Why do analytics
Netflix is one of analytical competitor. Its CEO Reed Hastings has a master’s in computer science from Stanford. They also created 1 million USD prize for quantitative analysts outside the company who can improve the cinematic algorithm by at least 10 %.
Analytics is the extensive use of data, statistical and quantitative analysis, explanatory and predictive models, and fact-based management to drive decision and actions. Analytics are a subset of what has come to be called business intelligence. Business intelligence includes both data access and reporting and analytics.
Analytics can support almost any business process. Maybe you strive to make money by being better at identifying profitable and loyal customers than your competition and charging them optimal price for your product or service. Perhaps you sell commodity products and need to have the lowest possible level of inventory while preventing your customer from being unable to find your product on the shelf – supply chain optimization. Perhaps you compete in a people-intensive business and are seeking to hire, retain and promote the best people in the industry.
Good decisions usually have systematically assembled data and analysis behind them.
If your business generates lots of transaction data – such as in financial services, travel and transportation or gaming – competing on analytics is a natural strategy.
Peter Keen and Charles Stabell are pioneers of decision support systems (DSS). DSS evolved into executive support systems.
There are some circumstances in which decisions can’t or shouldn’t be based on analytics. Gladwell’s book Blink is talking about such situations. A book is about intuitive decision making. Gladwell is talking about human being and their evolution to make accurate and quick decisions about each other’s personality and intentions. Intuition is a good guide to action only when it’s backed by many years of expertise.
M&A decisions were based on intuition of top management before analytics took over. In a few years, firms that do not employ extensive analytics in making major acquisition will be considered irresponsible.
Reasons for analytical competition: a combination of pressing business need, the availability of data and it that can crunch all the numbers.
Analytical competitor
Four common key characteristics:
- Analytics supported a strategic, distinctive capability
- The approach and management of analytics was enterprise-wide
- Senior management was committed to the use of analytics
- The company made a significant strategic bet on analytics-based competition
Without a distinctive capability, you can’t be an analytical competitor, because there is no clear process or activity for analytics to support. If strategic decisions an organization makes are intuitive or experience based and cannot be made analytically, it wouldn’t make sense to try to compete on statistics and fact-based decisions.
Fashion business could try to decompose and predict the elements of consumer taste. Executive search firms could build their businesses around a database of what kind of executives perform well under certain circumstances. Consultants could base their advice to clients on detailed statistical analytics. The most commonly used measure in consumer finance is the credit score or FICO score. FICO score is based on algorithm developed by Fair, Isaac and Company in 1989).
Analytics have largely been either an individual or a departmental activity in the past. An enterprise approach is needed to become analytical competitor. Individual analytics problem is that create multiple versions of truth. Enterprise-wide approach at Harrah’s is called centrally driven, broadly distributed. Some of the companies use BICC – business intelligence competency center. BICC according to SAS is a cross-functional team with a permanent, formal organizational structure.
The adoption of a broad analytical approach to business requires changes in culture, process, behavior and skills for multiple employees. Great analytical CEO’s: Gary Loveman – Harrah’s, Jeff Bezos – Amazon, Rich Fairbank – Capital One, Reed Hastings – Netflix, Barry Beracha – Sara Lee Bakery Group.
Some of the large-scale project are “yield management” – optimizing price of seat at American Airlines, inventory optimizing called “direct derivate estimation of non-stationary inventory” at Deere & Company, reorganizing sourcing and distribution at Procter & Gamble.
Four pillars of analytical competition are:
- Distinctive capability
- Enterprise-wide analytics
- Senior management commitment
- Large-scale ambition
Senior commitment is probably the most important, since it can make other possible.
Five stages of analytical competition:
- Stage 1 – Analytical impaired
- Stage 2 – Localized analytics
- Stage 3 – Analytical aspiration
- Stage 4 – Analytical companies
- Stage 5 – Analytical competitors
Most of stage 5 organizations were information-intensive service firms. One of them was Progressive – an auto insurance company with a history of technological and analytical innovation.
Analytics and business performance
In 1980’s two financial service consultants, Richard Fairbank and Nigel Morris identified a major problem in the credit card industry, as well as potential solution. The problem was that the industry lacked a focus on the individual customer and the solution came in the form of technology-driven analytics. They discovered that the most profitable customers were people who borrowed large amounts quickly and the paid off the balances slowly.
Today Capital One runs about three hundred experiments per business day. A few companies are truly set up to apply the principles of this test-and-learn approach.
