Archive for Management Consulting

Integrated business planning – How to successfully use it !

Integrated business planning – How to successfully use it !

The adoption of integrated business planning (IBP) over the last ten years has allowed organisations to develop an agile approach to planning and execution in an increasingly challenging external environment. It has also demonstrated its value in enabling the finance function to deliver excellence in its emerging role as a business partner across the organisation.

Whilst the deployment of IBP represents a significant organisational change, considerable insight on the key success factors for the execution of IBP has now been accumulated. This deployment learning is crucial when planning the adoption of IBP, and, as outlined below, the finance function plays a key role in this.

IBP as presented in this article is not integrated thinking or integrated reporting, as they might be used in developing an integrated report using the International Integrated Reporting Council’s Integrated Reporting Framework. The use of the terms “sustainable” and “sustainability” in the context of this article refer to adoption of an IBP approach as outlined in the article and not the adoption of a multi-capital approach to sustainable management.

IBP is a specific process for using specific business goals to develop precise financial and operational resource requirements with the goal of minimising risk and maximising either cash flow or profit. As presented in this article, IBP is an operational planning system with origins in supply chain planning systems. Sustainable growth in IBP refers to the maximum rate of growth that a company can support without taking on new financing, and not to broader environmental, social, and governance factors that are taken into account in a broader definition of sustainability.

Deployment insights

The graphic “Key Success Factors for IBP Deployment” summarises four points for effective adoption of IBP based on deployment experiences over a wide range of industry sectors.

Key success factors for IBP deployment

Source: Camelot Management Consultants.

Historically, due to its evolution from sales and operations planning (S&OP), IBP has often been seen as a supply-chain-centred process. However, experience shows that the enterprise-wide benefits of the process are not achieved or sustained when deployment is approached in this way.

A key feature of IBP deployment is the creation of genuine cross-functional collaboration to deliver enterprise goals as reflected in the key success factors:

  • Cross-functional C-suite sponsorship:IBP entails significant change not only in processes and systems but also in mindsets and behaviours across the business. For this reason, strong senior leadership sponsorship is critical. Furthermore, the cross-functional and interdependent nature of IBP has been shown to be more successfully deployed when the collaborative character of the process is reflected in the C-suite’s sponsorship of the programme. In particular, joint sponsorship by the key functional leaders (finance, commercial operations, and supply chain) has proved very powerful in role-modelling the teamworking that underpins effective IBP.
  • Senior enterprise leadership:Ideally, IBP deployment should be led by a senior manager with either a commercial or financial background and strong cross-functional awareness and connections. This differs from the traditional handling of IBP (and S&OP), as outlined above, where supply chain leaders have often been tasked with deployment. This senior enterprise leadership provides the credibility and commercial acumen to lead change across the various parts of the business.
  • Building cross-functional teams:Most organisations are not set up, either structurally or culturally, to excel in cross-functional working. It is therefore essential to invest time to support the development of the cross-functional teams that are at the core of creating value through IBP. This goes beyond basic team building to include, for example, the development of team-based reward-and-recognition approaches and cross-functional metrics and goals.
  • Coaching and mentoring:In common with many organisational changes, IBP deployment has often focused on the process and system aspects (which are undoubtedly important). However, applying new cross-functional leadership practices and executing a new corporate process requires hands-on support, especially for managers working in IBP for the first time. Coaching and mentoring have been shown to be very effective approaches for rapidly building new capability and driving quick wins from the process.

In addition to these broad deployment learnings, it is also clear that the finance function has a critical role in ensuring successful and sustainable deployment of IBP in four key areas:

CFO sponsorship

As one of the key C-suite sponsors, the CFO has a very significant role in the sponsorship of IBP adoption. IBP leads to a major change in the philosophy and execution of business planning (typically a key part of the CFO remit), from major annual or biannual activities to a rolling monthly financial planning cycle. This change has far-reaching effects across functions and, hence, the road map for change and its organisational benefits must be strongly owned and promoted by the CFO.

