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Tableau data in use: How to compute Login frequency

Updated: Aug 18, 2021

Computing login frequency

The modern business has a lot of touch points with its customers and the environment it operates in. All these point of interactions leave some trails which can tell a story on the health of business relationship with customers. This could be buried in different data sources ranging from social interaction data, point of sale data, supply chain data, finance data, marketing data or even log in data from various data monetization platforms.

The goal in this article is exploring a simple technique of computing login frequency. We’re assuming the position of IT manager of ‘Brand X’, which has recently launched a portal from which different subscribers can consume various industry metrics through a uniquely assigned login credentials.


As the person in charge of this portfolio, I would like to understand how frequently my clients use the portal, with a broad understanding of the metrics at different categories like Segment. This will help me understand whether the portal is delivering the projected numbers at the beginning of the project.

Using Superstores data set, my target columns for this article are ‘Order Date’ now renamed ‘Log in Date’, ‘Customer ID’ and ‘Segment’. Where ‘Customer ID’ is a unique identifier for each customer.

Sample of the dataset

Connect Superstores data set to the Tableau app and follow the guideline below.

Step 1: Compute First & Last log in date for every customer

Using the formula below.

computing first login date

computing last login date

Step 2: Compute the time lapsed between ‘First Log in Date’ and ‘Last Log in Date’

The time unit could vary between, days, months, years, weeks etc. However, in this article we’ll compute time lapsed in months using the formula below.

computing time lapsed

Step 3: Count number of times user logged in

Using the calculation below.

computing number of logins per cutomer

Step 4: Compute Log in frequency

Now, that we’ve the number of months the customer has been active and the number of time he or she has logged into the portal. We can compute the login frequency using the formula below.

computing login frequency

Rounding our calculation to the nearest integer we’ve.

rounding the calculation to the nearest integer

Step 5: Lets build a simple chart to present our findings

  • First drag the measure field Frequency bin to the dimension area.

  • Drag again now dimension field Frequency bin to the Columns shelf.

  • Drag count distinct Customer ID to the Rows shelf.

  • Drag dimension field Segment to the filters, Show Filter.

view visualizing login frequency

Adding Table calculation by computing ‘Percent of Total distinct count of Customer ID’ we’ve.

Using the quick filter, I can drill down to other segments. See below.

With this simple view, I can fully understand how actively my customers are using the portal, reach out to customers with fewer logins and seek to understand the challenges they face, monitor whether this problem is manifested in other customer categories and address the issues accordingly. With good understanding of my data as the IT manager, I can see crisis and respond before it’s too late.

I hope with this article you’ll find a good reason to begin questioning your data and stay informed on your business from the data point.

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About Me

More About the Author

Bernard K

Analytics Consultant | 3X Tableau Certified

Bernard is a data analytics consultant helping businesses reveal the true power of their data and bring clarity to their reporting dashboards. He loves building things and sharing knowledge on how to build dashboards that drive better outcomes.

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