Archive for the ‘Analytics’ Category

Getting started with Big Data

Leveraging Big Data can help every company significantly improve competitiveness and financial results. At the same time, poorly conceived and executed initiatives can lead to wasted investment and organizational distraction. Not surprisingly, the question of how best to reap the benefits of Big Data is triggering extensive deliberations in many companies. What is ‘best practice’ in launching a Big Data strategy?

Big Data is a set of activities for collecting and analyzing various types of data located within and outside the organization. The insights derived from this analysis are used to enhance business performance such as boosting advertising efficiency, improving supply chain responsiveness or improving service levels.

Most large companies are already in the Big Data business. The amount of data collected is growing exponentially thanks to the digitization of virtually every customer and operational interaction. In a typical Fortune 500 firm, terabytes of data are being amassed through regular business activities such as point-of-sale transactions, barcode tracking, web traffic or social media communications. This data torrent – when properly mined — affords management a valuable opportunity to learn about consumer behaviour or internal operations, enabling them to optimize tactics for better performance. At the same time, realizing the Big Data vision presents significant technical and organizational challenges. These challenges can increase the chances that managers will embark on expensive or poorly designed initiatives – or become paralyzed due to complexity.

In our experience, the best way to get into Big Data is to start with a sensible roll out plan and leverage best practices. This plan should consider four key elements:

1.  Data

Any plan should begin with a review of the relevant internal and external data, according to the 4 Vs: volume (the amount of data and its location); variety (types of data, both structured and unstructured); velocity (how quickly the data changes) and veracity (the accuracy and availability of the data). In many firms, data is siloed by function or business line; is not standardized and; it comes in various stages of completeness. Getting quality data can be difficult and time-consuming. It may be desirable to outsource this data integration and clean up to specialist firms who can make it ‘analytics-ready.’

2.  Hypotheses

It is easy to get side-tracked if you dive right into analysis without any strategic guideposts. Not all insights are equally important. Like other major initiatives, it is essential the Big Data effort links to business priorities and metrics. One way to do this is to start with a limited number of pilots based on specific hypotheses that directly impact strategic goals. Successful pilots can generate early wins that justify further investment, and can produce important insights around the business, as well as test out first generation capabilities.

3.  Analytics

To effectively and efficiently mine the data, the team should carefully choose the appropriate analytical methodology or model for each business problem. The analytics will vary whether the goal is workflow optimization (e.g., minimizing inventory levels, delivery times) or predictive analytics (e.g., anticipating consumer behaviour, forecasting events). However, managers can easily over-speculate on solutions, choosing costly and complicated tools that require expensive or scarce talent. Judicious CIOs will take a “great is the enemy of good’ approach to choosing their models and depth of analysis.

4.  Capabilities

Many IT environments are not conducive to quick or easy Big Data deployments. These infrastructures can be a heterogeneous mix of new and legacy hardware & software, lacking in data standardization and centralized control. To exploit Big Data opportunities, firms will need a unique combination of data experts, software tools and management capabilities as well as supporting governance practices. This capability should be developed with practicality in mind. Initially, CIOs could outsource Big Data needs to a cloud-based analytics service limiting upfront investment and accelerating time to value. Over the long term, the organization can look to develop world-class capabilities through employing specialized talent, bespoke software tools and private cloud architectures.

As with other strategic initiatives, a prudent way of getting into Big Data would be to start small and target actionable insights. Ongoing attention should be paid to ensure the learnings are understood by the staff and implemented into existing workflows. Where necessary, new processes or practices may be needed to fully leverage the insights. Learning by doing will prompt managers to connect different analytical models together to address wider problems that span functions and business units.

Firms that are winning with Big Data are often the quickest out of the gate with a practical plan, based on a thorough understanding of their data, staff and IT environment. Big Data will be a game changer for companies who can deploy the right analytics and capabilities against their most pressing business issues.

For more information on our services and work, please visit the Quanta Consulting Inc. web site.

Marketer as anthropologist

Many companies prioritize learning customer needs above any other marketing activity so that they can create better products and service experiences. Typically, marketers will use traditional qualitative techniques like focus groups, surveys and one-on-one interviews. Unfortunately, these tools often fail to generate breakthrough insights. Standard qualitative methods are good at telling firms what is happening but not the why it’s happening. To get to the root cause of a consumer’s actions, marketers need to explore the recesses of their mind to identify subconscious drivers of behaviour. Anthropology is a very effective way to do this.

Simply put, anthropology is the study of people and civilization, past and present. It incorporates teachings from a wide range of disciplines, from psychology and biology, to the humanities and sociology. Anthropology is increasingly being used by companies (Starbucks, Lego, Herman Miller and Nokia are pacesetters) to better understand latent consumer needs and as well as societal and religious influences on their behavior.

