PwC thought leaders featured in Financial Times as they explain the power of fact-based insights

PwC thought leaders Paul Blase, John Sviokla and Anand Rao weigh in on the power of fact-based insights to help drive profitability and growth, and to monetize an organization's data assets.

Productivity growth: the crucial link between investment and return

June 25, 2013

Productivity growth: the crucial link between investment and return

By Paul Blase and John Sviokla

What’s the critical link between investment and shareholder return for any business? Productivity. As the key to growth, it should be top of mind for the C-Suite at all times, and that means facing some tough, interrelated short and long term challenges: converting innovation investments into better products and services; building operating models to improve top and bottom line performance and reinvesting to create the right conditions for the next wave of growth. Getting a positive feedback loop working across these areas to drive productivity growth is a management triathlon, but one worth winning.

Productivity matters because it creates huge value. Across all sectors and at any point in the business cycle, a five per cent uptick in productivity – using earnings before interest, taxation, depreciation and amortisation per employee as a benchmark – leads to an average 11.9 per cent stock appreciation, a positive effect that should catch the eye of any CFO. In some sectors, five per cent productivity growth has coincided with up to a 50 per cent increase in total shareholder return. Numbers like that are hard to ignore. “We need to work on our productivity,” a VP at a large asset management firm said to us. Yes they do, and so do you.

The pursuit of productivity is nothing new, of course, but in our complex global and digital economy, ever-changing regulatory and competitive forces make it difficult for the ‘c’-suite to drive productivity growth consistently, and the traditional drivers no longer tell the whole story of how to achieve it. We have found that productivity isn’t solely determined by industry, isn’t just about high growth, doesn’t necessarily require headcount reduction, and doesn’t only come from new business models.

Management mainstays to increase productivity- automation of routine work, controlled, hierarchical, decision making, reliance on “lean methods”, optimising function performance – are running out of steam. Today’s business environment has more knowledge intensity, complexity, digitisation, codependent business networks, and global integration. It requires new approaches to drive productivity growth ranging from creating flexible platforms that reduce the complexity of creating custom products for segments of customers, to empowering talent with the knowledge to anticipate and resolve customer problems before they show up in social media forums and earn your company a national news headline.

How do we know all this? In PwC’s most recent Productivity Stars survey, we asked 300 US company executives for their perspectives on new economy drivers of productivity and the characteristics of “Productivity Stars” – leaders who achieve substantial productivity growth and enviable shareholder return.

Four core themes emerged: consistent productivity growth is critical to shareholder return; executives are not confident about sustaining productivity; productivity growth is viewed as a front office, not a back office issue and more collaboration is needed from strategy through execution to deliver productivity results.

Consistency wins. The 11 per cent of companies that grew productivity consistently over a five-year time period earned a 19.1 per cent shareholder return per year compared to an average of 4.6 per cent for everyone else. That fact tells us that planning for productivity means doing long-range planning, not indulging in short-term thinking.

Executives aren’t confident about sustaining productivity. Four out of five executives felt that productivity growth was important to their success, but only 23 per cent were very confident that they were doing the right things to increase productivity faster than their competitors in the midst of regulatory uncertainties and increased competition for scarce, high-skilled knowledge workers.

Almost 70 per cent of executives are concerned about the availability of key employee skills necessary for their company’s growth. “You need to have top rate staff and managers keeping a constant eye on productivity, or it can fall off very fast,” a division head at a food and beverage company told us. One business unit president of a services company described the shortage of skilled workers available as “the number one issue we face.”

Productivity growth is a front office issue. Four of the five top-ranked business areas that executives expect to drive future productivity growth are front-office functions such as sales, marketing, product development, pricing and risk management, not back-office functions such as finance and procurement.

More collaboration is needed from strategy through execution. The five most cited collaboration areas executives noted to drive productivity growth are market research (23 per cent), strategic planning (34 per cent), product development (21 per cent), sales & operations delivery (27 per cent) and customer service (27 per cent). Some of these are functions associated with strategy, some are execution oriented – collaboration across them is viewed as the key to continuously improve the operating model to increase productivity through top line and bottom line results.

