Bridging Marketing Strategy with Analytics & BI
I'm Ifeoma Chuks, a Product Marketing Data Analyst with 7+ years of experience transforming multi-channel funnels, customer support telemetry, and product behavior into revenue-driving decisions. Certified in Business Intelligence and Data Analytics.
About Me
I spent seven years in marketing learning how to make people pay attention to words, only to realise I was becoming increasingly curious about the numbers people left behind.
A campaign would end. The report would say engagement increased by 32%. Everyone would celebrate, close the report and move on to the next campaign.
Meanwhile, I’d still be staring at the 32%.
Why did it increase? Who responded? What changed? Was it the message, the audience, the timing—or something we hadn’t even considered?
That curiosity is what pulled me into data analytics.
Today, I use Excel, Google Sheets and SQL to clean data, investigate patterns and answer business questions. But I haven’t left my marketing brain behind, and I don’t intend to. Seven years of thinking about audiences taught me that there is usually a person behind every click, conversion and percentage—and context behind every number.
So when I look at a dataset, I’m not interested in producing a pretty chart and calling it a day. I want to know what happened, why it happened, what we can reasonably learn from it and, most importantly, what someone should do with that information.
Maybe that’s the marketer in me. Maybe that’s the analyst.
I think it’s useful to be both.
I like numbers, but I love what they mean—and I’m happiest somewhere between the spreadsheet and the sentence that finally makes it all make sense.
Technical Competencies
Business Intelligence & Viz
- Tableau & Looker Studio
- Interactive Dashboard Modeling
- ETL Concepts & Pipelines
- Executive Scorecards & KPIs
Querying & Analysis
- SQL (BigQuery, PostgreSQL)
- R Programming
- Exploratory Data Analysis (EDA)
- Data Cleaning Sensitivity Audits
Advanced Spreadsheets
- Exact VLOOKUP & XLOOKUP
- Nested Logic & Dynamic Arrays
- Pivot Tables & Variance Reporting
- Data Hygiene & Standardization
Product Marketing Analytics
- Behavioral Customer Segmentation
- A/B Testing & Statistical Inference
- SLA Compliance & CSAT Modeling
- Trial-to-Paid & ARR Forecasting
Featured Case Studies
01. Bellabeat Smart-Device Telemetry Analysis
Audited wearable tracker data in BigQuery and modeled an Activity × Tracking matrix in Tableau to formulate segmented retention and app engagement strategies.
02. E-Commerce Support Ticket & SLA Analysis
Analyzed 2,990 customer support tickets using Google Sheets to uncover root-cause friction, channel latency, and SLA bottlenecks impacting customer satisfaction.
03. In-App Onboarding A/B Test & Growth Model
Evaluated an interactive quick-start checklist against a legacy modal tour across 10,000 users, delivering a +23.95% setup activation lift and +$136.5K ARR run-rate.
Bellabeat Smart-Device Usage Analysis
Turning Wearable-Device Behavior into Actionable Product Marketing Insights
01. Business Task
Bellabeat manufactures high-end wellness trackers tailored for women. The objective was not merely calculating average step counts, but analyzing customer engagement telemetry to determine how consumers track activity, identify behavioral friction, and translate those findings into personalized in-app marketing campaigns.
02. Data Processing in BigQuery
Joined daily steps and intensity tables (940 observations across 33 participants) in Google BigQuery. Computed user-level aggregate baselines (mean, median, observation coverage %, sedentary minutes) so participants with 31 days would not disproportionately skew comparisons.
03 & 04. Data Cleaning Sensitivity Audit
The daily dataset contained 77 zero-step records. Rather than arbitrarily deleting records, a conservative quality rule flagged Possible Non-Wear when total_steps = 0 AND total_active_minutes = 0 AND sedentary_minutes = 1440 (72 records). A sensitivity test proved cleaning is part of the analytical result:
| Metric | All Observations (940 Rows) | Excluding Possible Non-Wear (868 Rows) | Material Impact (% Delta) |
|---|---|---|---|
| Average Daily Steps | 7,637.91 | 8,271.47 | +8.3% increase |
| Average Active Minutes | 227.54 min | 246.42 min | +8.3% increase |
| Average Sedentary Minutes | 991.21 min (16.5 hrs) | 953.98 min (15.9 hrs) | -3.8% decrease |
Tableau Visual: Activity Level × Tracking Coverage Matrix
Tableau Portfolio VisualScatterplot mapping Observation Coverage (%) against Average Daily Steps across all 33 participants[cite: 6]. Segmented by sample median activity (7,555.8 steps) and 90% tracking coverage threshold[cite: 6]. Pearson correlation ($r = 0.143$) confirmed that tracking frequently does not necessarily mean higher physical activity.
Tableau Visual: Day-of-Week Mean vs. Median Comparison
Tableau Portfolio VisualComparing daily Mean Steps vs. Median Steps reveals how outliers distort averages[cite: 5]. While Tuesday exhibited uniformly high activity (8,885 mean vs. 9,033 median), Saturday showed a massive 1,455-step gap (8,791 mean vs. 7,336 median), indicating that high weekend averages were driven by a small minority of users.
Product Marketing Recommendations
- Separate Inactivity from Workout Reminders: Because steps and sedentary time correlate weakly ($r = -0.258$), prompt daytime posture breaks rather than generic step pushes.
