Business Analytics Solution for Airline Industry

 

If the airline industry could be described in two words, it would be "intensely competitive". The airline industry generates billions of dollars every year and still has a cumulative profit margin of less than 1%.  The reason for this lies in this industry’s vast complexity. Airlines have a multitude of different business issues that need to be solved at once, such as globally uneven playing field, revenue vulnerability, an extremely variable planning horizon, high cyclicality and seasonality, fierce competition, excessive government intervention and high fixed and low marginal cost. To ensure the best chance for full economic recovery, airlines should fully leverage their most prolific asset - data.  Data used in conjunction with innovative technologies that would allow the creation of an Business Analytics Solution, will provide the capabilities for a comprehensive intelligent management and decision-making system throughout the enterprise.  The ultimate benefits of implementing and using an enterprise wide intelligence platform, together with airline business acumen and experience would include timely responses to current and future market demands, better planning and strategically aligned decision making, and clear understanding and monitoring of all key performance drivers relevant to the airline industry.

Achieving these benefits in a timely and intelligent manner will ultimately result in lower operating costs, better customer service, market leading competitiveness and increased profit margin and shareholder value. 

Airlines throughout the world are currently facing an unprecedented financial crisis. Factors contributing to this crisis are low customer satisfaction, overtraded markets, insufficient and under utilization of aircraft capacity, poor labor relations, excessive government intervention, high labor costs, ever increasing oil prices resulting in spiraling fuel costs, and generally  high operational costs. The low profit to turnover ratio of airlines have been further exacerbated by growing low-fare competition, increasing security costs, and frequent dynamic shifts in air travel consumer behavior. The historical business model of many network airlines now appears to be unable to support sustained profitability under any but the most favorable economic conditions. The industry is at a turning point.  The market dictates an “adapt or die” policy, and the airlines that whish to survive will face the challenge of having to make significant changes to their current archaic business model. To do this requires far more allowance for innovative technologies that would allow airlines to build an end-to-end Business Analytics Solution. The core capabilities of these technologies will ensure the flow of consistent, repeatable and reliable enterprise wide intelligence needed to tackle all the challenges the industry is facing.

Purpose of Business Analytics Solution for Airlines 

Purpose of an Business Analytics Solution for airlines is to bridge what is called the Information-to-intelligence gap.  The disparity between what an airlines has – which is prolific amounts of data from disparate source systems – and what an airline wants – which is to achieve strategy alignment for a competitive edge; whether it be through compliance, increased profitability, decreased risk, or to better manage performance, planning, etc.

 

  Addressing the Business issues 

Some of the challenges that can be successfully addressed by Business Analytics Solution are:

• The need for accurate daily and weekly performance measurement reports (e.g. “flash/estimated” revenue, operating costs and net contribution reports for every aircraft’s actual flight per sector/route).
• The Need to better manage all aspects of risk.
• The Need for better impact analysis and more effective optimization of all resources as well as being able to produce accurate passenger-revenue forecasts, 
• The Need for a holistic, 360 degrees view of the airline industries customers, suppliers, service providers and distributors.
• The Need for expense verification models in order to better control all industry cost aspects.

 
Issues related to Performance Management 

Airlines usually operate in a globally competitive environment and therefore require prompt and accurate enterprise performance measurements. Furthermore, airlines are volume driven and small variations (passengers flown, fuel spent/bought, load carried) can multiply into major effects – therefore appropriate and timely action is critical. Airlines suffer substantial difficulties to produce daily/weekly reliable performance measurements. Current airlines “legacy” IT systems such as Revenue Accounting, require several weeks after a month end to generate revenue results for every flight per sector/route.  Business Analytics Solution for Airlines can automate production of daily activity reports such as number of passenger flown per flight/sector, distance flown, etc which can be used to provide estimated performance measurements such as daily or weekly revenues for specific routes or sectors.

