Fehlentscheidungen durch unzureichende Datennutzung im Treueprogramm

Definition

Australasian loyalty research shows AI is increasingly used to enhance program relevance: 66% of members are aware of AI enhancements, and 58% are willing to share data for more relevant offers.[2] Market reports stress that Australian retailers are adopting AI and analytics to deliver customised experiences and that those failing to adapt risk losing market relevance.[1][3] Grocery has the highest loyalty levels and very high purchase frequency, making it particularly suited to fine‑grained optimisation of promotions and assortment.[2] If a major grocer spends several hundred million AUD annually on promotions and loyalty discounts, and under‑targeted offers yield just 80–90% of the possible ROI versus well‑optimised, data‑driven campaigns, the opportunity cost is significant. Assuming a promotable base of AUD 5b in annual sales and a realistic 1–3% incremental margin attainable via better targeting and reduced blanket discounting, the forgone profit is AUD 50–150m per year per large chain (logic estimate). This is a silent bleed because the spend is already committed; only the return is suboptimal.

Why This Matters

The Pitch: Australian grocery players forgo 1–3% of potential margin uplift because loyalty data is under‑utilised in pricing, promotion and range decisions. Deploying AI‑driven personalisation, as highlighted in current loyalty research, can convert this into several tens of millions of AUD in incremental profit for large chains.

Affected Stakeholders

Chief Data & Analytics Officer, Head of Loyalty, Category Management, Head of Pricing & Promotions, CFO

Related Business Risks