Key results
uplift in Return on Ad Spend (ROAS) was realized by linking ad campaign performance directly to SKU-level sales and margin contribution.
improvement in repeat purchase rate was achieved by identifying high-LTV cohorts and optimizing lifecycle-based reactivation strategies.
forecast accuracy was achieved (up from 68.3%) by combining heuristic rules with Python-based models, enabling precise demand planning, reduced stockouts, and optimized inventory decisions.
increase in overall revenue was driven by improved marketing effectiveness, inventory availability, and real-time performance visibility across SKUs and channels.
About the Client
A U.S.-based premium nutrition and wellness brand with a D2C-first model. Known for its performance-focused supplements, the brand invests heavily in digital marketing and depends on agile, data-driven decision-making to scale profitably.
Key Challenges
The client’s data was fragmented across Shopify, Meta Ads, Google Ads, and inventory systems, preventing a unified view of business performance across marketing, sales, and operations.
Business teams relied heavily on manual, Excel-based reporting, which slowed down decision-making and limited real-time visibility into key performance metrics.
Revenue forecasting and inventory planning were reactive and inconsistent, with no structured approach to incorporate historical trends or heuristic business rules into demand projections.
Marketing and sales data operated in silos, making it difficult to attribute spend to SKU-level profitability, track blended customer acquisition cost (CAC), or assess true campaign ROI.
Our Approach
- Integrated marketing and eCommerce data using DataChannel, extracting campaign metrics from Meta Ads and Google Ads, and sales data from Shopify into Amazon S3 as the centralized staging layer.
- Used AWS Glue to transform and structure the data, loading it into Amazon Redshift to serve as the unified data warehouse for all reporting and forecasting needs.
- Developed predictive revenue forecasting models in Python, combining historical trends, promotions, and heuristic business rules to improve demand accuracy and inventory planning.
- Built role-specific Tableau dashboards for marketing, sales, inventory, and finance teams—enabling real-time visibility into performance metrics like ROAS, repeat rate, contribution margin, and forecast variance.
Implemented Solution
Sales & Conversion Dashboards
- Shopify and marketplace data integrated via AWS → Redshift → Tableau
- Live reporting by SKU, region, device, and acquisition source
- Automated YoY, MoM, and WoW performance snapshots
Marketing Performance Analytics
- Meta Ads & Google Ads data piped via DataChannel
- Tableau dashboards track ROAS, CTR, CPA by campaign, product, and keyword
- Budget shift suggestions based on ROI and blended CAC trends
Customer Funnel & Repeat Behavior
- Repeat purchase metrics tracked via order history and cohort tagging
- Customer funnel performance segmented by first vs. repeat purchase
- Tableau dashboards highlight high-LTV segments and dormant cohorts
Inventory Health & Profitability
- SKU-level stock tracking with real-time sell-through, aging, and margin data
- Tableau dashboards for fast-moving, slow-moving, and dead stock
- Contribution margin visualized across products and campaigns
Revenue Forecasting with Python
- Built forecast models using Python (Prophet, SciKit-Learn, Pandas) for demand planning
- Factors included: past sales, seasonality, ad spend, promo periods, and product launches
- Forecast accuracy improved from 68.3% to 91.6%, resulting in:
- Reduced inventory cost
- Minimized stockouts
- Increased revenue reliability
- Reduced inventory cost
Technologies Used
