COMARCH SMART ANALYTICS WHITE PAPER

Transcription

1 COMARCH SMART ANALYTICS WHITE PAPER

2 Comarch Smart Analytics White Paper Introduction INTRODUCTION 3 FOR WHOM IS COMARCH SMART ANALYTICS? 3 MATERIALS, COMPONENTS, HALF-PRODUCTS 4 Demographic data 4 Psychographic data 5 Transaction data 5 WHERE DO WE GET MATERIALS FROM? 5 WHAT CAN WE GAIN FROM THE MATERIALS? 6 Analytical areas 6 Time-based analysis 10 Multiprogramming 10 Multipartner programs 11 Multi-currency 11 Real value of money 11 Contextual information 11 DATA MINING 12 Segmentation 12 Cluster analysis 12 Marketing scoring 13 Customer Lifetime Value 15 GENERAL ASSUMPTIONS FOR COMARCH SMART ANALYTICS IMPLEMENTATION 16 The aim of implementing Comarch Smart Analytics 16 TECHNICAL ARCHITECTURE 17 RESULT VISUALIZATION OF ANALYTICAL AREAS 18 Analytical form 18 The management cockpit form 18 Report form 18 Roles of system users 18 KEY QUALITIES OF COMARCH SMART ANALYTICS 19 SUMMARY 19 All companies introducing or already using a loyalty program seek to answer a number of questions arising in the management process. Informational requirements follow directly from the very aim of introducing a loyalty program. The following list presents only a narrow section of these requirements at random order, as the relative importance may vary from one company to another. Keeping up with competitors who already have their own loyalty programs Collecting more information about your customers Stimulating customer behavior Maximizing the number of customers Boosting your brand awareness Stimulating customer behavior with simultaneous profitability control of loyalty activities Catalyzing the synergy effect of a blanket offer combining the company s and partners offers Generating company s primary or additional income in case of partnership programs Naturally, the above list is overly simplified and by no means exhaustive. The important thing is to ask and answer is the question: Why do we need a loyalty program?, thus defining the aims, which are to be attained and efficiently measured so that effective decisions could be made depending on the indication of the measurement. Many enterprises are changing their market approach. A product-focused attitude is being replaced by a customer-focused approach. This change of tactics requires employing a new set of tools specialized to solve a new class of problems. Business Intelligence is the company s ability to make quick and apt decisions. A customer-focused Business Intelligence environment contains infrastructure required for making decisions necessary to maximize the greatest good customers. Such an environment combines data, channels and analytical methods with the aim of conducting a thorough customer analysis. The data comprises all the information about the customer that is collected in IT systems. The channels include the traditional, as well as rapidly developing electronic market. The analytical methods encompass customer behavior analysis, forecasting models, time series analysis and other methods. This new environment for informational requirements is the natural habitat of Comarch Smart Analytics. FOR WHOM IS COMARCH SMART ANALYTICS? This product is for everyone who manages a loyalty program. The information obtained thanks to Comarch Smart Analytics is useful to all decision-makers on operational, tactical and strategic levels. Comarch Smart Analytics affords fast implementation by utilizing predefined analytical areas. Much of the information coming from Comarch Smart Analytics may be used in marketing activity management systems or in marketing resource management. 2 3

3 PSYCHOGRAPHIC DATA This category encompasses: opinions, lifestyle, temperament etc. Normally such data is collected through a variety of surveys and market research. This information always expands on the attributes already contained in the customer profile. Although processing this kind of information presents a number of difficulties, this effort is well worth making, as few companies claim to use automated survey analysis and this may in fact constitute the main advantage over competitors who also use loyalty programs. The kind of information collected will depend on the industry and line of business. A company preoccupied with the customer s personality and individual preferences will pay more heed to psychographic rather than demographic data, whereas a company selling FMCG products will be more interested in data concerning time-related customer behavior. TRANSACTION DATA Transaction data is in fact the carrier of information about customer behavior. Each completed transaction may be treated as an indication of the program member s way of life. Transactions can tell us what our customer is really like as opposed to data collected through surveys. Materials, components, half-products This type of information is most frequently the subject of prediction. Depending on the line of business the information will include: date and place of a transaction, transaction content, form and payment deadline. A subgroup of this data is the information coming from an on-line shop or loyalty system portal indicating the customer s activity (when they used the system and what functionalities were used, what they viewed, which route they took to find information about the given product, and if they expressed interest in making a purchase). When use of an on-line shop or loyalty portal requires logging in, we obtain detailed information about a customer s