Abstract. Translating customer needs into faisable objects: marketing DB

Transcription

1 Insurance Application: marketing DB, forecasting model and cluster design. An integrated strategy used at the marketing department of Reale Mutua Assicurazioni in Turin, a major insurance company in italy. Abstract The application concerns: budget definition of insurance agencies and agencies performancebased segmentation. The paper will discuss the facility of building a marketing DB directly linked to the core of business mission. Some advantages will be illustrated in terms of parsimony and robustness of the forecasting model. A dynamic and self correcting estimation process will be presented. The same DB will be then accessed to establish a well defined clusters among the agencies in terms of local commercial opportunities. The absence of data redundancy will be outlined. The problem of upgrading such data and procedures will be focused. Of particular interest is the use of Enterprise Miner in the estimation process. As a final step a philosophical point of view will be offered to the discussion: data warehouse and data mining, empty words or a new industrial approach? Translating customer needs into faisable objects: marketing DB The starting point of each DB project is the exact identification of requested information. In other terms the Data Base scope has to be well established in advance, in such a way that the applied procedures will run in a natural way. That is without efforts or ad hoc adaptation. When we are faced to customer we have to do with a list of things listed in the natural language. In the case of study the list is: market, income, saving, financial, customer identification, revenue, turnover, earning, possible clients, clients over the time, clients prospect, location, economic life, territorial aspects, etc. The first impression is that all the listed words have only a casual order, although they have certainly something in common. Furthermore the implied information are on different analysis levels: individual, agency, local administrative unit level. Also different are the information sources (some are public, some private, other stored by the customer) and different are the collection methods and supports. This dispersion make us many difficulties and sometime is impossible to get important results. Often this appends because the conceptual framework is very poor. For this reason the data appear to be collected at random, without robust links among them. The solution we propose to order all the information is to move from ahead to the data. This solution is not original. In facts since origins IT workers are used to think first of all to the output and only in a second time to the input. For example one may think to a simple assignment instruction: often the output field comes first(on the left of the equal sign) and then the input field (on the right of the equal sign). Let me show you a prehistoric program code as an example (Cfr. B.W.Kernighan, D.M. Ritchie, The C programming language, p. 101): strcpy (s, t) /* copy t to s; pointer version 3 */ char *s, *t; { while (*s++ = *t++) ; } Also think ahead is our keyword and in this context its meaning is: first of all think to the project goal, and if the goal does not exist try to find out a plausible one. Then start to collect the necessary and only the necessary information, to get this goal. In our case we stated that the goal of our system is the core mission of an insurance company: earning money by selling insurance products. So all the information that help us to predict customer behaviour or agencies earnings is useful and must be taken in consideration. All other information, not related or 1

2 weakly related to our goal, does not enter in the marketing DB. The figure in TAB. 1 shows an example of variables pertinent to DB. TAB. 1 An example of DB variables at the territorial level 2

3 Outline of the entire project The entire project consists of three main modules and three support tools plus a simple DBMS, used for maintenance purposes. Forecasting model, client behaviour segmentation, agencies cluster are the main modules while the other modules form the system user interface: a browser, a table delivering system and an intranet based viewer. The DBMS has many things in common with a DATA WHAREHOUSE, although at the moment is written using traditional SAS tools, SAS/AF by the way. The other part of the system are written using SAS/STAT, SAS/INSIGHT and SAS/GRAPH (Forecasting Model, Agencies Cluster and Client Segmentation), and SAS/IntrNet (System Viewer). TAB. 2 The Insurance Application Forecasting Model Browser Intranet Viewer Agencies Cluster Delivering System Client Segmentation The core of the system is the DBMS serving all project modules. If we take the earnings as the model dependent variable and the company core mission as the object of our project, we may think the DBMS as an entity at which the other modules look from their specific point of view. 3

4 The budget forecasting model The aim of the model is to determine the budget that has to be assigned for the year t+1 to the agencies, taken as the company operating units. The model works at the agency level (the agencies are the observations) and has three order of independent variables: dimension of Agency at time t, the trend of earnings in the last k years, the local context in which they operate. Technically speaking a set of regression models has been carried out in order to estimate the parameters for the forecasting model. It has been tested over the past and the simulation results have been confronted with the earnings of the proper period. In 1998 the model performed well: we got an error of 1 0 / 000 over the entire amount of earnings, with slight variations among the different insurance product branches. TAB. 3 Estimated and real earnings of the whole insurance company per product branch rca rcg ard infortuni furto incendio malattie vita total 0 4