An important part of this sponsorship role is working directly with C-suite peers to maintain a disciplined adherence to the IBP process, especially in the early stages of deployment. IBP sets out a clear and structured approach for performance review, empowered decision-making, and forward planning. This approach can often challenge existing ways of working amongst senior leaders who over time have built personal networks and practices that allow them to access business data and make decisions, often from a siloed perspective.

A key role of the CFO is therefore to model the behaviours of the senior leader within IBP, supporting and leveraging delegated responsibilities in the IBP cycle and not overriding decisions outside the designated process.

CFO sponsorship of IBP is also critical across the finance function itself. The IBP approach shifts current practices in business and financial planning and creates new and increased expectations for finance staff to operate as business partners with a broader influence beyond the traditional accounting role.

The role of the finance partner in IBP is vital (as described further below), and, hence, the CFO must engage and inspire the team to respond to this opportunity. Whilst extending the expectations of the finance staff beyond the core of traditional accounting to a broader business role is exciting for many, it also presents challenges.

Leading this functional shift as part of IBP deployment is a key role for the CFO to ensure that high-quality finance support is present throughout the various cross-functional IBP teams and when decisions are made.

Cross-functional partnerships and influence

Finance leaders play a key role on the newly formed IBP teams to build the connection between commercial and supply chain teams. Traditionally, these teams have often not established an open and trusting partnership, with the supply chain team often seen as a back-office or “arms-length” provider to the commercial organisation. However, the finance team is typically experienced at working across teams in the process of aligning and reconciling financial plans.

This experience, and the trust built with these teams, enables finance to play a critical role in focusing the cross-functional IBP teams on enterprise outcomes and metrics. Over time, trust and collaboration is built through the positive experience of executing IBP, but finance is often a key catalyst to accelerate this.

A key foundation for enterprise-optimised decision-making in IBP is rigour and transparency to create a common, cross-silo view of business performance and outlook. This is a crucial role for finance, leveraging its traditional strengths in management accounting and financial stewardship.

An important feature of IBP is the application of standard data sources, definitions, and metrics (typically supported by the business ERP system). This consistent foundation for situation analysis and decision-making is essential in aligning cross-functional perspectives and creating transparency for decision-makers. This can often be problematic in the early stages of IBP adoption, where there can be considerable resistance to letting go of silo-based data or interpretations.

Here finance has a key responsibility to help educate cross-functional partners on the new data sets and metrics and their importance in supporting decision-making optimised at the enterprise (rather than the silo) level. Finance members of IBP teams often also take on a “champion” role to ensure adherence to this new approach in the monthly IBP meetings, and this has been shown to both improve the quality of individual IBP team outputs, but also the overall flow and decision-making efficiency of the overall IBP cycle.

Investing in new skills for finance

Given both the key role of finance in deploying IBP, and the degree of change in the finance team to deliver this, it is vital that the function invests to build the capabilities required.

Key finance capabilities for IBP

Source: Camelot Management Consultants.

This capability development can be categorised into three themes, as shown in the graphic “Key Finance Capabilities for IBP”:

  • Cross-functional leadership: Finance leaders have an important role in supporting the integration of the new cross-functional teams that are central to IBP and in enabling strong, coherent team outputs. Capability development should include the rational, task-focused aspects of building cross-functional teams, such as aligning around common goals, agreeing roles and responsibilities, and defining team metrics. However, it is important that this is complemented by capability building in softer topics such as an understanding of team dynamics, personality types, generational differences, and communication styles, and how to translate this understanding into effective team leadership.
  • Business acumen: The finance role in IBP demands a broad judgement of business issues to understand and influence responses to risks and opportunities. A common element of this role is also the development of scenario plans and advocating the optimal business response. It is important that finance staff members are able to leverage strong analytical inputs with business credibility. This can be developed, for example, through job rotations across functions and with coaching/mentoring support within finance.
  • Advising and influencing business leaders:IBP delegates decision-making throughout the various cross-functional teams. This requires that the decision-maker in each case is briefed in order to ensure that rational, enterprise-optimised decisions can be made. Finance has a key role to play in this, and this often creates a need for relatively junior finance staff to build their capability and confidence in advising and influencing the relevant business leader. Developing skills in presenting, influencing, and negotiating are therefore key for finance staff as increasing demands are made by IBP.

