn up unused leave balances—or maybe that person is just feeling burned out and needs to take some mental heath days to deal with on-the-job stress.

THE LINKEDIN TRICK

There’s a third method, too, that I’ve seen work wonders. A well-known tech firm that recently worked with my company was losing its precious engineers. Recruiters who spent a lot of time looking for coders on LinkedIn were already in the habit of noticing recently updated “Skills” sections, interpreting that as a sign an engineer might be interested in hearing about new opportunities. So it occurred to the tech company to apply this principle in reverse.

The managers realized that their own coders were probably doing the same thing–updating their LinkedIn profiles whenever they were ready to hear from other firms. So the company wrote a simple script to capture the LinkedIn update feed for the profiles of around 2,000 of its top-performing coders. That let managers to react quickly whenever one of those employees added new info. Similar to the call managers, supervisors then swooped in to discuss the career goals and professional-development opportunities with the coders who might be wavering.

As a result, turnover fell, and many of those engineers were moved to assignments or projects that suited their talents and interests much better.

USE YOUR DATA WISELY–AND FAST

Whatever patterns you decide to watch, make sure you’re gathering data for two weeks to two months, so you’ll have enough information to perform a reasonable analysis.

But once you do spot a certain trend, don’t wait to act. Start looking for the source of the dissatisfaction in the corner of the company where you’re picking up on it. Maybe a certain team just really needs flex schedules or better recognition, or they feel starved for information. Often the most effective remedies aren’t even monetary. Once you’ve determined a solution, measure its effectiveness to make sure it continues to produce the outcome you’re hoping for.

At the end of the day, most employees all want the same basic things. Done right, people analytics starts from that humane premise and doesn’t reduce people to numbers–it just helps companies understand why certain situations cause people to keep behaving in certain ways. Ideally, it’s good for everyone when there are fewer surprises, and there’s more happiness to go around.

Read More →

The habits of highly innovative companies

The habits of highly innovative companies

While companies continue to focus on in-house innovation, they understand that good ideas can come from anywhere. With technology quickening the pace of business change, research and development is taking on new meaning as it goes increasingly outside company walls.

Businesses are creating venture capital arms to mine for on-the-rise companies or new technologies that can be integrated into their operations.

In the first half of 2016, 53 new corporate venture capital units made their first investment, according to CB Insights data, the most recent available. That was on pace to continue a full-year growth trend that started in 2011.

And some investment in innovation is through acquisition. Microsoft bought LinkedIn for $26 billion, and Facebook bought Instagram.

Some lesser-known deals also help companies advance strategic efforts. For instance, Under Armour, a US-based athletic apparel company, has branched out into technology through the purchase of personal fitness applications Endomondo and MyFitnessPal. The acquisitions, combined with the company’s existing app MapMyFitness, give Under Armour data on the exercise habits of about 120 million users from around the world. That sort of insight can help the company tailor products to everyday athletes.

The Boston Consulting Group (BCG) annually ranks top corporate innovators, and more and more of those innovators are looking far afield. General Motors’ investment in tech start-ups such as Cruise Automation, which GM said in March would add “deep software talent and rapid development capability” to the company’s development of self-driving vehicles, was listed as an example in the group’s report.

Under Armour is ranked No. 22 and GM No. 27 in the BCG report, which bases its list on financial metrics and a survey of innovation executives (see below). The report lists three habits that separate strong innovators from their less innovative peers: They cast a wide net; they excel at using multiple data sources; and they use external data in multiple phases of the innovation process.

INNOVATORS LOOK INSIDE AND OUT FOR NEW IDEAS

At least two-thirds of strong innovators often used the following strategies to generate ideas: Employee idea forums (68%), customer suggestions (70%), competitive intelligence (72%), and internal sources (78%). Companies labelled as weak innovators are far less likely to use such strategies. For example, just 15% of weak innovators get ideas for new projects or growth from employees, and just 26% said they used customer suggestions.

Eighty-six per cent of strong innovators said proprietary company data were a strong part of innovation efforts, compared with 36% of weak innovators. Strong innovators also are skilled at using patent data and scientific literature to their advantage, according to the report. And another 86% of strong innovators said their ability to use data analytics was closely tied to their ability to reveal market trends; among weak innovators, just 29% thought that was the case.

It appears that thinking about the value of data collection and analysis has changed in just two years of the survey, when three-fourths of respondents said their companies were not targeting big data in innovation programmes.

THE RANKINGS

In the BCG rankings, Apple maintained the top spot for the 11th consecutive year. Google was second for the ninth time in 11 surveys, followed by Tesla Motors, Microsoft, and Amazon. Eleven companies entered the rankings for the first time, led by car-hailing service Uber at No. 17.

Tesla made its first appearance in the rankings in 2013, when automobile producers dominated the list, putting nine companies in the top 20. Netflix, which was ranked No. 6 on the current list, didn’t appear in the rankings until 2015.

Source : FM

4 ways innovative companies set themselves apart

4 ways innovative companies set themselves apart

Consider how you used to book a hotel room or flight 25 years ago, or how and where you watched a movie, or hailed a ride from the airport. Companies that have thrived as innovators have capitalised on digital advances more than their peers have, and as a result, they have built strong brands that customers keep coming back to.

