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Tutorial: Customer Clustering 101


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Tutorial: Customer Clustering 101

  1. 1. Tutorial Customer Clustering 101 Dhruv Bhargava Director Analytics, AgilOne
  2. 2. Clustering 101
  3. 3. Predictive Analytics 101 Clustering Recommendations Predictions 3
  4. 4. Let’s talk clustering! •  Your customers are different à Clustering reveals these different personae •  Understanding clusters à personalization •  Personalized campaigns à better marketing ROI
  5. 5. What is customer clustering? Athletes Chronic Returners Doting grandmothers 5 Creating homogenous groups of customers and identifying personae to drive marketing actions
  6. 6. Then: Broad strokes Basic retro-active segmentation Now: Clear picture Clustering drives relevancy & personalization •  •  What products do they need? •  12 month file versus 12+ month file •  What brands do they prefer? •  Buys best selling brands •  What offers will compel them to buy? •  6 1x buyer versus 2x+ buyers Has loyalty membership? •  What’s the right channel & frequency of contact?
  7. 7. 3 clustering models available •  Behavioral: Spending, channel, order interval, discounts, returns,… •  Product based •  Brand preference •  Clustering done mathematically via unsupervised learning •  •  •  •  •  Let data drive what clusters you have in your customer base No guesswork or leaning on intuition Multi-variable, not based just on revenue, or a single product Optimized for stability Data refreshed daily
  8. 8. Example: behavioral cluster DNA Long term, frequent buyers, medium sized orders High value, fewer orders, big spend on 1st order $99 average order $2,261 total revenue 24 days between orders 24 total orders 57 total items $76 first order revenue 1.7 products in first order 6% of orders on clearance +10 more $124 average order $595 total revenue 67 days between orders 5 total orders 14 total items $164 first order revenue 3.3 products in first order 3% of orders on clearance +10 more
  9. 9. Use the DNA to personalize Long term, frequent buyers medium sized orders High value, fewer orders, big spend on 1st order Goal: increase order size (AOV) Goal: decrease time to rebuy Campaign: Give double points if they spend 50% more than usual Campaign: Give double points that expire sooner than usual with purchase Result: 125% increase in AOV Result: Time to next purchase reduced to 2 months from 4 months
  10. 10. Example: product clusters Product Clusters Developed Cluster #1: Sweaters •  Based on what products customers buy Cluster #2: Stylish Men’s Wear Cluster #3: Active Wear Cluster #4: Gift Certificates/Family Cluster #5: Elegant Ladies Cluster #6: Kids Wear Cluster #7: Underwear Cluster #8: Accessories 10 4 March 2014 •  Once clusters are created, this reveals the natural groupings of products
  11. 11. Example: product cluster DNA 11 4 March 2014
  12. 12. Example: Brand clusters DNA Cluster 1 Brand Scale Least Interest Pleasure Doing Business Wow Couture Desigual 6126 L*Space Preferred Brands Tahari Arthur S. Levine Calvin Klein Eliza J Adrienne Vittadini Nine West Preferred Brands Cluster 3 Brand Scale Least Interest Collective Concepts Wow Couture Max and Cleo Rene Rofe Steve Desigual Dzhavael Couture Custo Barcelona Smash Wear Salvage Rock’N’Rebel
  13. 13. DNA for each cluster drives relevancy in campaigns Cluster 1: Sweaters Cluster 3: Active Wear Goal: Drive customer engagement Goal: Increase customer value Campaign: Target cluster with all main product related campaigns (E.g. New sweater arrivals) Campaign: Offer bundles of related products from same cluster at discount Result: 100% increase in AOV Result: Improved email open rate by 25% and click rate by 66%
  14. 14. Ÿ (877) 769 3047