More Than Data

Leveraging Data to Optimize FMCG Sales During Key Periods: A Case Study with More Than Data

Mar 06, 2025By More Than Data
More Than Data

Challenge: Understanding Sales Drivers During School Holidays, Promotions, and Competitor Influence

A media agency managing an FMCG brand, which produces a popular salad and sandwich sauce, reached out to More Than Data for insights into sales fluctuations during school holidays and promotional periods. The agency had several key questions:

  • Sales saw a noticeable increase at the end of school holidays. The agency wanted to identify the factors responsible for this uplift from a data perspective.
  • The brand frequently runs discounts and promotions, some of which overlap with the school holiday period. The agency needed to understand whether these promotions were driving the sales increase or if other factors were at play.
  • The agency used multiple media channels during this time, including OOH (digital panels in shopping centres, street furniture), TV, paid social, and online video (YouTube). The agency wanted to distinguish and understand the sole contribution of each media channel.
  • Additionally, the agency needed to factor in the impact of competitor media spend and promotions, as these could also influence the sales results.

Solution: Integrating Data into the Causal Inference Model

To address these challenges, More Than Data developed a comprehensive causal inference model. This model took into account not only media, promotions, and seasonality (school days vs. school holidays) but also incorporated competitor media spend and competitor promotions. Our approach was unique in that it went beyond traditional marketing mix modelling (MMM), showing not just direct links but also the indirect relationships between variables.

The model mapped out a network structure, connecting various offline and digital channels, competitor activities, pricing incentives, and seasonality. By including competitor media and promotions, we were able to measure the direct and indirect influence of competitor actions on the client’s sales. The model also quantified the unique contribution of each factor, providing the agency with a holistic understanding of what was driving the sales uptick during the back-to-school period.

Key Insights and Results

  • Combined Effects of Media, Pricing, and Competitor Activities: The sales uplift was not attributed to a single factor. Instead, it was a combined effect of media spend, pricing promotions, seasonality, and competitor activities. The model showed that competitor media spend and promotions had a noticeable influence on the client’s sales.
  • Confirming the Role of Seasonality: A/B testing and follow-up market research surveys confirmed that seasonality was a consistent sales driver. Even without media or promotional support, housewives were purchasing the sauce for their children’s lunchboxes during school terms.
  • Optimized Media Strategy: Based on these insights, the media agency adjusted their strategy for the following school holidays. They ran brand awareness campaigns via OOH and TV during the middle of the holiday period, followed by digital and paid social campaigns near the end of the school holidays, paired with in-store promotions. This shift resulted in a 7% sales uplift compared to the previous school holiday period and an 5% uplift compared to the same period the year before.
  • Improved Media ROI: The changes in strategy led to a 20% improvement in media ROI, while reducing media costs to only 87% of the original media plan. Despite maintaining a full-channel campaign, the agency maximized efficiency and delivered stronger results.

Conclusion

By leveraging More Than Data’s advanced causal inference model, which included competitor media and promotional activities, the media agency gained a deeper understanding of the factors driving sales during key periods. This data-driven approach led to an optimized media strategy, driving a significant sales increase and improving ROI while reducing media costs. The integration of multiple-source data provided a competitive edge, ensuring the agency’s strategy was both effective and efficient.