This startup was an online boutique specializing in sustainable home goods and eco-friendly products. As they began to scale beyond their initial product offerings, they faced challenges tracking their business metrics across multiple platforms (Shopify, social media advertising, and their logistics partners).
Here's how they leveraged Looker Studio:
Initial Challenge: The founders were spending 8-10 hours weekly compiling spreadsheets from different data sources to understand basic business metrics. Their growth was being limited by inefficient data analysis and reactive rather than proactive decision-making.
Looker Studio Implementation:
They connected Looker Studio directly to their Shopify store, Google Analytics, Facebook Ads, and Google Ads accounts
They set up a custom data pipeline to import inventory and fulfillment data from their 3PL (third-party logistics) provider
They created three main dashboards: Operations, Marketing Performance, and Financial Health
Key Metrics Tracked:
Customer Acquisition Cost (CAC) by channel
Customer Lifetime Value (CLV) segmented by acquisition source
Product margin analysis and contribution to profit
Inventory turnover rates by SKU
Return rates and reasons
Reorder timing optimization
Cohort retention analysis
Business Impact:
They identified that customers acquired through Instagram had a 32% higher lifetime value than those from Facebook, despite similar acquisition costs, leading them to shift budget allocation
Their inventory dashboard revealed that certain products had significantly higher return rates, prompting changes to product descriptions and sizing guides
They reduced stockouts by 47% by setting up automated alerts in Looker Studio when inventory reached threshold levels
The time spent on reporting decreased from 8-10 hours weekly to about 2 hours for review and analysis
Within six months of implementation, they saw a 28% improvement in marketing ROI and a 15% reduction in inventory holding costs
Implementation Approach: The startup began with simple dashboards focused on sales and inventory, then gradually expanded as they became more comfortable with the platform. A key factor in their success was starting with clean data structures and consistent naming conventions, making their reports more reliable and easier to maintain as they scaled.
E-commerce Startup Dashboard Design Details
Dashboard Design
The e-commerce startup developed three interconnected dashboards in Looker Studio:
1. Operations Dashboard
This served as their daily command center with:
Real-time sales tracking with hourly, daily, and weekly trends
Current inventory levels with visual indicators (green/yellow/red) for stock status
Order fulfillment metrics (processing time, shipping time, delivery performance)
Customer service metrics (ticket volume, resolution time, satisfaction scores)
Returns processing status and timeline
They used a top-row "scorecard" design showing KPIs with period-over-period comparisons, followed by time-series charts. The dashboard utilized consistent color coding: green for metrics above target, yellow for at-risk, and red for concerning values.
2. Marketing Performance Dashboard
This dashboard focused on acquisition and customer behavior:
Channel performance comparison (CAC, conversion rates, ROAS)
Campaign performance with drill-down capabilities
Customer journey visualization showing drop-off points
Audience segment performance analysis
Email marketing metrics (open rates, click rates, conversion)
Product performance by marketing channel
They implemented filters allowing team members to isolate specific date ranges, campaigns, or product categories, making it easier to identify patterns and opportunities.
3. Financial Health Dashboard
This more restricted-access dashboard included:
Gross and net profit margins by product category
Cash flow projections based on current orders and historical patterns
Customer cohort analysis showing LTV development over time
Unit economics breakdown
Inventory carrying costs and turnover efficiency
Operating expense tracking against budget
Metrics Selection Process
Their metrics selection followed a deliberate methodology:
Goal Alignment Workshop: The founding team held a session identifying their critical business questions and mapping them to measurable KPIs.
Metric Prioritization Matrix: They created a 2x2 matrix plotting metrics by:
Ease of data collection/calculation (x-axis)
Business impact potential (y-axis) This helped them focus initially on high-impact, accessible metrics.
Metrics Documentation: For each selected metric, they documented:
Precise calculation formula
Data sources required
Update frequency
The owner is responsible for data quality
Target/benchmark values
Metric Layering Strategy: They implemented metrics in three layers:
Foundation metrics (revenue, costs, conversion rate)
Operational metrics (inventory turnover, fulfillment time)
Strategic metrics (LTV
 ratio, cohort retention)
Regular Review Cycle: Quarterly reassessment of metrics relevance, where underutilized metrics were deprecated and new ones added based on evolving business needs.
Technical Implementation Details
The technical implementation involved several components:
Data Source Integration
Shopify: Connected via the native Looker Studio connector
Google Analytics/Google Ads: Direct connectors with refreshed authentication
Facebook/Instagram Ads: Used a third-party connector for daily data refresh
Inventory/3PL data: Created a Google Sheets integration that received daily automated exports from their logistics provider's system
Data Transformation
Implemented calculated fields in Looker Studio for basic metrics
For more complex transformations, they used Google Sheets as an intermediate layer with formulas to standardize metrics across platforms
Created a data extraction workflow that normalized campaign naming conventions across different ad platforms
Refresh Architecture
Critical operational metrics set to auto-refresh every 3 hours
Marketing data updated daily at 6AM
Financial dashboards refreshed weekly with some manual validation steps
Access Controls
Implemented tiered access permissions:
Executive view (all dashboards)
Marketing team view (operations and marketing dashboards)
Operations team view (operations dashboard with limited financial data)
Investor view (customized financial dashboard with only high-level metrics)
Technical Challenges Overcome
Initially struggled with data mismatches between advertising platforms and analytics; solved by implementing UTM parameter standardization and lookback window alignment
Had issues with Looker Studio performance as data volume grew; optimized by using data sampling for historical analysis and focusing on recent data for operational views
Solved attribution problems by creating a custom blended table in Google Sheets that applied their preferred attribution model before importing to Looker Studio
This comprehensive approach to dashboard design, metrics selection, and technical implementation allowed the startup to build a scalable analytics foundation that grew with their business while maintaining data integrity and accessibility for decision-making.