New mindsets and behaviours for finance

Expanding finance’s traditional skill base can be a key mindset change for the finance function, which requires clear direction and sponsorship from the CFO, as well as engagement and support from all functional leaders.

A further success factor for finance staff working in IBP is the ability to balance the need for championing financial rigour whilst adopting a dynamic and agile approach to driving the business.

The rolling monthly cycle requires a major mindset change from traditional (typically annual or biannual) financial planning processes. Monthly IBP discussions focus on changes, outliers, and closing performance gaps; and a key strength of the process is the ability to dynamically create robust updates to the business and financial outlook. This discipline brings finance closer to the operational edge of the business and is a key asset in ensuring the resilience and relevance of the business plan and associated financials. The capacity of finance to adapt to this new way of working is a key success factor in IBP, providing a competitive advantage in a volatile and complex market environment.

Source : FM

Problem-solving – Creating a culture of blameless 

Problem-solving – Creating a culture of blameless

Companies that fail exceptionally have the potential for greatness.

Finance is complex, and whenever you have complication and uncertainty, it is a given that things will go wrong at some point. When they do, the best way to deal with those mistakes is to use them to learn and grow. And the only way an organisation can be aware of issues while they’re still small-scale is to create an environment in which employees and managers at all levels feel safe voicing their concerns and thoughts.

“The reality is human beings will make mistakes,” said Amy C. Edmondson, Novartis professor of leadership and management at Harvard Business School and author of The Fearless Organization: Creating Psychological Safety in the Workplace for Learning, Innovation, and Growth. “When we’re in novel settings, beyond just mistakes, we’ll also have failures that aren’t, strictly speaking, mistakes because no one’s ever been in that situation before. The most important thing is that you hear about what went wrong in a timely way because that’s how you can jump on it and avoid larger-scale problems.”

Companies that foster a culture of blameless problem-solving have the potential to learn from what goes wrong, and also to innovate, through smart experimentation, while companies that habitually blame individuals are in danger of running into large-scale disasters without a hint of impending doom, according to Edmondson.

Here are some tips for creating a workplace environment in which people feel they can speak up about what’s happening and collectively work hard to improve and avoid big problems:

Promote smart experimentation. Experimentation is how companies innovate and develop tomorrow’s new offerings, but you want to make sure that the experimentation strategy is a smart one. Organisations should never experiment on a grand scale in uncertain domains. Experiments need to be big enough to get valid data about their viability, but not so big that the potential failure will be devastating to the business.

“For organisations to create a culture that doesn’t blame or punish mistakes, they must embrace entrepreneurship culture,” said Ebrima Sawaneh, a Lagos-based accountant and finance blogger. “Every employee should be trained and empowered to innovate solutions without fear of being punished if they make genuine mistakes. Employees should be encouraged to report any mistake, and organisations have to clearly set what is acceptable and create a line of sight.”

Once you have a clear experiment strategy of an appropriate scale, you must make sure that everyone’s expectations are aligned.

“Everyone (high and low) must know that this is an experiment, and the nature of an experiment is we don’t yet know what will happen,” Edmondson said. “Make sure everyone is aware of the fact that this may or may not work, and in both cases, what happens will provide great data.”

Invite input. Leaders need to make it clear to people that their voice is not only expected but also welcomed.

“A lot of times, especially when they are nervous that there might be layoffs, people have the tendency to hold back,” Edmondson said. “There’s an implicit belief that no one ever got fired for silence. I think the job of leaders is to flip that around. In the complex, uncertain industry in which we operate, the people that we’re not hearing from are not of much value.”

Because the tendency for employees is to remain silent about issues, leaders need to be proactive in inviting input. It’s one thing to say, “I’d love to hear from all of you,” but it’s another to turn to a specific employee and ask, “What do you think of this situation? I’d love your thoughts.”

“After painting the situation we find ourselves in in such a way that it becomes clear that voice is necessary, leaders must be proactive in asking ‘What are you seeing out there? Is there anything not going well? What are you excited about?’” Edmondson said.

Foster psychological safety. In her latest book, Edmondson discusses why it matters for company performance that people feel psychologically safe to speak up and what leaders can do to help bring it about.

