Mi-CORRELATE

Data analysis is the crucial step to create knowledge out of the collected data as rightly said by Peter Drucker "What gets measured, gets managed"

Data Analytics is the discovery, interpretation, and communication of meaningful patterns in the data. Analytics relies on the simultaneous application of statistics, computer programming and market research techniques to quantify information.

The marketing research process consists of 7 discrete steps as given below :

Step 1 - Understanding the business objective
Step 2 - Research objective and key research questions
Step 3 - Research instrument design
Step 4 - Data Collection
Step 5 - Data Analysis [MiCORRELATE]
Step 6 - Report Preparation to present or disseminate the findings
Step 7 - Recommendation/Business growth strategy

MiCORRELATE applies analytics to the collected data to describe, forecast, to fact-based decisions. Specifically, areas within analytics include predictive analytics, prescriptive analytics, enterprise decision management, descriptive analytics, cognitive analytics, Big Data Analytics, retail analytics, store assortment and stock-keeping unit optimization, marketing optimization and marketing mix modeling, web analytics, call analytics, speech analytics, sales force sizing and optimization, price and promotion modeling, predictive science, credit risk analysis, and fraud analytics.

MiCORRELATE is the advanced analytics solution used to complement the other two research solutions namely MiCOMPLIANCE & MiCONNECT

MiCORRELATE uses the most advanced statistical tool to analyze the data. Our team of data scientists is capable of finding meaningful patterns in the data, which generates insights for designing the future strategy

In the majority of the market research report, the data is either tabulated or frequency distribution is shown in bar charts or pie charts. The means or averages and other measures of dispersion are common ways of analyzing data for which frequency distributions are available. Rarely, advanced statistics and analytics solutions are used to maximize the information that can be extracted from research data. However, we ensure to apply advanced statistics by using MiCORRELATE in every data set so that all the required information is extracted from the date as we believe every dataset has a story provided the data is collected and analysed in the right way

MiCORRELATE ensures that the research is designed to produce statistically valid and should represent the market

Scientifically designedsample size and the sample plan :

Research is done to find a solution to a particular business problem formulated as a research objective which in turn is broken down into research questions. In a scenario where we have unlimited time and resources, the entire population should be covered however this is not possible because of limited budget and stringent timeline. Other than the census, which is conducted on each and every person of the population, all other studies are done on a limited sample size drawn from the target population known as "sample". The data obtained is analyzed and conclusions are drawn which are extrapolated to the population. Thus, for any research to produce meaningful results it is critical to understand the importance of sample plan which should be decided at the beginning of the research project.

Though sample size calculation may vary based on the type and objective of the study, the basic framework remains the same. The four main factors that need to be concerned during sample calculate the margin of error, confidence level, power and effect size.

Unbiased Analysis of data :

It's comfortable and easy to accept an analysis that supports our pre-conceived notions. The Mukraj-Insights team ensures that they look data without any bias. So our team of a researcher is always sceptical always believe that important results hold up to a deeper look. We make sure that we see more than one perspective on data.

Our approach of taking an unbiased approach to data opens the door to unseen insights. It provides a different perspective that might turn out to be far more important than you ever imagined.

Presenting data in a user-friendly way :

We do not stop ourselves at the analysis of data and finding the information, but we also dig deep and read every comment, slicing the data different ways in an effort to find gaps and opportunities and we make the data understandable so that our clients can easily comprehend it.

MiCORRELATE uses Python, an increasingly popular tool for data analysis Learn more about Python

Python was originally a general-purpose language. But, over the years, with strong community support, this language got a dedicated library for data analysis and predictive modelling.

Python is an interpreted, object-oriented, high-level programming language with dynamic semantics. Its high-level built in data structures, combined with dynamic typing and dynamic binding, make it very attractive for data analysis. Python supports modules and packages, which encourages program modularity and code reuse. The Python interpreter and the extensive standard library are available in source or binary form for all major platforms.

Python combines remarkable power with very clear syntax. It has modules, classes, exceptions, very high-level dynamic data types, and dynamic typing. There are interfaces to many systems calls and libraries, as well as to various windowing systems. Python is also usable as an extension language for applications written in other languages that need easy-to-use scripting or automation interfaces.

With the help of python, large datasets can be easily analyzed, and data can be presented in a user-friendly way.

MiCORRELATE can conduct the following type of analysis :

Canonical correlation

Canonical correlation analysis is a method for establishing the relationships between two sets of variables, all measured on the same subject.