In action

The following example shows anthropology in practice. A firm in the spa industry engaged us to help redesign its customer experience and service offering for female patrons. The client wanted to address any unmet customer needs and better differentiate their customer experience. Conventional research techniques regularly produced muted feedback, which led to copycat store designs and products. We wanted to go deeper into the consumer’s subconscious to find unmet needs and drivers that triggers behaviour. To get there, we employed anthropology to probe fundamental beliefs and values around their body image and wellness as well cultural influences. For example, how do women define beauty?  What role does human touch play? And, how can a spa experience help satisfy a women’s intrinsic needs? Our findings upended conventional thinking and led to a revamping of how the facilities were designed and how the services and benefits were communicated, resulting in higher client retention, an enhanced brand image and increased rates of cross selling.

Conventional qualitative research techniques take people at their word. This can be risky for brands.  At their core, consumers are often irrational, driven by motives or external influences that are unseen even to themselves. Using anthropology as complementary research can produce a more holistic and penetrating view of the consumer in their real life condition. Likewise, anthropology’s rigorous, academic-driven methodology preempts the emergence of erroneous assumptions around a customers’ behaviour that could have been shaped by a firm’s culture, the bias of its managers, or increasingly, the large but imperfect data stream flowing in.

Anthropologist have a number of data-collection instruments at their disposal including artifact analysis, quotidian diaries, and observational studies. Importantly, practitioners approach their research without hypotheses, gather­ing large quantities of information in an open-ended way, with no preconceptions about what they will find. The collected data is raw, personal, and first­hand — not the incomplete or artificial version of reality that is generated by most market research tools.

Anthropology is particularly helpful in understanding the dynamic world of social media. “Companies are beginning to use anthropology to understand the stream of consciousness within social medial that flows with ‘here’s what I’m doing/thinking/wanting now,’” says Lynn Coles a leading marketer. “Anthropological research helps us better understand and inhabit the social communities to identify behavioral patterns as well as the emerging dialect within a particular community so we can better communicate with our target consumers.”

Basic approach

1. Frame the issue

Anthropology requires the marketer to frame the problem in human — not business — terms. Doing so gets to the core of how a customer experiences a service or product. For example, a business problem could be:  How can a wireless provider reduce churn? The corresponding anthropological issue would be: How do our customers experience our service, and why are they leaving?

2. Assemble the data

The raw data is codified in a form of carefully organized diaries, videos, photographs, field notes, and objects such as packages. Although this open-ended data collection casts a very wide net, it requires a disciplined and structured pro­cess that needs to be overseen by anthropologists skilled in research design and organization.

3. Find patterns, insights

The anthropologist then undertakes a careful analysis of the data to uncover themes or patterns. When organized in themes, a variety of insights will emerge about how a customer feels, their goals and what drives their actions.

Of course, traditional quantitative and qualitative research methods have their place and should remain part of a marketer’s analytical tool kit. However, anthropology will play an increasing role in uncovering the consumer’s subconscious needs as well as societal/religious behavioral drivers, areas that are largely impervious to standard qualitative techniques. Producing this holistic view will allow marketers to design more relevant products and services that deliver higher value.

For more information on our services and work, please visit the Quanta Consulting Inc. web site.

Banking goes digital

The banking industry is changing, whether it likes it or not. In the past, the business was driven by capital-deployed, risk-management competencies and branch coverage. The financial meltdown of 2008, however, changed much of that. The sector now features low growth, increased regulations and leverage limits. To make matters more complex the emergence of digital technologies is transforming the way customers want to deal with banks and opening up market opportunities for aggressive and focused competitors. Increasingly, banks will need to address this “new normal“ by enabling their customers with “any time, any place” digital capabilities and capitalizing on Big Data insights that come from mining the reams of daily customer and operational interactions.

Despite the rise of transformational technologies, bankers in 2014 run their businesses pretty much as they did in 2008. Most of the executives we speak with continue to hope that traditional profit drivers — high fees, exchange-rate volatility and a growing economy — reassert themselves. However, hope is not a strategy especially when consumer behaviour has fundamentally changed.

The arrival of mobile computing, social media, digital payments, and web-based, face-to-face communications like Skype have radically changed the way people buy products and interact with organizations. Not surprisingly, these technologies have created opportunities for disrupters to enter the sector with low-cost, focused offerings unencumbered by legacy business models. In the United States, for example, Walmart has introduced reloadable pre-paid offerings that act like checking accounts. PayPal and Bitcoin are now enabling payments outside the banking system. Covestor links individual investors with portfolio managers who meet their investment needs.

It is bewildering how slow many financial institutions have been in adopting digital technologies and exploiting Big Data, compared to other industries. For example, while music stores, electronics stores, and other retailers have reduced or even eliminated physical distribution, large banks have expanded it. Many blue-chip firms like Cisco, Walmart and IBM already employ Big Data and mobile strategies to deliver new services, streamline their operations and reduce cost. If banks want to compete better and protect their franchise, they need to act more like mobile and digitally driven competitors like Apple, Google and Facebook — who not incidentally command much higher market capitalizations.

