Unlocking Retail Success: Key Data Metrics and Analysis Methods for the New Era

From traditional offline retail (department stores, chain stores, supermarkets) to the recent surge of online e‑commerce and the emerging new‑retail model that blends online and offline, the industry’s operation models have dramatically changed.

In the era of big data, retailers must analyze data across all links to quickly adapt to market changes and make scientific decisions. This article discusses the indicator system and methodology for data analysis in the new‑retail model.

Offline “People, Goods, Place”

1. Data analysis improves “people” efficiency

“People” includes both employees and consumers. In a consumer‑centric era, improving staff efficiency and boosting customer loyalty and lifetime value are crucial.

Employee management analysis covers efficiency (sales and service metrics) and structure (turnover rate, staffing composition, salary distribution) to avoid uneven staffing or unreasonable wages.

Customer management, especially for members, involves analyzing consumption behavior, tier segmentation, activity management, etc., to maintain and develop relationships.

2. “Goods” – Product analysis

Product analysis focuses on inventory flow (in‑stock, sales, out‑stock). Key metrics include product‑structure ratios, sell‑through rate, inventory‑to‑sales ratio, and other detailed indicators.

3. Data analysis improves “place” efficiency

“Place” refers to consumption scenarios such as physical stores, websites, apps, mini‑programs. Analysis monitors performance and operational indicators like store sales, tracking, and efficiency.

A key metric for physical stores is “sales per square meter” (坪效), calculated as sales ÷ area, where sales = traffic × conversion rate × average order value × repeat purchase rate.

When traffic growth slows, retailers increase online sales to boost overall traffic.

Online E‑commerce Data Analysis Indicators

Online retail also follows the “people‑goods‑place” framework, but emphasizes user and traffic analysis. Five key metrics are active users, conversion, retention, repeat purchase, and GMV.

Product analysis focuses on conversion rates to adjust operations and increase GMV. User analysis emphasizes retention and segmentation for targeted strategies. Promotional activities are evaluated for effectiveness and ad spend efficiency.

Common Data Analysis Methods in Retail

1. ABC analysis (Pareto analysis)

Classifies products into A, B, C based on sales contribution, guiding different management strategies. Example steps: collect annual sales volume and price, compute sales, cumulative sales, percentages, rank products, and assign categories (A: 0‑50%, B: 50‑90%, C: 90‑100%).

2. RFM model

Segments customers by Recency, Frequency, Monetary value into eight levels to assess value, adjust strategies, retain high‑value customers, and reduce churn.

3. Association analysis

Examines relationships between variables, commonly used for market‑basket analysis to discover product associations and design bundle promotions. It can also relate product quantity to sales or staff numbers to revenue.

4. Funnel analysis

Breaks down processes such as marketing funnels or the AARRR model, calculating conversion rates at each stage to identify optimization points and improve purchase conversion.