Issues related to Risk Management

The global airline industry has been subjected to major catastrophes over the past years.  It is accordingly imperative for airlines to develop various risk management models and strategies to protect themselves from negative impact of these types of events. Furthermore, due to the global playing field, airlines often earn its revenues and pay its costs in different baskets of currencies (e.g. USD, Euro, GBP etc). As a result there is frequently a mismatch between the flow of revenue receipts and expenses of each basket of currency - creating risk exposure. Using Business Analytics Solution for Airlines Infrastructure, relevant data can be gathered, consolidated and cleaned, risk can be modeled, and risk exposure can be measured and presented on “as and when” basis, as requested by business user. 

Issues related to Control and Verification

Airline carriers require a number of control and verification models to be able to control costs arising from its various operational activities. To enable this, airlines have a pressing need for a complete and integrated repository of flight information data gathered from all its disparate business units. This will enable computation of various efficiency analytics - e.g. planed fuel usage compared with actual fuel usage per aircraft, crew utilization (roster optimization). These issues could also be fully addressed by the Business Analytics Solution for Airlines, which will access, consolidate and analyze relevant flight and aircraft data. In turn this would help to create a 360 ° view of each flight and aircraft, allowing the business users to dramatically improve their control and verification systems. 

 

 Issues related to be able to better forecast

Airlines require the development of an effective and holistic forecasting model to regularly asses the impact of options and alternatives such as increasing aircraft seats available, adjusting fares, introducing new routes etc. Forecasts should also take account of actual statistical trends and results e.g. actual passengers carried and actual average fares earned. Such forecasts should then be compared against budgets and prior year performance. Business Analytics Solution for Airlines  has a market leading and powerful forecasting engine capable of generating large number of forecasts automatically and making them available to the people who would used them for sound decision making. 

        
 Issues related to a lack of a holistic view of core business components. 

Airlines would greatly benefit from knowing and understanding its business environment along some of the key business issues, such as performance, behavior, risk, profitability, etc. Using customers as an example - the main objective would be to enrich the knowledge about individual customers leading to new strategic customer segments. This intelligence would allow airlines to reap the host of benefits such as successful, targeted customer promotions, cross-selling and up-selling campaigns for different flights and booking classes leading to improved yield and revenue. For example, it would give airlines the power of knowing to limit discounts on flight routes which are usually over-booked, allowing the large number of passengers to compete for high profit seats immediately prior to departure. Such multidimensional views of the business can help the airline to better serve its customers through more effective, efficient and personalized service, receiving in return customer loyalty, support and market share, all leading to higher profitability.

 Conclusion

The Business Analytics Solution for Airlines is designed on Usable, Interoperable, Scalable, and Manageable technology, and encompasses all aspects of turning information into strategically aligned, powerful and accurate intelligence and empowering the business user into intelligent action by ensuring the delivery of the right intelligence to the right business user in the right format in a timely manner.  Solution is built on core technological components of Data Integration, Data Management, Data Analysis and Information Deployment, all of these components being fed by centrally shared enterprise wide metadata.  Built into these core technology components are airline specific data models, statistical and analytical models, pre-written reports and all necessary training and methodologies for a successful and sustainable solution for airlines implementation.  All of these items collectively give the  the capability and capacity to address the host of the burning issues prevalent in the airline industry.

 

Goran Dragosavac

Police using ‘predictive analytics’ to prevent crimes before they happen

 

By Agence France-Presse

Crime fighters have long used brains and brawn, but now a new kind of technology known as “predictive policing” promises to make them more efficient. A growing number of law enforcement agencies, in the US and elsewhere, have been adopting software tools with predictive analytics, based on algorithms that aim to predict crimes before they happen. The concept sounds like something out of science fiction and the thriller “Minority Report” based on a Philip K. Dick story.

 

Without some of the sci-fi gimmickry, police departments from Santa Cruz, California, to Memphis, Tennessee, and law enforcement agencies from Poland to Britain have adopted these new techniques.

The premise is simple: criminals follow patterns, and with software — the same kind that retailers like Wal-Mart and Amazon use to determine consumer purchasing trends — police can determine where the next crime will occur and sometimes prevent it.

 

Colleen McCue, a behavioral scientist at GeoEye, a firm that works with US Homeland Security and local law enforcement on predictive analytics, said studying criminal behavior was not that different from examining other types of behavior like shopping. “People are creatures of habit,” she said. “When you go shopping you go to a place where they have the things you’re looking for… the criminal wants to go where he will be successful also.” She said the technology could help in cities where tight budgets were forcing patrol reductions.

