Purple Analytics Indonesia was established as PT. Survey Prima Solusi Statindo in 1999 to help Clients improve their decision making using data and analytics.

As SPSS Software was acquired by IBM Company, then nowadays Purple Analytics is a IBM Business Partner in selling SPSS Software, conduct training, data analytics consulting and project implementation.

Purple Analytics Indonesia is an integral part of Purple Analytics Asia with offices in Bangkok, Jakarta and Kuala Lumpur, providing leading technology and methodology that transform data into insights (data mining), serve more than 350 Clients in Indonesia raging from private companies to government bodies, from Risk Management to Marketing Management (CRM).

Purple Analytics combines three unique expertise, Data Mining, Business and IT Software to enable Clients get business insight and act on it to have tangible benefits, such as increasing revenue, decreasing risk or cost and exceeding target ROI.

Our basic value proposition is IBM SPSS Software (IT), the leading analytics software in the world complete with training and implementation based on proper Data Mining Methodology. Different to IT Company, we ensure the IT and Data Mining are the right solution for business problems by perfect marriage with Business Acumen of our team. By this we offer complete Solution start from Data Mining guided by Business Acumen and IT Software.

Purple Analytics adheres to a universally accepted standard of Data Mining called CRISP-DM (Cross Industry Standard Process for Data Mining). As can be seen in the figure below, there are 6 very important phases in every analytic project:

Business Understanding

This initial phase focuses on understanding the project objectives and requirements from a business perspective, then converting this knowledge into a data mining problem definition and a preliminary plan designed to achieve the objectives.

Data Understanding

The data understanding phase starts with initial data collection and proceeds with activities that enable our team and Client to become familiar with the data, identify data quality problems, discover first insights into the data, and/or detect interesting subsets to form hypotheses regarding hidden information.

Data Preparation

The data preparation phase covers all activities needed to construct the final dataset [data that will be fed into the modeling tool(s)] from the initial raw data. Data preparation tasks are likely to be performed multiple times and not in any prescribed order. Tasks include table, record, and attribute selection, as well as transformation and cleaning of data for modeling tools.

Modeling

In this phase, various modeling techniques are selected and applied, and their parameters are calibrated to optimal values. Typically, there are several techniques for the same data mining problem type. Some techniques have specific requirements on the form of data. Therefore, going back to the data preparation phase is often necessary.

Evaluation

At this stage in the project, we would have built the models that appear to have high quality from a data analysis perspective. Before proceeding to final deployment of the model, it is important to thoroughly evaluate it and review the steps executed to create it, to be certain the model properly achieves the business objectives. A key objective is to determine if there is some important business issue that has not been sufficiently considered. At the end of this phase, a decision on the use of the data mining results should be reached.

Deployment

Creation of the model is generally not the end of the project. Even if the purpose of the model is to increase knowledge of the data, the knowledge gained will need to be organized and presented in a way that the customer can use it. It often involves applying "live" models within an organization's decision making processes-for example, real-time personalization of Web pages or repeated scoring of marketing databases. Depending on the requirements, the deployment phase can be as simple as generating a report or as complex as implementing a repeatable data mining process across the enterprise. In many cases, it is the customers' front line, not the data analyst, who carries out the deployment steps. However, even if the analyst will carry out the deployment effort, it is important for the customer to understand up front what actions need to be carried out in order to actually make use of the created models.

Purple Analytics is a IBM Business Partner in selling IBM SPSS software. In addition to selling software, we ensure Clients to have expertise to operate the software or even to maximize the investment through statistical/ data mining and business training to solve business problems.

Moreover we "sell" project implementation, aim to get insight or agreed Client's target achievement such as increased revenue or profit. Our project implementation cover following generally needed business analysis:

  • Risk Analytics
    • Developing scorecard models to determine which applicants are most probable to default, which customers are most probable to collect.
    • Developing methodology and tool to monitor model accuracy and determine cut-off score.
  • Customer Analytics
    • Analyzing customer to understand group of customer which have similar respond to marketing program, their value and future potential to have better understanding of Customer Life-time Value (profitability along period of engagement), list of customers who tend to defect, which customers are most probable to buy certain product category, product item or brand (cross/up-sell).
    • Developing an effective strategy based on growth potency and limitations, such as cross-selling.
    • Conduct Operational CRM such as: mobile apps for retention and promote sales.