TD recently identified digital transformation as a corporate priority, and built capabilities back from the customer’s needs and desired online experience. “When we’re working on new online or mobile banking features, we put ourselves in the customer’s shoes to see things from their perspective, says Rizwan Khalfan, senior vice-president, digital channels, TD Bank Group. “We know customers are quick to adopt new ways to bank that make managing their finances simpler. It’s not just about paying a bill on your mobile, it’s about creating a great customer experience across all our distribution channels.”

Prudent bankers are starting small, testing extensively and then boldly scaling. Khalfan says, “When we launched the ability to deposit cheques using your mobile phone in the U.S., we spent time perfecting the little features that will make it an overall better experience. We know it’s hard to hold your phone and take a photo by pressing a small button, so on our app, customers can press any part of the screen to take a photo of the cheque and the photo won’t be taken until the camera has focused properly. We’ll be leveraging those learnings when we roll out that capability in Canada later on this year.”

Across the pond, British bank Barclays is taking a bold approach to digital transformation. Its strategy is to use technology to get closer to customers and simplify their lives. In order to become the “Go-To Bank” for consumers, the firm rapidly launched some breakthrough services like Pingit (Euorpe’s first mobile payment app) and CloudIT (a cloud-based service that allows consumers to store documents and photos online). When launching Pingit, Barclay’s dispensed with their traditional multi-year business case. Mike Walters, head of UK Corporate Payments, was recently quoted in The Economist as saying: “The rate of change in mobile app technology is so fast that the best thing for us is to be aware of our customer trends, and then be fast to execute.”

Without a sustained top-down commitment, change won’t come easy or quickly. Traditional business and IT models, low digital literacy among many executives and a risk-averse culture will slow down digital adoption in some areas. Yet, bankers don’t have a choice if they want to protect their franchise and find new avenues of growth. They would be would be wise to heed the words of well-known British philosopher Allan Watts: “The only way to make sense out of change is to plunge into it, move with it, and join the dance.”

For more information on our services and work, please visit the Quanta Consulting Inc. web site.

6 Big Data Mistakes

Big Data is all the rage across many enterprises.   The potential payoffs are compelling.  For example, research out of MIT found that firms leveraging Big Data  achieve, on average, 5-6% greater productivity and profitability than their peers.  McKinsey calls Big Data a game changer for sales and marketing along with other areas of the business. But like anything else, getting the most out of data means knowing what not to do. Companies looking to extract value from their terabytes of data should make sure they avoid the following 6 mistakes.

Boiling the ocean

Big Data can be big work. It is easy to burn through a lot of time and cost finding insights that don’t materially address major business challenges like getting closer to customers or improving operational performance. One way to ensure value is to ask research questions whose answers will directly impact key corporate goals. This focused method also enables your company to ‘learn as they go’ and develop quick wins that justify further effort and investment.

Only considering data in silos

In many organizations, the majority of data resides in functional areas or business units — not in an enterprise-data warehouse. Only analyzing siloed data reduces your chances of finding key insight that can affect a company because you are limiting the number of variables and quantity of data under consideration.  However, bridging these silos is easier said than done; organizational and data issues may hinder an enterprise-wide data mining effort. In other cases, managers often limit their analysis to existing digital data. This approach could miss out on insights that are discovered when analog data (such as social feedback and qualitative research) is ‘datafied.’

Ignoring bias

There are good reasons why Big Data resembles science. The analytics can be challenging and methodological errors are not uncommon. “There is significant risk of the analytics being wrong,” says Neil Seeman, founder & CEO of global online data collection firm, The RIWI Corporation. “Systematic bias can easily slip into enormous data sets. Or, not understanding unknown bias in the data set results in false conclusions. Case in point was the early analysis of large HIV data sets; this did not consider the influence of intravenous drug use.”

Focusing on cause instead of correlation

Understanding precise cause and effect is difficult and impractical. What’s more actionable is uncovering correlations — patterns and associations that help predict what will happen next time. Managers should be mindful of looking for and expecting data perfection. For example, the shelf life of market-based insights could be measured in days or even hours. Often it is better to quickly make decisions with 80% confidence in the data than to wait for perfection farther out in the future.

Disregarding qualitative knowledge

Data analytics can deliver many insights but it often cannot tell the entire story. Take the drivers of consumer behaviour as an example. It is difficult to comprehend what drives action without looking at qualitative research tools like behavioural psychology or anthropology as well as expert opinion. These tools should be used to fill in knowledge gaps.

Forgetting about instinct and creativity

At a certain point in the future, the leaders in each sector will have comparable Big Data capabilities and access to the same data. To wit, the Open Data movement is making terabytes of the same data available to everyone. Like other innovations, the greatest returns will flow to those who use the tools and methodologies in the most creative way. As well, management instinct will continue to play a key role in setting Big Data priorities and figuring out how to combine disparate information into more powerful conclusions.