“When police departments are laying more sworn personnel, they can do more with less,” she said.

The key to success in predictive policing is getting as much data as possible to determine patterns. This can be especially useful in property crimes like auto theft and burglary, where patterns can be detected. “You can build a model that factors in attributes like the time of year, whether it is hot and humid or cold and snowy, if it is a payday when people are carrying a lot of cash,” says Mark Cleverly, who heads the analytical unit for predictive crime analytics. “It’s not saying a crime will occur at a particular time and place, no one can do that. But it can say you can expect a wave of vehicle thefts based one everything we know.”

 

In Memphis, officials said serious crimes fell 30 percent and violent crimes declined 15 percent since implementing predictive analytics in a program with IBM and the University of Memphis in 2006.

The program known as CRUSH — Criminal Reduction Utilizing Statistical History — targeted certain “hot spots” to allow police to deploy more efficiently. John Williams, crime analysis manager for the city’s police, said the system has had a dramatic impact, allowing Memphis to get off the list of worst US cities for crime. “If the data is indicating a hot spot, we are able to immediately deploy resources there. And in a lot of instances we are able to make quality arrests because we’re in the right area at the right time,” he told AFP.

 

Although beat officers can use their instincts for similar results, Williams said the software could be far more precise, such as predicting burglaries in a small geographic area between 10 pm and 2 am.

In one case, the software was able to help police break up a group that was committing armed robberies on the city’s Hispanic population. “There were 84 robberies, but we had no idea it was so organized,” Williams said. By crunching the numbers, police were able to pinpoint the zone and time of likely holdups: “We caught a group of robbers in progress, we had leads on additional robberies,” he said.

 

Williams said police officials from as far away as Hong Kong, Rio de Janeiro and Estonia have come to review the experience in Memphis. In Los Angeles, another program developed by scientists at the University of California-Los Angeles and Santa Clara University was tested in a single precinct, and resulted in a 12 percent drop in crime while the rest of the city saw a 0.2 percent increase. That test and others led to the creation of a company called PredPol. And Los Angeles will expand its use of the program under contract with PredPol, said CEO Caleb Baskin.

 

Baskin said the system is based on a model from mathematician George Mohler which “is very effective in predicting the time and location for crimes that have not yet taken place.” PredPol had begun working with other cities in California and “we’ve had inquiries from a lot of places in the US and international locations,” Baskin said. “The science that underlies the tool will work anywhere. The question is does the agency maintain a database that we can plug into.” While use of such analytics generally wins plaudits for helping “smarter” policing, it does raise concerns about Big Brother-like snooping.

 

Andrew Guthrie Ferguson, a law professor at the University of the District of Columbia, said the use of technology could be positive but that it could lower the threshold for constitutional protections on “unreasonable” searches. “To stop you and frisk you and search you, a police officer needs easonable suspicion, so my question is how will this affect reasonable suspicion?” he said. If the search is based on a computer algorithm, Ferguson said, and the case comes to court, “How do you cross-examine a computer?” IBM’s Cleverly said the technology can in many cases improve privacy.

“You can pinpoint the record of who has access to information, you have a solid history of what’s going on, so if someone is using the system for ill you have an audit trail,” he said. As for “The Minority Report” and its predictive software, Cleverly said, “It was a great film and great short story, but it’s science fiction and will remain science fiction. That’s not what this is about.”

What to do with False Positives?

 I often hear complaints from business  folk that their models need improvement because there are too many false positives. Just for clarity - in a case of fraud transactions – false positives are related to those transactions which were assigned to be fraudulent when in reality they weren’t.  Sure, one needs to always minimize occurrence of false positives as much as possible, but it is not always the model’s fault. Sometimes what looks  like a clear cut fraud – just isn’t. It is a fuzzy area where the difference between patterns of your event and non-event are completely blurred. Kind of – it could go either way!

