Market Research Conjoint Analysis Service

Conjoint Analysis is Statistical Analysis technique used in market research to determine how people value dissimilar features that make up an being product or service. Conjoint Analysis presents conception to respondents. However, instead of giving a single concept for each respondent, each of the respondents is showing too many concepts. The goal of Conjoint Analysis is to determine the alignments of limited number of attributes are the most influential on respondent choice or decision making. A controlled set of potential products or services is shown the respondents and by analyzing how they make preferences between these products, the implicit valuation of the individual elements making up the product or service can be determined. This implicit valuation can be used to create a market models that approximate market share, revenue and even effectiveness of new designs.

Conjoint Analysis is originated in mathematical psychology and was developed by the marketing professor Paul Green at the University of Pennsylvania and Data Chan. Other prominent Conjoint Analysis pioneers are professor V. Srinivasan of Stanford University who developed linear programming procedure for rank ordered data as well as self explicated approach. Today it is used in many of the social sciences and applied sciences including marketing, product management, and operations research. It is used to test frequently the customer who acceptance of new product designs, in assessing the appeal of advertisements and in service design. It has been used in product positioning, but there are some who raise problems with this application of Conjoint Analysis.

Conjoint Analysis techniques may also be referred as multi-attribute compositional modelling, isolated choice modelling, or stated preference research, and is part of broader set of trade off analysis tools used for systematic analysis of decisions. These tools are includes Brand Price Trade Off, Simalto, and mathematical approaches such as evolutionary algorithms or Rule Developing Experimentation.

Data for Conjoint Analysis is the most commonly gathered information through market research survey, although Conjoint Analysis can also be applied to carefully designed configuration or data from an correctly design test market research. Market research rules of thumb apply with regard to statistical sample size and accuracy when scheming Conjoint Analysis interviews.

The length of research survey depends on the number of attributes to be assessed and the method of Conjoint Analysis in use. The typical Adaptive Conjoint survey with 20-25 attributes may take more than 30 minutes to complete. Choice based conjoint, by using a smaller summary set circulated across the sample for entire steps completed in less than 15 minutes. Choice exercises may be displayed as store front type layout or in the some other replicated shopping environment.

Conjoint Analysis services from Informatics Outsourcing

• For each action, respondents are required to make hypothetical trade-offs between products.
• Each respondent is forced to make trade-offs between product features, much as consumers are forced to do when they are actually shopping.
• Each respondent answers a series of questions; in each question the combination of features shown together changes. In this way, a large number of product features can be estimated.

Offshore Quantitative Market Research Service

Market Research is any organized effort to gather information about markets or customers. It is very important component of business strategy. The term is commonly interchanged with marketing research; however, expert practitioners may wish to draw a distinction, in that marketing research is concerned specifically about marketing processes, while market research is concerned specifically with markets. Market Research is a key factor to get advantage over competitors. 

Market Research provides important information to identify and analyze the market need, market size and competition. Informatics Outsourcing is a full service research organization offering a comprehensive range of information gathering and analysis capabilities. Informatics Outsourcing has close to 10 years experience in the conduct of marketing research programs. At Informatics Outsourcing you will find a team of research professionals who are focused and committed to ensuring that the most pertinent, reliable, easy to interpret and actionable information is generated and delivered to all their clients. Informatics Outsourcing is one of the best networked marketing research companies in the World with a well trained and experienced team. 

A good marketing research consists of much more than just conducting interviews with interested individuals. When you work with Informatics Outsourcing you are working with one of the most experienced full service marketing research companies. Informatics Outsourcing customizes each research project to clients need. At Informatics Outsourcing, we are always ready to help our clients explore new opportunities, find solutions to marketing problems, learn more about prevailing market conditions, link with their customers, suppliers and other key business affiliates.

