Data is a valuable tool and most companies gather huge amount of data but majority of the companies cannot afford to analyze. GuiRes offers comprehensive data analysis services for wide range of industries that offers important conclusion and key decisions that might otherwise have been hidden. We use cutting edge statistical tools with best trained data management experts and statisticians. Our services includes, data cleaning, quality of data audit, missing data analysis, extreme observations, analysis of home and heterogeneity, and finally run inference or confirmatory analysis using advanced tools based on the normality and data structure. Some of the analysis that we offer are bivariate, multivariate, conjoint, survival, and many more.Data Analytics for Segmentation, Targeting and Positioning Analysis
Are your firm struggle to appropriately segement the market and package differentitaed products and services for target segments. We apply various clustering techniquest to identify these segements.
Our segmentation analysis would shed light on the different characteristics of customers and prospects is a common challenge experienced by firms today. Based on the consumer characteristics and their response to the product or services, we apply several techniques to segement a market. Our segementation analsysis would be classfied as follows Geographic segementation (region, urban/rural, locality), demographic (age, gender, income, education, family size, family life cycle, race and religion), sociographic (cultural, influence of social class, reference groups), pyschographic segmentation (lfiestyle and personality), behavioural segmentation (usage rate, user status, readiness stage, and buying motives)The following are the analysis
Clustering analysis enables to group hetergenous population into a number of homogenous sub-groups. This may indicate different market segements and mean differing customer communciation strategies and channel. Further, to position their prduct or service in terms of competitive offerings and consumers’ views about the types of people most likely to own the product, we use cluster analysis.
Factor Analysis: This model group the respondents on a mutually exclusive basis and followed by segmenting on a non-mutually exclusive basis to explore the overalapping of segements. This technique is important in deveoping targeting, positioning and marketing strategiesK-Means cluster analysis: Identified realtive similar groups of customers based on selected characteristics. Two step cluster analysis: It automtically selected the number of clusters by comparing the values of a model choice criterion. Latent class cluster analysis: It optmizes the number of clusters and then fit the segementation model to the data and predict pattern in multiple dependent variables. Latent class choice modeling: This analysis classifies customers into segements based on their prefered product benefits. This segementation enable the firm to customize their product offering to match segement preferences. Discriminate analysis: This analysis identifies the questions that are msot important in determining segement membership CHAID is used for determining customer segements in a market.
Conjoint Analysis : This analysis helps the firm to predict choice share for evaluated stimuli such as competitive brands. We use several analysis such as two-factor-at a-time tradeoff, full profile, Adaptive Conjoint Analysis (ACA), choice-based conjoint, self-explicated conjoint, hybrid conjoint, and Hierarchical Bayes (HB). Conjoint analysis is most conducive to predicting choice behavior when the product or service involves a relatively high resource commitment and tends to be “analyzable” by the purchaser (e.g., banking or insurance services, industrial products). This tool is applied in sectors like consumer durables, industrial goods and other products like hair shampoo, gasoline pricing, clothing, carpet cleaners, etc.
Multi-dimensional Scaling Analysis (MDS): This analysis helps firm to see how their brand is positioned in the minds of consumers, vis-à-vis competing brands.
Clustering techiqnues: We are pioneering in applying both hierachical and partitioning method. Our market segmentation techniques are, K-means algorithm and artifical neural networks (ANN) [for sales forcasting, bankruptcy prediction), Unsupervised neural networks, the self-organizing maps (SOMs), Fuzzy Delphi method, self-organizing maps (SOM) and a visualization technique to cluster customers according to their various characterististcs
Neural Networks: We use various models to examining advertisment effectiveness such as ANN model, backpropagation ANNs (forecasting of advertising and promotion impact), Marketing Decision Support System (MDSS),
Estimation: as part of a market segmentation process to establish some reasonable guess at an indeterminable value (Customer lifetime value)
Link Analysis: To establish links between entities within a data set especially to look at the closely connected groups of people.
Rule Induction Using Decision Trees: Decision tree analysis looks at a collection of data instances and given outcomes, evaluates the frequency and distribution of values across the set of variables, and constructs a decision model in the form of a tree.
Affinity Grouping:This technique evaluates relationships between data elements that demonstrates some kind of affinity between objects. For example, affinity grouping might be used to determine the likelihood that people who buy one product will be willing to try a different product. This analysis would be helpful for marketing campaigns when trying to cross-sell or up-sell a customer on additional or better products.
Market Basket Analysis:To identify the pattern in selection that occur from one shopper to another. We apply market basket analysis
Product Placement: to identify products that may often be purchased tother and arranging the placement of those itemsPhysical shelf arrangement: to increase the probability of additional impulses purchases.
Up-sell, cross-sell and bundling opportuntities: To enable the presentation of items for cross-selling, or may suggest that customers may be willing to buy more items when certain products are bundled together.
Customer retention: To determine the right incentive to offer in order to retain the customer business
Memory-Based Reasoning (MBR)
To apply known situation to form a model for analsis. New situations are compared against the model to find the closet matches, which can be reviewed to inform decision about classification or for prediction.
Perceptual Mapping: We apply this two dimensional graph to visually show where your product stands, or should stand, relative to your competitors, based on criteria (e.g price, quality, level of customer service) important to buyers.