Some implementations of analytics have been built on false positives. These are the people who look like buyers of particular brand – and yet they are not. Well, the logical assumption is that if some marketing stimuli is sent to these people – they are more likely to become buyers of that brand, due to its high-degree of look-alike-ness than randomly selected folk. I have completed several successful projects geared solely on acting on these ‘so called’ modeling mistakes.

 Another example is building a model capable of predicting who will be dormant customers within a period of time.  After building the model we score it on some existing base comprising of known (historical) dormant  customers as well as of those who are not. Then, we focus on false positives and compare them to one’s that are correctly predicted.  Often the difference is so small between the two groups in terms of their usage patterns – that we may as well call them all dormant customers. Even though false positives are technically not dormant yet –  for all intents and purposes they really are. So, we go back to the business definition of what constitutes dormant customer and we look at the whole phenomenon with a  new fresh angle. Thanks to comparative studies between accurate predictions and false positives.

So what I am trying to say in this article is that what appears to be modeling “mistake” can be turned into the value from more than one different angle. There is always a reason why models make mistakes – and tiredness is never one of them.     

 Goran Dragosavac

Analytics and Data Mining in Banking

With the increasing economic globalization and improvements in information technology, large amounts of financial data are being generated and stored. These can be subjected to data mining techniques to discover hidden patterns and obtain predictions for trends in the future and the behavior of the financial markets. This in turn would result in an improved market place responsiveness and awareness leading to reduced costs

 and increased revenue.

Analytics can contribute to solving business problems in banking and finance by finding patterns, causalities, and correlations in business information and market prices that are not immediately apparent to managers because the volume data is too large or is generated too quickly to screen by experts. The managers of the banks may go a step further to find the sequences, episodes and periodicity of the transaction behaviour of their customers which may help them in actually better segmenting, targeting, acquiring, retaining and maintaining a profitable customer base.

Business Intelligence and data mining techniques can also help them in identifying various classes of customers and come up with a class based product and/or pricing approach that may garner better revenue management as well. Analytics can help banks understand and drive decisions related to customer profitability, as well as enable banking institutions to segment customers according to a multitude of variables – demographics, geographies, account history, etc. – In order to create more meaningful and targeted marketing programs.  

Furthermore, analytics can help banks improve retention rates by determining the causes and predicting future customer attrition.  In addition, banks can apply analytics to historical data to find out which customers are good candidates for cross-selling and up-selling and as a result achieve increase in revenue and wallet share. For most banks analytics are used as the most powerful weapon in the fight against fraud.

Customer Relationship Management

Customer segmentation and profiling is a data mining process that builds customer profiles of different groups from the company’s existing customer database. The information obtained from this process can be used for different purposes, such as understanding business performance, making new marketing initiatives, market segmentation, risk analysis and revising company customer policies. The advantage of data mining is that it can handle large amounts of data and learn inherent structures and patterns in data. It can generate rules and models that are useful in enabling decisions that can be applied to future cases.

 In Banking - analytics and data mining is frequently used to assign a score to a particular customer or prospect indicating the likelihood that the individual will behave in a particular way. For example, a score could measure the propensity to respond to a particular insurance or credit card offer or to switch to a competitor’s product. Data mining can be useful in all the three phases of a customer relationship-cycle: customer acquisition, increasing value of the customer and customer retention.

Banks use their credit risk models to classify these respondents in good credit risk and bad credit risk classes. Seeing the huge cost and effort involved in such marketing process, data mining techniques can significantly improve the customer conversion rate by more focused marketing.

Because high competitions in the finance industry, intelligent business decisions in marketing are more important than ever for better customer targeting, acquisition, retention and customer relationship. There is a need for customer care and marketing strategies to be in place for the success and survival of the business. It is possible with the help of data mining and predictive analytics to make such strategies. Financial institutions are finding it more difficult to locate new previously unsolicited buyers, and as a result they are implementing aggressive marketing program to acquire new customer from their competitors.

  

With the advent of data mining and business intelligence tools it has become possible for banks to strengthen their customer acquisition by direct marketing and establish multi-channel contacts, to improve customer development by cross selling and up selling of products, and to increase customer retention by behaviour management. 