“Customers have more information today than ever before,” said Bill Swedish, a consultant in the western US state of Washington. “They have speed of obtaining that information and, because of that, they have the power.

“There is a call for companies to innovate today because of the rapid changes that are happening with their customers and in their markets,” he said. “… The imperative here for the organisations is to get back ahead of their customers and to be proactive in terms of establishing products and services and ways to interact that will win the hearts and minds of customers.”

Companies that have excelled in that realm have embraced four specific types of digital-related innovation, according to the Boston Consulting Group (BCG), which compiles an annual list of the world’s most innovative companies. The four types of innovation that are a strong focus among leading organisations are:

  • Big data analytics.
  • Fast adoption of new technologies.
  • Mobile products and capabilities.
  • Digital design.

BCG singled out those areas because they were the four that had the greatest year-over-year change in expected impact to their industry in the next three to five years. Overall, new products and technology platforms, at 41% each in a survey BCG conducts for the report, are tied atop the list, but their perceived impact shrunk in the past year. In particular, big data analytics (39%) and fast adoption of new technologies (38%) have narrowed the gap of what’s important for companies.

More than half of respondents said that their companies use data analytics for a variety of purposes connected with innovation, BCG said. These include “identifying new areas for exploration, providing input for idea generation, revealing market trends, informing innovation investment decisions, and setting portfolio priorities,” the report said.

One company is still No. 1

Not surprisingly, Apple is atop the BCG list. The Silicon Valley company has been No. 1 all 12 times the list has been compiled. Google, for the tenth time in the past 11 BCG lists, is No. 2. Amazon, which ranked No. 20 on the list ten years ago, is up to No. 4, just behind Microsoft. The rest of the top ten, in order are Samsung, Tesla, Facebook, IBM, Uber, and Alibaba.

Apple’s most recent financial results demonstrate how the company is winning with digital products around the world. Apple CEO Tim Cook said the company’s number of active installed devices, including the new iPhone X, reached 1.3 billion in January, a 30% increase since 2016. For the quarter that ended 30 December 2017, Apple reported revenue of $88.3 billion, its all-time high. Apple said nearly two-thirds of that revenue came from outside the US.

On the BCG list, North America remains the most represented region, with 27 companies in the top 50. Europe is next with 16 companies on the list, up from ten companies in 2016. Europe’s improvement includes first-time appearances by German companies Adidas and SAP.

BCG bases its list on financial metrics, such as three-year total shareholder return and a survey of innovation executives.

Source:  

Using algorithms to fight supply-chain fraud

Using algorithms to fight supply-chain fraud

More companies are turning to data analytics to detect supply chain fraud.Supply chain fraud is hard to avoid, with the contents of a missing cargo container ending up on the black market or an overseas worker skimming off the top being the inevitable reality of global supply chains.

Companies are increasingly turning to data analytics to detect and stop fraudulent schemes, a recent Deloitte poll found.

Nearly 35% of companies are using some type of data analytics to keep eyes on their supply chains, according to the poll of more than 3,200 professionals from a variety of industries.

That’s an increase of nearly 10 percentage points from 2014, and a sign that more companies, large and small, are adopting complex technologies to closely examine their supply chains.

Companies need to constantly look for fraudulent practices in the manufacturing and shipping of goods, with those looking to steal often as crafty and innovative as the technologies that stop them, said Guido van Drunen, a principal in KPMG’s forensic advisory group in Silicon Valley.

“Fraud goes where there is the least resistance,” he said, comparing fraud detection to a game of Whac-A-Mole. Nip one scheme in the bud and another one pops up elsewhere.

A major advantage of using data analytics to examine supply chain transactions is that detection can happen much faster, said Larry Kivett, CPA, a Houston-based Deloitte Advisory partner in the firm’s forensics and investigations division.

Schemes that might have gone on for years, such as a factory operator setting aside a pallet of electronics for black market sales, can now be found out shortly after the theft begins.

Now, complex algorithms can sift through thousands of incoming invoices and work orders from third-party suppliers to find instances of double-billing, overcharging, and theft.

That saves companies money, especially those with complex, global supply chains with hundreds of vendors and suppliers to keep track of, Kivett said. In many cases, the fraud can be detected before an invoice is paid, instead of having audits years down the road reveal the problem.

Advanced algorithms can also match bills of lading, forms filled out at ports that reflect cargo shipments and times, to ensure that goods are leaving when and where suppliers say they are with the correct number of manufactured products, Kivett said.

“If you can leverage data analysis to help out, you’re getting much earlier visibility” on fraud, he said.

Even DNA has a role. Instead of taking a swipe of a criminal suspect’s cheek, DNA swabs from a crate of clothing can tell a company where a product was produced, and ensure a contractor isn’t surreptitiously having goods made in another factory by underage workers, van Drunen said.

Here are tips from Kivett and van Drunen on how to shore up supply chains to detect and prevent fraud:

Consider the risk. Ignoring problems of graft and theft at factories and subsidiaries isn’t an option, van Drunen said, calling the supply chain the “lifeblood of the company”.

