Desafio
Promotional saturation and difficulty separating promotional effects from regular sales made campaign performance harder to assess.
20 de out. de 2026
Using predictive analytics to improve retail promotion planning
LTPlabs helped a consumer goods company optimize promotions with predictive analytics, improving forecast accuracy by 50% and supporting better planning decisions.
Em resumo
Solução
Historical promotion data, automated monitoring, and predictive scenario simulation supported evaluation and planning.
Resultados
The company identified weaker promotions and improved promotional planning, sales performance, and profitability.
Challenge
A consumer goods company operating in different edible oil categories faced a market increasingly saturated with promotions. With promotional sales difficult to distinguish from regular sales, the company struggled to measure the true impact of its campaigns and identify which actions were most effective. This limited its ability to plan future promotions and negotiate campaign terms with retail customers.
Solution
LTPlabs developed an analytical approach to connect the review of past promotions with the planning of future campaigns.
The work began by organizing historical promotional information in a centralized database, capturing the characteristics and outcomes of past actions. Automated monitoring helped the company assess promotional effectiveness and identify patterns in the results.
A predictive engine used machine learning to analyze historical sales and promotional data, including factors such as discount levels, timing, context, and competitive actions.
A user-friendly simulator enabled stakeholders to compare promotional scenarios and assess their potential impact before negotiating campaign plans with customers.
Together, these capabilities supported the identification of underperforming actions, the optimization of planned promotions, and the assessment of opportunities to strengthen profitability or market share.
Results
The project standardized promotional data and introduced intuitive dashboards to track past promotional actions.
A planning tool, powered by analytical models, enabled stakeholders to assess the effectiveness of multiple scenarios.
This helped the organization identify underperforming promotions, improve the accuracy of promotional forecasts by 50% (vs. the prior process of using a mirrored past promotion to infer impact), and make better informed planning decisions. These improvements contributed to stronger sales performance and profitability.
Estudos de Caso
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