Archive for the ‘Data Analytics’ Tag

Do you have too much IT?

These are heady times for technophiles.  New technologies like mobile computing, data analytics, social networking, and cloud computing has propelled IT back to the top of corporate agendas.  However, in the rush to exploit new applications, many companies can easily over indulge in IT with negative repercussions on cost, ROI and organizational performance.

In today’s competitive economy, IT exuberance is understandable.  Managers want to use breakthrough technologies to serve customers better, improve performance and ring out more cost savings from operations.  At the same time, nobody wants to go through the carnage of the early 2000s when firms threw away $130B in IT spending between 2000-2002 (source:  Morgan Stanley). Furthermore, CEOs can no longer ignore the high cost of IT in their search for bottom line savings.  In some firms, the IT budget is now approaching 12-15% of total corporate spending.

Managers are faced with a dilemma:  how do you take advantage of new technologies (if they are any good) without overspending and distracting the business?  Based on our research and client experience, we recommend the following maxims:

1.  IT must follow business strategy not the other way around – Typically, many managers look to get the latest applications, functionality and hardware before they understand how it would fit into the corporate strategy and workflows, or because they succumb to common phenomena like ‘feature creep’ or ‘keeping up with the Joneses.’  As a result, much of the IT purchased does not end up being deployed or effectively utilized.  There are a variety of reasons for this, including:  uneven management attention, insufficient employee training or poorly articulated requirements.

When strategy and goals dictates what resources are needed and when, less IT is inevitably purchased and more is utilized.  To make this happen, firms should tweak their cultures in two ways.  First, business sponsors should take the responsibility for better understanding existing IT assets and capabilities.  They should jointly propose with IT technical solutions that align to business needs and corporate strategy.  Second, the IT department must adopt an ‘inside-out’ approach to recommending technology.  To do this, they must be congruent with business goals, strategy and plans before seeking out the ideal IT solution.

2.  The organization is the focus – The role of IT is to support the organization, not the other way around.  It is common for impatient managers to throw IT resources at what appears to be a business problem, when in fact it is the workflows, structure and policies that are the issue.   Leaders need to first make sure the organization’s roles & responsibilities, decision rights and processes are optimized before considering new IT resources.

In addition, firms need to recognize that IT is an aid to judgment not a replacement for it.  A case in point is data analytics.  The potential of new DA technologies to better segment customers or identify operational improvements is hard to resist.  However, managers need to tread carefully to ensure their organizations have the capabilities, skills and focus to fully leverage the power of DA or implement its insights.

3.  IT simplicity should be the goal – Not surprisingly, the typical IT department is a mish mash of hardware, applications, operating systems, vendors and skills.  This complexity breeds more complexity when managers start to add capabilities while continuing to support legacy systems.   No wonder IT spending can quickly, quietly and unexpectedly spiral out of control.

Standardizing the computing platform across a company or business unit is one answer.  Many companies like Cisco and Zara have gained significant productivity improvements and enterprise-wide IT savings by standardizing on a limited number of platforms, applications and vendors.  In fact, firms can generate savings through scale economies and experience effects even when the individual asset is not the least expensive or the most capable.

Another way of getting more IT for less money is to move your computing into the cloud.  While valid security and technical concerns remain, there are enough case studies and organizational best practices to justify moving many IT operations and applications, particularly non-core activities.

4. Re-exert transparency and control – Mismanaged IT spending is a pervasive problem in large organizations, particularly where there are weak controls and spend opacity. We’ve seen companies with strict headcount ceilings simultaneously give free rein to junior IT managers to purchase hardware, software licenses and consulting services at their leisure.  A hospital we work with allows researchers to buy new hardware for every new project regardless of the presence of hundreds of under-utilized servers and licenses lying around.   In our experience, rogue purchases can account for up to 25% of an IT budget.

To counter this, management needs to apply the same spending rules and discipline to IT as they do with other functional groups and expense categories.  Furthermore, centralized purchase and finance departments should have more knowledge and visibility into existing IT assets and vendors in order to encourage the sharing of assets across business units and departments.

Many companies will flourish despite a minimalist approach to IT but to a large extent because of it.  A ‘less is more’ IT strategy can lead to lower spending, reduced business complexity and higher employee engagement. Achieving this is as much about strategic alignment and organizational optimization as it is about technology selection and resourcing.

