Salesforce Pulse is Tableau’s recent addition to Tableau Cloud that allows you to measure performance metrics over time with pre-built analysis capabilities. Arguably the most common and important aspect of business intelligence, metric tracking and analysis is critical for business leaders to keep their finger on the “pulse”.
In this article, we will explore the process for setting up metrics and discuss some important considerations while implementing metrics that will help you build excitement and adoption!
What is Tableau Pulse?
Tableau Pulse streamlines the tracking and analysis of business metrics within Tableau Cloud, focusing on simplicity and efficiency for users. Unlike building dashboards in Salesforce, which requires in-depth data manipulation and visualization efforts, Tableau Pulse offers an intuitive approach, enabling users to easily define, manage, and monitor key performance indicators.
The Tableau product team has provided a true plug-and-play solution for metrics that is a useful starting point. Pulse is simple by design, so with the right expectations and careful delivery, I think you can get a lot of value into a business user’s hands without too much effort. As someone who spends a lot of time designing and managing Salesforce metrics and KPIs, I was delighted to see the initial result with Pulse.
Its advantage lies in providing users with pre-built analytical capabilities that offer insights into factors influencing metric fluctuations over time without the need for extensive technical skills. This user-friendly method allows for a quicker and more accessible way to stay informed about important data trends, making it simpler to derive actionable insights.
Tracking Measures and Metrics – What’s the Difference?
Before we get into the technical details of Pulse, it’s important to understand the building blocks that make up metrics and KPIs that Pulse prepares and analyzes. Sometimes it is hard to discern measures, metrics, and KPIs as the terms are utilized across business, analytics, and technology teams.
When you implement Tableau Pulse and build metrics, Tableau is forcing some important best practices to ensure you define the what core components of a metric are, which may require some planning and scoping.
Key Performance Indicator (KPI): Metrics and KPIs are similar in concept and are interchangeable. Key performance indicators are synonymous with balanced scorecards but should be linked directly to organizational goals and objectives. Ideally, you have a small number of impactful KPIs that you monitor with any number of supporting metrics that help you understand why KPIs are performing well or not. A well-designed KPI should have a target, timeline, and owner who is accountable.
Measure: A measure is a numeric value that is typically aggregated and analyzed. A measure can be an event that occurs over time like Lead Conversions or it can be a value like Revenue.
Fact: A fact can be the same as a measure both in naming and structure. However, a fact can have its own business logic applied differently to a measure. Additionally, facts can be aggregated – if you wanted a fact that measured lead to opportunity conversion ratio you would represent the fact as:
Lead Conversion Ratio = COUNT(Id) / COUNT(ConvertedOpportunityId)
Metric: A metric should have a single, business definition that explains how a metric influences performance (good or bad), and a time dimension so you can understand the metric performance over time. When you define a metric you should generally understand if an increasing value is good or bad.
Choosing Your First Pulse Metrics
When I started using Tableau Pulse, I started with key performance indicators that already have been adopted and implemented in Tableau. This allows me to sanity-check the numbers and focus on delivering incremental value. My approach assumes that if I can get users to adopt Tableau Pulse for the most impactful metrics and KPIs, then I have the momentum to explore new metrics.
In my case for this article, I focused on my own business at DataToolsPro where my north star metric is “Activation Rate”. This is a KPI that measures the percentage of leads that successfully log in and use the service.
Key Features of Tableau Pulse
- Personalized insights and metrics tracking.
- Email and Slack digest for following metrics.
- Integration with Tableau AI for personalized insights summaries.
- Simplified metrics definition and management.
Enable Tableau Pulse
Before you can build with Pulse, you will need to enable Pulse in Tableau Cloud.
- From the main Tableau Cloud navigation menu, select Settings.
- Under Tableau Pulse Deployment, select Turn on Tableau Pulse.
- Choose whether to turn on Tableau Pulse for all users or for a specified group.
- If you choose to limit Tableau Pulse to a group, select the group.
- Select Save.
Create your First Pulse Metric
From Tableau cloud, select “Tableau Pulse” and then select “New Metric Definition”.
The first step in creating a metric is to select a data source that holds the data for your metric; you are not restricted to Tableau-specific data sources. In my case, all of our data is in Snowflake, so I selected “Lead with Web and Lead Info”. With your data source selected, we will explore field by field how to build your metric.
Metric Name
Your metric should align to business outcomes and shouldn’t indicate specific dimensions or periods.
Description
The description for your metric is extremely important – this is the context that your business users will have to understand what they are looking at. While this article won’t dive into the typical challenges and considerations for metrics, it is important to be descriptive and detail how your metric is formulated.
Measure
The measure is arguably the most important part of your metric as it ensures you are measuring the right metric. Within my data, I have NULL values, so I created a Tableau formula like the following:
Otherwise, with NULL values in your measure you limit the insights that Pulse will present:
Now with my activation measurement defined, I select an aggregation as “SUM”.