 

It is also possible to bundle various offers to meet the need of the valued customers. Analytics can also help the banks in customizing the various promotional offers. It is also possible for the banks to find out the problem customers who can be defaulters in the future, from their past payment records and the profile and the data patterns that are available. This can also help the banks in adjusting the relationship with these customers so that the loss in future is kept to its minimum.

Data Mining techniques can be of immense help to the banks and financial institutions in this arena for better targeting and acquiring new customers, fraud detection in real time, providing segment based products for better targeting the customers, analysis of the customers’ purchase patterns over time for better retention and relationship, detection of emerging trends to take proactive stance in a highly competitive market adding a lot more value to existing products and services and launching of new product and service bundles.

Risk Management

Managing and measurement of risk is at the core of every financial institution. Today’s major challenge in the banking and insurance world is therefore the implementation of risk management systems in order to identify, measure, and control business exposure. Here credit and market risk present the central challenge, one can observe a major change in the area of how to measure and deal with them, based on the advent of advanced database and data mining technology.( Other types of risk is also available in the banking and finance i.e., liquidity risk, operational risk, or concentration risk. ) Today, integrated measurement of different kinds of risk (i.e., market and credit risk) is moving into focus. These all are based on models representing single financial instruments or risk factors, their behaviour, and their interaction with overall market, making this field highly important topic of research.

Financial Market Risk

For single financial instruments, that is, stock indices, interest rates, or urrencies, market risk measurement is based on models depending on a set of underlying risk factor, such as interest rates, stock indices, or economic development. One is interested in a functional form between instrument price or risk and underlying risk factors as well as in functional dependency of the risk factors itself. Today different market risk measurement approaches exist. All of them rely on models representing single instrument, their behaviour and interaction with overall market. Many of this can only be built by using various data mining techniques.

Portfolio Management

Risk measurement approaches on an aggregated portfolio level quantify the risk of a set of instrument or customer including diversification effects. On the other hand, forecasting models give an induction of the expected return or price of a financial instrument. With the data mining and optimization techniques investors are able to allocate capital across trading activities to maximize profit or minimize risk. With data mining techniques it is possible to provide extensive scenario analysis capabilities concerning expected asset prices or returns and the risk involved. With this functionality what-if simulations of varying market conditions can be run to assess impact on the value and/or risk associated with portfolio. Profit and loss analyses allow users to access an asset class, region, counterparty, or custom sub-portfolio can be benchmarked against common international benchmarks.

  

Trading

For the last few years a major topic of research has been the building of quantitative trading tools using data mining methods based on past data as input to predict short term movements of important currencies, interest rates, or equities. The goal of this technique is to spot times when markets are cheap or expensive by identifying the factor that are important in determining market returns. The trading system examines the relationship between relevant information and piece of financial assets, and gives you buy or sell recommendations when they suspect an under or overvaluation.

Goran Dragosavac

What to do when the data doesn’t fit the analytical question?

Smart response to this question can be – well, either we get the new data, or new question!

Let’s imagine our task is to find similarity between members of the same group, for example – home loan customers. Now, imagine the situation where we ONLY have a data for the home loans customers.

We can certainly examine all their characteristics, but there is no guarantee that they will be different from purchases of some other banking products. What we need is some point of reference. We need additional data of customers who have any other product other than home loans. So, in order to find out what is something similar about them, we need to figure what is different between them and anyone else – which is pretty much one and a same thing.

This is invariably classification problem which we try to solve by unary target variable (where all purchasers having the same value of the product purchased). So, since we don’t have, or are able to get - additional data for customers that have other types of products – we need to go for second-best scenario. So, instead of “reformulating” data through the artful and creative data preparation to better fit analytical question – we have no other option but to do exactly opposite – reformulating analytical question to fit the data at hand.

This would mean that our new question should be what are the groups of similarity within the single class of loan customers, and how do they differ from other groups of loan customers – as oppose to the original question of what makes my “loan” customers similar? This is now very different question and by reformulating our question we are also picking new “tool” from our workbench, so instead of using some classification algorithm we are reverting to clustering method.

So, the usual premise where data and analytical methods are functions of business question – doesn’t work in this situation, so practical solution is to alter the initial objective.     

