Database marketing

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My invited presentation on Database Marketing/Marketing Analytics to BRANDWAGON forum of budding marketing managers

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Database marketing

  1. 1. DATABASE MARKETING also known as Marketing Analytics Invited presentation for “BRANDWAGON”March 3, 2013 ©Arup Guha - Indian Institute of Foreign Trade - New Delhi, India
  2. 2. • ARUP GUHA • ANALYSIS MANAGER (INNOVATIONS) • dunnhumby LTD • TWO DEGREES in ECONOMETRICS • ZERO DEGREES in MARKETINGMarch 3, 2013 ©Arup Guha - Indian Institute of Foreign Trade - New Delhi, India
  3. 3. THE DATA SCIENCE REVOLUTION When the Sloan Digitial Sky Survey started work in 2000, its telescope in New Mexico collected MORE data in a FEW WEEKS than in the entire HISTORY of astronomy Walmart, a retail giant, handles more than 1M customer transaction EVERY HOUR, feeding databases estimated at more than 2.5 PETABYTES Facebook, is home to 40B photosMarch 3, 2013 ©Arup Guha - Indian Institute of Foreign Trade - New Delhi, India
  4. 4. SNOWBALLING DATA AND RELATED ISSUES SPOT BUSINESS STORAGE, PRIVACY, SP TRENDS, PREVENT URIOUS TRENDS DISEASE, COMBAT CRIMES VASTGETTING ORACLE,VASTER IBM, SAP INTERNALISATION GADGETS, MEASUREMENT MIDDLE CLASS DATA SCIENTISTMarch 3, 2013 ©Arup Guha - Indian Institute of Foreign Trade - New Delhi, India
  5. 5. DATA IS THE NEW ASSET OF BIG AND SMALL BUSINESSADVICE CUSTOMERS DECISION TO LEAVE IS MADEWHETHER TO BUY AN BY CUSTOMERS MUCH BEFOREAIRLINE TICKET NOW OR THEY ACTUALLY DOWAIT FOR THE PRICE TOCOME DOWN INCREASING ARRAY OF POWERFUL TECHNIQUES AND ALGORITHMS MANAGERS PORED OVER EVERY MACHINE TO MAKE IT EFFICIENT. NOW STATISTICIANSSTOCK POPTARTS BEFORE PORE OVER THE DATA FROMSTORMS THE QUANTITATIVE ANALYST THESE MACHINES March 3, 2013 ©Arup Guha - Indian Institute of Foreign Trade - New Delhi, India
  6. 6. THE AGONY OF ANALYSIS ANALYTICS IS DATED IT SYSTEMSIT INDUSTRY IS PERFORMING STORING “TORTURE THE DATA PILING INTO STATISTICAL INNACURATE LONG ENOUGH AND BUSINESS OPERATIONS FOR INFORMATION AT IT WILL CONFESS TOINTELLIGENCE FORECASTING OR HUGE COST AND ANYTHING” UNCOVERING SPACE USAGEHal Varian, Google’s Nestle sells IPCC reported onlyChief Economist CORRELATIONS 100,000 products in the influence ofpredicts that the The Royal 200 countries using warming factorsjob of the Shakespearian 550,000 suppliers. that are raisingquantitative Company sifted Half of its records globalanalyst would be through 7 years of were obsolete or temperaturethe sexiest aroundIBM has invested sales data for a duplicated and without mentioning$12B in last 4 years. marketing more than 15% the coolingSAS, ORACLE, SAP all campaign that plain wrong forces, to makehave invested increased regular their figures lookheavily visitors by 70% more dramatic March 3, 2013 ©Arup Guha - Indian Institute of Foreign Trade - New Delhi, India
  7. 7. DIVE DEEP INTO MARKETING ANALYTICSMarch 3, 2013 ©Arup Guha - Indian Institute of Foreign Trade - New Delhi, India
  8. 8. EVOLUTION OF DATA BASED INSIGHTS IN MARKETING Simulatio Forecasti ng n MARKETERS Planning AND Post Analysis ADVERTISERDescriptiv es S ALWAYS KNEW THAT RESEARCH WAS THE MARKETING ANALYST IMPORTANT March 3, 2013 ©Arup Guha - Indian Institute of Foreign Trade - New Delhi, India
