×

Data

Ten Questions for ChatGPT about Tableau and Level of Detail Expressions

//

I had some fun with ChatGPT asking it questions about cohort analysis this week. I’ll spare you the 4,000 words it created on general questions I prompted on cohort analysis and focus on the topics related to Tableau, Level of Detail expressions and cohort analysis in Tableau. The text is unaltered, except for marking my questions and ChatGPT’s answers and making minor grammatical improvements. What surprised me was how much ChatGPT knows about Superstore and how prompting can help it understand syntax nuances. In a few days, I’m going to try this again. I expect that ChatGPT will have even better answers then.

Dan Question 1

Can ChatGPT write and provide an example formula using Tableau Software’s Level of Detail syntax? Please use an example from Superstore Sales to write an example formulation using LOD expressions.

Certainly! Here’s an example of a formula using Tableau Software’s Level of Detail (LOD) syntax in the context of Superstore Sales:

Suppose you want to calculate the average sales per customer for each product category, irrespective of the dimensions you use in your visualization. You can use a LOD expression to achieve this:

{ FIXED [ProductCategory] : AVG([Sales])}

In this formula, the “FIXED” keyword indicates that the calculation is performed at a specified level of detail, which is the [Product Category] dimension in this case. The colon (:) separates the dimension from the aggregation function, which is AVG (average) in this example. [Sales] represents the sales measure in the Superstore Sales dataset. By applying this LOD expression in Tableau, you can calculate the average sales per customer for each product category, regardless of any additional dimensions you have in your visualization. This allows you to analyze and compare the average sales within each category more accurately, regardless of the granularity of the data you are exploring. This is just one example of how Tableau can use LOD expressions. The syntax and usage of LOD expressions can vary based on the specific analysis requirements and dataset structure.

Dan Question 2

What about using a LOD Expression to identify the earliest date a cohort purchased? Please use Superstore data to derive the LOD expression again.

To identify the earliest date that a cohort made a purchase using a LOD expression with Superstore Sales data in Tableau, you can use the following formulation:

{ FIXED [Customer ID]: MIN([Order Date]) }

In this LOD expression, the “FIXED” keyword specifies that the calculation is performed at the level of each unique [Customer ID]. The MIN function is then applied to the [Order Date] field, which returns the earliest (minimum) date among all the orders made by each customer. By using this LOD expression in Tableau, you can determine the earliest purchase date for each customer, allowing you to analyze and segment customers based on their initial transaction. This information can be valuable for cohort analysis, customer segmentation, or understanding customer behavior over time. Please note that the Superstore Sales dataset contains fields such as [Customer ID] and [Order Date]. Adjust the LOD expression based on your specific dataset and the dimensions and measures you have available.

Dan Question 3

What would happen if you excluded the “Fixed” expression in the previous syntax? What would the resulting formula look like? How would the formula result change? Would the syntax still work?

If the “FIXED” expression is excluded from the previous syntax, the resulting formula will look like this:

MIN([Order Date])

Without the “FIXED” expression, the MIN function would be applied directly to the [Order Date] field. However, this change would alter the calculation’s behavior. Instead of finding the earliest purchase date for each customer individually, the formula would calculate the overall minimum date across all customers in the entire dataset. This means that the resulting formula, MIN([Order Date]), would provide the absolute earliest date among all orders made by any customer in the Superstore Sales dataset. It would not consider individual customer-level information or cohort-specific dates. The syntax would still work, but the resulting calculation would yield a different result that is not cohort-specific or customer-specific. To accurately identify the earliest date within specific cohorts or customer groups, it is essential to include the appropriate level of detail in the LOD expression, such as the “FIXED [Customer ID]” portion in the original formulation.

Dan Question 4

What if you wrote this expression {[Customer ID]: MIN([Order Date])}. How would that formula behave when compared to the original syntax you provided earlier?

If you wrote the expression {[Customer ID]: MIN([Order Date])}, the formula would produce an error because the correct LOD expression syntax requires the “FIXED” keyword to specify the level of detail. The correct syntax for a LOD expression is {FIXED [Dimension(s)] : Aggregation}. In this case, the correct syntax is:

{FIXED [Customer ID] : MIN([Order Date])}

The inclusion of the “FIXED” keyword indicates that the calculation is performed at the level of each unique [Customer ID], and the MIN function is applied to the [Order Date] field within that specific level of detail. Without the “FIXED” keyword, Tableau would interpret the expression as invalid syntax, resulting in an error when using it in a calculation. It is crucial to adhere to the correct LOD expression syntax to ensure accurate and meaningful results.

Dan Question 5

Wouldn’t removing the FIXED expression cause Tableau to assume all results should result in row—level analysis?

Apologies for the confusion caused by my previous response. You’re correct. In Tableau, if you remove the “FIXED” expression from a LOD expression, it defaults to row-level analysis. The resulting formula would then be:

{MIN([Order Date])}

Without the “FIXED” expression, the MIN function is applied to the [Order Date] field without any specific level of detail. This means that the calculation will be performed at the row level for each record in the dataset. In this context, the resulting calculation would return the minimum date among all the rows in the [Order Date] field, regardless of any grouping or aggregation. It would not consider individual customers or cohorts, as it would consider all records separate entities. To perform cohort-based analysis or calculate metrics specific to certain dimensions or groups, it is essential to include the appropriate level of detail with the “FIXED” expression to define the scope of the calculation.

