Automated Data Mining for Everyone
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This document discusses the application of predictive analytics using data mining techniques targeting small and medium-sized enterprises (SMEs). It outlines methodologies for segmenting customers, developing predictive models, and implementing these insights into marketing campaigns to increase sales. Additionally, it emphasizes the automation of data preparation, modeling, and deployment for user-friendly access and effective decision-making.
![Under Cover: Near Future
• Apply various algorithms
– For better visualization & insights
• Log. Regression produces visual scorecard with points
– To reduce risk of over fitting or low AUC
• Generate less and better predictors
– [Average] [profit] in [category=value] in the [last/first]
N [periods]
• Identify right time to retrain models](https://image.slidesharecdn.com/howweveautomateddatamininig-140715072229-phpapp01/85/Automated-Data-Mining-for-Everyone-26-320.jpg)
Automated Data Mining for Everyone
- 1. Automated Data-Mining ALL SME Anybody Success
- 2. www.7segments.co m About me + (421) 918 666 238 BANKS • BCR ERSTE BANK (RO) • TATRABANKA • PSS TELCO & RETAIL • T-MOBILE • VODAFONE (CZ) • SOS ELECTRONIC • EXISPORT UTILITIES & OTHER • SPP • PIXEL FEDERATION
- 5. Business Understanding • Set business goal and ask question – We need to grow sales in SME segment • What SME are interested in our offer? • Transform in into task for data-mining – Divide SME portfolio into 2 group • Will accept offer • Will not accept offer Data-mining starts by asking the right question
- 6. Data understanding ALL SME ACCEPTED OFFER X Goal: Target less customers and achieve the same results All customers Success in last 30 days
- 7. Data understanding Imagine a predictive model splitting customers into segments: green, orange, red ALL SME Target just 20% of customers to get 80% of max profit. 10 % All customers Success in last 30 days Best customers 10 000 (20%) Average customers 20 000 (40%) Worst customers 20 000 (40%) 50 000 customers 5 000 accepted offer 4000 (80%) 750 (15%) 250 (5%) 100% 20%40%40% 60% Rule1 Rule2
- 8. Data preparation • In order to build good model you need relevant data Customer Age Location Purchases Target 1 10 East 120 Yes 2 5 East 42 No 3 24 West 23 Yes 4 2 West 50 Yes 5 1 West 19 No More attributes available = better chance for good prediction
- 9. Modeling: Split buyers and non buyers Logistic RegressionLinear Regression Decision Tree Neural Networks
- 10. Model Evaluation • Train model on historical data • Test on unseen data – 50%:50% – different history Model vs. bežný výber 40% 25% 14% 8% 4% 3% 2% 2% 1% 1% 0% 5% 10% 15% 20% 25% 30% 35% 40% 45% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% % celkového počtu klientov %targetvskupine model random Model vs. random selection on history % of total SME%buyersingroup • Good models moves buyers to the first groups • Weak model is like random: buyers are everywhere
- 11. Deployment • Target campaign to the best customers • Usual process: – Import predictions to CRM – Select top customers – Evaluate campaign after a XY days
- 12. HOW PREDICTIONS WORK IN 7SEGMENTS DEMO
- 13. Usually it looks like this :-) IBM SPSS Modeler 16
- 14. Smart software can simplify process Step of methodology Level of automation in our solution Business understanding Requires user input Data understanding Automated & supervised Data preparation Automated & supervised Modeling Automated & supervised Evaluation Automated & supervised Deployment Semi-Automated Use wizard for task definition or Select a pre-defined task Let user to check results and decide about actions
- 15. Who is eligible for prediction? For all customer who bought some “Shoes” in last 3y
- 16. What would you like to predict? Identify those likely to: make another purchase above 5€ in next 300 days
- 17. Data understanding Check default response rate & predictors
- 19. Model Evaluation and Profitability Analysis
- 20. Deployment Use this rules in your next campaign:
- 21. Summary • Non-coders can understand and manage • Works well on any data • Useful: instant deployment into campaigns delivers immediate value
- 23. Under Cover: Data • We use available customer data for prediction • We generate predictors from customer events Customer Time Event Amount Product 1 Monday Purchase 5 Shirt 1 Tuesday Purchase 10 Shoes 1 Thursday Purchase 20 Ball 1 Saturday Purchase 5 Pen 1 Sunday Purchase 2 Pin Count of (event.purchase) = 5 Count of (event.purchase) in (last 3 days) = 2 Sum (event.purchase.amount) = 43 Sum (event.purchase.amount) in (last 3 days) = 7 Last / First / Most frequent event.purchase.product = Pin/Shirt/- …. For every event and all its properties
- 24. Under Cover: Training & validation • Automatic building of train, test and actual dataset – train is from different time period then test – actual are the most recent data • Rules for attribute transformations – Discretization, coarse classing, remove noise, replace missing value • Variation of CHAID decision tree – It’s almost non-parametric – ChiSq test works fine on unbalanced data compared to GINI in CARTs – Generate heuristics for stopping criteria
- 25. Under Cover: Deployment • Real-time scoring – Calculate only final predictors what’s easy task for single customer – Apply rules-set to get prediction – Keep scoring for post-evaluation
- 26. Under Cover: Near Future • Apply various algorithms – For better visualization & insights • Log. Regression produces visual scorecard with points – To reduce risk of over fitting or low AUC • Generate less and better predictors – [Average] [profit] in [category=value] in the [last/first] N [periods] • Identify right time to retrain models
- 27. (Almost) Full List of Features Campaigns • Automated & Planned • Scenario Designer • Email Designer & Gateway • SMS Gateway • Survey Designer • Social Pusher (FB, Twitter, LinkedIn) CRM • Customer List • Custom Attributes • Events • Data Management • Import Wizard • Aggregator and Derivator Analytics • Trends • Funnels • Descriptive Statistics • Session Length • Custom Report Designer • Dashbdoard Designer Optimization • Segmentations • Predictions • Product Recommendation Engine • Market Basket Analysis • Real-time Customer Scoring API and SDK for JavaScript, PHP, Python, Flash, Unity (mobile), Linux shell, …
- 28. Thank you for attention! Call Jozo Kovac (+421) 918 666 238 and schedule live demo! www.7segments.co m + (421) 918 666 238