Other analysis or solution such as Entity Matching (Data Management), Capacity Planning (Operation), Cost Allocation (Accounting) or HR Analytics (Human Resources) will be available on Custom Solution.

To make abstract information easy to understand and to make complex data easy to memorize, we provide Infographic Service, as you can see throughout this website.

In data analytics, IBM is the name people trust for quality, integration, and leadership. IBM SPSS offers following Modules in the entire analytical process:

  • SPSS Statistics: Solve research problems easily and efficiently
    IBM SPSS Statistics is a statistical tool for research activities, and integrates well to entire analytical process above.
  • SPSS Modeler: Make better decisions through predictive intelligence
    IBM SPSS Modeler is a data mining workbench that helps you build predictive models quickly and intuitively, without programming.


Gartner Magic Quadrant namely "Business Intelligence and Analytics Platforms" (previously "Business Intelligence Platforms"), emphasizing the growing importance of analysis capabilities to the information systems that organizations are now building. Gartner defines the business intelligence (BI) and analytics platform market as a software platform that delivers 15 capabilities across three categories: integration, information delivery and analysis, including:
Integration

1. BI infrastructure
2. Metadata management
3. Development tools: the platform should provide a set of programmatic and visual tools, coupled with a software developer's kit for creating analytic applications, integrating them into a business process, and/or embedding them in another application
4. Collaboration: Enables users to share and discuss information and analytic content, and/or to manage hierarchies and metrics via discussion threads, chat and annotations
Information Delivery

5. Reporting
6. Dashboards
7. Ad hoc query
8. Microsoft Office integration
9. Search-based BI
10. Mobile BI

Analysis
11. Online analytical processing (OLAP)
12. Interactive visualization
13. Predictive modeling and data mining
14. Scorecards: metrics displayed in a dashboard is applied to a strategy map (BSC)
15. Prescriptive modeling, simulation and optimization

From the Gartner Magic Quadrant "Business Intelligence and Analytics Platforms", IBM is the vendor with the most complete vision in BI and Analytics, supported with huge resources.

Purple Analytics provides following Solution for Credit Risk Management:

  • Default Parameter and/ or early warning indicator to monitor credit quality
  • Model of Application Scoring to predict applicant's risk which improve time-to-yes, efficiency and credit quality
  • Cut-off Application Score to optimize credit quality and sales
  • Model of Behavior Scoring to calibrate Application Score Model, manage collection, and predict which customer to top-up or close monitor which optimize customer share
  • Model of Collection Score to predict probability to collect of the defaulted customer
  • Model of Profitability to predict
  • Monitoring Tool in form of reports and dashboard, start using Microsoft Excel to more sophisticated BI tools to navigate credit acquisition process
  • Customized Risk Modeling, such as Operational Risk and Stress-Test*
  • Web-based Scoring System to enable scoring the loan application whenever and wherever as long as log-in to the internet
  • Training


Our Models in Credit Risk Management are complying to Basel-II and III Accords, and ready to be deployed on Clients IT environment.

*) in cooperation with Regional Office/ Partners

Purple Analytics provides following Solution for Customer Relationship and Loyalty Management:

  • Actionable Segmentation and Profiling based on transaction and/or descriptive data to group customer based on their value and response to marketing programs
  • Positioning and CRM Theme, to integrate all CRM activities into a singular, valuable and personal experience of customers
  • CRM Architecture, start from dB, CRM Analytics, and Operational CRM
  • CRM Analytics to generate insight:
    • What products which are generally bought together (Basket Analysis)
    • What products which will be bought considering customer profile (Propensity-to-buy Analysis, Cross/Up-sell Analysis)
    • Which customers will stay or churn in near future (Churn Analysis)
  • CRM / Loyalty programs* to generate more interaction or transaction, such as:
    • Sales Force Automation System, to contact, initiate and follow-up activities involving frontlines and customers
    • Point system, including point strategy, calculation engine, accumulation & redemption infrastructure
  • Strategic partnership* to develop retention, and cross loyalty
  • Branding and A&P* to increase awareness, interest and affection
  • Social Media Marketing to tap feeling of stakeholders for better marketing strategy
  • Monitoring Tool in form of reports and dashboard, start using Microsoft Excel to more sophisticated BI tools to navigate credit acquisition process
  • Training