“I don’t mean to say we have to get rid of all fear,” Edmondson said. “I think it’s fine to be afraid of missing a deadline or afraid of the competition. It’s not fine to be afraid of one another or the boss.”

Edmondson explained that while managers have an outsized influence on the climate at work, any employee can make a more psychologically safe space for colleagues simply by showing up with a spirit of openness, asking questions, and truly listening.

“When you listen thoughtfully to a colleague or a subordinate, you are making a difference. You are making work life that much more safe and enriching,” she said. “In addition to asking questions, when you say things to colleagues, subordinates, or managers such as ‘I made a mistake’ or ‘That didn’t work out the way I thought,’ it sets a shining example of a learning orientation. If you model a learning orientation and interest in others, you will make that small difference, in your vicinity, in helping create a learning organisation.”

Sawaneh agrees that fostering psychological safety can help create a high-performing financial organisation.

“When people fear that they will be blamed for mistakes, it can affect their active participation and sometimes result in their being too careful,” he said. “The key resource of accounting firms are their people, and when individuals are less concerned about mistakes, they will be willing to delegate, create a learning culture, become team players, and embrace change.”

Avoid stretched goals and closed ears. While there are several examples of organisations doing a good job of creating a culture of blameless problem-solving, there are also examples of companies that have faced the consequences of squelching safe and open communication.

Wells Fargo’s recent failure, in which millions of accounts were created without consumers’ consent, is one such example. According to Edmondson, the bank’s initial cross-selling strategy wasn’t fully in touch with the reality of customers’ limited wallets, which created immense pressure to have more and more products per customer, leading employees to activities that became fraudulent and problematic in other ways. Had employees felt able to speak up, push back, and say what they were learning, the strategy might have been tweaked.

“A recipe for failure is stretch goals and closed ears,” Edmondson said. “When managers, getting the messages from on high, are saying, ‘You better deliver on this,’ the implied rest of that sentence is, or else. People will deliver, at least on the illusion of creating the desired results, so then what you will often see is the illusion of good performance rather than good performance itself.”

Develop a productive response to bad news. Psychological safety in the workplace can be shattered the second a boss erupts in anger over a reported failure.

“Leaders need to train themselves not to overreact emotionally to bad news,” Edmondson said. “They need to pause, breathe, and disrupt what might be the natural, instantaneous reaction of emotion or disapproval, and say, ‘Thank you for that clear line of sight. Now what should we do next? What are your ideas? Here are my ideas.’ It’s what I call a productive response to bad news, as opposed to a natural, in many ways normal, response to bad news.”

Source : GCMA

Finance innovators- Watchlist for future

Finance innovators – Watchlist for future

At a recent accounting conference in Malaysia, the chief executive of the Malaysian Institute of Accountants opened the first panel discussion by presenting statistics from a World Economic Forum report on the future of jobs. In the report, accountants and auditors are classified under “redundant roles” that will see steep decline in demand over the next few years due to automation. Minutes later, one of the most popular questions from the audience was “How accurate are those statistics?” It reflects a sense of incredulity amongst professionals in the accounting and finance industry. Change can’t be that soon, can it?

You may share the same sentiment. You may have heard buzzwords like automation and artificial intelligence generate chatter in the office or at networking events. They have become ubiquitous in our business vocabulary, yet they are minimally understood. Perhaps you can’t help wondering, “How many of these new technologies are truly ‘disruptive’ and not passing fads?” (History tells us: Whatever is practically useful and reaches a tipping point in adoption survives).

Experts are saying that these new tools are not an evolution of current technology. Rather, it’s a revolution. Klaus Schwab, executive chairman of the World Economic Forum, wrote that the scale, scope, and complexity of this revolution is “unlike anything humankind has experienced before”. And these changes are not limited to technology. Climate change is also affecting the availability of natural resources like water and arable land. It is forcing businesses to rethink the sustainability of their practices.

There’s a silver lining amidst these changes: We are only at the beginning, and there’s still some time to learn about them. Management accountants are needed now more than ever to help navigate this tumultuous time. We dug deeper into six must-know topics from a recently published watchlist by the Association of International Certified Professional Accountants and listed resources to help you learn more. Here’s to future-proofing your career.

