The Future of Marketing with Analytics

by September 5, 2016 0 comments

Analytics is all about gleaning meaningful insights from data. You do not get surprised when your CEO asks you why your competitors are gaining more market share? Do they have better products? Every question then centres around how meaningfully you extracted insights from the data and embarked on a strategy which gives a competitive advantage in the market.

Business Analysts are regularly faced with tight deadlines, demands for results that are accurate and an abundance of data. As a result, they need to have an access to a much broader array of sophisticated techniques to explore data. Multivariate analysis is the only way which helps analysts to derive actionable findings quickly and effectively. These techniques can be applied to a large volume of data including demographics, sales and purchase patterns. Here is a list of five multivariate techniques which assist business analysts and marketing managers in knowing their customers better.

Data Checks:
Before you begin any statistical technique, it is important to know your data well. The form of the data refers to whether the data is non-metric or metric. Whether it is normally distributed, linear and has no outliers? Moreover, it is important to understand the magnitude of missing values in observations. We need to determine whether to ignore them or assign values to it.

Multiple Regression Analysis
Regression analysis is the most widely used multivariate technique to estimate the relationship among variables. It examines the relationship between a single metric dependent variable and one or more metric/non-metric independent variables. The statistical technique determines the linear relationship between the dependent and the independent variables, with the lowest sum of squared variances. In business, multiple regression analysis is commonly used as a forecasting tool.

Factor Analysis
The main application of factor analysis is to reduce the number of variables and detect structure in the relationships between variables. It is one of the most effective techniques to classify variables. This is an independence technique, in which there is no dependent variable. Factor Analysis is performed through two methods: Common Factor Analysis and Principal Component Analysis. The first method is based on the variance shared by the factors while the second method is used to find the fewest number of variables that explain the most variance. The factors are extracted as long as the eigenvalues are greater than 1.0. This is also represented by a scree plot.

Cluster Analysis
Cluster analysis is primarily used to group objects (eg. respondents, products, or other entities) based on similar characteristics. The grouping is done based on the distance between the objects. If plotted geometrically, the objects within the clusters remain together, while the distances between the clusters are farthest. Once the choice of distance measure is selected, the clustering algorithm groups members effectively. There are three clustering methods: Hierarchical Clustering, K-means Clustering, and Two- Step Clustering. Cluster analysis is majorly used in market segmentation. It helps marketing managers to segment customers into groups and formulate strategies.

Conjoint Analysis
Conjoint Analysis is a statistical technique used to design a product based on selected attributes. It enables marketers to determine what combinations of attributes are most influential on respondents’ choice. A part-worth, or utility, is calculated for each level of each attribute. The combinations of attributes at specific levels are summed to develop the overall preference for the attributes across levels. Conjoint analysis is actively used in product development, competitive positioning, product pricing, segmentation and resource allocation.

Discriminant Function Analysis
Discriminant Analysis is a technique to predict a categorical dependent variable (called a grouping variable) by one or more continuous or binary independent variables (called predictor variables). The purpose of the discriminant analysis is to maximally separate the groups and discard variables which are little related to group distinctions. The model is composed of a discriminant function based on linear combinations of predictor variables. Discriminant analysis formulates a linear discriminant function which describes the importance of the independent variables in differentiating observations of group membership. Discriminant analysis is one of the important tools for market segmentation.

The five techniques described above will act as an elixir to difficult marketing decision problems. These can be performed on different statistical packages including R, SAS or SPSP. Although these techniques are used to develop the blueprint of a successful marketing strategy, they are prone to misunderstanding. An experienced market researcher should avoid pitfalls in analysis and use his intuition to interpret the results. Great marketers follow their own instincts and sometimes it feels much more real than data and algorithms.


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