CHAID analysis: what it is, characteristics and how it is done

 CHAID analysis: what it is, characteristics and how it is done

CHAID analysis was developed with the purpose of segmenting a population into categories until there are no more important predictor variables or a certain criterion is satisfied.

In this article we will present you what exactly it consists of, what its characteristics are and how you can develop it.

What is CHAID analysis?

CHAID analysis (Chi-Square Automatic Interaction Detector) is a tool used as a market segmentation technique to discover the relationship between categorical response variables, as well as what characteristics define certain groups and outline which specific variables have a greater impact on the differentiation of the groups.

The analysis is based on the Chi-square test, finds patterns in data that has many categorical variables, creates segments, and then presents the data in a visual representation.

CHAID analyzes are used to segment different customer groups. This technique examines how they might respond to a market strategy and analyzes the data based on the attributes of each group.

Knowing some characteristics of the clients, the CHAID analysis can build a tree that divides the data set along the chosen variable, and shows the effect of the characteristics on the probability of response to the strategy.

Importance of CHAID analysis

1.One of the reasons for using CHAID analysis is that it divides the market based on the size or responsiveness of each category.

2.This technique contains no equations. Instead, it is visual, making it easy to understand, helping to segment the market through direct visual representation.

3.This segmentation makes it easy to prioritize marketing and market research resources, and you can analyze the response rate of the node based on a given benchmark.

4.It is then analyzed according to size. In this way, you can determine what you should dedicate more resources to.

5 Characteristics of CHAID Analysis

Here are 5 characteristics of CHAID analysis:

Predictive model.

 The analysis builds a predictive analysis, or tree, model to help determine how variables best combine to explain the outcome on the given dependent variable.

Nominal, ordinal and continuous data.

 Nominal data, ordinal data, and continuous data, in which continuous predictors are divided into categories with approximately the same number of observations, can be used in this analysis. CHAID analysis does not require the data to be normally distributed.

Cross tabulation

The analysis creates all possible cross tabulations for each categorical predictor until the best result is achieved and no further splitting can be performed.

Decision tree

In CHAID analysis we can visually see the relationships between the split variables and the associated related factor within the tree.

nodes.

 The analysis divides the target into two or more categories that are called initial, or parent, nodes, and then the nodes are partitioned using statistical algorithms into child nodes.

How to perform a CHAID analysis?

The analysis can be performed using a variety of inputs including scales (for example, satisfaction rating from 1 to 10) as well as categorical questions (for example, company demographics).

CHAID analysis can only be performed if the variables produce a statistically significant split in the research sample. Since the sample is repeatedly split, the technique works best if large sample sizes are used.

The first category of predictors used by the Chi-Square Automatic Interaction Detector analysis to divide the sample is the predictor that is most associated with the response variable, that is, it gives the most differentiated groups of respondents.

The "decision tree" is further built by dividing the customer base until the algorithm no longer finds any significantly discriminating predictors.

CHAID analysis has the advantage of providing details about the overall levels at each stage of the “decision tree”. As the analysis is used to identify specific groups and the characteristics they share, its applications are multiple.

Decision Tree Components in CHAID Analysis

In the CHAID-type analysis, the components of the decision tree are the following:

root node. It is the one that represents the dependent or destination variable.

parent nodes. They are the categories derived from the target variable by the algorithm.

Child nodes. They are the categories that are below the parent categories.

Terminal node: It is the category that presented the least influence on the dependent variable.

conclusion

Now that you know what CHAID analysis consists of and how you can carry it out, you would probably like to learn about other methodologies and techniques to carry out your research in a more practical and precise way.