1. BLOCKCHAIN

Invented as the technology behind bitcoin ten years ago, blockchain has garnered greater attention in recent years as a technology with the potential to transform financial transactions, supply chain management, and even large portions of the internet. A blockchain report by the American Institute of CPAs defines it as a distributed public ledger that makes a record of every transaction and adds it to a chain of all the transactions that have come before. In accounting and finance, blockchain may enable firms to create immutable and continually updated financial records that are difficult to tamper with. In other industries, companies such as Walmart and Pfizer have completed blockchain pilots to improve food safety and track medicine.

Large and consistent investments into blockchain startups are an indication of interest in the technology. In 2017, venture-capital funding for blockchain startups was up to $1 billion, according to a McKinsey report. In the same report, the consultancy identified some sectors that are inherently more suited for blockchain implementation; namely, financial services, government, and healthcare. In the short term, blockchain’s value lies in cost reduction, but meaningful scale is still three to five years away, according to the report. Some cite its instability, high cost, and complexity as causes for its stuttering path toward mainstream adoption.

Bottom line: Experiments with and implementation of blockchain will continue in 2019 as more applications are identified. But meaningful scale of blockchain implementation to realise its full value in reducing costs and generating revenue is still a few years away.

2. ARTIFICIAL INTELLIGENCE

Artificial intelligence (AI) refers to a branch of computer science that strives to create software that mimics human intelligence. AI-related applications already have been implemented in operations and finance to automate repetitive processes. In 2019, 20% of US organisations plan to implement AI enterprise-wide, according to a PwC survey.

Far from a single technology, AI is an umbrella term that covers a number of areas, among them machine learning, natural language processing, and deep learning. Banks are using AI surveillance tools to prevent financial crime, and insurers use automated underwriting tools in decision-making. In an FM magazine interview, UBS wealth managers spoke about building robo-advisers and using AI and natural language processing to conduct due diligence on the bank’s clients.

The software the bank built can conduct quicker and more convenient know your client (KYC) and anti-money laundering (AML) checks compared to a human worker. It parses hundreds of pages of search engine results for negative news and conducts checks on a potential client’s criminal history — a task that would have demanded a significant amount of a human employee’s time.

Technology expert Amy Vetter, CPA/CITP, CGMA, CEO of The B3 Method Institute, said in a series of video interviews with the Journal of Accountancy that “there are many who look at the technology that’s coming … that we’re going to be disrupted and accountants will go away. And I do not believe that is the case at all.” Instead, new technologies will free up time for finance professionals to provide analysis of financial data, she said. For that to happen, finance professionals will have to train on new technologies, whether through courses or hands-on experience. Communications skills will also be pivotal to help accounting and finance professionals explain their analyses effectively.

Bottom line: AI technology is already implemented in various finance functions and industries. The first step is to learn more about AI — its opportunities and limitations. Its potential is in freeing up human workers to provide more value-added services. Great opportunities await the eager student.

 

3. ROBOTICS

The inconsistent use of vocabulary to describe robotics, sometimes called automation, has created much confusion. Some may even imagine a physical robot sitting in an office. Robotics here refers to robotic process automation (RPA), and quite simply it’s a class of software used to process transactions, manipulate data, trigger responses, and communicate with other digital systems.

Although not an entirely new phenomenon, RPA’s capabilities have improved remarkably over time. Rob King, author of Digital Workforce and vice-president of education at RPA Academy, said in an Association podcast that “RPA has definitely arrived”.

Last year, FM magazine got an inside look at how a Koch Industries subsidiary successfully implemented RPA, freeing up almost 50,000 employee-hours after less than two years of implementation. At the company, RPA was used to automate invoice transactions and track third-party labour.

In 2019, companies will continue to explore RPA implementation and reskill employees to adopt the technology. Research firm Gartner estimates that global spending on RPA will reach $2.4 billion in 2022, compared to $680 million in 2018.

Bottom line: Robotics’ capabilities have matured over time. More companies will want to use RPA to automate repetitive tasks, increase efficiency, and reduce costs. Most large enterprises will embrace RPA in the next few years. For smaller firms, the cost barrier to implement RPA will gradually diminish.