Goran Dragosavac

If you are new to Web Mining…

If you selling products and services via web channel you may consider analyzing who is visiting your web site and how do people who buy differ from thos that don’t, and out of those who buy - what is their clickstream sequence and navigational pattern.

Each customer's action on a website generates data, and not just high-level interactions such as buying something but also something as simple as using a search engine or navigating through a site. All these interactions between digital service providers, and the consumer can be recorded, and stored in digital databases. These large data sets contain information helpful to business marketing strategies, both - for retrospective analysis, as well as for data-driven forecasting.

Companies today are in the unprecedented position of being able to collect vast amounts of customer information relatively easily. By using web mining, companies can analyze and predict the behavior of their customers. All web site visitors leave digital trails which web servers automatically store in log files. Web analysis tools analyze, and process these web server logs files to produce meaningful information. Essentially, a complete profile of site traffic is created which shows for example, how many visitors there were to the site, what sites they came from, and which pages on the site are most popular. Web analysis tools provide companies with previously unknown statistics, and useful insights into the behavior of their online customers. While the usage and popularity of such tools may continue to increase, many online retailers are now demanding more useful information about their customers, from the vast amounts of data generated by their web sites.

Organizations have typically invested large amounts of money into developing their web sites and web strategy and they would like to know what return they are receiving on their investment. Most sites use hits and page views as measure of success of the web site, which clearly is not going to answer their questions. A website is commonly used for:

-Selling products/services

-Providing product/company information

-Providing customer support

Typical questions that an e-retailer needs to answer are:

- How to increase browser to buyer conversion rate?

- How to increase web retention rate? (Defined as ratio of number of browsers who return to the web site within certain window of time to the total number of browsers.)

- How to reduce clicks-to-close value? (Smaller number indicates that customers are finding easier what they looking for. To reduce this value personalization of web services is a right approach.

- Does the web site design satisfy the needs of various customer segments?

Using page hits will NOT provide answer for any of these goals. Current traffic analysis tools are geared at providing high-level predefined reports about domain names, IP addresses, browsers, cookies and other machine-to-machine activity. These server activity reports simply do not provide the type of bottom-line analysis that e-tailers, service providers, marketers and advertisers in the business world have come to demand. These software packages (i.e., web analysis tools) originated from the need to report on the activity of the web server and not on the activity of the user.

Web mining may be subdivided into:

- Web-content mining

- Web-structure mining

- Web-usage mining.

- User profile data

Web-content mining is the mining of Internet pages, common in the next generation of XML/RKF-based search engines/Web spiders.

Web-structure mining is the application of data mining to reconstruct the structure of a Web site or sites.

Web-usage mining is mining of log files and associated data from a particular Web site to discover knowledge of browser and buyer behavior on that site. User profile data, such as demographic information about the users of the web-site, registration data and customer profile information can provide valuable information of its customers, and can be platform for segmentation and profiling. Web-usage mining is what is widely understood to be web mining and it is main subject of this introduction.

Goran Dragosavac

Data Mining in Retail Industry

Retail industry collects large amount of data on sales and customer shopping history. The quantity of data collected continues to expand rapidly, especially due to the increasing ease, availability and popularity of the business conducted on web, or e-commerce. Retail industry provides a rich source for data mining. Retail data mining can help identify customer behavior, discover customer shopping patterns and trends, improve the quality of customer service, achieve better customer retention and satisfaction, enhance goods consumption ratios design more effective goods transportation and distribution policies and reduce the cost of business.

Some of the retail applications of data mining are in following areas:

Customer Relationship Management

 Customer Segmentation: Customer segmentation is a vital ingredient in a retail organization's marketing recipe. It can offer insights into how different segments respond to shifts in demographics, fashions and trends. For example it can help classify customers in the following segments:

·          Customers who respond to new promotions

·          Customers who respond to new product launches

·          Customers who respond to discounts

·          Customers who show propensity to purchase specific products

 Campaign/ Promotion Effectiveness Analysis: Once a campaign is launched its effectiveness can be studied across different media and in terms of costs and benefits; this greatly helps in understanding what goes into a successful marketing campaign. Campaign/ promotion effectiveness analysis can answer questions like:

·         Which media channels have been most successful in the past for various campaigns?