actions. Acquiring this type of data demands employing the greatest IT resources. The difficulty lies in processing a large amount of data in an acceptable amount of time, and then presenting it in a clear and legible manner. Equally important is optimization of time elapsed from the moment the data is demanded by the client until it is presented. Where do we get materials from? When we consider loyalty systems regarding the information it gathers, we will find it to be the best source in terms of the quantity and quality of customer data. A lot of information is outright inaccessible through traditional CRM or ERP systems. DEMOGRAPHIC DATA This describes our customer and/or their family. This category includes data such as age, marital status, income, number of children, place of residence, acquired profession and occupation, position in the company etc. Demographic data has a lot of strong points: it is very stable and the rare changes it undergoes strongly affect customer behavior; it allows us to define customer characteristics used for predicting their further behavior. Analyzed data may come from many sources, the main one being a loyalty system, which contains demographic and customer behavior data. The former performs a technical function in the system in the case of direct communication with the program member essential for managing the program (points, account statements, reward order etc.) Also the majority of loyalty systems possess information about transactions made by their members. The scope of this data depends on the client s specific requirements during system implementation. Notwithstanding these differences, the information we always get is the time and value of purchase. Collecting this data is very simple it is contained in the customer profile that is created when filling in an application form for the program. However, a weakness of this category is connected with the fact that many people want to avoid giving away personal information to businesses and if forced to do so, provide false information so that only the mandatory fields are filled. To obtain information regarding the entire customer population (a loyalty program only includes a section) we require data from the system recording all transactions. This system provides information about time, content and value of purchase but is usually unable to identify the customer (unlike a loyalty program, where customer identification is essential). Despite this drawback, this information gives 4 5

4 a more comprehensive view of the behavior of customers as a group and allows us to compare members of the loyalty program with the rest of the customers. This in turn gives us the indication of the extent to which members of the loyalty programs can be treated as a representative sample of the population. In the age of increased revenue and cost control (due to economic crisis) information from accounting and shopping systems is required. What is it for? Without it we are unable to determine the cost of products/services, direct and indirect sales costs or of maintaining a management program. This means we cannot find out whether there is real profit from a primary activity or from a marketing action, which costs real money and should therefore be compensated by a profit margin. Additional information may come from many other systems, such as: On-line shop hit counters Booking systems (cinemas, theaters, concert and exhibition halls) Direct Internet sales systems (on-line shops, auction systems) External customer classification systems External customer database in the form of an order database, profiles in on-line shops Exchange rate services Weather services MEMBERS OF THE PROGRAM Demographic analysis: Place of residence Age, gender, number of children Analysis of member acquisition for the loyalty program change in the number of members, member acquisition dynamics, index of program sign-outs, customer activity index Place of enrollment Time of enrollment Classification of a member as inactive, indicating the period of inactivity understood as: No purchases of products or services No activities unrelated to purchases (logging into websites, phoning a Contact Center) Analysis of marketing costs per member cost data may come from the Comarch Campaign Management or external financial and accounting systems. In the latter case, a model of cost allocation per individual member of the program must be developed. What can we gain from the materials? SEGMENTATION ANALYTICAL AREAS Segmentation allows us to divide clients into groups regarding their behavior. A program member is allocated to a segment, which may be used as a target group for promotion as well as for specialized services. The aim of dedicated analytical areas is to gather in one place data concerning a specific area. Source information may be collected from one or many sources. In the dedicated analytical areas the data is stored in an orderly manner typical for data warehouses, combining the logic of information by means of measures and dimensions. Measure is created by the quantitative values which are analyzed. Examples of these are costs, income, profit margin, deviation, circulation, results etc. Measures are located in the facts table. A measure is always a number. In reports it is usually located at the section of analyzed dimensions. Analytical dimension is a phenomenon describing measure attributes. It can be associated with axes in a coordinate system. Examples of dimensions