  9. 9. DESCRIPTIVES HELP SUMMARISE A DATASET AND REVEAL UNEXPECTED PATTERNS In 2004, Walmart peered into its massive databases and found that before a hurricane strikes, there was a run on flashlights and batteries, as might be expected, but also on pop tarts, a sugary American snack product store shop Sum spend by product name for shop dates name name spend units date corresponding to the week before hurricane 12-12-telephone grimgold 4,000 345 2012 13-11-sack ciplo 29,900 5214 2012 10-06-ground beef mezol 48,57,659 245425 2012 product name spend shop date 12-12-flashlight forte 3,84,734 976776 2012 flashlight 3,84,734 12-12-2012 04-04-printer kong 2,84,859 43525 2012 batteries 2,84,85,905 12-12-2012 12-12-batteries forte 2,84,85,905 523523 2012 Pop tarts 2,99,494 12-12-2012 04-02-plugs ciplo 2,94,959 4576 2013 12-12-poptarts brim 2,99,494 43124 2012 March 3, 2013 ©Arup Guha - Indian Institute of Foreign Trade - New Delhi, India
  10. 10. DESCRIPTIVES PACKED INTO A REPORT ARE A VERYPOWERFUL DECISION MAKING TOOLBest Buy, an American electronics retailer, found that 7% of its customersaccount for 43% of its sales. So, it executed a customer survey andreorganised its stores to suit their needs and tastes 100%SIMPLE TRANSACTIONS TABLE CUMULATIVE SALES Customer ID spend 12345 4,000 43% 12346 29,900 WHO ARE MY BEST 21385 48,57,659 CUSTOMERS? 496848 3,84,734 3988476 2,84,859 7% 100% `2367 2,84,85,905 CUMULATIVE CUSTOMERS 1345987 2,94,959 236567 2,99,494 March 3, 2013 ©Arup Guha - Indian Institute of Foreign Trade - New Delhi, India
  11. 11. DESCRIPTIVES HELP COMBINE PATTERNS AND FINALISE BUSINESS STRATEGY PRODUCT SEGMENTATION GERMINATING PRODUCT STRATEGY BEST BEST CUSTOMER CUSTOMER BEST CUSTOMERS LIKE BEST CUSTOMERS DONT LIKEBEST CUSTOMERS MOST THAT MUCH S DON’T S LIKE It is good idea to remain Why they don’t like these products? LIKE MUCH MOST competitive on these Is the price wrong? Is the quality products, give frequent wrong? Can be use bundling? What offers, display widely is happening in the outside world? Are they niche products? NOT BEST NOT BEST NOT BEST CUSTOMERS NOT BEST CUSTOMERS LIKE CUSTOMER CUSTOMER DONT LIKE MUCH MOST S DON’T S LIKE Why are they here? Again is Are my stores inadequate in LIKE MUCH MOST the pricing wrong? Just get rid these products? What is the of them attitude of my best customers towards these products? What would be the conversion if I focus on these products? PURCHASE FREQUENCY March 3, 2013 ©Arup Guha - Indian Institute of Foreign Trade - New Delhi, India
  12. 12. POST ANALYSIS HELPS UNDERSTAND WHAT WORKEDAND WHAT DID NOT40% of all purchases in US supermarkets take place under some kind ofpromotion. Are they Effective? DID IT WORK? IF WE DO IT AGAIN WILL IT WORK AGAIN? DID IT HAPPEN BECAUSE OF THE EVENT OR SOME OTHER FACTOR? WILL IT WORK IN ALL GEOGRAPHIES? FOR ALL CUSTOMERS? ANSWER ANCOVA ANSWERMarch 3, 2013 ©Arup Guha - Indian Institute of Foreign Trade - New Delhi, India
  13. 13. ANALYTICS FOR PLANNING HELPS UNDERSTAND THE DRIVERS OF OBSERVED TRENDS Cablecom, a Swiss Telecom operator, reduced its annual attrition from 20% to 5% by crunching numbers FIRST, THE SECOND, THE THIRD, THE TREND WAS DRIVER WAS STRATEGY WAS IDENTIFIED UNCOVERED FORMULATED 25% ARRESTING 20% CUSTOMER ATTRITION Send special deals toATTRITION 15% The X or the best customers at risk during 10% indicator of the 7th month into their 5% attrition was found subscription to be the number of calls to customer 0% 0 2 4 6 8 10 12 14 16 support services. ANSWER This fell around the SURVIVAL ANALYSIS MONTHS 9th month for attritors ANSWER March 3, 2013 ©Arup Guha - Indian Institute of Foreign Trade - New Delhi, India