- Segmented Micro-Goals: Target users who track frequently but record low steps with gradual 15-minute walking goals.
- Friday Habit Nudges: Mitigate the high Saturday variance by testing pre-weekend consistency reminders.
Methodological Constraints
- Sample Constraints: 33 participants across a 30-day window (2016); directional hypotheses rather than population-wide baselines.
- Demographic Blindspots: Lacks demographic segmentation to prove participants represent Bellabeat’s female customer base.
- Correlation ≠ Causation: Observable relationships describe historical associations requiring controlled A/B testing before feature release.
E-Commerce Customer Support Ticket Analysis
Diagnosing Channel Bottlenecks, SLA Deficits, and Post-Purchase Friction
Data Preparation & Google Sheets Formulas
Deduplicated 10 raw rows, normalized mixed-case issue strings using =PROPER(TRIM()), and verified timestamp formats[cite: 4]. Enriched individual ticket records by pulling transaction metadata from the separate Orders table via exact-match VLOOKUP[cite: 4]:
=IF($B2="","",VLOOKUP($B2, Orders!$A$2:$L$2401, 8, FALSE))
Resolution duration was engineered as (resolved_at - created_at) * 24[cite: 4]. SLA benchmarks were dynamically assigned by priority tier (Urgent: 4h, High: 12h, Normal: 24h, Low: 48h) to evaluate breach rates against operational targets[cite: 4].
1. Post-Purchase Fulfillment Burden
Delivery Delays (1,211 tickets; 40.5%) and Damaged Items (467 tickets; 15.6%) generated over 56% of total inbound volume[cite: 4]. Post-purchase friction consumed customer support capacity, rather than pre-sale discovery questions[cite: 4].
2. Channel Efficiency & CSAT Divergence
Email proved to be the slowest channel (111 min first response; 25.8h resolution)[cite: 4]. Conversely, Live Chat achieved the fastest resolution (15.5h) and highest satisfaction score (3.66/5.0)[cite: 4], proving synchronous resolution preserves customer retention.
Support Strategy & Retention Interventions
- Automated Self-Service Tracking: Deploy proactive SMS order tracking updates to deflect up to 30% of "Where is my order?" delivery delay tickets.
- Channel Routing & Reallocation: Route high-urgency refund requests away from email queues toward Live Chat to reduce resolution latency.
- Packaging Quality Review: High return volumes in fragile categories require vendor packaging audits to reduce damaged item claims at the source.
Optimizing In-App Onboarding to Accelerate Activation & ARR
Evaluating Behavioral Setup Checklists vs. Static Modal Tours Across 10,000 Users
A randomized 14-day controlled growth experiment ($N = 10,000$) testing whether replacing a passive 4-slide carousel (Control) with an interactive, role-tailored quick-start checklist (Challenger Variant) accelerates product time-to-value and downstream ARR conversion.
Experiment Performance Scorecard
| Lifecycle Stage / Metric | Control (Version A) | Challenger (Version B) | Delta (Absolute) | Relative Lift | Confidence Level |
|---|---|---|---|---|---|
| Exposed Unique Visitors | 4,923 | 5,077 | +154 | — | Clean 50/50 Traffic Split |
| Completed Activation | 611 (12.41%) | 781 (15.38%) | +170 users (+2.97%) | +23.95% | 99.9% ($z = 4.29$) |
| Paid Customer Upgrades | 111 (2.25%) | 186 (3.66%) | +75 paid (+1.41%) | +62.48% (Overall) | 99.9% ($z = 4.15$) |
| Trial-to-Paid Efficiency | 18.17% | 23.82% | +5.65% | +31.09% | Retained Deal Quality |
| Total MRR Captured | $14,789 | $26,164 | +$11,375 | +76.92% | Higher Enterprise Mix |
Funnel Comparison: Control vs. Challenger Variant
User progression across initial exposure, completed setup activation, and paid subscription conversions ($N = 10,000$).
Key Analytical Observations
- Slashed Blank-Slate Friction: Replacing a passive 4-slide tour with a role-based setup checklist lowered time-to-first-project from 28.4 to 6.2 minutes, unlocking immediate activation.
- No Quality Dilution: Paid upgrades grew by +62.5% without depressing ARPU ($5.15 vs. $3.00), demonstrating stronger user intent.
- Device Parity: Desktop activation reached 15.04% and mobile reached 15.64%, proving the lightweight checklist avoided small-screen usability drops.
GTM Rollout Recommendations
- 100% Production Rollout: Fully decommission the legacy modal walkthrough across all signup channels to capture +$136.5K incremental ARR.
- Triggered Lifecycle Emails: Implement automated behavioral re-engagement emails for users who complete Step 1 but stall before Step 2 within 6 hours.
- Mobile App Alignment: Maintain checklist parity across mobile apps to prevent cross-platform conversion leakage.
Professional Credentials
Google Business Intelligence
Dashboard modeling, BigQuery SQL data pipelines, ETL concepts, Tableau development, and stakeholder metrics.
Google Data Analytics
Exploratory data analysis (EDA), data cleaning, SQL querying, R programming, and narrative storytelling.
Google IT Support
System infrastructure administration, networking fundamentals, and enterprise technical troubleshooting.
Let's Connect
Whether you are seeking to optimize your product funnel, understand your numbers, or translate behavioral telemetry into sustainable pipeline growth, let's start a conversation.