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Analytics Capabilities. Content DATAMATICS’ RESEARCH & ANALYTICS ADVANCE ANALYTICS CAPABILITIES OUR EXPERIENCES CONJOINT ANALYSIS RESEARCH TOOLS – SIMULATORS.

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Presentation on theme: "Analytics Capabilities. Content DATAMATICS’ RESEARCH & ANALYTICS ADVANCE ANALYTICS CAPABILITIES OUR EXPERIENCES CONJOINT ANALYSIS RESEARCH TOOLS – SIMULATORS."— Presentation transcript:

1 Analytics Capabilities

2 Content DATAMATICS’ RESEARCH & ANALYTICS ADVANCE ANALYTICS CAPABILITIES OUR EXPERIENCES CONJOINT ANALYSIS RESEARCH TOOLS – SIMULATORS TECHNIQUES USED AT DATAMATICS Quality Check

3 Datamatics’ Research & Analytics Datamatics’ Research and Analytics(R&A) business unit has established itself as a leading domain player for providing market research support services and technology solutions woven around them. Global firms such as Nielsen, Ipsos/Synovate, ResearchNow as well as specialized firms like Morpace, BuzzBack, C&R, ISA etc. have engaged Datamatics for services over the last few years. Our differentiators and relevant experience include:  Sharp focus on the market research support services  Expertise in advanced analytics and data visualization  Significant experience around automation, process reengineering of research projects, including application development, mobile application development  A unique MR + Tech blend. Domain layer comes via key folks who come from MR industry, with operations grounding, and demonstrating thought leadership via papers presented at ESOMAR and CASRO).  US based Subject matter experts adding value to projects  Execution of numerous engagements aimed at improving quality, turnaround time and significant cost saving for Market Research Project and borrowing best practices from other industry segments serviced by Datamatics

4 Advance Analytics Capabilities Multiple Linear RegressionFactor AnalysisDiscreet Choice ModelingMultidimensional ScalingCHAID AnalysisChurn AnalysisDecision Tree designTURF AnalysisConjoint AnalysisMaxDiff design and Analysis

5 Our Experiences Include… Regression Models – Multiple Linear Regression Regression Models – Logistics Regression Factor Analysis Decision Tree Design / CHAID Analysis Target Variable

6 Our Experiences Include… Conjoint Techniques - CBC Share of Preference MaxDiff Analysis and More…

7 Conjoint Analysis We are experienced in many conjoint methods such as CBC, ACA and full profile conjoint We use Sawtooth software SSI Web to script the conjoint survey and host it on web server for the clients We also use CBC Hierarchical Bays to generate respondent level utilities We have experience in building customized simulators for our clients to enable them to calculate preference share for various possible combinations of product attributes and features We also have experience in in finding relative importance of attributes using Maxdiff Technique For conjoint studies at Design level we can  Design the choice tasks with 2-4 product concepts on each card with optional “None” option  Create multiple versions of these choice tasks  Program it in SSI web and upload on web server and provide a link  Alternatively send the card design to client to include in their online or offline questionnaire  Include hold out cards  Apply conditional pricing  Can put prohibitions on certain combination

8 Conjoint Analysis At Analysis level we can  Generate overall utilities for each level of each attribute  Calculate importance of attributes  In each attribute the level preference  Run Simulations  Create respondent level utilities  Design and create customized simulator

9 Research Tools – Simulators Datamatics has proficiency in designing various research tools for market research applications. Examples:  BPTO Simulator  Claims Optimization Simulator  Max-Diff Share Simulator  Discreet choice simulator  Segments allocation tool  TURF Simulator BPTO Simulator gives relative shares for different price levels The data can be calibrated to current market shares if current price levels are part of BPTO price levels For new products, it can show from which existing brands it will draw its shares Also it can calculate the index to show from which brand it will draw maximum share

10 Maxdiff Simulators 10 Output

11 Discreet Choice Model – Simulators We have designed discreet choice simulator from the respondent level utilities. The simulator can take different no. of attributes and different levels for each attribute  It will have a user screen where the products can be defined  It will generate relative share of the products defined  These shares can be generated at total and subgroup level  For sub group level we should have classification data TURF Simulators: If we have several variants of a brand and wondering how many variants should be launched so that we have maximize reach then this tool is very useful and gives the share if you launch 2, 3, 4, 5, or more If after taking 4 variants if there is no significant incremental reach we may decide to launch only 4 variants

12 Turf Analysis – Simulators TURF Analysis and Simulators Development

13 CHAID Analysis CHAID Analysis – Key Outputs

14 Brand Positioning Map

15 Brand Performance Map (Quadrant Analysis) Importance rating on attributes are taken on five point scale, from Very important to not important at all. On the same attributes the performance ratings, from Excellent to poor, are taken for each brand. A perceptual map visually plots the attributes as per ratings by customers on their importance and how brand performance. Attributes appearing in fourth quadrant are not important but brand is performing well hence they can be used in advertising.

16 Techniques Used At Datamatics  One way ANOVA  Regression Models  Multiple Linear Regression  Logistic Regression  Multinomial Regression  Ridge Regression  Factor Analysis  Segmentation Techniques  Hierarchical Cluster Analysis  K-Means Cluster Analysis  Latent Class Segmentation  Discriminant Function Analysis  Corresponding Analysis  Multidimensional Scaling (MDS)  CHAID Analysis  TURF Analysis  Path Analysis (Structural Equation Modeling)  Conjoint Analysis  Full Profile Conjoint  Choice Based Conjoint (CBC)  CBC / HB (Hierarchical Bayes)  Adaptive Conjoint Analysis (ACA)  Maxdiff Analysis  SSI Web

17 Thank YOU


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