 

4. NATURAL CAPITAL CONCERNS

The only nontechnology trend on this list, but no less significant, is natural capital concerns or environmental risks, which have been brewing in the background for years. We would need 1.7 planets to sustain our current global levels of consumption, according to the World Wildlife Fund (WWF) Living Planet Report 2018. The study estimated that at our current rate, we are using up natural resources faster than they are replenishing themselves. In relation to the Financial Times WWF Water Summit last year, Margaret Kuhlow, WWF’s finance practice leader, wrote that businesses need to get serious about water risks and disclose greater asset-specific location data to identify water investment opportunities.

Former New York City mayor Michael Bloomberg has led the establishment of the Task Force on Climate-related Financial Disclosures (TCFD), encouraging organisations to provide voluntary financial disclosures related to climate that can paint a fuller picture of their businesses. Momentum has also been gathering on nonfinancial reporting. FM magazine reported on a recent debate organised by Oxford Saïd Business School where speakers posed arguments on whether standardised nonfinancial reporting should be mandated for it to be useful to investors. “We need to make sure that climate change, biodiversity, and inequality are dealt with in the future. … There is an urgency,” said Paul Druckman, former CEO of the International Integrated Reporting Council, an organisation that advocates for natural capital and other nonfinancial information to be included in corporate reporting.

Bottom line: Businesses are realising that environmental sustainability must be given greater consideration in business decisions. In 2019, there may be greater support and practice of disclosing climate-related information in corporate reporting.

5. BANKING EVOLUTION

Last year in an issue of the IMF magazine Finance and Development, Stefan Ingves, the governor of the world’s oldest central bank, Sweden’s Riksbank, pondered the question, “In a cashless society, what would legal tender mean?” The question is not far-fetched because Swedish society has almost stopped using paper money, preferring transactions through cards and digital platforms.

Other than changing consumer preferences, banking is also evolving because of the rise of bitcoin and other cryptocurrencies. Although many do not consider cryptocurrency a reliable substitute for cash due to its price volatility, consumers and businesses are already performing transactions using these virtual currencies. Among many are Microsoft, Subway sandwich shops, and e-commerce site Shopify.

In 2019, Sweden’s central bank is expected to issue its first cryptocurrency, e-krona. In the UK, the Bank of England published a working paper last year to understand the implications of a central bank-issued digital currency. In this banking evolution, traditional functions of banks as clearing houses will also change, causing banks to rethink their business models. According to Deloitte, 2019 will see retail banks race to be digital leaders in embracing a mobile-centric customer experience, while fintechs attempt to devour a larger piece of the market share by providing faster payments that work seamlessly across borders.

Bottom line: Debates on whether cryptocurrency can be money will continue in 2019. But consumer preference for hassle-free banking introduced by fintechs is already the standard baseline. What happens in fintech doesn’t stay in fintech — “Why can’t I pay with my e-wallet?”

6. QUANTUM COMPUTING

IBM kicked off 2019 by launching the world’s first stand-alone quantum computer, the Q System One, for commercial applications. Quantum computers are radically different from today’s computers. Instead of running on bits, they run on quantum bits or qubits, and promise to surpass even the fastest supercomputers of today. But don’t run off to get a quantum computer just yet. IBM is not producing quantum computers for sale, and the quantum computer’s processing powers are only accessible through IBM’s computing cloud, similar to what the US’s Rigetti Computing and Canada’s D-Wave are offering. More importantly, the Q System One is still an experimental device used to figure out how quantum computers might work.

Winfried Hensinger, professor of quantum technologies at the University of Sussex, told The Verge that “it’s more like a stepping stone than a practical quantum computer. … Think of it as a prototype machine that allows you to test and further develop some of the programming that might be useful in the future.”

That said, banks like Barclays and JP Morgan Chase are already experimenting with quantum computing. IBM, as quoted in the American Banker, said that organisations are interested in using quantum computing to develop a competitive advantage. Financial institutions are testing it to help minimise risk and maximise gains from their portfolios.

In the US, a law was signed in December to provide $1.2 billion over five years to boost US quantum technology. In China, quantum technology was deployed in the Micius satellite to develop new forms of secure communications.