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

Transformational analytics

Companies regularly collect reams of data from their customer interactions and operations. Increasingly, they are looking to build capabilities that can synthesize this raw asset into actionable insights (a competency known as Data Analytics), dramatically improving operational performance, enabling promotion & product ‘mass customization’ and spawning new business models. Leveraging data, however, is easier said than done.  Many companies do not have a data-analytics vision and, therefore, tend to underestimate its potential impact.  Before investing in capabilities (the combination of talent, technology and math), managers should first consider how analytics can transform their business.

For select firms, data and the capabilities that manage it is a competitive differentiator, on par with other valuable assets like a brand. Recent academic research shows that companies that use DA to guide decision making are more productive and experience higher returns on equity than competitors that don’t. However, not all industries offer the same opportunities.  Some sectors like entertainment, construction and services will have modest requirements for high performance analytics, given their market dynamics and structure,.  Based on our consulting experience, we believe that companies in retail, manufacturing, banking, telecom, wholesale, and healthcare industries are best positioned to exploit the breakthrough opportunities provided by data analytics.

Brian Ross, president of Precima, a part of the LoyaltyOne analytics solution is on the front lines of transformational DA.  “We believe that today’s advantage is quickly becoming tomorrow’s necessity.  The first step in this transformation starts at the top. The C-Suite has to establish the long-term vision and align the organization to build the capabilities, processes and tools.”  Strategy-minded leaders should consider the following four areas for breakthrough DA:

1. Optimizing operations

Powerful analytics can significantly improve operational performance, reduce cost and minimize risk.  For example, collating supply chain data onto one integrated platform will allow manufacturers to better collaborate with suppliers during product development, reducing cost, shrinking development time and minimizing the risk of costly errors. In other cases, analytics can enable the deployment of self-optimizing manufacturing systems. McKinsey has written about impact of data analytics on the oil industry.  Operational data from wells, pipelines, and mechanical systems can be collected and analyzed, feeding back real-time commands to control systems that adjust oil flows to optimize production and minimize downtimes. One major oil company has used this approach to cut operating and staffing costs by 10-25% while increasing production by 5%.

2. Transforming marketing & productsAdvertisement

Real time data analytics enables companies to quickly customize products and promotional offers on the fly for different customer segments.  As an example, retailers can track the behavior of individual customers through their usage patterns — both at their site, through social media and from location-specific smartphones — and predict their likely behavior in real time. Once they can predict behavior, retailers can better drive purchases by triggering customized offers, special discounts, or product bundles. In another example, McKinsey works with a personal-line insurer client who leverages DA to tailor insurance policies for each customer, using constantly updated profiles of customer risk, home asset value and changes in wealth.

According to Brian Ross, “Our most telling case studies today lie in enhanced one-to-one communications between vendor and customer. We have seen DA deliver impressive results of 90+% sales lifts, direct response rates as high as 87% and 4% retention gains.”

3. Enhancing decision making

The capability to quickly process and synthesize large amounts of data opens up the possibility of using controlled experiments to test different scenarios around important investment, marketing and operational decisions.  For example, Amazon assigns a number of their web page views to run experiments; they seek to understand which factors promote sales and drive higher user engagement.  McDonald’s has equipped some restaurants with sensors that gathers operational data through tracking store traffic and ordering patterns. The gleaned insights are used to model the impact of variations in menus, restaurant designs, and training on sales and operational productivity.

4. Enabling new business models

Firms with world class analytics competencies have the opportunity to germinate totally new business models and services.  McKinsey has worked with a global manufacturer that learned so much from analyzing its own data that it decided to create a new business doing similar work for other companies. This service business now outperforms the company’s manufacturing one. Information aggregation is another business model that can be spawned from analytics.  Consider this:  UPS regularly collects a mountain of data on shipment patterns, energy usage etc. on the estimated 3-5% of U.S. GDP they ship annually. This data could be mined, synthesized and then sold to organizations that provide economic forecasting services.

Within five years, analytics will be a game-changer for many companies.  However, building capabilities will not be easy or inexpensive. Developing a bold vision of analytics; transformational impact is a good first step.

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

In retail, are you experienced?

Now more than ever, creating a rich shopping “experience” is de rigueur for retailers seeking to be a destination of choice.  Competing on the basis of the shopping experience – for example, delivering useful education, ‘best n class’ service, welcoming aesthetics and engaging entertainment – is not a novel idea.  Many world class firms like Walt Disney, Southwest Airlines and Hard Rock Hotels have built significant businesses by leveraging an experiential-based business and marketing model. Its now the retail sector’s turn.