Marriot uses revenues management – the process by which hotels establish the optimal price for their rooms. Enterprise-wide revenue management system is called One Yield. They also use Marriot Rewards loyalty program to identify the most profitable customers.
Progressive was the first insurance company to offer auto insurance online in real time and the first to allow online rate comparisons.
Companies can take several approaches to gain a competitive advantage with data. Some can collect unique data over time about their customers and prospects that competitors cannot match. Others can organize, standardize and manipulate data that is available to others in a unique fashion. Still others might develop a proprietary algorithm that leads to better, more insightful analyses upon which to make decisions. And some differentiate themselves by embedding analytics into a distinctive business process.
Companies that successfully compete on analytics have analytical capabilities that are:
- Hard to duplicate
- Unique
- Adaptable to many situations
- Better than the competition
- Renewable
Money from analytics can came also from offering market – to customers; as stand-alone offering or to augmented other products and services. Accenture is offering consulting. Dunnhumby worked closely with Tesco on their loyalty card. Catalina Marketing sell analytical services to the grocery industry that help it understand the effects of coupons and other promotions.
Internal processes and analytics
Internal functions such as general management, finance and accounting, R&D, manufacturing and HR. results can be improved financial and operational performance.
Typical analytical applications for Internal Processes:
- Activity-based cost (ABC)
- Bayesian inference (predicting revenue)
- Bio simulation (pharmaceutical “in silico” research)
- Combinatorial optimization (optimizing a product portfolio)
- Constraint analysis (product configuration)
- Experimental design (Web site analysis)
- Future-value analysis
- Monte Carlo simulation (R&D project valuation)
- Multiple regression analysis (how nonfinancial factors affect financial performance)
- Neural network analysis (predict the onset of disease)
- Textual analysis (assess intangible capabilities)
- Yield analysis (semiconductor manufacturing)
Reporting and scorecards, both financial and operational, are some of the most common applications of business intelligence and decision support. Balanced scorecards are mostly not balanced at all, focusing primarily on financial side.
While the math to compute activity-based costs isn’t that difficult, coming up with the needed data does require a substantial degree of diligent investigation.
Merger and acquisitions (M&A) have historically not been the focus of a lot of analytical activity, perhaps with the exception of detailed cash flow analyses. There is usually little attention to operational analytics involving supply chain efficiencies, predicted customer reactions and impact on costs within the combined organization.
One analytical domain that has long existed in companies is operations, especially manufacturing and quality. Total Quality Management and Six Sigma, when done seriously, involve detailed statistical analysis of process variations, defect rates and sources of problem. Analytics can also be applied to assess manufactured quality. Another key aspect of manufacturing analytics is to ensure that the right products are being manufactured. We’ll refer to it as the configuration problem – making sure that the products offered to the market are those that the market wants. The mismatch between consumer desires and available product has been one of the biggest problems facing Ford and General Motors.
R&D has been perhaps the most analytical function within companies. Hypothesis testing, control groups and statistical analysis. In several industries, research has become more mathematical and statistical in nature as computational methods replace or augment traditional experimental approaches. Vertex uses analytics to automate and enhance clinical trial operations; examples include tools for patient accruals and electronic data capture (EDC). Vertex’s approach to drug design is known as rational or structural. This approach seeks to “design-in” drug-lie properties from the beginning of a drug development project and it enables Vertex to determine as early as possible whether a compound will have drug-like attributes.
Shift to talent management is also driving the move toward HR analytics. Another factor driving the shift to HR analytics is increasing rigor in staffing and recruiting processes.
External processes and analytics
Once kept strictly segregated, the boundaries between customer relationship management (CRM) processes such as sales and marketing and supply chain management (SCM) process such as procurement and logistic have been broken down by organizations seeking to align supply and demand more accurately.
The roots of analytics in business come from the customer side as much as the supplier side. More recently, marketing organization have radically increased their analytical orientations with the rise of campaign management software.
Acquiring and retaining customers is getting more expensive, especially in service-based industries such as telecommunications and financial services.
Typical Analytical Applications in Marketing:
- CHAID – Chi-square automatic interactions detection. The analysis creates a segmentation “tree”.
- Conjoint analysis – typically used to evaluate the strength and direction of customer preferences for a combination of product or service attributes.
- Lifetime value analysis
- Market experiments
- Multiple regression analysis – while basic regression assumes linear relationships, modification of the model can deal with nonlinearity, logarithmic relationships and so forth.
