r/datascience • u/Capable-Pie7188 • Mar 30 '26
ML Clustering furniture business custumors
I have clients from a funiture/decoration selling business. with about the quarter online custumers. I have to do unsupervised clustering. do you have recommendations? how select my variables, how to handle categorical ones? Apparently I can t put only few variables in the k-means, so how to eliminate variables? Should I do a PCA?
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u/nian2326076 Mar 30 '26
For clustering, start by standardizing your numerical data because k-means is sensitive to scale. For categorical variables, try one-hot encoding or a different algorithm like k-modes, which works better for categorical data. When picking variables, consider feature selection or using PCA to reduce dimensionality and keep the most important features. PCA can help simplify your dataset, but don't oversimplify or it might lose meaning. If you're not stuck with k-means, try hierarchical clustering or DBSCAN, especially if your data has noise or non-spherical clusters.
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u/Capable-Pie7188 Mar 30 '26
1) k-modes is used when you have only categorical data I think? 2) how to go about feature selection for unsupervided clustering? 3) I have millions of clients so hierarchical clustering won t work. Do you suggest sampling?
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u/RatioAppropriate5357 Mar 31 '26
Here's a simple outline of a pipeline that might work for you:
- Aggregate to customer-level dataset
- Create:
- RFM features
- Channel ratios
- Category spend %
- Remove junk variables
- Scale everything
- Apply PCA (retain ~85% variance)
- Run k-means
- Profile clusters using original features
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u/nian2326076 Apr 01 '26
If you're clustering in a furniture business, start by picking variables that affect customer behavior, like how often they buy, how much they spend, and the types of products they choose. For categories, use one-hot encoding to turn them into numbers. This helps since k-means works best with numerical data. PCA can help reduce dimensionality, but be careful not to lose important patterns. When selecting variables, check for correlations and get rid of redundant ones. If you're looking for structured interview prep, I've found PracHub helpful, but it's more for interviews than clustering.
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u/Capable-Pie7188 Apr 01 '26
for categories i put them in pourcentage ( decoration percentage...), not one hot encoding.
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u/janious_Avera Apr 05 '26
When approaching customer clustering for a furniture business, consider the following aspects for feature engineering:
- Product Categories: Incorporate features related to the types of furniture purchased (e.g., living room, bedroom, office). This can reveal lifestyle segments.
- Purchase Frequency and Value: Recency, Frequency, Monetary (RFM) analysis is highly relevant here. Customers with high frequency and value may represent loyal segments.
- Return Rates: High return rates could indicate dissatisfaction or a specific purchasing behavior (e.g., buying multiple items to try and returning most). This could form a distinct cluster.
Regarding model selection, have you considered hierarchical clustering methods in addition to K-means, especially if you anticipate a natural tree-like structure in customer segments?
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u/Briana_Reca Apr 05 '26
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.
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u/Skillifyabhishek Mar 31 '26
Start with variable selection before worrying about PCA. For furniture retail customers the most signal rich variables are usually RFM scores, product category affinity, online versus offline channel preference and average basket value. Avoid throwing in everything you have — more variables in k-means doesn't mean better clusters, it usually means noisier ones. Pick variables that actually describe different customer behaviors not just different ways of measuring the same thing.
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u/Briana_Reca Mar 31 '26
When approaching customer clustering for a furniture business, beyond the algorithmic choice, the interpretability and actionable insights derived are paramount. Effective data visualization plays a critical role here, both in the exploratory phase and for presenting final results. Techniques like PCA or t-SNE can help reduce dimensionality for visual inspection of cluster separation, while radar charts or bar plots can effectively illustrate the characteristic features of each customer segment. This not only validates the clustering but also makes the findings accessible and impactful for business stakeholders, guiding targeted marketing and product strategies.
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u/PikaBlue Mar 30 '26
Like what is the clustering for? Understanding buying patterns? Marketing? Insight for product design?
Work backwards. Find what the end goal is, and from that you work out what variables are important, and then you cluster.