The evolving landscape

Recent market, technological and consumer developments have put the shopping experience at the center stage of retail marketing and operations. Stores and chains are increasingly seen by buyers as undifferentiated and unexciting; consumer spending has stalled and; pure-play Web retailers and online shopping tools have come into their own.  At the same time, social networking’s popularity is exploding while bored, confused or fickle customers increasingly demand education, aesthetics and entertainment as part of their retail experience.  To stay relevant and competitive particularly in the mid-market and premium segments, merchants will need to regularly orchestrate and promote compelling on and offline shopping memories.  That memory itself will become the one of the products – i.e. the “experience.” 

Evolving consumer behavior is a major factor impacting the customer experience. For one thing, sales channels are changing.  Online sales, already 7% of all transactions, are forecasted to hit 15% by 2019 (source:  Google).  Although in its infancy, mobile commerce is expected to explode over the next few years.  Increasingly, buying decisions are being made before the consumer visits the web or traditional store.  Twenty years ago, 70-80% of all grocery purchasing decisions were made at a store.  Today, it is less than 50% according to Joel Rubinson, former chief research officer of the Advertising Research Foundation.  Typically, consumers will first research products online or canvas their friends and then decide what they will buy before going to a store.  Or, they may forego a trip to the store altogether and make purchases via the Internet. 

Moreover, social networking is enabling consumers to take control of how marketing messages are distributed and consumed – at the expense of company-sponsored sites.  Buyers now attach significant value to online reviews and often spend more money online after reading product recommendations by other consumers.  Facebook, for example, accounts for 38% of social media page referrals online (source: MDC Partners, a marketing firm).  According to pundits, reviews like these are trusted some 12 times more than advertising messages.

These changes have important consequences. When shoppers think about a brand, they now consider their entire buying and service experience.  To be successful, marketing campaigns will now have to encompass and integrate all of the touch points of a company’s relationship with a consumer, including the first moment of contact when a product search begins, through the sale and service, and eventual product disposal and replacement. Though consumers have far more control over marketing messages than they ever have, retailers still have plenty of scope to guide opinions. That’s where experiential marketing comes in.

Who is getting it

Recently, a number of retailers have pushed marketing boundaries to create innovative experiences that integrate entertainment, social media, information and traditional advertising. Two examples were highlighted in a recent issue of the Knowledge@Wharton newsletter:


In 2010, Macy’s hit the mark with a couple of highly successful marketing programs that integrated a number of different experiential elements.   During New York’s Fashion Week, the retailer provided customers with a “magic fitting booth,” which allowed them to “try on” virtual outfits and post images of themselves in their new clothes on Facebook. All year round, shoppers were able to use their phones to watch videos for fashion tips from designers and makeup artists. For the holiday season, Macy’s launched an animated website with an online tool enabling customers to send letters to Santa Claus. The site, which received more than 1M letters, was combined with a fundraising drive for a children’s charity and a mail order guide.


This merchant understands the critical role of human contact in delivering powerful retail experiences.  Bloomingdale’s 8,000 in-store “associates” play a key role in delivering and enabling experiences.  Their responsibilities include collecting customer information in databases.  This information is then used for marketing programs such as birthday alerts or shopping recommendations based on previous purchases. “What we don’t want to lose is the assurance and authenticity of human contact: the touch, the feel, the laugh, the personal knowledge” says Tony Spring, President and COO of Bloomingdale’s.

Making it real

Incorporating the above elements – plus emerging mobile and location-based services – into the marketing and operational model will lead to further evolution of the shopping experience. To remain competitive and efficient, retailers will need to develop a holistic “customer experience” vision and strategy that integrates both traditional and new media, while addressing every point of the purchase/service continuum.  Making this vision a reality will requires firms to revamp their organizational structure in order to bridge functional silos like digital marketing, public relations, store design, advertising and merchandising.

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

Retailing discovers science

More than at any other time, Canadian retailers face a myriad of business challenges, from a slowing economy to the entry of American giants like Target and Marshalls.  To drive sales, improve customer service and increase profitability, Canadian retailers should consider the insights of Professor Marshall Fisher, an operations professor at Wharton.  Fisher argues that operational “science” can help merchants better match product supply with customer demand. OS is already being practiced by some leading retailers including Zara, Walmart and World, a successful Japanese clothing manufacturer. 