In case of two sets of variables, canonical correlation analysis describes the relationship between the first set of variables and the second set of variables.

How can a business benefit from Canonical correlation technique?

Canonical correlation is used frequently when we are studying very complicated relationships in market research which helps in finding the hard to find the pattern

Cluster Analysis

Cluster analysis is grouping a set of data objects into clusters or in simple terms it is unsupervised classification with no predefined classes

The major categories of clustering methods are partitioning, hierarchical, density-based and grid-based. Customer clustering emerges as the most important technique for segmentation and positioning based on demographics as well as the psychographic profile of the customers

How businesses can benefit from cluster analysis :

Clustering can help businesses to manage their data better example image segmentation, grouping web pages, market segmentation and positioning, shopping behaviour, sales campaigns, customer retention, fraud detection, risk factor identification and customer retention efforts, customer segmentation, credit scoring and analyzing customer profitability.

Conjoint Analysis :

Conjoint analysis is a technique that is used to determine how people value different features or attributes that make up an individual product or service.

A market research technique in which consumers make tradeoffs between two or more features and benefits of a product on a scalecoupled with techniques such as simulation analysis, conjoint analysis helps in evaluation of different pricing points.

The key characteristic of conjoint analysis is that a product is composed of multiple conjoined elements (attributes or features). Based on how the combined elements (product concepts) are evaluated, the underlying preference structure can be determined.

How can businesses benefit from conjoint analysis? :

When making choices between products and services, every consumer is faced with trade-offs. Choices involve trade-offs and compromises.

Replicates consumer choice and trade-off behaviour, Selection of a product among multiple alternatives, Test objective not obvious for respondents, can include compensatory and non-compensatory models, Flexible experimental research designs, Optimal product configuration and item prioritization from a business or consumer standpoint, and Predicts preference shares

Correspondence Analysis& Multi-dimensional scaling :

Correspondence analysis is a statistical technique that provides a graphical representation of data. It is a compositional approach to perceptual mapping that is based on categories and helps in deriving a multidimensional map

Multidimensional scaling (MDS) is a very useful technique for market researchers because it produces an invaluable "perceptual map" revealing like and unlike products, thus making it useful in brand similarity studies, product positioning, and market segmentation.

It is often used to plot data such as the perception of products or brands; this will display both the number and nature of the dataset in an easy to interpret, visual way

How can correspondence analysis benefit businesses? :

Marketers are often faced with the challenge of changing perceptions in the marketplace or even within their company, for any number of issues including brand or company image. Correspondence analysis is the ideal tool that enables quick representation of current perceptions, providing guidance in strategy development for building a new image or positioning a brand.

• Image measurement
• Market segmentation
• New product development (positioning)
• Assessing advertising effectiveness
• Pricing analysis
• Channel decisions

Discriminant Analysis :

Discriminant analysis is a regression-based statistical technique used in determining which particular group belongs to on the basis of its characteristics or essential features.

How can discriminant analysis benefit customers? :

Discriminant analysis has widespread application in situations in which the primary objective is to identify the group to which an object e.g. person, firm or product belongs potential application include predicting the success or failure of a new product, determining the category of credit risk for a person, or predicting whether a new product will be successful or not

Factor Analysis :

Factor analysis is a technique that is used to reduce a large number of variables into fewer numbers of factors. The primary purpose is to define the underlying structure among the variables in the analysis so that the information contained in the original number of variables can be condensed into factors with minimum loss of information

How can factor analysis benefit businesses? :

Factor analysis has been successfully used in a wide variety of industries and fields. It is usually applied in market research related to product attributes and perceptions. The construction of 'Perceptual Map' and product positioning studies are some crucial areas where factor analysis is widely used

Factor analysis in market research is often used in customer satisfaction studies to identify underlying service dimensions, and in profiling studies to determine core attitudes

Market research and analysis of large volumes of data are necessary when it comes to analyzing and determining the right market segment, potential demand, and potential areas of competition, product development requirements and all other facets of the business marketing portfolio

It can also prove to be useful when a lengthy questionnaire needs to be shortened but still, retain key questions. Factor analysis indicates which questions can be omitted without losing information.

Structural Equation Modeling :

Structural equation modelling is a multivariate statistical analysis technique that is used to analyze structural relationships. This technique is the combination of factor analysis and multiple regression analysis, and it is used to examine the structure of interrelationships which helps in identifying complex patterns in the datasets