A dangerous assumption made by some companies is to think your entire team should be made up only of credentialed “data science” experts. According to Seeman, “Experience in this fledgling field of data science often eclipses the value of fancy degrees from prestigious universities. What are needed are curiosity seekers with demonstrable experience in pattern recognition and exploiting data for real value. In my case, everything I learned about Big Data came through experimentation, failure, and asking really dumb questions — through efforts to solve the problem of how to collect a unique data stream from every country and territory in the world.”

Big Data is still in its infancy. The first cases studies are still being written. Of course, success will be a product of a strong top-down mandate, having sufficient resources and working with competent partners. At the same time, managers should be mindful of hamstringing themselves by not following a common-sense and continuous-learning approach to project design and implementation.

For more information on our services and work, please visit the Quanta Consulting Inc. web site.

Don’t plan, tack

Companies that fail to plan, plan to fail. Or do they?  It is very common for organizations to spend significant time and effort each year developing strategic plans that are used to analyze the market, set goals and priorities and allocate resources.  However, there is often no correlation between plan quality and market results.  Relying on a formal, long-term strategic plan is not for everyone and can, in fact, be dangerous for many companies.  Here’s why.

Strategic planning is predicated on a number of shaky assumptions, three of which are:

  1. Managers can predict the future. It is virtually impossible to dependably predict the future given the unknowns — competitive reaction, technological change and likelihood of Black Swan events.  No matter how thorough a plan is, it cannot account for every contingency. For example, few firms anticipated the 2008 Financial Crisis and the speed at which it unfolded.  When unforeseen change happens, an inflexible plan can turn into a strategic blindfold.
  2. Target goals are attainable. Many plans feature BHAGs (Big Hairy Audacious Goals) that are used to motivate employees and spur breakthrough thinking and effort. The problem with having these stretch goals is that often they are  divorced from market and financial realities, and are set for other reasons like management incentives, ego or head office needs.
  3. Everyone aligns to the plan. Though strategic planning is typically a cross-functional exercise, its implementation is often uneven or compromised. Poor implementation arises when metrics or objectives that are at odds with the plan’s targets and measures are used as incentives for departments. Promoting unrealistic goals can demotivate employees or lead to unethical behaviour.

Strategic planning has other drawbacks. The exercise can consume an inordinate amount of time, effort and resources.  The value of the plan is highly dependent on having customer, channel and cost data, which is often unavailable or of poor quality.  Finally, the planning process can often lead to win-lose outcomes, triggering infighting and limiting collaboration.

There is another approach to coping with competition and uncertain future.  We call this method, “strategic tacking” — a sailing term used to describe how a boat sails towards the finish line in an indirect way making allowances for changing weather and water conditions plus race position. Through strategic tacking, companies pay less attention to creating a plan and focus instead on producing the essential knowledge and operational adaptability to compete well in a dynamic environment. Strategic tacking does not mean an enterprise is not systematic or rigorous in its thinking. Rather, management prioritizes process over a finite outcome or plan.  Strategic tacking includes the following elements:

Core assets

Whether they know it or not, every firm competes best across one to two dimensions such as customer satisfaction, product performance or low cost. Understanding this differentiation and the capabilities that support it (taken together are core assets) is the first step to achieving clarity of purpose and action. These assets would act as a strategic lens to help leaders decide what to prioritize with what resources, given the potential benefits and risks.

Analytical competencies

Companies need a regular and objective view of their competitors, customers and costs.  To get this, managers need to undertake a thorough analytical process that includes as many internal and external people as possible. This activity will help management quickly identify and meet competitive challenge and exploit new opportunities without over-stretching capabilities and partners.  Where possible, simulation-based analytical tools like war gaming should be used to explore real-life, risk scenarios and drive internal buy-in.  As well, the firm should have market intelligence mechanisms that capture information, turn it into knowledge and share it quickly.

Organizational Agility

The right structure and processes need to be in place to make quick, fact-based decisions that lead to rapid enterprise mobilization.  We have seen firms maintain a SWOT-like, decision making group, made up of senior managers and experts from across the company.  Instead of earmarking 12 months of investment in a budget, firms can hold back discretionary funds to capitalize on opportunities.  Once decisions are made, there also needs to be mechanisms for deploying people, capital and expertise.  We have created market-specific, rapid-deployment teams made up of project managers, marketers and product managers who are ready to execute high-priority initiatives.

Many companies strategically tack whether they recognize it or not.  This approach may be one reason why leaders like Apple, Google, Amazon, Open Text and Nike can consistently outflank and preempt competition. Unsurprisingly, strategic tacking is not a realistic option for every company based on shareholder expectations and organizational issues.  Furthermore, well-crafted plans are still best suited for slow-moving sectors such as consumer & industrial goods, not-for-profits and services.

For more information on our services and work, please visit the Quanta Consulting Inc. web site.