for analyzing sales could be: geographical region, customer service representative or the product sold. A primary and commonly identified dimension in an economic event is time. Example Assumptions for the senior management segment: top-level managers frequently flying first class and traveling on various routes (not particularly price-sensitive) accrue a large number of points over a short period of time; rarely take advantage of official discounts; statussensitive. Such a group may be the target for special rewards offered by the program at a very high price in points yet also very exclusive, as they are not offered to all customers. Advanced segmentation may be built based on: Purchase value for individual product groups Participation in marketing campaigns The number of campaigns in which a client could participate Preferred purchase location Loyalty program partners Communication channels Shopping frequency Activity frequency (logging, transactions) 6 7

5 TRANSACTIONS Contact center transactions for many it may seem as just another channel of communication with the client. Due to the complexity of operation of such a system (large number of employees, number of phone calls), the efficiency of this area of activity is the key factor There are many types of transactions in loyalty systems and each type affords a different analysis. Transaction attributes, if present, for many companies. may carry different importance. Other transactions an example of such a transaction is enrollment to the program or completing a questionnaire Purchase transactions all the transactions related to a customer who buys products or services. Such a transaction may be connected with benefits awarded to a program member. Whether the benefit is points, miles, discount or all at the same time depends on loyalty LOYALTY TRANSACTIONS program configuration. Information about customer behavior is found here. The analysis of loyalty transactions in terms of geography of points of sales, time of transaction, time of transaction processing (when Redemption transactions all transactions related to spending benefits. A member program exchanges benefits for prizes, gift cards, and the CLM system works off-line), customers geography, types of transactions, analyzing the number of transactions, value of transactions, discount coupons. All such events are connected to loyalty transactions. Here is where we can find information regarding what a member number of loyalty points awarded/redeemed, number of customers. needs or what they need but would never buy, e.g. a night at a hotel, a book, music It is always a certain substitute for a dream come true. LOYALTY PROMOTIONS Loyalty transactions all transactions connected to the benefits accrued by loyalty program members. Some of these transactions may be linked to sale and redemption transactions, other loyalty transactions may be an outcome of manual operations or bonuses for Sales analysis. Before, during and after the promotion, with relation to promoted products or services on a time and territorial basis filling in a questionnaire. Information about expired points is also contained here, as well as the most important information for the loyalty Effectiveness of promotion. Analysis of customer behavior before, during and after the promotion program operator (connected with sales and redemption) it makes it possible to determine, whether the program is profitable per se or Effectiveness of the promotion in individual segments and regions establishes the level of obligations towards program members (as all obligations will need to be paid sometime). 8 9

6 MARKETING COMMUNICATION MULTIPARTNER PROGRAMS Analysis of communication connected with a given marketing action Comarch Smart Analytics contains an analytical area responsible for the analysis of cooperation with the partners of the loyalty program. Comparison of promotion effectiveness between a target group and control group The great majority of analyses may be conducted in the context of transactions connected with a certain partner. Comparing the results Analysis of marketing action effectiveness in relation to communication channel (SMS, , www, press), points of sales geography, of partner cooperation allows us to establish which of the partners produce the most profits and which one generate losses. promoted products Questionnaire analysis a questionnaire is one of the sources of information the customer wishes to provide. Why wishes to provide rather than provides? Because what the customer says may not always be true what we say is closer to what we would like MULTI-CURRENCY to be rather than what we really are. Today s questionnaires are highly complex creations: they include questions relating to many aspects and ones which verify the truthfulness, or rather consequences of the answers. Questions concerning different aspects Comarch Smart Analytics supports multi-currency transactions, which is an essential requirement in loyalty programs in which products (quality, price, availability, satisfaction etc.) are scattered throughout the form. With the large number of responses, there is a need are sold in different currency areas. Value in money is presented in a chosen currency, calculated according to the current exchange rate for a global analysis of the data collected and for calculating indices referring to particular areas. A Net Promoter Score (NPS) is also on the day of the transaction. calculated for the questionnaire. REWARD