Enhancing Root Cause Analysis with Contribution Explorer

Mastering Snowflake
5 min readJun 19, 2024


Thank you for reading my latest article Enhancing Root Cause Analysis with Contribution Explorer

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Root cause analysis is a crucial aspect of data-driven decision-making, enabling organizations to pinpoint the factors behind changes in key metrics. Snowflake’s Contribution Explorer is a powerful tool designed to streamline and enhance this process. In this blog post, we will explore what Contribution Explorer is, its benefits, and how to utilize it effectively.

What is Contribution Explorer?

Contribution Explorer is a tool developed to simplify and improve the process of root cause analysis for changes in observed metrics. By analyzing the values of a specific metric over time, Contribution Explorer identifies the data segments driving these shifts. This helps businesses understand the underlying factors influencing their key performance indicators (KPIs).

In our series on Snowflake Cortex, we’ve covered the Classification, Anomaly Detection and Forecasting ML functions which leaves one more; the Contribution explorer. This works in a different way to the others which require you to build and train and model before passing new data in. The Contribution explorer acts more like a table function analysing the data set you pass into it. Also, this function isn’t called contribution explorer — the function is actually called top_insights!

Why is Contribution Explorer Useful?

Contribution Explorer is invaluable for organizations looking to quickly identify and address issues affecting their metrics. For instance, if a company notices a decline in sales, Contribution Explorer can determine whether the drop is due to specific locations, salespeople, customers, industry verticals, or other factors. This allows for immediate, targeted corrective actions, minimizing the impact of negative trends.

Key Features and Capabilities

  1. Segmentation of Data: Contribution Explorer excels at segmenting data based on various dimensions. These dimensions can be categorical, such as location or market segment, or continuous, like temperature or attendance. This flexibility ensures that Contribution Explorer can handle diverse datasets and provide insights tailored to different business needs.
  2. Root Cause Analysis: By focusing on shifts in data concerning a particular metric, Contribution Explorer highlights the segments that have the most significant impact. This makes it easier to identify the root causes of changes and take corrective actions promptly.

Using Contribution Explorer

To leverage Contribution Explorer in your queries and pipelines, you can use the TOP_INSIGHTS (SNOWFLAKE.ML) table function. This function is designed to find the most important dimensions within a dataset, build segments from those dimensions, and detect which segments have influenced the metric in question. Here’s how to get started:

  1. Identify Candidate Datasets: Good candidate datasets for analysis with Contribution Explorer should have columns or dimensions that can be used to segment the data. These columns are often categorical but can also be continuous.
  2. Call the TOP_INSIGHTS Function: Incorporate the TOP_INSIGHTS (SNOWFLAKE.ML) function in your queries. This function will analyze your dataset, identify the critical dimensions, and highlight the segments driving changes in your metric.

Practical Example: Sales Analysis

Imagine you are tracking sales performance and notice a revenue shortfall. Using Contribution Explorer, you can quickly identify which factors are contributing to this decline. Here’s a step-by-step example:

  1. Prepare Your Data: Ensure your sales data includes various dimensions such as location, salesperson, customer, and industry vertical.
  2. Apply Contribution Explorer: Use the TOP_INSIGHTS function to analyze the sales data.
  3. Interpret the Results: Contribution Explorer will highlight which locations, salespeople, or customer segments are driving the revenue shortfall.
  4. Take Action: Based on the insights, you can implement targeted strategies to address the identified issues, such as providing additional training to underperforming salespeople or focusing marketing efforts on struggling regions.

Check out our demo

In this video we use Snowflake’s native functions to create mortgage application data which contains a bunch of fraudulent activity. We apply our contribution explorer using the top_insights function to help identify those segments linked to fraudulent applications.


Contribution Explorer is a powerful tool for enhancing root cause analysis, providing organizations with the insights needed to make data-driven decisions quickly and effectively. By segmenting data and identifying the key drivers behind metric changes, Contribution Explorer enables businesses to take targeted actions and improve their performance.

To stay up to date with the latest business and tech trends in data and analytics, make sure to subscribe to my newsletter, follow me on LinkedIn, and YouTube, and, if you’re interested in taking a deeper dive into Snowflake check out my books ‘Mastering Snowflake Solutions and SnowPro Core Certification Study Guide’.

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About Adam Morton

Adam Morton is an experienced data leader and author in the field of data and analytics with a passion for delivering tangible business value. Over the past two decades Adam has accumulated a wealth of valuable, real-world experiences designing and implementing enterprise-wide data strategies, advanced data and analytics solutions as well as building high-performing data teams across the UK, Europe, and Australia.

Adam’s continued commitment to the data and analytics community has seen him formally recognised as an international leader in his field when he was awarded a Global Talent Visa by the Australian Government in 2019.

Today, Adam is dedicated to helping his clients to overcome challenges with data while extracting the most value from their data and analytics implementations. You can find out more information by visiting his website here.

He has also developed a signature training program that includes an intensive online curriculum, weekly live consulting Q&A calls with Adam, and an exclusive mastermind of supportive data and analytics professionals helping you to become an expert in Snowflake. If you’re interested in finding out more, check out the latest Mastering Snowflake details.



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