Bottom line: Albeit in its infancy, quantum computing will have huge political and economic implications once the technology matures. It will be enterprise-ready when it can deliver reliable performance. IBM expects revenue from its quantum computers in two years.

Source : GCFM

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

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

How visionary CFOs approach tech investment

How visionary CFOs approach tech investment

Customer experience

Digital transformation is on the minds of CFOs, who expect to invest more in advanced analytics and artificial intelligence (AI) that can transform their businesses by improving customer experience.

That’s according to a recent Grant Thornton report, which shows that 69% of CFOs and senior finance executives plan to increase investment in technologies that quicken business change. CFOs themselves will need to have more technical skills, and they are divided in how to improve their overall workforce’s financial and technical expertise.

“It’s really a question of who’s more visionary as a CFO in moving forward with their products and services and reaching their customers,” said Srikant Sastry, Grant Thornton’s national managing principal for advisory services.

IMPROVEMENT TIED TO DIGITAL INITIATIVES

Companies that have figured out a way to reach customers more effectively through digital advances are reaping benefits. In one notable increase, Costco saw its second-quarter 2018 e-commerce sales grow about 29% year-over-year, to $1.5 billion, CFO Richard Galanti told analysts and reporters on a recent earnings call.

Additionally, firms that embraced digital transformation averaged a 55% increase in gross margins over a three-year period, according to a 2016 Harvard Business School study. Companies that were slow to adapt generated lower margin growth on average (37%) during the same period.

Meanwhile, International Data Corporation estimates that, by 2019, enterprises will spend $1.7 trillion on digital transformation — a 42% increase compared with 2017.

A year ago, Sastry said CFOs likened strategising on digital transformation to gazing into a crystal ball. Now, he says they are trying to gain a clearer picture of what is inside the sphere.

Accordingly, CFOs are not necessarily seeking digital transformation to improve efficiencies in their IT systems. The goals now are to enhance the customer experience, grow the business, and outperform the competition, according to the Grant Thornton report.

CHANGING THE VIEW OF ANALYTICS

CFOs have recognised that they were not thinking enough about analytics. Consequently, 24% of respondents said their finance team is currently adopting advanced analytics, another 24% will do likewise over the next year, and an additional 25% plan to adopt advanced analytics within two years. But CFOs will have to adapt, too.

CFOs have traditionally been focused on operational performance, cost reduction, and business management, but now they want to drive strategy and clear a path to digital transformation by leveraging information and technology, Sastry said.

“They have to make sure that they have the right skillset and innovation to leverage advanced analytics,” he said. “So the crystal ball is still there, but I think they’re trying to clarify the fog in the ball.”

Forty-one per cent of respondents do not believe they have good financial metrics that show the return on IT investments. And only 12% strongly agree that they possess an effective system to measure financial performance tied to newly implemented technology.

The report points out tension between companies’ current need to invest in maintenance and system updates and their desire to allocate funds to new automation technologies, such as AI. Investment in AI is projected to increase significantly: Beyond the 7% who say they have already adopted AI, an additional 47% expect to adopt it over the course of five years. A similar number of CFO respondents expect implementation of innovations such as distributed-ledger technology (also known as blockchain), machine learning, robotic-process automation, and optical-character recognition within five years.

“Ostensibly, AI will help improve quality, improve accuracy, and streamline the number of people required to perform tasks,” Sastry said. “It’ll change the face of business, including financial management.”

The top IT challenges in the survey are:

  • Systems complexity, including enterprise-wide systems integration;
  • Upkeep of legacy systems; and
  • IT talent.

Regarding the talent challenge, most executives — 52% — would prefer to retrain existing staff. Twenty per cent want to recruit new, technically skilled employees, and 17% aim to outsource tech hiring.

In addition to being aware of AI concepts, Sastry said, CFOs will need to know how AI systems work and how they can improve the business through a better customer experience.

“Those skillsets have, historically, resided in the technology space,” he said. “They’ve resided in the IT shop and the CIO [chief information officer] function. So CFOs need to really embrace the technology portions of their business, or the CIOs.”

Source : FM

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.

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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.

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