Waggle: From Local Pet Shop to Sales Powerhouse

It hit the manager during the third weekly report the charts weren’t just cleaner; the insights were actionable. For years, they’d relied on hunches and Excel scraps.

Client

Waggle

VIEW PROJECT [↗]

Challenges

Sales uses Salesforce, finance uses Excel, and ops uses legacy software nobody trusts the same numbers. Teams waste 20+ hours/month reconciling reports.
No Track for pet fitness engagement (to use it for customer and re-sell programs for their pets)
Decline Customer satisfactions rate
Demographics targeting gaps

How does pet fitness tracking differ between cats and dogs? In this Power BI analytics project, I helped Waggle compare user engagement and satisfaction across their two flagship products: the Lapdog (dog activity tracker) and Lapcat (cat activity tracker).
I went in a meeting with the CEO and CMO to understand the problem.

Step 1: Understanding the problem and previous implemented solutions.When Waggle first launched its Lapdog activity tracker, the results were promising—dog owners loved monitoring their pets’ fitness, and the data showed a clear increase in daily steps over time. But when the company introduced the Lapcat tracker for cats, leadership faced a critical question:
"Is this product working as well as Lapdog did?"
The CEO needed proof:
"Are cats actually moving more with Lapcat, or is this product falling short?"
"Do cat owners love the device as much as dog owners do, or are we missing something?"
Meanwhile, the CMO had branding concerns:
"Our reports look generic—how do we make this feel uniquely Waggle?"
"We need visuals that match our playful, pet-friendly identity—not just dry charts."
Before this project, Waggle relied on disconnected Excel reports—sales had one dataset, customer support had another, and product teams struggled to merge insights. My mission? Turn scattered data into a clear, interactive story

Step 2: Collect and store data

Waggle’s data was scattered across multiple sources:1-Device Logs: Raw step counts and activity timestamps from Lapdog & Lapcat collars2-Customer Surveys: Satisfaction scores, open-ended feedback, and owner demographics3-Sales Records: Purchase dates, subscription renewals, and regional trends
The biggest hurdle? No single source of truth. The product team worked with CSV exports, marketing tracked NPS scores in Google Sheets, and support had their own ticket logs. Merging this data manually was error-prone and time-consuming.
The SolutionConsolidated Data Sources:1-Imported device logs (API pulls), survey responses (Google Sheets), and sales data (SQL database) into Power BI.2-Standardized date formats, unique IDs, and metric definitions.
Built a Data Model:Created relationships between tables (e.g., linking device IDs to owner demographics).Added calculated columns.
Ensured Refreshability:Set up automated weekly refreshes so leadership always saw the latest stats.

Step 3: Data Cleaning and manipulation

The Problem: Messy, Unreliable Raw Data
Before analysis could begin, the raw data needed serious cleanup:Inconsistent entries: “Labrador Retriever” vs. “Labrador,” “NY” vs. “New York”Missing values: 15% of survey responses lacked owner age or pet weightOutliers: A “cat” with 50,000 daily steps (likely a device error)Mismatched formats: Timestamps in UTC, EST, and even some plain-text dates
The Fix: Rigorous Data WranglingUsing Power Query in Power BI, I:✅ Standardized categories (e.g., dog breeds → AKC-standardized names)✅ Imputed missing data (e.g., used median pet weight by breed)✅ Flagged outliers (e.g., steps >3 standard deviations = “suspect” filter)✅ Harmonized dates/timezones → all converted to owner-local time✅ Merged duplicates (e.g., “LapCat v1” and “Lapcat-V1” → single product)

Step 4: Data Analysis & Insights

1. Lapcat vs. Lapdog Activity TrendsObjective: Compare average daily step counts over time.Method:Calculated rolling 7-day averages for both devices.Applied seasonal decomposition to adjust for weekends/holidays.
Key Findings:Dogs: Significant upward trend (+3.2% steps).Cats: Flat trendline (+0.5% steps).Outliers: 12% of cats showed negative step trends.
2. Customer Satisfaction AnalysisObjective: Quantify Lapcat vs. Lapdog owner sentiment.Method:Scored survey responses (1–10) → normalized to industry benchmarks.
Key Findings:
Lapdog:68% "Very Satisfied" (≥8/10)Top keywords: "accurate," "durable," "fun"
Lapcat:41% "Very Satisfied"Top complaints: "falls off," "hard to charge"
3. Demographic Drivers4. Device Performance

Step 5: Visualize & Communicate Data

Using the Power BI visualizations, I delivered clear insights that directly addressed the CEO’s and CMO’s key questions, driving actionable business decisions:

Key Findings & Impact

Lapdog Outperforms Lapcat in Engagement

Visual: Bar chart comparing avg. daily steps (Lapdog: 6K steps vs. Lapcat: 3K).

Insight: Dogs showed 2x higher activity levels, confirming Lapdog’s success.

Action: CEO prioritized R&D for Lapdog 2.0 and paused Lapcat expansion.

Visual: Gender Distribution Donut Chart (Male: 57.6% vs Female: 42.4%)

Correlated Finding: While analyzing pet genders, we discovered:

Female cat owners submitted 30% more complaints about device fit

Male cat owners reported 2x more battery issues

Action Taken:

Redesigned collars for better female cat fit

Added battery-saving mode preferred by male owners