MANAGEMENT REAL VALUE OF MONEY Analysis of prize inventory In these changing economic conditions sometimes there is a need to conduct analyses based on the real value of money. In case of Analysis of reward orders economies where inflation is a significant factor, we receive information about the real values of business growth. SERVICES AND PRODUCT SALES CONTEXTUAL INFORMATION Analysis of sales in terms of the geography of sales locations Analysis of the correlation between products purchased. Which products go into the same basket? Which products are purchased A great number of elements included in the analytical areas require a detailed description. Comarch Smart Analytics provides contextual within a specified period of time after buying a given product? information for key elements. It allows the user to quickly identify the element: the description, calculation assumptions, calculation Analysis of current customer purchase value. The change of purchase value over time. Current frequency of purchases and its change method, suggested interpretation of values. The information is presented in web form. over time Analysis of sales from the point of view of a member according to the appraisal in points based on RFM analysis GENERAL PROGRAM ANALYSIS Analysis of loyalty obligations including age and accumulation of loyalty points Analysis determining probable level of future obligations based on the predicted earning and redemption of loyalty points TIME-BASED ANALYSIS All analyses containing the context of time facilitate automated comparison of current valuable and quantitative data with: Previous period (value, change, dynamics of change) Corresponding period of previous year (value, change, dynamics of change) Accumulated value since the beginning of the year Accumulated value since implementing the loyalty program Average value of the last 3/6/9/12 periods MULTIPROGRAMMING Comarch Smart Analytics supports many instances of loyalty programs. It allows us to conduct analyses of certain programs in isolation from other programs. It makes it possible to compare one program with another or against all other programs. This functionality is of great importance for loyalty program operators, for whom managing several loyalty programs is a primary part of their activity

7 Data mining MARKETING SCORING Scoring models are used in all contexts where a simple customer description is needed. Usually it is limited to a binary value, for instance profitable customer/ unprofitable customer, loyal customer/migrating customer. An elaborate version attributes a numerical value that facilitates a more flexible interpretation, for instance we grade this customer with 68 point out of 100. Data mining is a buzzword, the Holy Grail for analysts, the last resort when all other analyses have failed. It is not entirely unlike the legendary creature, the Yeti. People claim to use this technology, however without presenting the results. This method is probably particularly evocative of strip mining. At first we can only see the landscape. But when we strip the outer layer, we may find real gold. Models usually grade: Customers inclination to resign from buying services (churn) Customers inclination to buy certain products Customers general performance based on RFM analysis simplified to a numerical index BENEFITS OF USING SCORING MODELS The same is true of customer data, we can see what they purchased and when, in which shop, on what day and where they live. But it is only a part of their characteristics that we can see at first glance. Only the discovery of deeper structure gives us a full overview of our customer s world. The authors of Comarch Smart Analytics are aware of these needs from the very start of the creative process. At first one had to describe the world, as one sees it, observe certain phenomena, and only then could one begin to analyze them. Cost reduction through limiting communication to the customer group potentially interested in the offer Increasing customer satisfaction, as customers appreciate individual treatment and being approached with offers that might really interest them Rapid customer classification, used in call center applications BASKET ANALYSIS AND PURCHASE SEQUENCE ANALYSIS The part preceding this chapter aims at describing the phenomena one can observe. In this chapter we focus on what can be discovered beneath. SEGMENTATION Segmentation in the case of CSA means identification of customer groups with common characteristics. In particular, a group of customers with similar reactions to the marketing instruments applied to them. Dividing customers into segments allows us to optimize actions through applying an individual approach. In CSA the segments are created in the following ways: During data analysis a segment is created, which is seen as a list of customers with common attributes and a common transaction history Segments are the result of applying clustering algorithms CLUSTER ANALYSIS This analysis aims at identifying among other items those that are similar, and grouping them together. As a result of this analysis from one non-homogenous set of data we arrive at a group of several homogenous subsets. Segments in CSA (regardless of their source of origin) may be applied in the following ways: Sending a segment to the loyalty system. For such segment a special promotion may be defined or marketing information adjusted Adding a segment to an analytical system in order to conduct further data