In laymen’s terms, OS looks to ensure that customers can consistently and easily find the items they are looking for.  At the same time, OS emphasizes product supply management to minimize over-stocking,  a situation which leads to expensive discounting.  At the heart of OS is the use of advanced marketing and IT methodologies known as data analytics.

Marshall Fisher was recently interviewed in Knowledge@Wharton, a newsletter published by the Wharton School at the University of Pennsylvania.  Below are some of his key conclusions:    

Poor operational performance is costly

Over-stocks create inventory problems, leading to cash flow issues and expensive discounting.  For perspective, the average item now sells for 40% off its full price, up from 33% in the mid 1990s.  Furthermore, out-of-stocks and poor merchandising decisions are resulting in sizeable revenue losses.  According to Fisher,  up to one-third of potential sales are lost when customers walk into a store clearly intending to buy something and walk out empty-handed because they couldn’t find the item.

Small operational gains can drive big bottom line improvements in a high fixed cost business like retail.

Assuming a merchant has a gross margin of 50%, a small 5% increase in sales can generate a 2.5% increase in profit.  For those retailers who lose one-third of potential customer sales via out-of-stocks, modest operational improvements could lead to revenue increases that double their profits.

Retailers are not effectively using the data they have. 

Most firms are awash with point of sale, customer satisfaction and demographic data.  To fully leverage this data, managers should apply data analytics methodologies, such as: 1) determine what of the collected data (e.g. product sales by form by store) is relevant to its corporate strategy;  2)  ensure the collected data is granular enough to be actionable by store;  and; 3) understand what and how factors like weather, merchandising and promotion impact these numbers. 

There is a “science” to deciding which products to add. 

Deciding which products or stock keeping units to add is more difficult than figuring what to cull – that is, eliminating low performing SKUs by store etc.  One data analytics approach is to  compare sales results of different SKUs by their attributes like color and style.  New products that have attribute profiles that mirror the most successful SKUs or fill obvious gaps (e.g., needed for the local selling area) would be added to the merchandising mix.

In-store execution is crucial.   

OS strategies will flounder if the customer experience is poor, merchandising strategies are counter-productive or front-line staff are poorly trained or lacking sufficient numbers.

According to Fisher, most merchants are (on average) under-staffed, and they tend to under-invest in the people they have.  A human capital deficit arises from the fact that retailers typically view labor as an expense rather than an investment. This deficit creates an in-store execution gap that translates into poor customer service and lower revenues.  For perspective, Fisher’s research with customer satisfaction surveys suggest that for every extra $1 invested in adding employees, an incremental $10 will be generated in revenue.

Explore greater supply chain speed and agility

Inflexible or slow supply chains are often a root cause of the supply and demand mismatch.  Increasing speed can be accomplished in many ways, including:  producing more accurate demand forecasts, optimizing the product mix and enhancing supply chain management.  To accomplish the latter, managers could analyze the optimal (read:  fastest and cheapest) way of getting product from offshore to the store.  For example, will operational performance increase with faster but more expensive shipping as opposed to slower but cheaper shipping?  This analysis could prompt retailers to backshore production previously sourced in Asia or consider faster shipment strategies like air freight.

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

Digital marketing: the power of social apponomics

One challenge continues to beset a large number of companies looking to generate higher returns from the Web:  How do you enhance your online business model to increase revenues and customer satisfaction while reducing costs?

According to strategy+business, a newsletter published by the consultancy Booze & Company, the secret lies in the development of a new go-to-market model called “social apponomics.” A new concept, SA is the blend of three compelling and symbiotic online developments: social media-powered community mobilization and interaction; the maturation of CRM-based e-commerce applications and; the emergence of powerful mobile functionality.  For firms that have discovered the SA success formula, the payoff has been impressive:  higher product sales & loyalty; an improved customer experience and; lower operating costs.

SA deployments are a work in progress in many companies.  However, some market leaders have got it right: 


This e-tailing colossus ($31B 2010 revenues) has led the way in delivering a world class sales and customer experience platform.  Amazon’s shoppers are regularly exposed to useful features such as product recommendations, customer reviews and special offers.  With only one click, customers can purchase Amazon products directly or through a huge and governed third-party marketplace.  This market access is enabled through a variety of mobile devices including the Kindle, their own e-reader.   


