A guide to CHAID: a decision tree algorithm for data analysis

A guide to CHAID: a decision tree algorithm for data analysis

Advanced statistical techniques offer a deeper analysis into your market research insights. By scientifically modeling scenarios on proprietary data, you can explore beyond customers’ claimed behavior and get more comprehensive answers to your research questions. 

Chi-square automatic interaction detection (CHAID) is one such tool. By performing a regression analysis on customer or respondent data, you learn how different factors affect your sales and marketing activities.

The insights inform how to personalize your approach with different customer groups. And fast-growing customers drive 40% more of their revenue from personalization compared to their competitors, according to McKinsey.

Arguably, CHAID’s key advantage over trade-off analysis techniques is its highly visual outputs that are easy to understand and share. That makes it a great tool to use when planning to share insights across the business for different teams to review. 

Purposes for using CHAID in a B2B research project include:

Contents

What is CHAID?

CHAID use cases

Interpreting CHAID decision trees

Advantages of using CHAID

Using CHAID for data analysis: best practices in B2B

 

 

What is CHAID?

CHAID is a predictive model used to forecast scenarios and draw conclusions. It’s based on significance testing and came into use during the 1980s after Gordon Kass published An Exploratory Technique for Investigating Large Quantities of Categorical Data.

It involves:

  • Regression: A statistical analysis to estimate the relationships between a dependent response variable and other independent ones.
  • Machine learning: Artificial intelligence that leverages data to absorb information, then makes logical predictions or decisions.
  • Decision trees: Branching models of decisions or attributes, followed by their event outcomes.

Early applications included medical and psychiatric research but today, it’s a useful market research and statistics tool.

CHAID determines and analyzes the relationship between a response variable and others, so you can forecast how to have the biggest impact. The CHAID algorithm splits nodes to produce chi-square values.

A chi-square value is the difference between a standard, expected scenario and the actual results observed in your data. 

The maximum chi-square value is the most statistically significant result in your CHAID decision tree. In other words, it’s the strongest relationship between two variables out of found chi-square values.

Splits with higher total expected chi-square values suggest stronger associations between the variables – i.e. more significant differences in the decision tree.

By finding these associations in B2B market research, you can discover different segments in your customer base – each with specific traits that will inform your targeting tactics.

It may be a legacy decision tree algorithm, but CHAID continues to be a widely used method for analyzing data. It also supports Python implementation, often used in market research for predictive modeling studies.

CHAID use cases

Now that we’ve established CHAID’s principles, what are some of the potential use cases in B2B market research?

  • Market segmentation: Statistical analysis techniques such as CHAID can help you identify segments in your customer base. Understanding the variety of traits, wants, needs, and behaviors in your target audience should inform a tailored approach to sales and marketing.
  • Perception/brand tracking: Understanding the different segments for specific brand perceptions, plus these groups’ notable characteristics. For example, you could use CHAID to analyze different groups’ customer satisfaction or likelihood to recommend.
  • New product development: Testing a detailed concept with fleshed-out features using CHAID can support product development. A decision tree can show how different groups would respond to a product concept and reveal the impact of different characteristics. Additionally, you could use CHAID to evaluate how customers would choose between multiple concepts.
  • Marcomms testing: As with product testing, you could test how customer appeal for different messages varies, or how different segments would respond to a single marketing message.

Interpreting CHAID decision trees

Resembling a flow chart with multiple paths, CHAID decision trees are a highly visual way to display data and are simple to interpret (once you know how to, that is).

A CHAID diagram typically includes:

  • Root: This is the starting point for the decision tree, with lines – a.k.a. branches – stemming from it. In market research, the root shows the overall factor used to find contrasting segments.
  • Branches: Branches connect the nodes, also known as leaves. In research, where the prediction model identifies several customer segments, there will be many branches – each with its own leaves.
  • Leaves: Leaves show the criteria included in the final prediction model. In market research analysis, leaves are the variables or traits found in a specific customer segment.

Here is a simple decision tree root with branches and leaves. This example explores hypothetical segments around likelihood to purchase a new product from Brand X after launch, to help the company understand the customer journey.