- Price optimization – also known as yield or revenue management
- Time series experiment
Martin Sorrell from WPP – there is no doubt in my mind that scientific analysis, including econometrics, is one of the most important areas in the marketing-services industry. The other dramatic change in advertising is the rise of online. Web-based ads.
Norwegian bank DnB NOR build analytics on top of Teradata warehouse to more effectively build CRM. They use “event triggers” in the data warehouse. They achieved a conversion rate on cross-selling between 40-50 %.
While the direct marketing industry’s average response is only 2 percent. Tesco’s coupon redemption rate is 20 percent and ranges as high as 50 percent.
Analytics also make it easier to engage in dynamic pricing – the practice of adjusting the price for a good or service in real time in response to market conditions such as demand, inventory level, competitor behavior and customer history. Most retailers experience a 5 percent to 10 percent increase in gross margin as a result of using price optimization systems. JC Penney few years ago began an analytically intensive program that integrated merchandising, pricing optimization and the supply chain. They add five points of gross margin, increase inventory turn by 10 percent and grow top-line and comparative store sales for four consecutive years.
BudNet is system used by Anheuser-Busch in which data from thousand distributor sales reps gives them unparalleled insight into what happens to its products at the retail level. Best Buy used analytics to build their customer-centric approach. Sprint also takes a keen interest in customer life cycles. It uses analytics to address forty-two attributes that characterize the interactions, perceptions and emotions of customer across a six-stage life cycle, from initial product awareness through service renewal or upgrade. A final strategy for using analytics to win over customers is to tailor offerings to individual preferences.
Typical Analytical Applications in Supply Chains:
- Capacity planning
- Demand-supply matching
- Location analysis
- Modelling
- Routing
- Scheduling
Amazon.com recruited Gang YU, a professor of management science and a software entrepreneur who is one of the world’s leading authorities on optimization analytics, as the head of its global supply chain.
The optimal sourcing strategy (determining the right mix of joint replenishment, coordinated replenishment and single sourcing) is determined by advanced optimization and supply chain management methodologies and techniques across its fulfillment, capacity expansion, inventory management, procurement and logistic functions.
Enhanced capabilities in analytics
Software applications, technology, data, processes, metrics, incentives, skills, culture and sponsorship; all those elements are important for development of analytical capability.
Five stages of development in life of analytical competitors:
- Stage 1: Prerequisites to Analytical Competition
- Stage 2: Prove-It Detour
- Stage 3: Analytical Aspirations
- Stage 4: Analytical Companies
- Stage 5: Analytical Competitors
It takes 18-36 months working with data to start developing a steady stream of rich insight that can be translated into practice.
Key elements of analytical capability:
- Organization
- Insight into performance drivers
- Choosing a distinctive capability
- Performance management and strategy execution
- Process redesign and integration
- Human
- Leadership and senior executive commitment
- Establishing a fact-based culture
- Securing and building skills
- Managing analytical people
- Technology
- Quality data
- Analytic technologies
Analytical competitors design effective decision making into their processes to ensure that analytical insights get translated into action and ultimately enhance business performance. To ensure that strategy is converted into operational results, organizations must define and monitor metrics that are tied to strategic enterprise objectives and align individual incentives and metrics with business objectives. Top priority is integrating analytics into the organization’s distinctive capability, with an eye to building competitive differentiations.
Prove-it approach will add additional year to analytics journey. You should keep it simple and in narrow scope.
Stage 3 is triggered when analytics gain executive sponsorship. IT organization must develop a vision and program plan (an analytical architecture) to support analytical competition.
The primary focus in stage 4 is on building world-class analytical capabilities at the enterprise level.
In stage 5 analytics move from being a very important capability for an organization to the key and competitive advantage. Proprietary metrics, analytics, processes and data create a strong barrier to competitors.
To measure performance of analytical initiatives, you need to observe: behaviors, processes and programs and financial results.
A common error is to assume that merely having analytical technology is sufficient to transform an organization. The Field of Dreams approach – if you build it, they will come – usually disappoints.
Some mistakes are also: choosing the wrong problem, not understanding the problem sufficiently, using the wrong analytical technique or the wrong analytical software.
Analytical people
Analytically based actions usually require a close, trusting relationship between analyst and decision makers. There are really three groups, then, whose analytical skills and organizations are at issue within organizations: senior management team, the professional analyst (gather and analyze the data) and analytical amateurs, a very large group of “everybody else”.