analysis Here one can find information on which products are usually bought together or in certain time intervals and in which order. These analyses allow us to identify the purchase patterns applied by customers. Basket analysis customers buying product A usually also buy product B and product C Purchase sequence analysis customers buying product A are most likely to buy product B in their next purchase The Basket Analysis module enables us to: Discover association rules (establish rules related to certain products being bought together), which allows us to construct a functionality the person who bought this product, also bought Carry out a sequence analysis (research into the order in which events follow one another) CUSTOMER MIGRATION ANALYSIS (CHURN ANALYSIS) This sort of analysis is applied in many branches. Churning means a customer ceasing to make any purchases due to their move to, for instance, a competing operator or competing airlines. In practice, this may mean losing customers to competitors. In retail the situation is highly complicated. One can observe a phenomenon of partial churners a person is still a customer of shop A, but a portion of their purchases are made in shop B. There is a danger of this partial disloyalty turning into complete emigration in the long run. In constructing a model analyzing the churn phenomenon, the following are used: Purchase frequency the value of this index exceeding the mathematic average (or median) for all cases gives an overview of customers loyalty Time elapsed between purchases (interpurchase rate, IPT) informing of purchase regularity in a given shop 12 13

8 It must be stressed that neither criteria indicates the basket value but only a potential value of this customer group and a possibility of losing this group. available to predict customer behavior and predicted behavior based on previous behavior is a much more accurate and effective method than predicting behavior based on known factors. To build models in this branch the following independent variables are used: Time interval between purchases (number of days since the last purchase, average number of days in the studied period, standard deviation from this average and the quotient of the standard deviation and the average) Purchase frequency (number of visits within the studied period, quotient of the number of visits and the length of the period of cooperation) Basket value (total sum spent within the studied period, quotient of this sum and the length of the period of cooperation, the number of visits resulting in exceeding the average spending sum) Category of purchased products (binary variables indicating the purchase of products from certain departments: fruit, fish, meat, bread, liquor, beverage, detergent etc.) Brand of purchased products (own brand, domestic brand) Period of cooperation (number of days since the first purchase) Time of purchase (time of day, time of day of the last visit) Form of payment (sums paid in cash, by credit cards, debit cards, checks, Sodexho vouchers, loyalty vouchers) Information about promotion instruments (the number of vouchers used from a given shop, number of visits since the day when the first voucher was used, number of points accumulated in the loyalty program etc.) Demographic variables (size of household, language spoken at home, zip code, pet ownership, information about the lack of demographic data) A model constructed in such a manner provides a comprehensive analysis of the churners. The system of snapshots of the model state in time allows us to indicate the characteristics of people, who may become churners in the future. RFM ANALYSIS RFM analysis is a fundamental marketing analysis based on customer behavior theory. Its main assumptions are as follows: Customers who have made a recent purchase are more likely to buy again than those who have not made a purchase for a long time Frequent buyers are more likely to make a purchase than those who buy rarely Customers who have spent large sums of money are more likely to spend more than those who have so far spent less. For CSA, RFM analysis is the base for CLV modeling defined as the current value of future cash flow connected with a customer. CUSTOMER LIFETIME VALUE Developing prediction models connected with customer life cycle is a typical area for using data mining in managing customer relations. Models based on regression may be used, however these models are meant for predicting behavior in the next period in relation to the present. The main assumption of the model employed in CSA is: the sum spent in a transaction is independent of the transaction process. This means that the buyer behavior model may be divided into submodels for: transaction flow and income from the transaction. TRANSACTION FLOW The model for transaction flow is based on the following assumptions: Customer-company relations are divided into two stages. The customer is live for an unnoticed period of time and then becomes permanently inactive. But the time of inactivity may not be short-term or medium-term. In some cases it may not happen at all During the active stage the customer buys randomly around the median value of their transactions The value of transactions and inter-transaction periods may vary from one customer to another INCOME FROM TRANSACTIONS The model for transaction income is based on the following assumption: Transaction value of a given customer changes randomly within their average transaction value Average