Violations of child labour and corruption statutes could have major implications for a company, with exposure opening up the chance of a public relations disaster and other fines and sanctions. The US Foreign Corrupt Practices Act could also shut down a production line, for example, if a company’s representatives are linked to bribery or other corrupt practices.

That’s why companies, especially those that produce and manufacture goods in areas of the world with looser regulations, need to be diligent in looking for problems.

Go in-house or contract? It all depends on your company’s strengths and weaknesses. Companies with robust technology departments and data scientists may do well to develop their own data analytics and internal controls.

But companies that don’t have a team of analytic experts may want to bring in outside help to examine their processes and make recommendations of how to best monitor their supply chain, Kivett said. “You really don’t understand where your susceptibilities are,” he said.

Experts in fraud can help point out weaknesses and conduct an audit of current controls by using experience of dealing with widespread cases of fraud.

Realise that no one is immune from fraud. Supply chain fraud risk is fairly consistent, regardless of industry and size of the global operation, Kivett said, citing Deloitte research. It’s an indication that companies need strong internal controls and need to periodically examine the controls and see if additional measures are need.

“For organisations, there’s the tendency to say that wouldn’t happen here; we’ve got good people,” Kivett said.

That’s a foolhardy stance to take in today’s environment, especially in a business that depends on cross-border trade and can be infiltrated through cyberattacks or by other means.

WHERE TO LOOK

There’s not a single technology or area to look for fraud, Kivett said. Instead, companies should examine all their processes and try to move as much information to electronic sources as possible.

With data such as timekeeping systems, financial reports, and other information available in a searchable way, companies can periodically examine operations to look for suspect patterns.

“You can see what the baseline patterns are like and identify any anomalies,” he said.

Similarly, companies should make sure new acquisitions and new vendors are using the same internal control and data analysis procedures.

Source : FM

How to develop a global mindset

How to develop a global mindset

Today’s business world is a far cry from yesteryear. An increasing number of organizations operate worldwide, and they are more diverse internally. And that means professionals — including CPAs — must be adept at dealing not only with employees from various backgrounds, but with workers and clients in different countries as well.

But how do leaders ensure that they and their organizations are culturally savvy and prepared to deal with diversity? This was the subject of “Developing Your Global Mindset,” a one-hour talk given by Kim Drumgo, director of Diversity & Inclusion at the Association of International Certified Professional Accountants. Drumgo’s talk was the second in a series of CPA Diversity & Inclusion webcasts aired by the Association.

“In this digital age, geographical borders are no longer clearly defined, so having a global mindset while working globally has become critically important for the success of business leaders, especially in the accounting profession,” Drumgo said following her talk.

Drumgo defines “global mindset” as the “ability to adapt to a culture and influence individuals or groups whose ways of doing business are different than your own.” By having this mindset, by asking questions and engaging in dialogue with others, leaders can improve employee morale, generate greater insight into untapped markets, and gain more credibility with clients. Those who do not develop a global mindset could miss out on client and talent potential, she noted.

She outlined three work environments:

  • Multicultural environments contain several cultures or ethnic groups alongside one another, but who operate independently.
  • Cross-cultural environments include people from different cultures and some acknowledgement of the differences, though one culture remains dominant.
  • Intercultural environments are the “gold” standard for organizations to achieve, as they encompass a deep understanding and respect for different cultures and ideas.

Drumgo also described the “global mindset inventory,” a concept created by the Thunderbird School of Global Management at Arizona State University. Individuals with global intellectual capital or global business savvy have strong analytical and problem-solving skills and an ability to understand international business. Next is global psychological capital, which is an individual’s innate passion for diversity. Then, global social capital is described as a more enthusiastic and outgoing quest to “collaborate with people from different perspectives,” she noted. Those who possess each type of capital are often more effective leaders since they engage and learn across cultures. Psychological capital is the most difficult to grasp as you are “changing your thought process, breaking down biases, and beginning to challenge your old way of thinking,” Drumgo said.

Drumgo offered the following five tips for changing your global mindset:

Forget the golden rule and use the platinum rule. “Treat people the way they want to be treated. Find the positive in other approaches,” she said.

Don’t underestimate the challenge. Dealing with cultural and individual differences can be difficult, and you cannot assume that you know how to handle every situation that can arise. “Having many stamps in your passport doesn’t mean you have a global mindset,” Drumgo said. So don’t underestimate the challenge of leading and working with others across the globe.

Apply multiple strategies. “There isn’t one silver bullet as to how you can interact with everyone. There is not one proven strategy that will help you relate to your entire team better,” Drumgo said. “Applying multiple strategies is really important.”

Be sensitive to differences in language. Communicating isn’t always easy for those who use English as a second language. Be empathetic, kindhearted, and understanding.

Be patient and ask for feedback. “You can’t flip a switch and know how to interact with everyone around the globe,” Drumgo said. “You can’t be everything to everyone all of the time,” she said. “But be the best you can to somebody when it’s time.” Then, she added, you will make a huge difference in developing your global mindset.