Time Dimension
With your metric defined, you now need to define a time dimension so you can measure the results of your metric over time. Considering historical context to understand how you got to the current state and improving/declining performance is a staple for Tableau Pulse.
In my case, I have a “First Activated” date in my data source.
The “Compared to” provides standard period comparison functions that will tell you if your performance has improved or is decreasing over time.
Create Advanced Definition
Tableau Pulse provides an advanced configuration option, allowing you to use a familiar Tableau experience to define your measure, time dimension, and analysis dimensions:
In addition to showing a running total, you can also let Tableau Pulse know to render your metric as “non-cumulative.” For example, if your data is formatted as a point in time like balances or customer count, you would not want Tableau to sum that data over time.
Dimensionality for Analysis
Adding dimensionality to your metric will give you real insights into what is influencing the increasing or decreasing performance of your metric. To define your dimensions, you should ask yourself and business leaders “What influences my metric over time?”.
In my scenario, the geography, the source of the lead, and the pre-qualification status all influence my activation rate.
Number Format
The number format will ensure Tableau Pulse renders data thematically correct. You can also adjust the metric taxonomy so that when Pulse delivers automated insights, it reads correctly.
Insights
With your metric created, it’s time to get the real “pulse” of what’s influencing your metric and lead your users down the path of understanding. Pulse Insights was created to automate common, logical data analysis processes historically, which requires human analysis.
This section will require some experience and common sense to ensure the insights you provide are relevant to your metric. As Insights is a data-driven solution, you may sometimes find insights redundant or not useful.
Configuring Insights
The first thing you need to do for configuring insights is let Tableau Pulse know the meaning for when your metric is increasing. When the value goes up for the activation rate, that is a favorable action, so I set the value to “favorable”. If I was measuring customer churn for example, the value going up would be “unfavorable”.
Insight Types
For each metric you can select or deselect a number of insight types that Tableau Pulse will perform and summarize. I have provided a summary of each analysis type.
Risky Monopoly
The “Risky Monopoly” analysis identifies when a disproportionate amount of value, such as sales or activations, is concentrated within a specific segment, indicating potential vulnerability if that segment’s performance declines.
For example, if 80% of your product activations are coming from leads generated via social media ads, this analysis would highlight the risk of relying too heavily on a single lead source for your businesses’ success.
Top Drivers
The “Top Drivers” analysis pinpoints the segments or factors that have contributed the most to an increase in a specific metric, such as sales or user activations, over a defined time period.
For instance, if your activations saw a significant spike last month, this analysis could reveal that the majority of this growth was driven by leads from email marketing campaigns targeting the European market, identifying it as a key growth lever during that period.
Current Trend
The “Current Trend” analysis examines the recent momentum of a metric, assessing its rate of change, direction (increase or decrease), and any fluctuations, to provide insights into its immediate trajectory.
For example, if the analysis of your activations metric reveals a consistent 10% month-over-month increase with minor fluctuations, it indicates a strong and stable upward trend in user activations across all segments.
Bottom Contributors
The “Bottom Contributors” analysis identifies the segments or factors that contribute the least to a particular metric, such as sales or activations, highlighting areas that may need improvement or strategic reevaluation.
For example, this analysis might show that despite a broad marketing strategy, activations from direct mail campaigns in the Southeast region are significantly lower than other channels and regions, pointing to a potential area for optimization or reallocation of resources.
Top Detractors
The “Top Detractors” analysis focuses on identifying the segments or factors that have led to the most significant decreases in a specific metric, such as sales or activations, over a defined period, pinpointing areas of concern that may require immediate attention or strategy adjustments.
For instance, if the analysis reveals that activations through paid search campaigns have seen the largest decline in the past quarter, particularly in the tech industry segment, it highlights this channel and market as the top detractors of your overall growth, suggesting a need for reassessment or intervention.
New Trend
The “New Trend” analysis identifies emerging patterns within a metric, focusing on the rate of change and fluctuation to highlight nascent trends that could influence future strategy or performance.
For example, this analysis might uncover that after introducing a new product feature, there’s a rapidly growing interest from a previously untapped demographic, signaling an emerging trend with a high rate of adoption and potential for significant impact on your overall activations.
Conclusion
Salesforce Pulse is a significant leap forward in making data analytics more accessible and actionable for business users. By automating the complex process of data analysis, Pulse empowers organizations to swiftly identify trends, understand performance drivers, and make informed decisions. Its suite of pre-built analysis types, such as “Risky Monopoly,” “Top Drivers,” and “New Trend,” among others, demystifies data analysis, allowing users to focus on leveraging insights rather than getting bogged down by the intricacies of data manipulation.
Tableau Pulse, with its emphasis on simplicity, efficiency, and actionable intelligence, is a powerful tool in the arsenal of any organization looking to harness the power of their data to drive success.
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