*) in cooperation with Regional Office/ Partners

Purple Analytics provides following Solution for Credit Risk Management:

  • Default Parameter and/ or early warning indicator to monitor credit quality
  • Model of Application Scoring to predict applicant's risk which improve time-to-yes, efficiency and credit quality
  • Cut-off Application Score to optimize credit quality and sales
  • Model of Behavior Scoring to calibrate Application Score Model, manage collection, and predict which customer to top-up or close monitor which optimize customer share
  • Model of Collection Score to predict probability to collect of the defaulted customer
  • Model of Profitability to predict
  • Monitoring Tool in form of reports and dashboard, start using Microsoft Excel to more sophisticated BI tools to navigate credit acquisition process
  • Customized Risk Modeling, such as Operational Risk and Stress-Test*
  • Web-based Scoring System to enable scoring the loan application whenever and wherever as long as log-in to the internet
  • Training


Our Models in Credit Risk Management are complying to Basel-II and III Accords, and ready to be deployed on Clients IT environment.

*) in cooperation with Regional Office/ Partners

Purple Analytics provides Infographic Solution to amplify your media presence by making your data looks and feel sexier than ever.

We believe that insight from analytics must be shared in interesting ways, as people love to discern information by examining visual presentation of data. Our Infographics Solution characterized by:

  1. Attractive visual. We develop infographics those accommodate audiences visual comprehension
  2. Knowledgeable content. Our visual not only capable of representing facts, statistics or other references, but also make your knowledge or abstract concept is presented in clear and credible way
  3. Remembrance story of your data. Visual content is not only nice to see but more important, it is easily remembered


Our Infographics Solution is available as a stand-alone project or as a package to accompany our other Solution, such as your Customer Analytics Update more attractive, remembered, but also knowledgeable.

Nowadays many vendors offer training, so below are our propositions:

  • We have real experience across industry in using the software to solve business problem.
  • We are a partner of the IBM Company who develops the software.
  • We are able to deliver on-line training and/ or assessment for your staff.
  • Our Facilitators are well selected from reputable universities.

SW 01 MODULE - TECHNICAL IBM SPSS STATISTICS BASE (18 hours)

  • Requirements: able to operates Microsoft Windows
  • Learning Target: able to operate IBM SPSS Statistics Base
  • Course Outline:
    • Data access using IBM SPSS Statistics Base
    • Data and file management using IBM SPSS Statistics Base
    • Operations of data and file : create, check, modify, etc
    • Operations of table and charts

SW 02 MODULE - TECHNICAL IBM SPSS FORECASTING (6 hours)

  • Requirements: able to operates Microsoft Windows
  • Learning Target: able to operate IBM SPSS Forecasting
  • Course Outline:
    • Forecasting wizard to build time-series model
    • Analysis of time series: Exponential Smoothing and ARIMA
    • Model evaluation
    • Case study

SW 03 MODULE - TECHNICAL IBM SPSS DECISION TREE (6 hours)

  • Requirements: able to operates Microsoft Windows
  • Learning Target: able to operate IBM SPSS Decision Tree
  • Course Outline:
    • Developing decision tree model
    • CHAID, C&RT and QUEST
    • Model evaluation
    • Case study

ST 01 MODULE - STATISTICAL ANALYSIS 1 (12 hours)

  • Requirements: SW 01
  • Learning Target: understand to descriptive and relational statistical analysis
  • Course Outline:
    • Basic principle of research
    • Central tendency and dispersion of data
    • Summarizing data
    • Distribution of data
    • Population and sample
    • Parameter and statistics
    • Graphical analysis of categorical and continuous data
    • Analyzing relationship of categorical variables
    • Case study

ST 02 MODULE - STATISTICAL ANALYSIS 2 (12 hours)

  • Requirements: ST 01, SW 01
  • Learning Target: understand to inferential statistics and testing of mean difference across groups within population
  • Course Outline:
    • Basic principle of inferential statistics
    • Cross tabulation
    • Testing differences between mean (t-test)
    • Testing of mean differences: 1 factor ANOVA
    • Testing of mean differences: 2 way ANOVA
    • Analyzing relationship of continuous variables using Correlation test
    • Case study