transaction values differ depending on customers but do not change in time for one individual Knowledge of the entire customer transaction history is not required. RFM indices, albeit collective, are sufficient to determine customer behavior. This means it is possible to draw conclusions concerning individual hidden predispositions (i.e. frequency, last purchase, monetary value) In other words good customers are likely to remain as such. The reason for the popularity of RFM analysis is the fact that it is a highly cost-effective, simple and useful method of customer classification according to their behavior (as opposed to demographic-social-economic data). It is often a very accurate indicator of the number of customers who will respond to special promotions or offers based on the reactions of a small test group. By combining the above models one can determine the number of discounted expected transactions (DET). DET may be scaled by net money flow the factor for the overall calculation of expected CLV: E (CLV) = (net cash flow / transactions) x DET. The result is the precise CLV of the customer. The use of iso-curves for visualization of the results of the model operation allows us to understand customer behavior and not only determine future values, as is the case with regression-based models. The other reason for RFM s advantage over methods based on demographic-social data analysis is the fact that all RFM components are behavioral: when they made the last purchase, how often they buy and how much money they spend. We use factors that are readily 14 15

9 General assumptions for Comarch Smart Analytics implementation Technical architecture THE AIM OF IMPLEMENTING COMARCH SMART ANALYTICS The aim of implementing Comarch Smart Analytics is providing aggregate, pure, multi-section data, facilitating decision making in managing loyalty programs. Comarch Smart Analytics is a specialized analytical system containing predefined multidimensional analytical areas employed in managing loyalty programs. The most important part of implementation is adjusting all analytical areas to individual business needs. Special attention is paid to: Analytical rationality; meaning the mutual interrelations of data in an analytical area Availability of elementary data. Gathering of requirements concerning data coming from source systems, in particular data outside the loyalty system The main goals for a Comarch Smart Analytics implementation may be categorized into the following areas: Integration of analytical information available in source systems, in particular in the Comarch Loyalty Management system Parameterization of a relational and multidimensional analytical system, which processes and provides analytical data Providing end users with the results of operations of analytical areas. The process of implementation encompasses the following elements: Service work, including the following stages: Analysis of business requirements through presenting the system s functionalities and describing the any required changes. The end product of this stage is a document describing the required parameterization of the system. Developing a technical project containing: range of data, data processing scenarios. Parameterization of the system and implementation of additional functionalities. Providing the necessary hardware infrastructure Providing the necessary licenses Providing the necessary third party licenses Installation, configuration and operation of the system The structure of Comarch Smart Analytics consists of the following logical architectural elements: Source systems The area of extract, transform and load procedures (ETL) Topical analytical areas consisting of a relational and multidimensional layer Result visualization system Sources of data Comarch Campaign Management Marketing Resource Management Comarch Loyalty Management 3rd party sources ETL procedures ETL procedures COMARCH SMART ANALYTICS Relation storage Reports & Dashboards Server Multidimensional storage Reporting Solution Report users Report engineers Ad hoc reporting Presentation over WWW Comarch presents the results of the operation of analytical areas via external software. We recommend using the Microsoft presentation environment, which is mature, efficient and popular among our clients. The results of Comarch Smart Analytics operations may be presented in data visualization systems produced by other companies. Supporting the XML/A protocol or a native possibility to use Analysis Services, a part of Microsoft SQL Server 2008 R2, is a necessary condition. Many companies already have their own report systems, which can use Comarch Smart Analytics. In this case there is no cost connected with buying new software and training staff. Enterprises using SAP Business Objects, IBM Cognos, Targit, Tagetik may use Comarch Smart Analytics directly through the report environment they have been using so far