·         Which geographic locations responded well to a particular campaign?

·         What were the relative costs and benefits of this campaign?

·         Which customer segments responded to the campaign?

 Customer Lifetime Value (CLV): Not all customers are equally profitable. CLV attempts to calculate some projected relative measure of value by calculating Risk Adjusted Revenue (probability of customer owning categories/products in his portfolio that he currently doesn ‘t have), as well as Risk Adjusted Loss (probability of customer dropping categories/products in his portfolio that he currently owns) and adding to some Net Present Value, and deducting the value of servicing the customer.

Customer Potential: Also, there are those customers who are not very profitable today may have the potential of being profitable in future. Hence it is absolutely essential to identify customers with high potential before deciding what the best way to realize that potential is through the right marketing stimully..

 Customer Loyalty Analysis: It is more economical to retain an existing customer than to acquire a new one. To develop effective customer retention programs it is vital to analyze the reasons for customer attrition. Business Intelligence helps in understanding customer attrition with respect to various factors influencing a customer and at times one can drill down to individual transactions, which might have resulted in the change of loyalty.

 Cross Selling: Retailers use the vast amount of customer information available with them to cross sell other products at the time of purchase. This can be done through product portfolio analysis and then selling the products that are missing from typical portfolios. Also market basket analysis can be another food method for effective cross selling. Look-a-like modeling is yet another strategy where model is produce that produce some quantitative measure of affinity of the customer to a specific product.

 Product Pricing: Pricing is one of the most crucial marketing decisions taken by retailers. Often an increase in price of a product can result in lower sales and customer adoption of replacement products. Using data warehousing and data mining, retailers can develop sophisticated price models for different products, which can establish price - sales relationships for the product and how changes in prices affect the sales of other products.

 Target Marketing/Response Modeling: Retailers can optimize the overall marketing and promotion effort by targeting campaigns to specific customers or groups of customers. Target marketing can be based on a very simple analysis of the buying habits of the customer or the customer group; but increasingly data mining tools are being used to define specific customer segments that are likely to respond to particular types of campaigns.

Supply Chain Management & Procurement

Supply chain management (SCM) promises unprecedented efficiencies in inventory control and procurement to the retailers. With cash registers equipped with bar-code scanners, retailers can now automatically manage the flow of products and transmit stock replenishment orders to the vendors. The data collected for this purpose can provide deep insights into the dynamics of the supply chain. However, most of the commercial SCM applications provide only transaction-based functionality for inventory management and procurement; they lack sophisticated analytical capabilities required to provide an integrated view of the supply chain.

 Vendor Performance Analysis: Performance of each vendor can be analyzed on the basis of a number of factors like cost, delivery time, quality of products delivered, payment lead time, etc. In addition to this, the role of suppliers in specific product outages can be critically analyzed.

 Inventory Control (Inventory levels, safety stock, lot size, and lead time analysis): Both current and historic reports on key inventory indicators like inventory levels, lot size, etc. can be generated from the data warehouse, thereby helping in both operational and strategic decisions relating to the inventory.

 Product Movement and the Supply Chain: Some products move much faster off the shelf than others. On-time replenishment orders are very critical for these products. Analyzing the movement of specific products - using BI tools - can help in predicting when there will be need for re-order.

 Demand Forecasting: Complex demand forecasting models can be created using a number of factors like sales figures, basic economic indicators, environmental conditions, etc. If correctly implemented, a data warehouse can significantly help in improving the retailer’s relations with suppliers and can complement the existing SCM application.

Storefront Operations

The information needs of the store manager are no longer restricted to the day to day operations. Today’s consumer is much more sophisticated and she demands a compelling shopping experience. For this the store manager needs to have an in-depth understanding of her tastes and purchasing behavior. Data warehousing and data mining can help the manager gain this insight. Following are some of the uses of BI in storefront operations:

Store Segmentation: This analysis takes the data that is common for different stores, and finds out which stores are similar in terms of product or customer dimensions. In other words – what stores are similar based on products that are sold quickly or more slowly in comparison to rest of the stores. Next step is to build the profile of the customers that buys from specific store.