These findings highlight important focus areas for executives as they formulate their plans to improve shareholder return. You should spend more time thinking about how productivity links directly to growth and how your innovation investments can create a sustainable platform for improving your future productivity. If you don’t take a holistic approach to productivity and have no effective way to measure it accurately, you may always struggle to improve shareholder return.

How will you do it? We’ve developed five concrete steps you can take to make the move toward more sustainable productivity growth, and we’ve also developed a new kind of productivity benchmark that you can use to evaluate your own management team compared to businesses inside or outside your sector. In our next article, we’ll outline the path to improved productivity growth.

Paul Blase is a principal at PwC leading PwC’s US Advisory Analytics Services. John Sviokla is a principal and US Advisory Innovation Leader with PwC.

Copyright The Financial Times Limited 2013

Five steps to sustainable productivity growth

July 25, 2013

Five steps to sustainable productivity growth

By Paul Blase and John Sviokla

In Part 1 of our Productivity series, we illustrated the vital link between increasing productivity growth in your company and shareholder return. Our increasingly global, digital, and knowledge-based business environments create more complexity for executives to manage and make it harder to devise and execute plans to sustain productivity growth.

In PwC’s most recent Productivity Stars survey, we asked 300 US company executives for their thoughts on the opportunities and challenges presented by new trends on productivity.

Companies may be overestimating how well they are doing - only three per cent put themselves in the bottom quartile of performance. Many companies have a significant opportunity for improvement yet, only 35 per cent of respondents thought that improving productivity was quite important to their business success.

Executives are paying attention to how new drivers such as knowledge intensity, complexity, relationship networks, globalisation, digitisation of products and services, and moving operations to the cloud are impacting productivity, but say they struggle with what to do about them. And only 19 per cent say their companies use integrated financial and operational metrics to drive productivity.

What can you as a c-suite executive, do to improve your business’s productivity growth? We’ve defined five concrete steps you can take to create policies that will drive your productivity growth.

1. Determine how good you are at sustaining productivity growth. The more we’ve analysed productivity, the more we’ve come to realise that there needs to be a new way to measure productivity growth over time. PwC’s new Productivity Alpha measures how fast the c-suite grows earning before interest, tax, depreciation and amortisation per employee compared to its competition, adjusting for market and industry volatility, stripping away as many secondary and non-essential performance factors as possible.

Why use this kind of measurement? Because it gives a clear idea of how well the c-suite manages the business, netting out its financing or capital structure, to increase its value through converting investments into tangible top line and bottom line over time.

For example, a leading pharmacy retailer invested in its portfolio of offerings by adding mail order pharmacies to drive demand in new segments while expanding productivity through lower operating costs. It invested in adding higher value added services to its retail footprints, changing store layouts to improve customer experience, and upgrading their rewards program to create a platform that is expanding short and long term productivity.

2. Specify productivity improvement in innovation and growth plans. How can your company’s core assets create productivity growth inside and outside your current business portfolio while you pinpoint capabilities that will create step-function productivity increases in critical business areas?

Pro-forma planning doesn’t cut it. You must dig deep. “We constantly need to innovate. That is key to productivity,” the division head of a food and beverage company told us. As an example, a leading global beverages company is exploring the potential of using new sources of customer data from social and mobile channels by proactively reacting to trends that impact types and amounts of beverages consumed such as health or sustainability concerns. It plans to increase productivity by shortening R&D, product development, and forecasting cycles to respond to new trends with products outside the portfolio while minimising brand risks.

3. Simulate productivity growth scenarios and their impact on objectives and measures. Once innovation investments and targeted productivity impacts are identified, it is critical to conduct scenario analyses to test the impact of market forces and operating changes on your productivity growth.

You’ll come to understand the complexity of all the interdependent variables you will encounter and determine how to make appropriate adjustments along the way. This is not a “once and done” exercise. It’s the connection between strategy and execution.

As an example, a leading global financial services group conducted scenario analysis to assess the market dynamics and operating capabilities needed to become the dominant, industry wide mobile wallet platform. The scenario analysis helped them determine a sequenced investment path to gain market share while maximising the return of platform investments by using a partnership strategy.