10 Result visualization of analytical areas Key qualities of Comarch Smart Analytics ANALYTICAL FORM Business analyses providing a general overview of the loyalty program. Possibility of creating your own analyses with the use of collected business data. In this form, full reporting functionality is readily available through a user-friendly graphical interface. In Comarch, this form is achieved by the ProClarity module. ProcClarity is an element of PerformacePoint Server THE MANAGEMENT COCKPIT FORM A set of management cockpits with selected efficiency indicators. The indicators allow the user to asses the current state of loyalty program operation. Each of the cockpits is interactive i.e. it presents data in accordance with the previously established set of selected parameters. This form gives a more general view on business reality but is not as flexible in creating responses to business questions as the analytical form. Comarch realizes this form through the monitoring module included in PerformancePoint Server REPORT FORM Combining demographic, psychographic and transaction data Elaborate analyses regarding: loyalty program members, loyalty promotions, marketing communication effectiveness, reward management, day-to-day maintenance of a loyalty program Data mining module containing: basket analysis and sequence analysis, RFM analysis, CLV analysis Operation of loyalty systems which include: multiprogram, multipartner, multicurrency, offering many kinds of benefits. Expanding analyses with information from other systems Each analytical area has a number of selected key efficiency indices Advanced data aggregation on a time and space basis Presenting monetary value in actual money Contextual information Summary A set of predefined reports generated according to an established schedule and a decision-maker receives these ready-made reports. While the reports are being generated, a cross-section of data is set e.g. data concerning new program members in the current month compared to the previous month. Comarch realizes this form through reporting services available through MS SQL Server 2008 R2. ROLES OF SYSTEM USERS The system for analytical area visualization allows the end user (analyst, report recipient) to communicate with Comarch Smart Analytics to achieve development and publication of the necessary reports, accounts and analyses. In our experience the following are the most common user categories: Business analyst highly empowered system user Has access to all or most analytical areas. Views data in search of emerging business solutions. Constructs reports for the less empowered groups of recipients. Manager moderately empowered system user Has access to all analytical areas. Data presented only applies to the area of business they are responsible for. Benefits from the effects of an analyst s work through passive analysis of presented data or by changing the scope of analysis through penetrating, sorting, grouping or ranking. Such a user constructs analyses much less frequently than a business analyst. Passive recipient of information, reports and accounts (lower level manager) Launches selected reports with current data; access to data restricted by the person compiling the report. Comarch Smart Analytics is a perfect complement to the Enterprise Marketing Management or Comarch Airline Suite application set. Thanks to the possibility of obtaining data from many sources, it is not limited to information derived solely from the loyalty program but is expanded with information coming from other systems. The use of preconfigured analytical areas shortens the implementation time and reduces costs. The set of dimensions, measures and indices is a result of knowledge that has been acquired during many implementations. Such background knowledge may be a strong complement to the current competence of marketing managers

11 Comarch headquarters Al. Jana Pawla II 39 a Krakow Poland phone: fax: info@comarch.pl Comarch Inc. 10 W 35th Street Chicago, IL United States phone: fax: info@comarch.com Comarch OOO Bakhrushina Street 32, bldg Moscow Russian Federation phone: Comarch AG Chemnitzer Str Dresden Germany phone: fax: info@comarch.de Comarch Software Sarl 19 Avenue LeCorbusier Lille France phone.: fax: lille@comarch.com Poland Krakow, Gdansk, Katowice, Lublin, Lodz, Poznan, Warsaw, Wroclaw Austria Vienna Belgium Brussels China Shanghai Finland Helsinki France Lille Germany Dresden, Frankfurt/Main Lithuania Vilnius Panama Panama City Russia Moscow Slovakia Bratislava UAE Dubai Ukraine Kiev USA Chicago Vietnam Ho Chi Minh City Comarch is a leading Central European IT business solutions provider specializing in forging business relationships that maximize customer profitability while optimizing business and operational processes. Comarch s primary advantage lies in the vast domain of knowledge accumulated in and applied to our software products. These products incorporate highly sophisticated IT solutions for businesses in all vertical sectors. Comarch has a multinational network of offices employing over 3500 highly-experienced IT specialists in Europe, the Middle East and the Americas. Comarch Spółka Akcyjna with its registered seat in Krakow at Aleja Jana Pawła II 39A, entered in the National Court Register kept by the District Court for Kraków-Śródmieście in Krakow, the 11th Commercial Division of the National Court Register under no. KRS The share capital amounts to 7,960, zł. The share capital was fully paid. NIP Copyright Comarch All Rights Reserved. No part of this document may be reproduced in any form without the prior written consent of Comarch. Comarch reserves the right to revise this document and to make changes in the content from time to time without notice. Comarch may make improvements and/or changes to the product(s) and/or programs described in this document any time. The trademarks and service marks of Comarch are the exclusive property of Comarch, and may not be used without permission. All other marks are the property of their respective owners. PL-2011