The root shows the average likelihood to purchase among customers based on an online survey, in this case, 6/10. From there, the CHAID algorithm creates branches to separate those who are above or below the average.

This produces two contrasting leaves – customers who are 8/10 likely to buy versus those who are only 4/10 likely.

CHAID then splits these nodes again looking for segments with common traits. It finds that common ground for customer groups is based on criteria including CSAT scores, previous purchases, spend value, membership type, having an account manager, and so on.

Advantages of using CHAID

CHAID’s benefits include:

  • Reading between the lines: By scientifically evaluating the relationship between variables, you could get more accurate results – compared to asking respondents directly. Sometimes, respondents do not know exactly why they make certain decisions and cannot pinpoint the driving factors themselves.
  • Detailed outputs: CHAID’s decision trees can be broad e.g. with several long branches and lots of leaves. In contrast, some other decision tree techniques are binary i.e. they only produce two long branches showing a couple of outcomes.
  • Simple to understand and share: Decision trees are more visual than, for example, data tables. They have a logical flow and show results in a more self-explanatory way than many other statistical techniques. Therefore, the results are easier to share with different teams across your business.

CHAID isn’t right for every market research project. Sampling is a key factor, for example. Our best practices explain how to get the most out of it and see if it works for your research objectives.

Using CHAID for data analysis: best practices in B2B

#1 Prune the decision trees

While CHAID makes use of machine learning, it won’t have the full context of what matters most to your business.

Some human input helps shape the analysis to maximize its usefulness. In CHAID, that often requires some ‘pruning’ of your decision tree outputs.

Your initial decision tree results may include leaves that are relatively less important to stakeholders, for example.

By taking these out of the equation, you can simplify a tree – making it simpler to understand and more useful for meeting your research objectives.

#2 Prioritize the big picture over methodological detail

Setting up a CHAID analysis can be complex. Therefore, it’s easy and common to debate small tweaks to parameters that could alter the research methodology or approach.

Ultimately, minor changes tend to have little impact on the overall results. But if discussed in detail, they can have a major impact on project timelines and slow down the process.

Prioritize the end goal of a statistical market simulation, rather than getting every precise methodological detail right. CHAID is a quick technique and you can re-run it if necessary.

#3 Use a robust sample size

CHAID analysis requires a large sample size, to split customers into multiple groups and test for a significant difference between them.

It means that quantitative market research projects with a low sample size won’t get good results from CHAID.

So, check that your B2B market research projects are robust. Take into account the difficulty of recruiting large numbers of genuine, senior decision-makers in your target market that are willing to participate in market research.

Some other trade-off analysis techniques could accommodate lower sample sizes.

 

Summary

A guide to CHAID: a decision tree algorithm for data analysis

Purposes for using CHAID in a B2B market research project include: refining buyer personas and segments; understanding data behind brand perceptions; informing product or service features; exploring customer reactions to marketing; predicting and influencing the buying process.

What is CHAID?

CHAID is a predictive model used to forecast scenarios and draw conclusions, involving regression, machine learning, and decision trees.

It analyzes the data to apply chi-square tests. The algorithm runs chi-square tests on the variables, shown as nodes.

CHAID splits the chi-square test value again, then any subsequent ones, and so on. It only stops when it cannot find chi-square values with significant differences.

CHAID use cases

Use cases for CHAID in B2B market research include: market segmentation; perception/brand tracking: new product development; marcomms testing.

Interpreting CHAID decision trees

A CHAID diagram typically includes:

Root: The overall factor used to find contrasting segments.

Branches: Showing the variables contributing to the segments.

Leaves: Individual traits or characteristics found by CHAID.

Advantages of CHAID

CHAID’s benefits include: Reading between the lines of customers’ claims and reported behavior; Detailed outputs – CHAID’s decision trees are broad, not binary; simple to understand and share with teams across your business.

Using CHAID for data analysis: best practices in B2B

When using CHAID, we recommend that you: Prune the decision trees; prioritize the big picture over methodological detail; use a robust sample size.

Chris Wells
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