Executives in an analytical competitor:
- They should be passionate believers in analytical and fact-based decision making
- They should have some appreciation of analytical tools and methods
- They should be willing to act on results of analyses
- They should be willing to manage a meritocracy
Roles involved with analytics are for sure CFO and CIO. CIO is involved with information. Information orientation consists of information behaviors and values, information management practices and information technology practices – whereas many CIOs only address the latter category.
The need is for analytical experts who also understand the business in general and the particular business need of a specific decision maker. One company referred to such individuals as “front room statisticians”. Back room ones can do statistic, but are terrible at business.
Critical success factors:
- Building a sustainable pipeline – projects, client relationships and analytical technologies.
- Relationship to IT
- Governance and funding
- Managing politics
- Don’t get ahead of users
When a company is an analytical competitor, it will need to ensure that a wide variety of employees have some exposure to analytics.
Spreadsheets (by which we really mean Microsoft Excel, of course) are the predominant tool by which amateurs manipulate data and perform analytics.
Among analytical competitors, we found automated decision technologies being used for a variety of operational decisions, including extension of credit, pricing, yield management and insurance underwriting.
Architecture of BI
While improvements in technology’s ability to store data can be astonishing, most organizations’ ability to manage, analyze and apply data has not kept pace.
Responsibility for getting the data, technology and processes right is the job of the IT architect. The business intelligence architecture is an umbrella term for an enterprise-wide set of systems, applications and governance processes that enable sophisticated analytics, by allowing data, content and analyses to flow to those who need it, when they need it.
Business intelligence architecture:
- Data management
- Transformation tools and processes
- Repositories
- Applications and other software tools
- Presentation tools and applications
- Operational processes
Greatest data challenge facing companies is “dirty” data. Information that is inconsistent, fragmented and out of context.
There should be “single version of truth” on enterprise level for data. Sources can be different, but enterprise system to handle them should be one. Enterprise systems automate, connect and manage information flows for business processes, they provide consistent, accurate and timely data for such tasks as financial reporting and supply chain optimization. They can develop sales forecasts and model alternative solutions to business problems.
Two pitfalls must be balanced against a need for large quantities of data. Resist the temptation to collect all possible data “just in case”. Avoid collecting data that is easy to capture but not necessarily important.
Several characteristics increase the value of data:
- It is correct
- It is complete
- It is current
- It is consistent
- It is context
- It is controlled
Stages of data management life cycle:
- Data acquisition
- Data cleansing (25-30% BI initiatives are about that)
- Data organization and storage
- Data maintenance
Both business and IT must expend significant effort in order to transform data into usable information. For every dollar spent on integration technology, around seven to eight dollars is spent on labor.
Organizing and storing data:
- Data warehouse
- A data mart – usually used to support a single business function or process
- A metadata repository
There are technologies that both structure the workflow and provide decision rules.
Typical analytical technologies:
- Spreadsheets
- OLAP – online analytical processors. They organize data in “data cubes”
- Statistical or quantitative algorithms
- Data mining tools – their objective is to identify patterns in complex and ill-defined data sets
- Text mining tools – spiders and data crawlers
- Simulation tools
Emerging analytical technologies
- Text categorization
- Genetic algorithms
- Expert systems
- Audio and video mining
- Swarn intelligence
- Information extraction
Presentation tolls like reporting tools, scorecards and portal should allow users to create ad hoc reports, to interactively visualize complex data.
Future
William Gibson: “Future is already here but unevenly distributed.”[1]
Some practices that already exists and will be expanded in the future:
- Pervasive BI software
- Increasing use of dedicated “business intelligence appliances”
- More automated decisions
- Consistent with the preceding trend
- Greater use of alerts
- More visual analytics
- More predictions and less reporting
- More mining of text
Humans don’t change as rapidly as information technologies. Business intelligence will be increasingly extended to the frontline analytical amateurs withing organizations. Use of outsourced and offshore analytical resources will also grow.
Analytical software applications can guide an analyst through a decision processes, either making the decision itself or ensuring that human decision maker has all needed information and isn’t violating any important statistical assumptions.
We anticipate that a number of changes in the analytical environment will be driven by business strategies. We also expect to see more analytics embedded in or augmenting product and services. Supplying analytics to customers and suppliers. Someday perhaps intangible assets will be reported upon by companies as a part of their regular financial reporting processes. Technology could play a substantial (but not exclusive) role in generating and analyzing intangible capabilities.
[1] In the book on page 175