ST 03 MODULE - BUILDING PREDICTIVE MODELS USING IBM SPSS REGRESSION (18 hours)

  • Requirements: ST 01, ST 02, SW 01
  • Learning Target: understand to predictive model building technique using IBM SPSS Regression
  • Course Outline:
    • Introduction to modeling
    • Simple linear regression
    • Multiple linear regression
    • Multicollinearity
    • Logistic regression
    • Case study

ST 04 MODULE - SEGMENTATION AND PROFILING (18 hours)

  • Requirements: ST 01, ST 02, SW 01, SW 03
  • Learning Target: understand to segmentation technique using individual variable, multiple variables and understand to profiling technique
  • Course Outline:
    • Introduction to segmentation and profiling
    • Factor analysis
    • Cluster analysis
    • Discriminant analysis
    • Classification tree
    • Case study

ST 05 MODULE - STATISTICAL PROCESS CONTROL (12 hours)

  • Requirements: able to operate Microsoft Windows
  • Learning Target: understand how to make control chart to improve product quality through process control and improvement
  • Course Outline:
    • Introduction to SPSS analytical tool
    • Introduction to basic statistics for SPC (Statistical Process Control)
      • Data measurement
      • Central tendency and dispersion of data
      • Distribution of data
      • Population and sample
      • Parameter and statistics
    • Statistical Process Control
      • SPC and its benefits
      • Control charts: definition, interpretation and control rules
      • Type of control charts and their usage
      • Developing control chart using IBM SPSS STATISTICS BASE
      • Developing pareto chart using IBM SPSS STATISTICS BASE

DM 01 MODULE - INTRODUCTION TO DATA MINING WITH IBM SPSS MODELER (30 hours)

  • Requirements: able to operate Microsoft Windows
  • Learning Target:
    • understand how to do data mining to answer business problem
    • able to operate IBM SPSS Modeler
  • Course Outline:
    • Introduction to data mining
    • Introduction to visual programming using IBM SPSS Modeler
    • Data operation from various dB: access, retrieve, merge, etc.
    • Data preparation for modeling: data cleansing, transformation, etc.
    • Introduction to statistical model in data mining, including machine learning and artificial intelligence
    • Model evaluation
    • Model interpretation
    • Model deployment to dB
    • Case study

At Purple Analytics, the ONLY reason for training is to improve business. In Customized Module, we start by defining your business needs, not your training needs. Then we clarify the required business result (for instance sales revenue), compared to current performance in 2 major dimension, those are employee behavior (for instance sales call of salesman) and work result (for instance winning ratio). Based on these understanding we develop customized training complete with evaluation module to ensure business result.

In training, we provide customization on:

  1. Delivery. Delivery is everything, since learning is a process. One significant factor of learning is matching between audience's learning type to the learning situation, and materials. For instance the Sales people which generally active on field, typically have kinesthetic learning type (Neil Fleming's VARK Model), so the situation and materials should be experience based-moving, touching, and doing/ practicing. Customization on delivery includes:
    1. Style of material presentation (wordy vs visual, pointer vs detail, etc)
    2. Presentation style, and the speaker (instructional, inspiring, concrete idea, etc)
    3. Room, light and sound
    4. Evaluation (written test, practice, etc)
  2. Knowledge and Skill Level. Customization on the course content includes:
    1. Level of complexity: new entry, staff level, and managerial level
    2. Level of business understanding: functional (for instance marketing), inter-function (for instance maximizing sales revenue through risk and expense segmentation)
  3. Special topics. We provides customized topical training on:
    1. Statistics in practical business, for instance:
      1. SQC (Statistical Quality Control)
      2. Statistics for decision making
      3. Demand seasonality
      4. Price sensitivity
      5. Product survival
    2. Core statistical domain, for instance:
      1. Descriptive Statistics
      2. Multivariate Statistics
      3. Forecasting
      4. Monte Carlo Simulation/ Methods
      5. Bayesian Statistics & Decision Theory
      6. Probability and stochastic Model