 Market Basket Analysis: It is used to study natural affinities between products. One of the classic examples of market basket analysis is the beer-diaper affinity, which states that men who buy diapers are also likely to buy beer. This is an example of 'two-product affinity'. But in real life, market basket analysis can get extremely complex resulting in hitherto unknown affinities between a number of products. This analysis has various uses in the retail organization. One very common use is for in-store product placement. Another popular use is product bundling, i.e.grouping products to be sold in a single package deal. Other uses include design ing the company's e-commerce web site and product catalogs.

 Category Management: It gives the retailer an insight into the right number of SKUs to stock in a particular category. The objective is to achieve maximum profitability from a category; too few SKUs would mean that the customer is not provided withadequate choice, and too many would mean that the SKUs are cannibalizing each other. It goes without saying that effective category management is vital for a retailer's survival in this market.

 Out-Of-Stock Analysis: This analysis probes into the various reasons resulting into an out of stock situation. Typically a number of variables are involved and it can get very complicated. An integral part of the analysis is calculating the lost revenue due to product stock out.

Alternative Sales Channels

 E Business Analysis: The Internet has emerged as a powerful alternative channel for established retailers. Increasing competition from retailers operating purely over the Internet - commonly known as 'e-tailers' - has forced the 'Bricks and Mortar' retailers to quickly adopt this channel. Their success would largely depend on how they use the Net to complement their existing channels. Web logs and Information forms filled over the web are very rich sources of data that can provide insightful information about customer's browsing behavior, purchasing patterns, likes and dislikes, etc. Two main types of analysis done on the web site data are:

·         Web Log Analysis: This involves analyzing the basic traffic information over the e-commerce web site. This analysis is primarily required to optimize the operations over the Internet. It typically includes following analyses:

·         Site Navigation: An analysis of the typical route followed by the user while navigating the web site. It also includes an analysis of the most popular pages in the web site. This can significantly help in site optimization by making it more user- friendly.

·         Referrer Analysis: An analysis of the sites, which are very prolific in diverting traffic to the company’s web site.

·         Error Analysis: An analysis of the errors encountered by the user while navigating the web site. This can help in solving the errors and making the browsing experience more pleasurable. n Keyword Analysis: An analysis of the most popular keywords used by various users in Internet search engines to reach the retailer’s e-commerce web site.

·         Product Recommendation: If someone buys product A which other product he may buy. Usually there are 3 different angles to exploit when setting up recommendation engine: natural product affinities, customers affinities and preferences, peer dynamics and wisdom of the crowds.

 Channel Profitability: Data mining can help analyze channel profitability, and whether it makes sense for the retailer to continue building up expertise in that channel. The decision of continuing with a channel would also include a number of subjective factors like outlook of key enabling technologies for that channel.

 Product – Channel Affinity: Some product categories sell particularly well on certain channels. Data mining can help identify hidden product-channel affinities and help the retailer design better promotion and marketing campaigns.

Finance and Fixed Asset Management

The role of financial reporting has undergone a paradigm shift during the last decade. It is no longer restricted to just financial statements required by the law; increasingly it is being used to help in strategic decision making. Also, many organizations have embraced a free information architecture, whereby financial information is openly available for internal use. Many analytics described till now use financial data. Many companies, across industries,have integrated financial data in their enterprise wide data warehouse or established separate Financial Data Warehouse (FDW). Following are some of the uses of BI in finance:

 Budgetary Analysis: Data warehousing facilitates analysis of budgeted versus actual expenditure for various cost heads like promotion  overruns can be analyzed in more detail. It can also be used to allocate budgets for the coming financial period.

Fixed Asset Return Analysis: This is used to analyze financial viability of the fixed assets owned or leased by the company. It would typically involve measures like profitability per sq. foot of store space, total lease cost vs. profitability, etc.

 Financial Ratio Analysis: Various financial ratios like debt-equity, liquidity ratios, etc. can be analyzed over a period of time. The ability to drill down and join inter-related reports and analyses – provided by all major OLAP tool vendors – can make ratio analysis much more intuitive.

 Profitability Analysis: This includes profitability of individual stores, departments within the store, product categories, brands, and individual SKUs.