5 The agencies clusters Starting from three variables types we established three different clusters of Agencies. The first cluster refers to territorial characteristics, the second to agency portfolio, while the third refers to the organisation. The first ranges from desert to luxuriant, the second from tradition to solid, the latest from well to few organised. We used FASTCLUS PRUCEDURE to accomplish this work. It s interesting to note that the clusters originated a series of dummy variables that are now tested in the model for prediction optimisation. In the following graph we show the results obtained in the latest cluster, the organisational one. We note that the dimension found is not a continuum but really multidimensional, just as the technique suggests. The dimensions are related to elasticity, personal stress and a mixture of different styles in organisation. The graph shows the different clusters with different colours using 3 of 4 dimensions used in analysis. TAB. 4 Graphic representation of organisational agencies cluster 5

6 The customer segmentation We worked with a random sample of clients of Reale Mutua Assicurazioni, with n=6000 as a train sample. The next two tables show the logistic model of prediction (TAB. 5) and a graphic tool illustrating the results of simulation (TAB. 6). The model has the dependent dummy variable The client has an insurance not related to his car (fire insurance, life assurance, and so on) and the probability to have this insurance type is modelled. The source of information are the electronic archives of the company, hosted in the system DBMS. TAB. 5 - Logit model 6

7 TAB. 6 Device showing the effects of the logit model 7

8 The browser It is the way we adopted to show our DBMS. It is build with a multilevel data mart storing the information at the agency level with all related information stored at that level (territorial and economic measures). The information is accessed via AF menu, as usual in many SAS applications. Many sources contribute to DB; a mixture of internal and external: financial, demographic, accounting data are put together in a big melting pot, in which the user is driven by menus. The DB is dynamic not only because the data are updated, it would be obvious. The system supports the automatic upgrade of menus too. The agent may confront his situation with surrounding region and with the entire company for the most important variables used by the analysis tools of the system. TAB. 7 The browser: a comparison table example 8

9 The Intranet based Viewer The results of the model may be corrected, and often they are, by the insurance agents. They control the provided estimation and then they confirm or not the data. We provided an easy way to both, to send the relevant information to the agents from the headquarter and to provide the obtained responses to headquarter. It s possible to run the model in real time to get new estimations and the process may continue until the results are judged to be acceptable. Numbers in tables are invented for privacy. TAB. 8 The multilevel accessing menu 9

10 TAB. 9 The results: from headquarter to the agent 10

11 TAB. 10 The corrections: from the agent to headquarter 11

12 The application tools: traditional approach versus Enterprise Miner The entire process of the cluster and forecast models is entirely contained in the following single screen of Entreprise Miner. The flow chart represents two possible paths in cluster and logistic regression analysis. In the second case the user defined logistic model described above is compared with the one produced by using EM. Simplicity reach here his maximum, we think. The next screen (TAB.12) give an example of cluster graphic analysis. The graphic illustrates how the clusters are related to one dimension used in the analysis process (the traditional composition of client portfolio). The last screen presents the results of a lift chart applied to the logistic model of the client segmentation model. It shows the gain obtained by using the model to target the clients, instead of working with random sample. TAB. 11 The Enterprise Miner power: the semplicity 12

13 Tab. 12 The Enterprise Miner power: the analysis tools, CLUSTERING. 13

14 Tab. 13 The Enterprise Miner power: the analysis tools, MODELING 14

15 It would be interesting to use Enterprise Miner flow chart schema for all the analysis processes illustrated in this paper. It s what we want to show you now, during the remaining time of this presentation 15

16 Data Warehouse and Data Mining, empty words or a new industrial approach? Sometimes it appends that changing the dimensions of work, changes the way we work. When procedures becomes more and more complicated, absorbing many more resources than before, we are faced to this problem: how we can continue to work maintaining the quality of our products? Historically this question had different answers depending on the type of work. If for example we take in account the automotive industry we discover soon that, after the early times where craftsmen made the cars, the taylorism, with his labour division in chain was the preferred solution. This solution had many advantages over the past: the cars became less expensive and the quantity of production increased. In more recent times the extensive use of robotics permitted to overcame the chain by more efficient solutions. In these example one thing must be pointed out: the ability to build efficient tools for production is sometimes most important than the knowledge of the specific product. To build a Stradivari Violin is not enough to play good music, but the music sounds better if a Stradivari Violin is used. We must think to EM as a tool to do statistic in an industrial environment, as a robot of statistical analysis. We have a lot of data to be analysed, often we have the right tools (DATA WHAREHOUSE) to get the right ones too, and so the most important question now becomes: how can we do the right statistics in the right time? EM, with his powerful capabilities is one possible answer to this question. In this way we may continue to preserve quality in the quantity world. Last but not least do not forget that a Stradivari do not play well if there are not good musicians. Acknowledgements I am grateful to many persons in Reale Mutua Assicurazioni. Particularly I would like to thank Dr. Oreste Porreca, the marketing director, and Dr. Daniela Bianco, that worked with me for this project. 16