The Data Mismatch Problem
The e-commerce startup encountered a common issue where data reported in their advertising platforms (like Facebook Ads and Google Ads) didn't match what appeared in their analytics platform (Google Analytics). For example:
Facebook Ads might show 150 conversions from a campaign
Google Analytics might only show 120 conversions attributed to that same campaign
Their Shopify sales data might show yet another number
This discrepancy made it difficult to accurately calculate metrics like CAC (Customer Acquisition Cost) and ROAS (Return on Ad Spend), because the numbers varied depending on which data source they used.
Root Causes of the Mismatches
Two primary issues were causing these discrepancies:
Inconsistent UTM Parameters: UTM parameters are tags added to URLs to track where traffic comes from. The startup was using inconsistent naming conventions across platforms:
Some campaigns used "fb_campaign" in UTM parameters
Others used "facebook_campaign" or "FB_promo"
Some lacked proper UTM tagging altogether
Different Lookback Windows: Each platform had different attribution windows:
Facebook Ads was using a 28-day click, 1-day view attribution window
Google Analytics was using a 30-day lookback window
Their internal reporting was using a 7-day window
These differences meant that conversions were being counted differently across systems.
The Solution
UTM Parameter Standardization
They implemented a strict UTM naming convention across all platforms:
Created a centralized UTM builder tool (a simple spreadsheet)
Established consistent parameter formats:
utm_source: always lowercase, representing the platform (facebook, google, etc.)
utm_medium: always lowercase, representing the ad type (cpc, display, etc.)
utm_campaign: using a standardized format like "YYYYMM_campaign-objective_product-category"
This ensured that traffic from all advertising efforts was consistently tagged and could be accurately tracked across analytics platforms.
Lookback Window Alignment
They aligned attribution windows across platforms where possible:
Set Facebook and Google Ads to use the same attribution window (7-day click)
Configured custom channel groupings in Google Analytics to match their advertising platform definitions
When building reports in Looker Studio, they added notes about attribution windows to explain any remaining discrepancies
For internal reporting purposes, they standardized on a single attribution model across all channels
By addressing these two issues, they were able to reduce data discrepancies from 20-30% down to about 5%, which was considered acceptable for their decision-making purposes. This more consistent data allowed them to make more confident budget allocation decisions and better understand their true customer acquisition costs.
Custom Channel Groupings in Google Analytics
When the e-commerce startup configured custom channel groupings in Google Analytics, they were essentially redefining how traffic sources are categorized and reported. This was a critical step in aligning their Google Analytics data with their advertising platforms. Here's how they approached it:
The Problem in Detail
By default, Google Analytics has predefined channel groupings (like Organic Search, Direct, Social, Paid Search, etc.). However, these default groupings didn't perfectly match how their advertising platforms categorized traffic. For example:
Some Facebook traffic might be categorized as "Social" instead of "Paid Social"
Certain display ads might be grouped under "Referral" rather than "Display Advertising"
Influencer marketing links were inconsistently categorized
The Custom Channel Grouping Solution
They created custom channel groupings in Google Analytics with rules specifically designed to match their advertising platform categorizations:
Defined Classification Priority Order: They established a hierarchical processing order for their rules to ensure traffic was classified consistently.
Created Precise Channel Definitions: They built rules using combinations of:
Source/Medium patterns
Campaign naming conventions
UTM parameter values
Referring domain information
Specific Examples of Rules They Created:
Paid Social Rule: Defined all traffic with source containing "facebook", "instagram", "pinterest", etc., AND medium containing "cpc", "paid", "ppc" to be categorized as "Paid Social"
Organic Social Rule: Defined traffic from social platforms without paid indicators as "Organic Social"
Branded Search Rule: Created a rule to identify paid search traffic containing their brand name keywords, separating it from non-branded paid search
Influencer Traffic Rule: Established patterns to identify traffic from specific influencer UTM codes and categorize it separately from other referral traffic
Rule Testing and Refinement: They tested these rules on historical data to ensure proper classification before applying them to live reporting.
Implementation Process
The technical implementation involved:
Accessing the Admin section of Google Analytics
Navigating to "Channel Settings" > "Channel Grouping"
Creating a new custom channel grouping named "Advertising Platform Alignment"
Building each rule with specific conditions, using regular expressions where needed to match patterns
Ordering the rules based on processing priority
Applying this custom channel grouping in their Looker Studio reports instead of the default channel grouping
Benefits Realized
This custom channel grouping configuration allowed them to:
Compare performance metrics across platforms with greater accuracy
Create more consistent cross-platform reports in Looker Studio
Make better budget allocation decisions with properly attributed conversion data
Track customer journey paths more accurately
Analyze ROAS (Return on Ad Spend) with greater confidence
By ensuring that Google Analytics categorized traffic in ways that aligned with how Facebook Ads, Google Ads, and other platforms defined their traffic, they created a more unified measurement framework that reduced discrepancies and improved decision-making confidence.