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Marketing Funnel Dashboard
This Tableau dashboard transforms raw marketing data into actionable insights, tracking prospects from first touch to conversion.
Open Source Project
Challenges
Wasted Ad Spend
Invisible Customer Bottlenecks
where the leads are coming from which source
demographics
Step 1: Understanding Business Context & Metadata.
I started by defining the key objective: to visualize how leads move through the marketing funnel and identify where drop-offs occur. Since there was no dedicated marketing team, I conducted my own research to understand standard funnel stages and relevant KPIs.
I gathered and organized the metadata, including:Funnel stages: Impressions → Clicks → Leads → Opportunities → SalesLead sources (e.g., social media, email, organic traffic)Campaign details, timestamps, and customer segments
Step 2: Data Collection.With the funnel structure defined, I identified the data sources required for each stage. I collected data from CSV exports and internal tools that tracked user interactions and campaign performance.
The key data points included:
Number of impressions, clicks, and conversionsLead source and campaign IDTimestamps and funnel stage tags
I ensured each dataset had a unique identifier to link stages together and maintain consistency across the funnel. Basic cleaning (removing duplicates, handling nulls) was performed to prepare for the transformation stage.
I gathered and organized the metadata, including:Funnel stages: Impressions → Clicks → Leads → Opportunities → SalesLead sources (e.g., social media, email, organic traffic)Campaign details, timestamps, and customer segments
Step 2: Data Collection.With the funnel structure defined, I identified the data sources required for each stage. I collected data from CSV exports and internal tools that tracked user interactions and campaign performance.
The key data points included:
Number of impressions, clicks, and conversionsLead source and campaign IDTimestamps and funnel stage tags
I ensured each dataset had a unique identifier to link stages together and maintain consistency across the funnel. Basic cleaning (removing duplicates, handling nulls) was performed to prepare for the transformation stage.
Step 3: Data Cleaning.
To ensure accuracy, I performed thorough data cleaning across all funnel stages. This included:
Removing duplicates and irrelevant entries (e.g., test data or bots)Handling missing values by applying default rules or flagging themStandardizing column names and data formats (e.g., date/time, funnel stages)Validating consistency between stages using unique lead IDs
I also checked for anomalies like negative conversions or skipped funnel stages, and resolved them through cross-referencing with source data. Clean data was essential to ensure reliable insights in the dashboard.
Step 4: Analyze Data.
With clean data, I began analyzing how leads moved through the funnel. I calculated key metrics such as:
Conversion rates between each stage (e.g., Click-to-Lead, Lead-to-Sale)Drop-off rates to identify bottlenecksLead performance by source and campaign
I also explored trends over time and segmented data by region and channel to uncover patterns. This analysis helped me identify which stages needed improvement and which sources brought the highest-quality leads—insights that would shape the dashboard visuals.
Step 5: Creating Dashboard & Communicating Findings.
I built an interactive dashboard to visualize lead performance across the funnel and uncover insights for decision-making. Key KPIs displayed at the top include:
Total Leads: 130Expected Amount from Converted Leads: $7.5KConversion Rate: 3.1%Converted Accounts: 4Converted Opportunities: 4
To enhance usability, I added filters for Lead Source, Status, and State/Province. Visuals include:
A map showing lead distribution by geography with size for volume and color for conversion rate
A bar chart comparing conversion status by lead source
A treemap showing leads by industry, with conversion rate represented by color and count by size
This dashboard made it easy to spot high-performing regions, sources, and industries, helping guide strategic focus areas.
Removing duplicates and irrelevant entries (e.g., test data or bots)Handling missing values by applying default rules or flagging themStandardizing column names and data formats (e.g., date/time, funnel stages)Validating consistency between stages using unique lead IDs
I also checked for anomalies like negative conversions or skipped funnel stages, and resolved them through cross-referencing with source data. Clean data was essential to ensure reliable insights in the dashboard.
Step 4: Analyze Data.
With clean data, I began analyzing how leads moved through the funnel. I calculated key metrics such as:
Conversion rates between each stage (e.g., Click-to-Lead, Lead-to-Sale)Drop-off rates to identify bottlenecksLead performance by source and campaign
I also explored trends over time and segmented data by region and channel to uncover patterns. This analysis helped me identify which stages needed improvement and which sources brought the highest-quality leads—insights that would shape the dashboard visuals.
Step 5: Creating Dashboard & Communicating Findings.
I built an interactive dashboard to visualize lead performance across the funnel and uncover insights for decision-making. Key KPIs displayed at the top include:
Total Leads: 130Expected Amount from Converted Leads: $7.5KConversion Rate: 3.1%Converted Accounts: 4Converted Opportunities: 4
To enhance usability, I added filters for Lead Source, Status, and State/Province. Visuals include:
A map showing lead distribution by geography with size for volume and color for conversion rate
A bar chart comparing conversion status by lead source
A treemap showing leads by industry, with conversion rate represented by color and count by size
This dashboard made it easy to spot high-performing regions, sources, and industries, helping guide strategic focus areas.
Tech Stack used: Tableau, Salesforce CRM, Excel
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