Powerful predictive analytics

Mark Twain was foreshadowing when he said, “the past does not repeat itself, but it rhymes.” Thanks to recent advances in predictive software, Big Data and cloud services, organizations now have real tools to predict the future with some degree of certainty. These technologies can (relatively) quickly mine terabytes of data on the Internet for clues and patterns to predict major events such as drought, flooding, and disease outbreaks, enabling organizations to improve their planning and risk mitigation efforts.  Taken a step further, companies will soon be able to predict sales patterns and the likelihood of success for new products or promotions with a higher degree of confidence.

A variety of forecasting software tools have been available to help predict major events (with modest accuracy), as well as identify transactional risks and opportunities.  Many are already in use in a variety of sectors including:  retail, insurance, travel, and healthcare.  What is different now is the availability of next-generation data mining software that leverages cloud-delivered processing power to parse vast amounts of current and historical data from numerous online sites and archives (think Big Data on steroids).

A good example of this forecasting software comes in the form of a research project from Microsoft Research’s managing director Eric Horvitz and former intern, Kira Radinsky, who worked together to develop a program that analyzes public news and archival websites for patterns and clues that have preceded outbreaks of disease, riots, and deaths.  The algorithm then compares those patterns to current conditions to make predictions on the probability of those events happening again. It’s a unique and advanced form of data mining that digs deep into the Web and other data bases, enabling a sophisticated, causative analysis of seemingly unrelated incidents and seeing how and when they repeat themselves over time.

One example where their research made the right call was the occurrence of two cholera outbreaks:  one in 2012 in Cuba and the other in Bangladesh in 2011.  Given their different times and lack of proximity, one would have simply considered them random events. However, the program suggested that this was not the case. Searching 150 years of news reports and historical archives, the software identified a specific correlation in developing countries (with substandard or non-existent flood control) between a drought condition followed by major flooding, which subsequently led to a cholera outbreak — exactly what happened in both Bangladesh and Cuba.

Microsoft sees the potential.  “When we look at trends in technology like cloud services, Big Data and business intelligence, and combine those with advanced machine learning, computer scientists will able to advance the use of the data to help predict catastrophic events more accurately in the future”, says Grad Conn, senior director of marketing at Microsoft.Advertisement

Their software is far from foolproof but has demonstrated an accuracy rate of between 70% and 80%, making it better than the more modest success rate of current tools.  This improvement in accuracy is meaningful on a global level.  Combined with preemption, enhanced planning and risk mitigation measures, better forecasting means that tens of thousands of lives plus billions of dollars could be saved.

Importantly, these same systems may be used to predict important trends and events in many other areas when combined with a companies’ own Big Data programs.  For example, car companies can figure out which are the best years to introduce new convertibles by looking at weather patterns, levels of disposable income and competing vehicles over the past 50 years.  Insurance firms can adjust their coverage, services and premiums based on the likelihood of different natural disasters occurring. Finally, travel firms that put together vacation packages can readjust their destinations, prices and itineraries (especially those to Cuba) to reflect the likelihood of other cholera outbreaks or hurricanes.

“What becomes critical is how the mounds of data collected are used to drive insights and make decisions”, says Mr. Conn. “The ability to use sophisticated insights to develop innovative products and services, prioritize privacy, and reach and engage high-value customers is clearly a prized competitive advantage.”

Even so, powerful data-mining software like this will not give every organization a clear crystal ball.  All forecasting tools — no matter how sophisticated — should be used cautiously.  There are enough gaps in the data and analytics to preclude most organizations from betting the farm on one predictive tool.  You need a comprehensive approach to forecasting.   Moreover, though powerful, these instruments may not be able to provide enough detail around timing or event severity to sensibly plan or take risk-reducing measures — or even overcome management inertia.  Lastly, it is not clear how ‘Black Swan’ or unexpected events could be anticipated, especially in highly complex environments or where people’s fickle actions or attitudes can play a major role in shaping circumstances.

For more information on services and work, please visit the Quanta Consulting Inc. web site.

 

Curbing avoidable employee absences

Avoidable employee absences are a hidden killer of corporate profitability. Many leaders don’t realize that short-term, unplanned absences can cost the average medium-sized company millions of dollars in payroll expenses not to mention lost productivity and business disruption. To get this financial sinkhole under control, HR leaders must get a handle on the problem and consider some innovative technological and business fixes.

Many types of worker absences are inevitable.  However, the unplanned and avoidable ones may be the most harmful.  Unmanaged or misunderstood, they can quickly lead to operational disruptions and cultural toxicity.  A variety of studies have estimated the aggregate costs of unplanned absences such as sick days and casual non-attendance.  A recent Conference Board of Canada study of 401 medium- to large-sized public and private firms found Canadian workers miss an average of 9.3 work days per year.  This costs employers 2.4% of their gross annual payroll (a $16.6B hit to the economy).  This number is likely understated as it does not include indirect costs like finding replacement worker costs, project delays or missed deadlines.   The absentee problem may be even bigger in the U.S.  A 2010 online survey of employees from 276 organizations conducted by Kronos/Mercer Consulting  found employee absenteeism produced 5.8% of extra payroll costs not including indirect costs.

