Analytics Capabilities
Content
DATAMATICS’ RESEARCH & ANALYTICS
ADVANCE ANALYTICS CAPABILITIES
OUR EXPERIENCES
CONJOINT ANALYSIS
RESEARCH TOOLS – SIMULATORS
TECHNIQUES USED AT DATAMATICS
Quality Check
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
Advance Analytics Capabilities
Multiple Linear Regression
Factor Analysis
Discreet Choice Modeling
Multidimensional Scaling
CHAID Analysis
Churn Analysis
Decision Tree design
TURF Analysis
Conjoint Analysis
MaxDiff design and Analysis
Our Experiences Include…
Regression Models – Multiple Linear Regression
Regression Models – Logistics Regression
Factor Analysis
Decision Tree Design / CHAID Analysis
Target Variable
Scree Plot
6
5
Eigenvalue
4
3
2
1
0
1
2
3
4
5
6
Component Number
7
8
9
Our Experiences Include…
Importance of Factors
32%
Average Ball Life
23%
45%
Average Driving Distance
0%
Average Driving Distance
Factors
Price
Importance of Levels
225 Yards
17%
250 Yards
20%
275 Yards
10% 20% 30% 40% 50%
63%
0%
10%
20%
% Importance
Conjoint Techniques - CBC
Set 1
Best
31
19
1
10
26
18
30%
40%
% Im portance
Share of Preference
CARDS - VERSION 1
Features
Shopping
Dual SIM
Camera
Expandable / removable memory
Chat
Twitter
Worst
31
19
1
10
26
18
MaxDiff Analysis and More…
50%
60%
70%
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
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
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
Maxdiff Simulators
Output
10
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
Turf Analysis – Simulators
TURF Analysis and Simulators
Development
CHAID Analysis
CHAID Analysis – Key Outputs
Brand Positioning Map
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.
Techniques Used At Datamatics
 One way ANOVA
 CHAID Analysis
 Regression Models
 TURF Analysis
 Multiple Linear Regression
 Path Analysis (Structural Equation Modeling)
 Logistic Regression
 Conjoint Analysis
 Multinomial Regression
 Full Profile Conjoint
 Ridge Regression
 Choice Based Conjoint (CBC)
 Factor Analysis
 CBC / HB (Hierarchical Bayes)
 Segmentation Techniques
 Adaptive Conjoint Analysis (ACA)
 Hierarchical Cluster Analysis
 Maxdiff Analysis
 K-Means Cluster Analysis
 SSI Web
 Latent Class Segmentation
 Discriminant Function Analysis
 Corresponding Analysis
 Multidimensional Scaling (MDS)
Thank
YOU
www.datamatics.com