HOME// PROJECTS// ATHLETE-LED VIRAL MARKETING — IMPACT ANALYSIS
Campaign Analytics | Product Analytics 2026-03-28 CASE FILE // LOG-01

Athlete-Led Viral Marketing — Impact Analysis

Analytics case study examining whether an athlete-led viral campaign converted reach into gym memberships — across channels, funnel stages, brand lift signals, and offline behaviour.

#CampaignAnalytics #Python #PowerBI #FunnelAnalysis
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Problem Wellness brands investing in athlete-led viral campaigns face a measurement challenge: reach metrics spike, but it is rarely clear how much of that reach converts into actual product adoption. Without a structured analytics layer, spend decisions default to gut feel and last-click attribution — both of which distort the true channel contribution picture.
Focus The project structures a synthetic but realistic campaign dataset into a full analytics pipeline — from raw channel data through to funnel conversion rates, cost-per-acquisition by channel, brand lift tracking, and offline gym activity signals. The data model is built for Power BI consumption and designed to answer real marketing accountability questions.
Outcome Stakeholders can review channel efficiency beyond impressions, understand where in the funnel spend is most effective, and see how brand awareness translates — with a lag — into offline membership behaviour.
// analyst.signals
Multi-channel campaign attribution and efficiency analysis Full funnel conversion from impressions to memberships Brand lift and offline behaviour measurement

Overview

Athlete-Led Viral Marketing Impact Analysis is a product analytics case study examining a TikTok-anchored wellness campaign. The analysis moves from reach metrics through conversion efficiency to brand lift and offline signals — using a star-schema data model built for direct BI tool consumption.

Intelligence Layer

Brands routinely over-index on reach when evaluating influencer and athlete campaigns. Impressions are visible and immediate; conversion and brand lift are delayed and harder to attribute. This project builds the measurement framework that sits between campaign activity and acquisition outcomes.

Problem

Wellness brands investing in athlete-led viral campaigns face a measurement challenge: reach metrics spike, but it is rarely clear how much of that reach converts into actual product adoption. Without a structured analytics layer, spend decisions default to gut feel and last-click attribution — both of which distort the true channel contribution picture.

Data / Signals

Analyst Objective

Structure campaign data to enable marketing and product teams to:

  • determine which channels drove reach versus which drove efficient acquisition,
  • identify where in the funnel conversion broke down by channel,
  • and measure brand lift and offline behaviour signals relative to campaign timing.

Stakeholders

  • Marketing leads evaluating channel ROI and spend reallocation decisions.
  • Product teams tracking acquisition funnel health and conversion benchmarks.
  • Finance and partnerships teams requiring cost-per-acquisition evidence.

Key Questions

  • Did TikTok’s reach volume translate into proportional acquisition volume?
  • Which channel had the lowest cost per gym membership?
  • How long was the consideration-to-action lag between campaign exposure and offline behaviour?
  • What does the brand search index tell us about awareness that conversion metrics miss?

KPI Framework

  • Reach: impressions, CTR by channel and date.
  • Conversion: signup rate, trial rate, membership rate through funnel stages.
  • Efficiency: CPA (spend / memberships) by channel.
  • Brand: brand search index trend across pre, during, and post-campaign windows.
  • Offline: gym visits and new membership trend relative to campaign timeline.

Insight

  • TikTok delivered the highest reach volume but the weakest direct conversion efficiency — consistent with its role as an upper-funnel awareness channel.
  • Paid search and email produced the lowest cost-per-membership despite significantly lower impression volumes.
  • Brand search index showed a measurable uplift during the campaign window — suggesting awareness was building even where immediate conversion was not occurring.
  • Offline gym activity spiked approximately two weeks post-campaign, consistent with a consideration-to-action lag typical of wellness product adoption.

Implication

  • Last-click attribution significantly undervalues TikTok’s contribution to the overall acquisition picture.
  • Budget optimisation should evaluate upper-funnel channels on brand lift and assisted conversion, not direct CPA alone.
  • The two-week offline lag is a predictable signal — one that could be modelled forward to improve campaign timing and crew readiness for membership intake surges.

Closing

Deliverables

  • Python data pipeline covering synthetic data generation, cleaning, and feature engineering.
  • Star-schema data model ready for Power BI consumption.
  • Interactive HTML dashboard for stakeholder review.
  • KPI framework covering reach, conversion, efficiency, brand lift, and offline signals.

Outcome

The project demonstrates how structured campaign analytics moves beyond vanity metrics — connecting reach, conversion, brand lift, and offline behaviour into a single coherent measurement framework.

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