Commercial Intelligence | Category Performance 2026-04-04 CASE FILE // LOG-19

Heineken South Africa: Premium Beer Channel & Pack Performance Intelligence

Built a commercial intelligence case study showing how pack formats, channels, and promotions shape premium beer growth in Gauteng.

#Python #CommercialAnalytics #FMCG #SyntheticData
Problem Premium beer growth depends on channel mix and pack strategy, but those trade-offs are not always visible in one view.
Focus I built a synthetic category dataset and tested how volume, repeat purchase, and margin move across packs, channels, and promotions.
Outcome The analysis highlights where premium beer growth looks strongest in Gauteng once commercial quality is considered alongside scale.
// analyst.signals
Pack and channel analysis Promotion trade-off logic Market-focused growth thinking

Overview

This case study examines premium beer performance in Gauteng, with a focus on how pack formats and sales channels influence volume, repeat purchase, and margin quality.

Business Context

Premium beer portfolios do not grow through brand equity alone. The commercial outcome is shaped by the mix of single-serve and multi-pack formats, the role of take-home retail versus on-premise consumption, and the degree to which promotions are helping or distorting performance.

Problem Statement

The category needed a clearer view of which pack and channel combinations were creating healthy growth, rather than simply moving sales volume at the expense of margin.

Analyst Objective

Build a commercial intelligence case study that tests how pack strategy, channel mix, and promotions affect premium beer performance in Gauteng, and identify where the strongest growth opportunities sit.

Stakeholders

  • Commercial and category teams need a clearer view of pack and channel trade-offs.
  • Growth teams need evidence on where premium beer expansion looks most commercially attractive.
  • Brand and promotion teams need better visibility into where uplift supports or weakens margin quality.

Key Questions

  • Which pack formats drive the most meaningful volume and repeat purchase?
  • Which channels create the strongest balance of value, margin, and commercial scale?
  • Where do promotions create useful momentum, and where do they weaken the economics?
  • Which pack and channel combinations look strongest in Gauteng?

Workflow Thinking

  • Pack choice shapes both shopper behaviour and margin structure.
  • Channel choice affects how volume, value, and repeat purchase convert into commercially useful growth.
  • Promotion analysis only becomes useful when it is read alongside channel role and pack economics.

KPI Framework

  • Commercial metrics: volume, value, average margin contribution, promo uplift.
  • Behaviour metrics: repeat purchase rate, pack-format preference, channel concentration.
  • Market lens: Gauteng volume share, Gauteng opportunity score, top-performing pack-channel combinations.

These metrics mattered because the purpose was to compare growth quality, not just total sales activity.

Approach

  • Designed a synthetic beer-category dataset with believable patterns across customers, transactions, packs, and promotions.
  • Analysed pack performance across single-serve and multi-pack formats.
  • Compared channel trade-offs across take-home retail, on-premise, and wholesale.
  • Reviewed promotion uplift against margin pressure and isolated Gauteng opportunity areas.

Insights

  • 6_pack_can is the strongest repeat-purchase format in the portfolio.
  • Take-home retail remains the main driver of premium beer volume.
  • Promotions lift demand, but the margin trade-off is visible and needs active management.
  • Gauteng offers the clearest growth opportunity when pack strength and channel economics are considered together.

Deliverables

  • Synthetic FMCG dataset across transactions, customers, packs, and promotions
  • Jupyter notebook for category analysis
  • Exported charts for pack performance, channel mix, promotion trade-offs, and Gauteng opportunity
  • Summary tables for commercial review
  • Published GitHub Pages case study

Results

  • Overall repeat purchase rate landed at 50.8%.
  • 6_pack_can in take-home retail was the highest-volume pack-channel combination.
  • Take-home retail delivered the strongest average margin contribution in the final model.
  • 12_pack in take-home retail surfaced as the top Gauteng growth opportunity.

Next Steps

  • Extend the model to compare Heineken and Amstel more explicitly at brand level.
  • Add retailer-specific pack and promo scenarios to test channel execution choices.
  • Build a second FMCG case study to compare alcohol with another repeat-purchase category.

Embedded Project

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