Panel: Driving Factors for Prospective Sectors Moderated by: Angel Mitev Panelists: Hristo Hadjitchonev, Angel Marchev Jr., Nikola Toshev, Emil

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1 Panel: Driving Factors for Prospective Sectors Moderated by: Angel Mitev Panelists: Hristo Hadjitchonev, Angel Marchev Jr., Nikola Toshev, Emil Ivanov, Sergi Sergiev

2 ROI of Data Science Projects Improve Revenue Reduce Cost Optimize Performance

3 A4E Case Studies - Nedelya Real-time forecasting of demand Automation of Supply-Demand chain decision process and Production Facility and Distribution Analytics for Location and Marketing performances Waste minimized ~2% (7% industry average) Solution delivered via A4E proprietary Analytics cloud based platform Usage of big data enriched weather data (historical and forecast) MRR Industry: Pastry and cakes retail and production Size: 37 + retail locations Revenue: 9M (2016) Web: a4everyone.com

4 A4E Case Studies Coca Cola Analysis of past marketing performances Geo targeting of marketing Campaign (300,000 households) New product marketing - 750ml glass pack 20% better performance compared to the previous campaigns Big Data utilization rent per sq.m., income per district, NIS public data (age, gender, households, etc. distributions) Project delivered based on proprietary modeling algorithms ARR Industry: Beverages Size: Web: a4everyone.com

5 A4E Case Studies Sport Depot Analysis of historical sales data Model and forecast of next winter season demand Supply order recommendations for 23 stores and distribution based on: Sport Gender Color Size Pricing category Project delivery based on proprietary forecasting algorithms Project based and ARR Industry: Sporting goods retail Brands: 60+ Locations: 21 Revenue: 35M Web: sportdepot.bg a4everyone.com

6 A4E Case Studies Credissimo Credit Score as a Service Automated Scorecard updated on biweekly bases 5 seconds response time per request Integration of the business rules 80% automated decisions Industry: Financial Services Users: 2M + Markets: Bulgaria, Macedonia, Poland Revenue: 11M Web: credissimo.bg a4everyone.com

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8 Project Next best offer, DSK Bank (Data mining driven sales) Essence: Computing individual probabilities to buy a credit product, using statistical methods, to extract maximum sales potential from the data of own customers. Target group: Retail banking Goals: 1.Discover implicit models (outside of the normal business logic) and better understanding motives for buying a credit product; 2.Generating leads with high propensity to buy on individual level; 3.Deeper understanding of the (groups of) factors which determine the propensity to buy; 4.Targeting bigger groups ( nuggets ) of customers with high propensity to buy. Business understanding Data understanding & preparation Modelling Evaluation & implementation 8

9 Target variable: Applied for <personal> loans Project Next best offer, DSK Bank (Data mining driven sales) Predictor variables: 3965 features, describing products, demographics, and behaviors customers Main challenges: Non-structured data which lead to the large amount of additional work to be structured and merged Too sparse data (many missing values which lead to the dropping out cases and/or variables) Data prep: Reduction of the 3965 features to 413 significant, most important features and factors, increasing propensity to buy General data cleansing Business understanding Data understanding & preparation Modelling Evaluation & implementation

10 Best Method: CHAID Chi-squared Automatic Interaction Detection (Gordon Kass, 1980): Numeric & categorial variables Uses cases with missing values Project Next best offer, DSK Bank (Data mining driven sales) ChiSq procedure which distinguishes more than two splits The model with best relations among the nodes Take away s: Big data needs of strong computing power (hours of calculation) The model requires update after time Business understanding Data understanding & preparation Modelling Evaluation & implementation

11 Project Next best offer, DSK Bank (Data mining driven sales) Evaluation: Accuracy is high at 76,8% for actual/predicted customers and 81.3% overall Area under curve (AUC) = Main outputs: Individual Raw Propensity (RP) score to be used for identifying prospective (groups of) customers Ruleset for generation of leads towards groups of customers and for further analyses. Business understanding Data understanding & preparation Modelling Evaluation & implementation

12 Project Next best offer, DSK Bank (Data mining driven sales) Comparison by efficiency: Two campaigns with the same time lengths using leads from: - data mining results from the model - business rules regularly practiced at the bank Campaign Credit applications Credits deals signed Contacted leads Applications to contacts (%) Deals to contacts (%) Leads by Data mining Leads by Business rules Increased efficiency: 99.8% 24.3% Business understanding Data understanding & preparation Modelling Evaluation & implementation

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14 Travel Agent Hotel Chain

15 Look to book

16 KPIs Goal KPI Target Shield suppliers from high transaction volume Utilisation 70-80% Look to Book 4,000 Protect booking opportunities by maintaining high cache accuracy Accuracy 95% Booking Error <1% Enable certain channel access to rich rate diversity Booking Growth 10% Drive low average response times by answering from cache Response time <300ms

17 Concept Build a cache based on machine learning to improve performance (KPIs): Recognize patterns in history: Availability Frequency of rate change Revenue management hierarchy Predict search requests Infer expiration time Use historical transaction data

18 Solution No proactive search requests Estimate validity, not expiration time Travel-specific feature engineering to capture the right correlations New cache structure

19 Smart Cache

20 Feature Engineering Lead time to arrival day Cache entry age Booking spike in area Hotel type All key request fields All cached response fields Weather prediction... Is cache entry valid? 200h 12.5h 3σ Airport No 48h 1h -0.5σ Resort Yes σ City

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22 Online data vs offline data?

23 Retail Statistics 91% of all purchases happen in the physical stores* 20% 71% of purchases are tracked by loyalty cards of customers data is not collected and utilized >90% of shoppers use their mobile while shopping ** Source: RetailNext * SessionM **

24 Technology 3G / 4G WiFi Bluetooth Unique key % Link Online Patterns Behaviour Mobile App Cashier Free WiFi Bills Loyalty cards CRM

25 Customer Behavior Analytics Add-on for every brick and mortar

26 ShopUp Combine Analyze Predict WiFi Data Traffic Outside Dwell time Traffic Shifts Cross-Chopping Cannibalization Door Counters Venue Analytics Heatmap Retention Maintenance Sales Databases Customer Behaviour Free loyalty card Customer Flow Recommendation engine Notifications Weather Databases Profiling Up-Sell Cross-Sell Customer basket Preferences Products Promotions Mobile Applications Customer cross-products Event recommendation

27 Customer understanding (revenue) Holistic view Employees feedback (performance) Optimized shifts, KPI and realistic metrics Merchandising (performance) Hot and cold zones, early notifications and alerts Cross and us-sell mechaniques (revenue)

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