Intuit, a $3B provider of tax and accounting solutions for individuals, accountants and small businesses, brings its consumers directly into its product development and testing process.  Through, the firm has been able to rapidly roll out more compelling product upgrades.  In addition, Intuit cultivates a vibrant user community by:  bringing together small businesses and entrepreneurs, offering local classified ads and providing an audience-driven wiki for educational purposes.


A unique SA model has helped this online movie distributor attract 15 million North American members and generate $1.3B in revenues.  Netflix goes beyond customized interfaces, personalized recommendations and iterative filtering technology; the Company’s offering allows customers to access movies on-demand as well as manage lists, explore 3rd party reviews and add movies to their queues, all through variety of regular and mobile channels. 

Many lessons can be gleaned from these early SA adopters, including:

Humanize the experience

Customers are more likely to transact at a site that is focused on them versus corporate interests.  Firms like Amazon engage individuals in free and open dialogue, foster trust and feature unique, personalized service.  They also provide intuitive user interfaces, ease of support and aesthetically pleasing visuals.

Think local

Product and customer experience designers should heed research that suggests that local tastes, preferences and physical proximity are still important to customers in a virtual environment.  Moreover, firms should facilitate mobile consumers by providing location specific products, services and offers.  

Target micro segments

New web analytics tools allow companies to track customer need states in real time on a highly granular level.  Access to richer amounts of customer data enables marketers to target high potential, hitherto unexploited segments with tailored products, customized promotions and user experiences.

Recruit your customers

Firms now have the ability to inexpensively and rapidly enlist their customer as product reviewers, referral sources and support staff through the use of Crowdsourcing strategies that leverage “the wisdom of crowds” and reduce operating costs.  Market leaders like Apple have gained significant competitive advantage using Crowdsourcing programs.

The above examples suggest that there is no “one size fits all” SA solution.  How SA is exploited will depend on the firm’s business strategy, culture, partners and IT infrastructure.  Fortunately, there are now a number of best practices to learn from.

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

Using IT to drive Insurer growth

Despite an improving economic climate, Canadian insurers continue to face considerable challenges including growing price competition, rising distribution & delivery costs and increasing competition from banks.  At the same time, consumers – particularly younger, more educated and more affluent ones – are rapidly turning to online tools to purchase and service products.

Emerging technologies offer Insurers the potential to increase market penetration, grow share of wallet and reduce sales, marketing and support costs.  However, Canadian firms are appreciably behind when it comes to using IT to drive revenues and bolster customer service.  There are understandable reasons for this conservatism.  As an industry built on trust developed through personal connections, Insurers are hesitant to adopt arm’s-length ways of developing relationships.  Moreover, with up to 20% of its operating budget spent on technology and its supporting services, Insurers tend to frame IT as an expensive cost center prone to high-priced cost overruns not as a strategic business driver.

Yet, many global insurances companies are using new web and mobile technologies to drive revenues and outflank competition.  In particular, four areas offer rich rewards for those company’s that can truly understand their customer’s needs, make IT a strategic imperative and execute with excellence.

1.  Establish new sales channels

New wireless platforms like the iPhone, iPad and BlackBerry enable Insurers to reach customers more effectively and efficiently.  In one case, South African Metropolitan Life is piloting a new short-term life insurance product, Cover2Go, which allows new customers to purchase a contract by simply by sending an SMS.

2.  Leverage Social Networking

Popular sites such as Facebook and LinkedIn offer tremendous potential to build brand communities, enhance the customer experience and engage a national workforce. In one example, State Farm is using Facebook as a national training platform.  Approximately 17,000 agents have mobilized into groups of “friends” to discuss new products and exchange best practices around customer service and claims processing.

Some Insurers have invited customers into their product development process. Allstate has set up social-network forums to facilitate interactions among motorcycle customers and enthusiasts. The forums solicit customer feedback and use it to inspire new products and services.

3.  Develop custom products

Forward-thinking insurance firms are using powerful data analytics capabilities to identify unexploited customer segments and then target them with tailored, “mass customized” products.  One large Insurer I know is combining sophisticated “rules-based” algorithms with high performance computing to enable product designers to adapt policies both to customers’ preferences and to specific market regulations.