Blind spot

Surprisingly, only 46% of employers admitted to tracking absences and exploring their causes, according to the Conference Board.  There are understandable reasons for this neglect.  Firstly, many firms cannot quantify the problem or understand its root causes because they do not have the right tracking systems, or because the data is siloed.  Bill Shapiro, CEO of Workplace Medical Corporation, says “If absence costs showed up as an expense line on the divisional P&L statement, it would it would get a lot more attention. The problem is that it is has been too difficult to get a hard number for that cost.”  Secondly, when it comes to reducing labour costs, unplanned absences play second fiddle to other priorities like headcount rationalization since these direct costs are easier to calculate.

Help is on the way

New methodologies and technologies are now available to better diagnose the problem and reverse its negative effects:

Big Data

Anecdotally, we all know that days preceding or following a long weekend or important game will tend to spike absences.  Big Data strategies — understanding what is really going on with attendance and staffing data across the organization and how it correlates to other variables like weather or sporting events — can give firms the insights and predictive tools to fix the problem and optimize practices.   To wit, theFinancial Times relayed a story about a British retailer who submitted the staffing records for thousands of its employees for an independent Big Data analysis.  This analysis discovered the retailer was paying more than 150 employees who had called in sick years earlier and had simply disappeared from the workplace.  Moreover, Big Data learnings can also help managers refine workflow design to minimize physical stress on employees.

Dedicated solutions

Traditionally, unplanned absences are handled manually or within a larger HR information management system. This approach is too primitive to address the issue in real-time, objectively, and proactively.  New, specialized systems address the problem head on by monitoring absences, aggregating the data and tracking the case, from day one.   In Workplace Medical’s solution, an absent employee would first contact a call centre. A service representative would log the absence in specialized software, provide the employee next steps and immediately notify his or her supervisor and HR department.  The rules-based software automates the management of the case including facilitating early intervention, tracking the length and cause of absence and identifying employee patterns.

Gamification

Integrating gamification strategies — a combination of game principles, behavioral psychology and enabling technologies — into attendance practices and processes could minimize the number unplanned absences.   Many firms like SAP and Microsoft have used game playing to promote long-term behavioural change around the adoption of new initiatives and the alteration of long-established practices. They have also used it to increase productivity for mundane or repetitive tasks. Gamification programs work by providing each employee or team significant intrinsic rewards — through enhanced status, feedback or recognition — when they play the game (i.e. comply with attendance policies).  Considerable research has shown  incorporating intrinsic rewards into workflows and practices is more effective than using extrinsic rewards (e.g., pay) or punishment.

Challenges

Dealing with this problem should be a corporate priority.  However, the fix should be designed and implemented with care.  The strategies mentioned above could breed mistrust and resentment; some employees may perceive management as Big Brother watching over them or manipulating them. Moreover, the HR group may be resistant to giving up control of the process to a third party that could expose HR’s dirty laundry, as was the case with the British retailer.

For more information on our services and work, please visit the Quanta Consulting Inc. web site.

Boost marketing performance

The marketing landscape of 2013 is a menagerie of media vehicles, digital tools & platforms, Big Data initiatives, sales channels and influencer programs — all requiring coordination, integration and funding.  Making sense of this complex world is a challenge; it’s hard to know what’s working and what’s not.  Conventional evaluation tools like ROI have their place but they are not strategic and don’t go deep enough in analyzing plan effectiveness.  To really evaluate marketing’s worth, it pays to go back to basic principles. We have found that asking five questions can help determine whether the marketing function is “doing the right things, right.”

Do we really understand our customer and what influences them?

For many companies, the answer is an unfortunate no.  This knowledge gap goes beyond understanding basic needs; it touches on how consumers interact with companies, who or what influences their actions, and what sub-conscious triggers drive purchase behaviour. Even data-intensive companies often know little about how specific tactics — like social media or TV advertising — drive purchase, as well as how various programs interact with each other.

To maximize performance, managers need a new, 360-degree view of today’s consumer.   “Connecting with clients is much tougher now than it used to be,” says Lynne Coles, a senior B2B and B2C marketer.  “They are one click away from becoming as well or even better informed than the firms trying to sell to them. They’re also just one click away from sharing their opinions and experiences with an ever-expanding activist community. Marketers need a holistic approach to understanding the entire customer experience across all channels.”

Is our value proposition relevant and differentiated? 

Whenever I give a speech on branding, I ask the audience members what their value proposition is, and can it be supported.  Typically,  80% or more list the same benefits, like great service or lowest cost.  Furthermore, less than 20% of the audience will link a meaningful outcome and explanation to the benefit (e.g., thanks to newer technology, XYZ brand delivers an 80% savings versus the leading brand). These ad hoc surveys usually point to serious flaws in a company’s core positioning, that no amount of spending or technology can fix.