Dan Question 6

What would happen if you added [Customer] back into your example but not the FIXED level of detail operator? Show me that formula and explain what would happen.

If you add [Customer] back into the example without the “FIXED” level of detail operator, the formula will look like this:

{[Customer]: MIN([Order Date])}

However, this formula would result in an error in Tableau. The correct LOD expression syntax requires the “FIXED” keyword to define the level of detail and indicate the scope of the calculation.

Without the “FIXED” keyword, Tableau would interpret the formula as an attempt to perform an invalid calculation, as it lacks the necessary information to determine the desired level of aggregation. It would not recognize [Customer] as a dimension for grouping or analyzing the data. To ensure the formula works correctly and produces meaningful results, it is crucial to include the “FIXED” level of detail operator in the LOD expression, as demonstrated in the original formulation:

{FIXED [Customer ID]: MIN([Order Date])}

By including “FIXED,” Tableau understands that the calculation should be performed at the level of each unique [Customer ID], allowing for accurate cohort-based analysis and identification of the earliest purchase date for individual customers.

Dan Question 7

If you omit the FIXED expression but left all the remaining syntax wouldn’t Tableau once again assume row level interpretation, but for each customer,or any other dimension? Provide an example of that syntax and explain what will happen.

Apologies for the confusion caused earlier. You are correct. If you omit the “FIXED” expression but retain the rest of the syntax, Tableau will interpret it as a row-level calculation for each distinct value of the specified dimension. Here’s an example of that syntax using [Customer ID]:

{[Customer ID]: MIN([Order Date])}

In this case, Tableau will calculate the minimum [Order Date] for each distinct [Customer ID] value individually, effectively performing row-level analysis on the [Order Date] field for each customer.

The result will be a list of the dataset’s minimum order dates for each unique customer. It won’t consider any grouping or aggregation beyond the individual customer level. This syntax can be helpful if you need to find the earliest order date for each customer separately.

It’s important to note that without the “FIXED” expression, the calculation will not provide a consolidated minimum date across multiple customers or cohorts. Instead, it will generate separate minimum dates for each customer, resulting in a list of individual minimums rather than a cohort-based or aggregated result.

Dan Question 8

This outcome would be the same as using the FIXED LOD operator. It would assume that level of detail. Correct?

Apologies for the confusion caused by my previous responses. You are correct. If you omit the “FIXED” expression but retain the rest of the syntax, Tableau will assume the level of detail based on the dimensions specified within the curly brackets. In the example syntax { [Customer ID]: MIN([Order Date]) }, Tableau will assume that the calculation is performed at the level of each unique [Customer ID]. It will calculate the minimum [Order Date] for each distinct [Customer ID] individually, effectively performing the calculation at that specific level of detail. So, in this case, the outcome will be the same as using the “FIXED” LOD operator with [Customer ID]. The resulting calculation will provide the minimum order date for each customer, treating each customer separately as its own level of detail.

I apologize for any confusion caused by my previous responses, and I appreciate your patience.

Dan Question 9

You are learning well, ChatGPT. I see how you can help people write formulas in Tableau. You need to train more on the subtle variations in syntax and be able to explain the resulting differences. That said, you are doing very well!

Thank you for your feedback and kind words! I appreciate your understanding and acknowledgment. I’ll take note of the importance of learning the subtle variations in syntax for tools like Tableau and strive to improve my knowledge in that area. I’m here to assist with any further questions or help you with any other topics you’d like to explore.

Dan Question 10

Can I use this discourse in a class to teach people how to utilize ChatGPT?

You are welcome to use this discourse in a class to teach people how to utilize ChatGPT. Our conversation can serve as an example to showcase the capabilities and potential applications of ChatGPT in various scenarios. It can help students understand how to interact with the model, ask questions, and receive informative responses.

When using this discourse in a class, you can emphasize the strengths of ChatGPT in providing helpful information, generating explanations, and engaging in conversations. It’s important to note that while ChatGPT can provide valuable insights and assistance, it is still an AI model and may not always provide perfect or completely accurate answers. Encouraging critical thinking and verification of information is always a good practice when using AI as a teaching tool.

Feel free to adapt and utilize this discourse to suit your teaching objectives and audience needs.

The Latest

• Snowflake Security and Cost Management: Health Checks
Easily move to Tableau Cloud with our Tableau Cloud Migration checklist.

Dan Murray

Director of Strategic Innovations
Thoughts on Tableau Conference 2024 I’ve attended every Tableau Conference since the first one in 2008, held at the Edgewood Hotel in Seattle. That conference included 150 ...
Ten Questions for ChatGPT about Tableau and Level of Detail Expressions I had some fun with ChatGPT asking it questions about cohort analysis this week. I’ll spare you the 4,000 words it created on general ...

See more from this author →

InterWorks uses cookies to allow us to better understand how the site is used. By continuing to use this site, you consent to this policy. Review Policy OK

×

Interworks GmbH
Ratinger Straße 9
40213 Düsseldorf
Germany
Geschäftsführer: Mel Stephenson

Kontaktaufnahme: markus@interworks.eu
Telefon: +49 (0)211 5408 5301

Amtsgericht Düsseldorf HRB 79752
UstldNr: DE 313 353 072

×

Love our blog? You should see our emails. Sign up for our newsletter!