In another case, some auto insurers are using IT to develop dynamic coverage models based on driving patterns and behavior. One leading carrier in the United States, for example, uses GPS technology to monitor drivers and then apply discounts to policies as a reward for safe driving.

4.  Streamline customer service

Even with extensive IT, insurance remains a labour intensive and complex business.  However, a growing number of firms are using off-the-shelf technology to streamline customer service. For example, AXA and Zurich Insurance have location-based iPhone applications that enable customers to use their phones to record damaged areas and then to send the accident photos to reporting centers to expedite claims handling.

In the future…

Social networking has the potential to reorder the traditional Insurance business model. With its ability to mobilize people quickly, sites like Facebook could quickly spawn large affinity groups which can negotiate preferential insurance rates much like Groupon has been offering since 2008.

To use IT as a growth driver, executives need to look beyond their industry in order to identify promising business opportunities that are enabled by new technologies and business models.

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

The Intelligent Enterprise takes shape

Business leaders are beginning to see the transformative power of data analytics to increase competitiveness, drive differentiation, reduce cost and foster business agility.  According to a recent MIT Sloan School of Business survey, many senior executives are now actively looking at how their collected data is being synthesized and used to dramatically redesign the way their organization’s go to market, get closer to their customers, and enable new business models.

In 2010, the Sloan School of Business surveyed almost 3,000 global executives on their goals, lessons and results around using data analytics.  Below are some of the highlights of the research:

Increased use of analytics correlates with higher performance

The results were striking:  There is a strong correlation between the degree of data analytics in an organization and their business performance.  Top performing firms were three times more likely to employ sophisticated data analytics than lower performing firms.  As an example, thought leaders referenced in the survey reported that higher levels of IT and analytics capabilities correspond to disproportionate increases in productivity gains. Moreover, top analytics performers reported greater ease and skill in handling the copious amounts of collected data than less advanced organizations.

Innovation is the number one business priority

 “Innovation to achieve competitive differentiation” was seen as the most prominent business priority (> 60% of respondents) as opposed to growing revenues, reducing costs and getting closer to customers.  Top performing companies were two times more likely to see analytics as a means of enabling innovation.  Examples of this innovation include new ways for companies to collect, synthesize and utilize data as well as the organizational structures and processes to support them.

Powerful analytics is more than just CRM

Building analytics excellence goes beyond ubiquitous data collection and data mining.  Intelligent Enterprises employ other powerful capabilities to help turn raw data into usable information that improves customer segmentation & targeting, fosters 1:1 relationships and enables supply chain efficiencies. These other components include data visualization, choice modeling & mathematical optimization and simulation & scenario building. 

Limited analytics knowledge is the major short-term adoption barrier

According to the survey, two out of the top three adoption barriers centered on a lack of specialized knowledge, resources and management vision.  Furthermore, this knowledge gap extended beyond employees directly responsible for analytics.  Most knowledge workers in areas like marketing, sales and operations need to be more comfortable and proficient in the new data-driven workplace.

Culture is critical to making analytics stick in the organization

Wishful thinking will not bring about the Intelligent Enterprise.  New technologies and methodologies must be accompanied by a shift in culture and organizational design.  In particular, management must be amenable to data-driven insights becoming core to decision-making (as opposed to hunches, history or best practice); information rights and communication flows must be expanded across the organizational and; traditional roles and structure must be tweaked to best exploit the use of the insights.  At the same time, responsibility for analytics must be centralized to ensure data integrity, clear ownership, easy access and operational efficiency.

Experimentation is the most practical implementation strategy

Most respondents emphasized the importance of conducting multiple experiments – as opposed to detailed planning – in order to best determine where and how the Intelligent Enterprise can take root.  Moreover, a ‘testing and learning’ approach was seen as a lower risk strategy for gauging organizational and cultural fit as well as setting priorities and generating early wins.  The respondents also reported that IT is not the driving force in the Intelligent Enterprise – although they are integral to its success.  Other departments (e.g., marketing, operations) as well as autonomous business units that have their own P&Ls are typically leading the charge.

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

Choice Modeling: Polishing the Crystal Ball

When it comes to designing products that customers will buy in droves, the stakes have never been higher.  Despite billions of dollars of investment and countless hours of R&D, 90% of all new product launches will fail within 3 years of hitting the market. Furthermore, many products live as ‘walking wounded’, suffering from low market share, profitability and market differentiation.  The challenge is deceptively simple:  what is the ideal product that balances customer appeal, product profitability and supply chain fit?  The answer can be devilishly complicated, given the myriad of product combinations that could be delivered through different supply chains and sold in a range of markets.  

