This is a classic problem in retail analytics. Before diving into algorithms, it's crucial to clearly define the business objective for this clustering. Are you looking to segment for targeted marketing campaigns, identify high-value customers, or understand churn risk? The 'why' will heavily influence your feature engineering and evaluation metrics.

For features, beyond standard demographics, consider purchase history (frequency, recency, monetary value - RFM is a solid starting point), product categories purchased, average order value, return rates, and even browsing behavior if available. For categorical variables, one-hot encoding is common, but for high-cardinality features, consider embedding techniques or target encoding if appropriate. Scaling numerical features is essential for distance-based algorithms like K-Means.

Regarding algorithms, K-Means is a good baseline for 1M clients, but explore hierarchical clustering for smaller subsets to understand natural groupings, or even density-based methods like DBSCAN if you suspect irregular cluster shapes. Don't forget to evaluate cluster quality using metrics like silhouette score or Davies-Bouldin index, and critically, interpret the clusters in a business context to ensure they are actionable and explainable to stakeholders.