Discover the CHAID Segmentation Model
CHAID (Chi-Square Automatic Interaction Detector) analysis is a marketing segmentation technique. It is useful for identifying the relationships between categorical response variables. It also works for other combinations of variables. CHAID finds patterns in data that has a lot of categorical variables. It creates segments and then presents the data in a visual representation.
In practice, we use CHAID analysis as a market research tool. We use it to segment different groups of customers. CHAID looks at how they might respond to a marketing campaign. It analyzes according to the attributes of each group. For instance, let’s say we are running a marketing campaign. We’ll need to know some customer characteristics. For example, we’ll need a geographical location, socio-economic status, and gender. These characteristics have the most impact on response rates. With CHAID analysis, we build a tree that splits the data set along the chosen variable. It shows the effect of the characteristics on the probability of a response to the campaign.
CHAID as a Market Segmentation Technique
One advantage of the CHAID analysis is that it does not contain equations. Instead, it is visual, making it easier to understand. It segments the market under consideration in a straightforward visual representation. For instance, in the image above, the bottom nodes represent the market segmentation. It divides up your market based on the size or responsiveness of each category. Such segmentation makes it easy to rank marketing resources according to priority. You can analyze the response rate of the node according to a given benchmark. Next, you analyze according to size. In this way, you can determine where you need to dedicate more resources. Segments with higher response rates represent low hanging fruit. They will yield high response rates yet do not need a lot of marketing effort. You can thus avoid the segment that has smaller than average response rates. You will need more resources to market to this segment, and you’ll reap fewer rewards.
CHAID Analysis determines the relationship between independent variables for analytical studies. Businesses also use it as a predictive tool. By employing CHAID analysis, you can uncover essential relationships between dependent variables. These could include prior purchases, frequency, recency, promotion, price, and product. These variables may affect response to market campaigns such as mail solicitation. The CHAID regression tree makes it easy to predict these responses.
You can also use the CHAID analysis to do a quantitative analysis. You use it by testing for association between two variables. You apply Chi-Square tests when building the CHAID tree. In this way, you determine the statistical significance of predictors of response rates. For instance, you can use Bonferroni corrections to account for false positives. Market research using CHAID can thus use regression analysis for data mining. You can also use it for statistical modeling and analysis.