Our Quantitative Market Research services include,

1. Market Research processes

•           Data Collection
•           Survey
•           Focus Group
•           CATI
•           Market Research data processing
•           Data Cleansing

2. Statistical Market Research services

•           Data Analysis
•           Statistical Modeling
•           Charting
•           Factor and Cluster Analysis
•           Conjoint Analysis
•           Regression Analysis
•           Report Generation
•           Insight Generation
•           Forecasting Analysis
•           Significance Testing

Market Research Data Cleansing Service

Data cleansing is the process of detecting and correcting corrupt or inaccurate records from a record set, table, or database. Used mainly in database, the term refers to identifying incomplete, incorrect, inaccurate, irrelevant data, Parts of the data and then replacing, modifying or deleting this dirty data. After cleansing, a data or data set will be consistent with other similar data sets in the system. The inconsistencies are detected or removed may have been originally caused by different data dictionary definitions of similar entities in different stores, may have been caused by user entry errors, or may have been corrupted in transmission or storage.

Data cleansing differs from data validation, validation means data is rejected from the system at entry and is performed at entry time, rather than on batches of data. The actual process of data cleansing may involve removing typographical errors or validating and correcting values against a known list of entities. The validation may be strict such as rejecting any address that does not have a valid postal code or fuzzy such as correcting records that partially match existing, known records.

High quality of data needs to pass a set of quality criteria. Those include:

    Accuracy: An aggregated value over the criteria of integrity, consistency and density
    Integrity: An aggregated value over the criteria of completeness and validity
    Completeness: Achieved by correcting data containing anomalies
    Validity: Approximately the amount of data satisfying integrity constraints
    Consistency: Concerns contradiction and syntactical anomalies
    Uniformity: Directly related to irregularity and in compliance with the set ‘Unit of Measure’
    Density: The quotient of missing values in the data and the number of total values ought to be known
    Uniqueness: Find the related number of duplicates in the data

Data Cleansing Process

    Data Auditing: The data is audited with the use of statistical methods to detect anomalies and contradictions. This eventually gives an indication for the characteristics of the anomalies and their locations.

    Workflow specification: The detection and removal of anomalies is performed by a sequence of operations on the data known as the workflow. It is specified after the process of auditing the data and is crucial in achieving the end of the product of high quality data. In order to achieve a proper workflow, the causes of the anomalies and errors in the data have to be closely considered. If for instance we find that an anomaly is typing errors in data input stages, the layout of keyboard can help in manifesting possible solutions.

    Workflow Execution: In this stage, the workflow is executed after its specification is completed and the correctness of data is verified. The implementation of the workflow should be efficient even on large sets of data which necessarily poses a trade-off, because the execution of the data cleansing operation can be computationally expensive.

    Post Processing and Controlling: After executing the cleansing workflow, the results are inspected to verify correctness. Data that could not be corrected during execution of the workflow are manually corrected if possible. The result is the new cycle in the data cleansing process where the data is audited again to allow the specification of an additional workflow to further cleansing the data by automatic processing.

Methods of Data Cleansing:

    Parsing: In data cleansing Parsing is detection of syntax errors. A parser decides whether a string of data is acceptable within the allowed data specification or not. In this similar way a parser works with grammars and languages.

    Data Transformation: Data Transformation allows the mapping of the data from their given format into expected format by the appropriate application. This includes value conversions or translation functions as well as normalizing numeric values to conform to minimum and maximum values.

    Duplicate Elimination: Duplicate detection requires an algorithm for determining whether data contains duplicate representations of the same entity or not. Usually, data is sorted by a key that would bring duplicate entries closer together for faster identification.

    Statistical Methods: By analyzing the data using the values of mean, standard deviation, range, or clustering algorithms, it is possible for an expert to find values that are unexpected and thus erroneous. Although the correction of such data is difficult, the true value is not known, it can be resolved by setting the values of an average or other statistical value. Statistical methods can also be used to handle missing values which can be replaced by one or more possible values that are usually obtained by extensive data augmentation algorithms.