4. Create a shared company business design with an operational path to short, medium, and long-term productivity growth. You should set goals several years out to ensure that productivity growth is prioritised on the agenda, is implemented in phases in the business, and stays on track.

This is the way to move from strategy through execution. How do you plan to embed new drivers of productivity into your business design to get you from point A to point B, specifying how you’ll measure it along the way? Recently, an international B2B lender identified a future state operating model to dramatically increase productivity while accommodating plans for growth in new products. The company created target short, medium and long-term business designs with specific client value propositions linked to capability deployment and performance targets to improve credit evaluation, loan acceptance, and servicing productivity by 35-40 per cent over time.

5. Align on a more comprehensive, simplified set of productivity measures. Does your c-suite share a common set of benchmarks to evaluate productivity growth within and across your most critical business areas?

Companies across industries are re-evaluating business intelligence solutions that do a good job reporting but don’t necessarily provide insight into how operating drivers impact productivity and financial metrics.

A leading US insurer is using an innovative, flexible large-scale visualisation approach to link investment level, operations drivers, and financial impact. It allows its management team to assess the investment by geographic segment that creates the maximum growth potential, while quickly assessing the impact of operating drivers on conversion productivity such as multi-channel coverage and agent capacity on financial metrics such as maximum loss. It is an end-to-end measurement model to help management grow productivity.

In a world of scarce high-end talent, more operating complexity, and increasing volatility, executive teams should be held accountable for how well they leverage their talent and their resources to create economic value, and how consistently they do it relative to their competition.

Paul Blase is a principal at PwC’s US Advisory Analytics Services. John Sviokla is a principal and US Advisory Innovation Leader with PwC.

Copyright The Financial Times Limited 2013

Information as an asset

December 9, 2013

Information as an asset

By Paul Blase and Anand Rao

Companies have long made habit of using talent to convert the inputs of capital and raw materials into amazing outputs as complex as jet engines, elegant as smartphones, convenient as refrigerators and vital as soybeans.

While information has always been an important ingredient to driving business models, now, it’s frequently the most important ingredient that talent and algorithms transform into the company’s primary product or service output. As “Big Data” and more sophisticated analytics become a critical business differentiator, three lenses help explain how companies can reframe their strategies to maximise market impact – and the capabilities they need to do it effectively.

First, five information economics concepts describe how the paradigm of production, distribution and consumption of products and services are dramatically altered by Big Data and predictive analytics.

Scope of Proprietary Knowledge: Proprietary knowledge can generate enormous option value that can be monetised – specifically when its scope crosses traditional industry value chains to create new classes of products and services. For example, a Fortune 100 company has used its geo-satellite mapping software to provide acritical foundation of proprietary location information and direction algorithms and the competency to make millions of calculations at sub-second speeds to provide the foundation for its driverless car.

This expands the scope of their proprietary knowledge and creates new option value in the massive automotive and government services value chain. In healthcare, companies that can crack the code on integrating information from medical journals, claims data, social media disease management forums, EHRs and personal health behaviour “fit-bit” type data will have the scope of priority knowledge with option value that extends from informing patients about treatment options and quality providers based on outcomes to helping providers deliver high quality care while managing costs.

Information Driven Feedback Loops: Companies exploiting information driven positive feedback loops generate more value for customers by providing access to a valuable network of connections, knowledge and associated services which incentivises customers to contribute more data, thereby increasing the value of the network. Social network and financial payments companies use their customer populations to create information feedback loops that dramatically reduce the cost of the production, distribution and consumption of data allowing them to create numerous information based products and services.

Information Marginal Costs & Benefits: Business models that reduce the marginal cost of accumulating and managing information and maximise benefits from continuously using additional information to make better decisions or create new products and services have the power to continuously monetise their information assets.

For example, a leading consumer financial services software provider allows customers, at a low marginal cost of adding additional data, to consolidate their login credentials and financial data in one place. They then create greater marginal benefit for customers through services that help them understand how “customers like them” save and/or spend their money.

As the company learns more about their customers,, they can integrate the new customer information at a low marginal cost that expands marginal benefits by adding additional customer services such as projecting tax impact on their income or recommending financial products.