Most firms can differentiate.  Many already do, they just don’t realize it.  The marketing challenge is around finding meaningful strategic differentiation and then driving the message through all marketing tactics as well as the customer experience.

Do we have enough information and wisdom to make decisions?

Exploiting Big Data — using advanced methodologies and tools to mine data for insights — is all the rage these days.  Making it work is another story.  Companies may collect a lot of data but it is not always accessible and usable. Furthermore, much of the data may not be germane to marketing goals.  Finally, data mining skills does not necessarily translate into wisdom that would help in areas like fostering innovation or creative development.

Modern marketing remains as much an art as a science.  Companies require proven analytical and IT capabilities plus common sense to ask better questions and to make better strategic and tactical decisions.

Are we using the right metrics?

Einstein said, “not everything that counts can be counted, and not everything that can be counted counts.” Many firms use metrics that cannot be effectively measured, are unrelated to strategic goals and lack organizational buy-in. The choice of metrics is important.  They play a major role in driving management focus & behavior, allocating resources and in framing the evaluation of marketing plans and vehicles.

Leaders should regularly confirm that each tactic is properly measured, evaluated and then linked to the long-term, strategic goals of the company.  Managers should also be mindful that a slavish focus on metrics is not a replacement for solid business and creative judgment.

Is the organization enabling marketing?

Many leaders believe their firms are market(ing) driven, with a mission to fully satisfy in a differentiated way the needs of consumers in attractive markets.  The organizational reality, however, is often different.  Other internal groups frequently have different agendas, a disproportionate share of internal resources and dissimilar perspectives on what drives long-term performance.  These dynamics are natural but could result in strife, lack of focus and poor resourcing.

Marketing performance — and ultimately competitiveness — will suffer without adequate alignment, investment and capability-building. Making the marketing mission real will be part cultural change, part priority-setting and part talent management.

For more information on our services and work, please visit the Quanta Consulting Inc. web site.

 

Big Data boosts advertising

Much has been written about the transformational role of Big Data in improving business performance, but the usefulness of data analysis has spread to almost all aspects of business. Most recently, ad-development managers have been able to make use of Big Data to measure and improve the performance of their traditional and digital advertising programs and tie them more closely to corporate goals. A thought leadership piece by Wes Nichols published in the March 2013 issue of the Harvard Business Review highlights a new framework for designing and implementing cutting-edge advertising analytics.

In the dynamic world of digital and traditional advertising, channel proliferation and social media, any improvement in measuring and refining performance will have an immediate impact on the bottom line and the brand. Traditionally, advertisers have been challenged to realistically measure the performance of their creative and media plans. They have been forced to link sales data with a small number of variables such as media reach and frequency, using a limited number of rudimentary analytical tools like media-mix modeling, surveys, measuring clicks and focus groups.

This popular approach has some significant drawbacks.  It evaluates each medium (e.g., TV, print, digital) independently, and not collectively as consumers in the real world experience them.  Secondly, it is very difficult to measure the impact of one advertising variable, (increased banner ads, for example), on another variable like awareness. Finally, these tools do not easily connect advertising activity back to changes in consumer behaviour like purchase.

Recently, a new set of specialized Big Data methodologies have emerged that allow managers to improve both the effectiveness and efficiency of the advertising plans.  Powerful techniques and technologies can now mine terabytes of data in real time across hundreds of different marketing and business variables in search of key correlations.  The insights gleaned can then be used to dynamically adjust media spend and creative execution for optimal performance.

In his Harvard Business Review article, Mr. Nichols, outline a three-step approach to leveraging next-generation advertising analytics:

Attribution: Gathering and attributing the revenue and strategic contribution of each tactic.  In many companies, this exercise could involve hundreds of variables, ranging from marketing initiatives to economic factors and competitive actions.

Optimization: Using predictive analytics to measure the potential outcomes of different business scenarios based on the interrelationship between tactics and changing market variables. For example, what will happen to sales revenue if you boost online advertising in Ontario, cut it in Quebec and increase prices in the Maritimes?

Allocation: Re-allocating marketing and advertising spend based on the learnings gleaned from the Optimization phase. Ideally, the most successful programs would gain additional funding while others would see less support.

We have witnessed a number of companies use an approach similar to Mr. Nichols’ to generate a 20-40% improvement in marketing effectiveness and efficiency.

Case in point is Electronic Arts, one of world’s leading software gaming companies.  They were looking to boost marketing performance by going beyond simple measurement tools and managerial judgment.  The company decided to use the attribution, optimization and allocation process on the marketing plan of a new game, Battlefield 3.  Hundreds of variables were analyzed including sales results, online chatter, pricing data, advertising reviews and distribution information.  The predictive analytics uncovered some important insights.  For example, a favoured tactic (in-theatre advertising) was under-performing.  Second, digital marketing performed better than previously thought.  And finally, the media launch plan was sub-optimal.  These learnings helped the firm revamp the introductory marketing plan of Battlefield 3, making this launch the most successful in the company’s history.