Fortunately, there are powerful, new analytical tools and methodologies that can help.   One of these techniques, Choice Modeling (CM), can improve new product success rates,  reduce business risk, increase customer knowledge and help define the optimal combination of features, services and prices for existing products. (Another powerful tool is CRM-driven data analytics)

Choice Modeling

As an approach, CM is part science and part art.  CM uses high-performance computer simulations and econometrics to understand and predict customer choice under various product configurations, market and environmental conditions.  In the past, marketers could only rely on simple statistical tools like regression analysis to understand a small set of cause and effect relationships between variables.  Thanks to CM, a firm can now dramatically accelerate the scope, depth and speed of their product analytical capabilities.

CM is being used to design products, services and supply chains in a wide variety of industries including consumer & industrial goods, financial services, hospitality, telecom and retail.

Using Choice Modeling

There are 3 basic steps to utilizing CM:

  1. The first step identifies the number of possible product, service or experiential features  (choices) that could influence a customer’s preference for your offering.  For a new car, the choices would include color, engine size, sales experience and options.  Information on choices can be gleaned from many sources including current product information, customer interviews, surveys and industry data.
  2. The second step is where the art comes in.  Marketers would design a series of simulations that ask customers to choose between a small number of choice options within a series of choice sets.  Using the car example, a simulation could be designed that asks customers to make choices between 2 different luxury packages (choice options) within a series of different feature collections (choice sets).
  3. The final step is where the science takes over.  Powerful econometric models are applied to a representative sample of respondents to identify empirical relationships between their selections of choice options and choice sets.  Unlike traditional tools, CM allows marketers to rapidly model and understand the relationships between hundreds of choices in hundreds of scenarios. Back to the car analogy, analysts would be able to test the impact of various option packages with different features on market share, segment profitability and customer satisfaction, before finalizing the product design and without guessing.

Poised for Growth

With the penetration of Web 2.0 technologies and higher bandwidth, it is now feasible to quickly gather key data and run simulations across multiple geographies, regulatory environments and customer segments.  Importantly, designers can now model unique and customized solutions to individual respondents or micro-segments using new advances in Bayesian statistics.

A Final Caveat

Like other analytical tools, CM is susceptible to “garbage in, garbage out” effects.  Problematic data, shaky assumptions and poorly designed simulations will inevitably lead to misleading results.  Furthermore, the most powerful CM models will not overcome incorrect findings arising from organizational effects like management bias or cultural influences.

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

Getting More Out of Digital Marketing

Companies don’t lack choices when it comes to following their customers online.  Most firms are rapidly introducing Web 2.0 programs including social media, sponsored content and online promotions. Unfortunately, in their haste many marketers are not paying enough attention to the bigger strategic picture or spending efficiencies.  For example, how does the new investment fit their consumer’s actual on and offline purchase behavior. Or, how do digital programs align to their brand and corporate strategies?

Before jumping on the next technological bandwagon, marketers would be prudent to revisit the efficiency and effectiveness of their existing digital programs. I have found that the most successful digital marketers consider three core principles before they increase digital’s percentage of total marketing spending: 

Integrate on and offline programs through a digital customer experience

Customers these days, whether by receiving direct mail, searching for products online, or using mobile devices to find retail coupons, use a combination of analog and digital vehicles to interact with brands throughout the purchase continuum. However, in most companies completely different parts of the organization manage each interaction, thereby reducing overall marketing coordination and brand integration while creating duplication and missed sales opportunities.

Digital channels have the ability to unify these disparate connections into one seamless and synergistic customer experience, as well as increase program efficiency and minimize revenue leakage. For example, TV advertising should feature the same words used in keyword programs.  To be sure, it is not easy to coordinate content and activities across the entire on and offline marketing experience. And, while a single customer experience is desirable, many managers must be careful not to invest in any one activity out of proportion to its actual role in revenue generation.   Despite the challenges, pursuing a single, digitally-driven customer experience is a worthy stretch goal. 

