CHAID Analysis

Chi-square automatic interaction detection (CHAID) analysis is a statistical analysis tool that uses a decision tree algorithm. You can use it to deepen understanding of your audience segments by uncovering interactions between different variables. You’re not just looking at what respondents directly tell you. But uncovering, sometimes subconscious, meaning behind their various decisions along the path to purchase. 

CHAID analysis uncovers valuable patterns in datasets with lots of categorical variables. It uncovers relationships between a response variable and predictor variables, and displays the information in a clear format that reduces the time needed for interpretation.

The decision tree diagram is essentially a multi-level flow chart. The CHAID analysis decision tree is divided into 3 different components:

• Root (or trunk): The main categorical response variable that’s the beginning of your decision tree analysis. This comes from the overarching question you want to investigate with this market research. For example: How successful was our latest email marketing campaign? The root of your CHAID analysis is the overall open rate from your marketing campaign, shown as a number and/or percentage. 
• Branch: We move down from the root, along the branches to the first, most significant factor that determines the open rate. For example, by industry – when there’s a particularly high number of opened emails in financial services, but less across higher education and human resources. 
• Leaves (or nodes): These first groupings become new leaves and the next branch leads to further subgroups (predictor variables) that demonstrate a statistically significant connection to the original response variable. In this case, email open rates. For example, geographical location or role in the buying team. These subdivisions continue until there are no remaining factors that contain useful information to contribute to the initial question. 

Rather counterintuitively, most CHAID diagrams start with the root at the top and you move down the branches to the leaves. ‘Upside down decision tree diagram’ is perhaps a more accurate name. Leaving literal interpretations aside, the clear visual summary of your dataset patterns is why CHAID analysis remains a popular tool in market research.

You can use CHAID analysis insights to put your audiences under the microscope and get even more value from your market segmentation research. Advanced statistical analysis can add molecular-level details to the brand personas of your target audience. 

Zooming out, you have prioritized segments that reveal where you should next target your resources. NewtonX CHAID analysis gives you detailed profiles of each of your market segments based on verified data, at the scale necessary for actionable insights. Discovering this extra value from your data means that you can better answer the question, ‘What’s important to our clients?’ Your sales, marketing and product departments can all use this information to make decisions that are finely tuned around your audiences’ needs, pain points, wants and behaviors.