Despite Analytics 2.0’s potential, firms need to approach it systematically and with common sense as implementation could be a challenge. We have seen analytics projects flounder due to poor data quality and reporting, weak compliance (e.g., data hugging), insignificant management support and insufficient IT capabilities.  Moreover, good judgment and creativity is still vital in the creative and media planning process.  Glen Hunt, creator of many memorable ads including Molson Canadian’s “I am Canadian”, says:  “Big Data represents a big opportunity, but it does not negate the importance of ‘blink’ test intuition and experience.  After all, ‘not everything that counts can be counted, not everything that can be counted counts.’   Or so says, Einstein.”

Big Data has the potential to revolutionize advertising measurement and evaluation, truly delivering higher marketing performance at less cost.  Companies looking to build and leverage these new capabilities would be wise to make them strategic priorities, choose the right business or product beachhead to kick-off and earmark the necessary mandate, resources and investment.

For more information on our services and work, please visit the Quanta Consulting Inc. web site.

Big Data 1-2-3

Taking advantage of the insights buried in Big Data is all the rage in many companies. An omnibus term, Big Data is the accumulation and synthesis of all kinds of customer data collected but lying dormant within an organization. However, exploiting its potential could be a daunting task for many managers. Here are three foundational steps to help kick off a high return, low risk analytics program:

Begin with a hypothesis

Big Data presents so many opportunities it’s often difficult to know where to start.  The journey can finish at many end-states, some providing real business value but others offering nothing actionable.  Moreover, data analytics competencies are not easy to assemble. Analytics experts are expensive and often difficult to find. You need to know where you are going if you want to extract value and not waste time and money.

One way to ensure you are on the right track is to create a hypothesis about your customers that is directly linked to corporate strategies and metrics. For example, an explicit hypothesis could be that the existing digital marketing plan is not effectively targeting the needs of the highest potential customer segments.   This hypothesis would then be tested against the insights produced from an analysis of the pertinent customer and operational data.

According to Casey Futterer, vice-president of  strategic new business at Nielsen Canada,  “Coping with large amounts of data with few analytical resources creates an imperative for laser focus — what issue to solve, what action to take.  Important issues will relate to questions of: who? (consumer/shopper); what? (proposition); and/or, how? (plan).”

Balance left and right brain thinking

Many assume Big Data is a mathematical and IT exercise based on customer relationship management data.  While these three drivers are critical to producing meaningful insights, they cannot tell the entire picture about the customer, particularly if the data is internally siloed or incomplete.  For example, firms can find in Big Data a link between nice weather and increased purchase behaviour but they often can’t tell you why these correlations occur.  Do people buy more because it’s sunny outside, springtime or because of a recent price promotion? Without knowing the ‘why’, marketers will have a difficult time turning the insight into something actionable that generates solid financial returns.

To get to root causes of behaviour and a critical 360-degree view of the customer, managers need to look elsewhere at non-quantitative factors — the right brain or emotional side of behaviour — through tools such as ethnography, neuroscience and qualitative consumer research.  In addition, managers should round out their quantitative analysis with a holistic examination of the customer experience including service, channel interactions and their actions with competitive offerings.

“There is no magic box that spits out the answer,” says Futterer. “Managers need to combine analytics with emerging tools and your team’s collective experience and brain power to extract insight and drive action.”

Test and scale

Once you know where you are going and have the right approach to get there, its time to put your strategy into action.  When it comes to high-impact initiatives like Big Data, prudent firms walk before they run.  This is often done for practical reasons.  For one thing, few senior managers have direct experience with complex analytical tools or methodologies. Secondly, Big Data programs can be costly to implement. Finally, organizational and IT challenges may initially limit data accessibility and quality.

Using pilots is a common sense approach when experience and investment are low, and uncertainty is high. By running a number of small tests, managers can identify resource requirements, learn by doing and build internal momentum behind quick wins.  Pilots could be structured around important questions such as which purchased products trigger the cross-sell of other items.  Or, they could be run in specific geographies, lines of business or with single products.

Finally, collecting and analyzing more data does not always lead to better results.  According to the former CIO of CIBC and McGraw-Hill Companies, Peter Watkins, “There is a common fallacy that more data is better. Best practice research shows that it is not the volume, rather it is the variety of data, and the better quality of that data, particularly on customer behaviour and characteristics, that enables smart analytics to produce rapid insight and speedy action.”

Properly executed, analytics has the potential to transform an organization. Tapping this opportunity need not be intimidating or unmanageable. Following an analytics strategy that aligns to marketing goals up front, adopting a holistic analytical approach and focusing on generating quick wins and learning will increase your firm’s chances of success.

For more information on our services and work, please visit the Quanta Consulting Inc. web site.

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