Maximize the potential of user-generated content

For many brands, active digital consumers generate, consume and distribute considerable amounts of unique and relevant content through channels such as user-forums, YouTube and social media.  By using powerful user-generated content, smart marketers can (with the right technology investments) accelerate and optimize their own branding efforts in two important ways.  First, combining user-generated content with your own messages can create a multiplier effect, substantially enhancing the brand’s impact. Conversely, utilizing user-generated content may enable firms to spend significantly less on marketing as a percentage of sales, with little or no performance deterioration. 

Allowing consumers to shape your brand inevitably raises concerns among companies that are fearful of losing control over brands. The key is to strike a balance between retaining control and creating opportunities for consumers to embrace and extend your content.

Turn data-driven insights into business results 

Savvy digital marketers deploy intelligence-gathering tools and processes that analyze what customers are seeing, doing and saying within their digital journey.  Significant value can be created when these digital insights are turned into actions that improve business performance, increase revenues, and drive efficiencies.   To accomplish this, marketers need a proactive data management strategy and system that links data collected to marketing activities, target segments and corporate priorities.  Part of this strategy would involve regularly deploying active and passive analytical tools & skills that assess and distil important consumer intelligence in real-time.  Marketers would develop action items from the data ensuring that they are consistent with key priorities and metrics. A cross-functional group could then review the actions for operational & marketing applicability and suggest course corrections that could be quickly deployed.  Overall, the key would be to create a tight feedback loop between data-driven consumer insights, enhanced programs and improved results.

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

Analytics: Data x Math x Computing = Profit

The holy grail of CRM, the ability to leverage and monetize internal data, is now within reach of most medium to large enterprises. Low cost computing power, new software tools and sophisticated math skills have converged to enable high-level data analytics, a powerful capability that can drive incremental revenue, improve workflow efficiency and enhance customer satisfaction.  Basically, advanced analytics uses special algorithms to comb through large databases of transactions looking for important causal relationships between variables that can be leveraged to improve the efficiency and effectiveness of a program or process. For example, Internet Services and Retail companies are mining millions of their transactions to uncover critical (and hitherto unseen) insights about consumer and supplier behavior.  In other cases, some firms in the Consulting and Software industries are using observations from their own mountain’s of data (as well as the clients they serve) to launch new practices focusing on data management and consulting. 

The business of helping firms make sense out of proliferating data is growing quickly. This industry, which includes leading IT players such as IBM, SAP, Microsoft and Oracle, has estimated revenues in excess of $100B.  The markets is growing at almost 10% a year, roughly twice as fast as the software market as a whole.

I found 3 ‘best practices’ firms, in MIT’s Technology Review and The Economist magazine, who are using data analytics with great success:


IBM is a pioneer in the use of mathematical models to analyze huge data sets.  IBM’s analytics business began as an internal project undertaken by in-house mathematicians, who wanted to learn how to maximize revenue per client by analyzing years of sales data.  The insights discovered in their work prompted them to retool their sales teams by account size & industry and to tweak their service offering.   The result was $1B in new revenues and better sales coverage.  Not surprisingly, IBM concluded that others could benefit from these capabilities and they built an entirely new business analytics and optimization group within IBM Global Business Services to support it.  To date, this group has already trained 4,000 consultants

And they are busy.  IBM mathematicians are using high-quantile modeling in its workforce analytics practice to help clients make decisions about human resources issues such as how best to deploy their sales people.  In other cases, their mathematicians are using stochastic optimization algorithms in their human resources and marketing practice areas to help clients find new customers and determine the right mix of experienced and junior programmers to staff large software projects.


Walmart generates reams of data through their Retail Link inventory management system.  The Company is using sophisticated analytics to crunch this data in a myriad of ways, turning information into a powerful profit accelerator.  In one impressive example, Walmart’s analysis showed that they should offload inventory management to their suppliers and not to take ownership of the products until the point of sale.  This new strategy allowed the firm to decrease inventory risk, conserve cash flow and reduce its costs.


Like many telecoms providers, Cablecom has grappled with churn.  Using advanced data analytics, Cablecom discovered that although customer defections peaked in the 13th month, the decision to leave was typically around the 9th month (as indicated by things like the number of calls to customer support services).  To reduce defections, Cablecom offered at-risk customers special deals 7 months into their subscription.  The results were impressive:  customer defections fell from 20% of subscribers a year to under 5%, enabling the firm to save significant marketing acquisition costs while boosting customer satisfaction.  

Regardless of your data management objectives and strategy, there is gold in those terabytes of data.

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


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