Multidimensional Data Model for Marketing Information System

Zlatinka Svetoslavova Kovacheva Centre for Information Technologies in Communications of

Bulgarian Telecommunications Company (BTC)

A B S T R A C T

The present talk deals with the basic moments in the process of design and development of the Marketing Information System (MkIS) of BTC. The MkIS analyses the evolution of marketing indicators such as capacity, usage, revenue of the services, etc. on the base of monthly information from BTC regions.

The Multidimensional data model design is considered. The program environment for developing the model is based on the Data Warehouse technology and includes OLAP (On-Line Analytical Processing) tools for structuring and analysis of the data into multidimensional arrays. This model provides representation of the information in a lot of interconnected tables and graphs, which can be viewed in different aspects according to the defined dimensions and their hierarchical levels. Data can be easily aggregated, disaggregated and rotated according to the requirements of the experts and managers. A generation of ad-hoc reports is available. It provides the users a fast direct access to that part of the comprehensive data structure, which is useful for their concrete purposes.

One of the most interesting features of the multidimensional data model is what-if-analysis. It provides managers creating hypothetical situations by changing the values of variables in the multidimensional data model. These changes are temporary and concern all formulas including corresponding variables. This is the way to observe the influence of changing some parameters to other ones. It is particularly important for the marketing decision making.

Another advantage of the model is forecasting facility. The following basic forecasting methods are available: linear trend, exponential trend, single, double and triple exponential smoothing, percentage change, moving average, Holt-Winters.

The Multidimensional data model provides a powerful tool for the decision makers in the field of marketing and other activities concerning the firm management.

MARKETING INFORMATION SYSTEM

The key areas of competitiveness in today’s market place are: Market awareness; Speed of response; Adaptability; Innovation; Efficiency.

Marketing information system (MkIS) is an ongoing, organized set of procedures and methods for creation, storage, retrieval, dissemination and analysis of information for marketing decision support.

DATA WAREHOUSE (DWH) The data warehouse (DWH) is a process supported by products,

services and partners, that collects, integrates, stores and delivers data to the organization (From a report produced by IDC: A Study of the Financial Impact of Data Warehouses (1996)).

DWH is an enterprise structured repository of subject oriented, integrated,

non volatile, time variant data. Types of Warehouse Data: Fact data – Measures of the business (detail data); Dimension data – Query drivers (an attribute by wich data may be analyzed); Reference data – Text look up (contains relatively small volume of data); Summary data – Precalculated data; Metadata – Warehouse “map”.

DWH vs OLTP:

Property Operational DWHUser activities Operations Analysis, forecasting,

etc.Response Time Sub. sec. to seconds Sec. to hoursAccess Read and write Primarily read only

Nature of data(time period)

Current data (30-60days)

Historical data(snapshots over time)

Data sources Internal Internal and external

Database Size Small to large (<100GB)

Large to very large (50GB to 2 TB)

Types of DecisionMaking

Productionmanagement

Strategic decisions

DWH vs. DATA MARTS

DWH

DM1

Legacy data

Finance

Marketing

Operational data

External data Personnelsources

DM2

DMk

DMn

…..

DWH scope – enterprise DM scope – department single multiple subjects subject

DWH size – 100 GB DM size – up to 100 GB to more than 1 TB

EXPRESS SERVER Express Server is a multidimensional engine for online

analytical processing (OLAP) with the following features: Multidimensional analysis; Measures with different dimensionality; SQL support; Robust development environment; Open API; Distributed; Scalable.

Multi-dimensionalData Base

Product Manager view Regional Manager view

Financial Manager View Ad-hoc view

EXPRESS SERVER APPLICATION

Applications:

Performing in-depth competitive analyses; Tracking new product introductions and promotional

response rates; Conducting pricing, distribution, and promotion

comparisons across regions; Analyzing income and expense ; Tracking manufacturing inventory.

EXPRESS SERVER APPLICATIONS

EXPRESS SERVER OBJECTS Dimensions Relations Variables Formulas Programs Composites Valuesets Worksheets

MOST TYPICAL DIMENSIONS :

dimension time periods: years – quarters – months; dimension geographical regions – regions in the

country dimension countries, etc.

FOR THE MARKETING PURPOSES:

dimension products or services dimension clients or types of clients dimension distributors, etc.

VARIABLES

FOR THE MARKETING PURPOSES:

variable products or capacity – contains quantity characteristics of products or services;

variable sales or usage – characterizes the realization of the products or services;

variable costs – describes the expended resources; variable revenue – describes the financial results of

the firm activities;

MARKETING INFORMATION SYSTEM of BTC

Marketing manager

Queries Decision making information

MkIS

DWH

THE MAIN FUNCTIONS OF THE MKIS SYSTEM

user friendly reports and ad hoc studies generation; historical and up to date data integration for the

purposes of tendency analysis and forecasting; real data mathematical models representation; what - if analysis.

lines

subscribers

exchanges

regionsperiods

changes

subscribers

exchanges

regionsperiods

services

subscribers

exchanges

regionsperiods

services

subscribers

exchanges

regionsperiods

THE MAIN VARIABLES IN THE MkIS

capacity

usage

changed lines

revenue

FORECASTING METHODS:

LINEAR TREND – models the data as a straight line; EXPONENTIAL TREND – models the data as an exponential

curve; SINGLE, DOUBLE AND TRIPLE EXPONENTIAL SMOOTHING –

a system of weighted averages which effectively smoothes the data;

PERCENTAGE CHANGE – applies a variable’s observed period-to-period percentage changes directly to the user-defined set of forecast time periods;

MOVING AVERAGE – calculates a moving average of a set of data;

HOLT-WINTERS – decomposes data into three related components: a “smoothed” series, a seasonal series, and a trend series.

CHOOSING A FORECASTING METHOD

Method Time Horizon Data Pattern Minimum numberof observations

SingleExponentioalSmoothing

Immediate, short Stationary 2

DoubleExponentioalSmoothing

Immediate, short Linear 3

TrippleExponentioalSmoothing

Immediate, short Non-linear 4

Moving Average Immediate, short Stationary 3Holt-Winters Short to medium Seasonal 2 seasonsLinear Trend Medium, long Linear 3Exponential trend Medium, long Non-linear 3Percentage change Medium, long Stationary, linear 2

USER ACCESS TO MARKETING DATA BASE

Marketing data base

Oracle Express Server Instance

Cached data cubes

Stored procedures

User id

Windows Client Applications

(Oracle Express Objects)

SNAPICalls

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