Overview
During a high-stakes internal hackathon at a global healthcare leader, our team aimed to tackle persistent challenges in sales forecasting and promotion ranking. The goal was to demonstrate how advanced data analytics could uncover actionable insights, even from a constrained dataset. By introducing causal inference, I spearheaded an innovative approach that revealed hidden drivers of success and showcased the untapped potential of the organization’s data.
Hackathon Context
The hackathon provided an opportunity to develop a proof of concept, showcasing how advanced techniques like causal inference could address business-critical problems. While the results were not implemented at scale, the projected impacts highlighted the value of adopting this methodology for broader organizational use
Approach
To maximize the hackathon’s impact, I led the team in developing a causal inference framework to identify the true drivers of sales and promotional success. Our steps included:
- Data Exploration: Evaluating the limited dataset to identify key variables influencing sales performance.
- Causal Modeling: Building a model that distinguished correlation from causation, focusing on actionable insights rather than descriptive trends.
- Promotion Ranking: Surfacing the best-performing promotions, highlighting opportunities for optimizing ROI.
- Stakeholder Engagement: Presenting findings to stakeholders, sparking excitement about the approach’s potential applications.
Insights Delivered
Despite the dataset’s limitations, our analysis revealed:
- Hidden Drivers of Success: Insights that stakeholders previously believed were unattainable due to data constraints.
- Strategic Recommendations: Clear identification of high-ROI promotions, enabling precise resource allocation.
- Methodological Innovation: The use of causal inference demonstrated a scalable approach for data-driven decision-making.
Projected Impacts
The hackathon results indicated significant potential benefits for the organization, including:
- Up to 25% Improvement in Forecast Accuracy: Early models showed promise in outperforming existing forecasting methods.
- Potential for 20% Increase in Promotional ROI: Ranking the highest-impact campaigns revealed opportunities to optimize promotional spending.
- Time Savings: Automating the forecasting and ranking process could save an estimated 200 person-hours annually.
Stakeholder Reactions
The innovative use of causal inference intrigued stakeholders, leading to discussions about integrating the approach into operational workflows. Feedback highlighted the excitement about exploring previously overlooked opportunities within the data.
Key Takeaways
- The Power of Causal Inference: Even limited datasets can yield transformative insights when analyzed with advanced methodologies.
- Engaging Stakeholders: Presenting projected outcomes effectively builds confidence and garners buy-in for new approaches.
- Hackathons as Innovation Catalysts: Short-term initiatives like hackathons can pave the way for long-term organizational change.
Conclusion
This hackathon project demonstrated the potential of causal inference to revolutionize sales forecasting and promotional strategies. By surfacing actionable insights, the initiative showcased how advanced analytics can unlock the value hidden in constrained datasets. Organizations seeking similar breakthroughs can leverage these methods to drive better decisions and achieve measurable results.