Information Enhanced Learning Curves: Business models that accelerate learning through more targeted, faster exchange and application of information create new types of value. For example, industrial products companies are using sensors to monitor and accumulate information about potential equipment risks related to material quality and reliability under specific manufacturing conditions. Quickly expanding their knowledge with better information helps them learn how to quickly adjust risk management and safety programs to mitigate hazards, reduce insurance costs, and avoid supply chain disruption.

Information Aggregation / Decay Ratio: Medical providers have situations where they can’t access the right information quickly enough to make good patient treatment decisions. The longer providers take to get information from medical tests the value of the information can decay such that it is useless in recommending the appropriate care management.

For example, bacteria culture tests to identify drug resistant tuberculosis frequently take six weeks to return. By the time the results are delivered the tuberculosis frequently can’t be treated. Biotech companies that continuously innovate faster ways to sequence disease genomes and conduct diagnostic tests for a multitude of diseases are aggregating information and deploying it through timely, targeted treatment recommendations before its value decays. In the case of TB, diagnostic tests can identify the drug resistant forms of the disease within 2 hours so that life saving treatments can be implemented.

The second lens pinpoints how to quantify the value of monetising information assets by making multi-billion dollar decisions more proactively as opposed to reactively in areas such as determining when to enter and exit emerging markets, anticipating how to redirect capital to more productive uses or sensing when to take actions to reduce risk.

Better data and new analysis techniques make proactive decisions more possible and cost effective.

First companies can access more granular information – and increase accuracy. It can be transmitted in more timely and customised ways to users. And information can be visualised to draw quicker insights. For example, now farmers can access decades of integrated, granular weather patterns, soil content and past harvest yields, analyse the current situation quickly through intuitive visual maps and make better decisions to maximise crop yield and revenue.

Similarly, many online vendors provide customers with personalised recommendations by using granular purchase history and understanding customer profiles to quickly, more accurately and visually present appealing product choices.

The third lens informs how companies can improve decision making by systematically building analytics and information capabilities to monetise more of their data assets.

First companies can build a capability to quickly and cost effectively source and aggregate more data from open, commercial, and proprietary information sources into a mineable format.

Second is to go beyond aggregating data, and explore novel combinations to invent new data with new potential uses. Third is to build processes and architecture to rapidly develop and test new analytic models driven by new data, increase their processing speed through better model coding, improve their insights overtime and quickly launch them into production.

For example, a start-up in the telecommunications space analyses cell tower traffic to identify the location of cell phones at different times of the day, but can then create a behaviour pattern for each cell phone to probabilistically predict where a phone will be at a given day/time in the future.

The new information they have created is of immense value to mobile advertisers targeting users with location-specific and time-specific offers. As they collect more data about customers they can add new variables to their model to increase customer conversion rates and thereby the value of their advertising real estate.

Lastly, given the complexity of representing data, analysis, insights and scenarios of actions, companies are incorporating better visualisations in their business intelligence and decision making processes. For example m, San Diego University’s Calit2 (‘The Wall’) is a large scale visualisation format that can be programmed with ‘analytic apps’ to help executive teams evaluate strategic growth options through scenario analyses that take into account a number of strategic and operational factors.

A Fortune 100 US insurer used the capability to design a growth strategy in the high value home market. They used simple visual controls to adjust thresholds for market attractiveness, competitive intensity and customer behaviours – which highlighted specific zip codes that meet the criteria on giant geo-satellite map. Then they can quickly choose from 50 variables dragging them on to graphs that calculate correlations to quickly test hypotheses, such as determining if attractive markets always have high competitive intensity.

They can then select promising markets at the zip code level, and overlay their agency presence and performance to determine areas to expand coverage. Lastly, they run forward looking scenarios to evaluate the growth potential of different strategies and types of investments.

It is nothing new for businesses to use information and analyse it to make better decisions. But the impact of information economics on the traditional production, distribution and consumption of products and services, the vast amounts of data, the analytics that can be applied and the capabilities required to win are radically changing. Companies that don’t mine and cultivate their information as if it is their most important asset ultimately are leaving it to be monetised by their competitors.

Paul Blase and Anand Rao are both advisory principals with PwC focused on data analytics. Paul can be reached at Anand can be reached at

Copyright The Financial Times Limited 2013