  14. 14. ANALYTICS FOR PLANNING HELPS IDENTIFY VALUABLETRENDS FOR MARKETINGAirline cost management improved because analytical techniques helpeduncover the best predictor that a customer would actually catch a flight hehad booked: that he had ordered a vegetarian meal Travellers who PROBABILITY OF FLYING order a vegetarian meal have a higher probability of actually flying Customers who have not ordered a vegetarian meal have a low probability of Vegetaria ANSWER actually catching n meal LOGISTIC REGRESSION N Y the plane ANSWERMarch 3, 2013 ©Arup Guha - Indian Institute of Foreign Trade - New Delhi, India
  15. 15. PATTERN MINING HELPS IN REAL TIME ANALYTICS ANDMOREAmazon, Flipkart, provide real time recommendations. Practically all sitesrearrange their links according to past browsing behaviour. Retailers givebundled offers. Shopper’s Stop rearranged its store layout MAGIC FORMULA If most people who have bought book B also bought DVD A, then maybe you would like it too? You always go from one formal section to another, so we will skip the links for the casuals? P(A|B) P(A) I am getting a higher cut from lipstick X but lipstick Y is what most people buy. There is a high chance of buying lipstick after mascara. What if I package lipstick X with that popular mascara brand? Women have a high tendency to buy shoes after salwar kamiz and in my store they are at the opposite ends. Wonder how much sales uplift I can get by bringing them next to each other? ANSWER ASSOCIATION ANALYSIS ANSWER March 3, 2013 ©Arup Guha - Indian Institute of Foreign Trade - New Delhi, India
  16. 16. MORE VISUAL PATTERN MINING TECHNIQUES HELPCOMMUNICATE BETTERApparel Retailer J. C . Penney shifted to an EDLP program while its biggestcustomer group liked coupons ALL CUSTOM Y ERS You Call That a Strategyeeeeee !!!! MALE FEMALE 40% 60% Y <30 30+ 20% 80% Y NO COUPONS COUPON USERS ANSWER 10% 90% DECISION TREE ANSWERMarch 3, 2013 ©Arup Guha - Indian Institute of Foreign Trade - New Delhi, India
  17. 17. FORECASTING ALLOWS US TO CAST THE SERIES OFINTEREST MULTI-PERIODS INTO THE FUTUREForecasting New Product Demand over time for future saves a fortune instocking and advertising costs A relevant curvature is assumed for the diffusion curve which is dependant on two parameters Based on Sales data we estimate two spread parameters: “word of mouth” and “advertising” These parameters enable us to forecast future sales for new and ANSWER similar products BASS MODEL ANSWERMarch 3, 2013 ©Arup Guha - Indian Institute of Foreign Trade - New Delhi, India
  18. 18. ADVANCED SIMULATION TECHNIQUES ARE USED TOSIMULATE OUTCOMES TOO COMPLICATED TO MODELAfter remaining dormant for several decades, Hush Puppy Sales suddenlyskyrocketed in early 1994 Heterogeneous AGENTS, Complex interactions, Dynamics Just enter simple rules for agents to interact Watch complex unimagined macro outcomes appear Software: Netlogo Unlike in traditional modelling you ANSWER don’t have to know the macro outcome and the interaction is too AGENT BASED MODELS complex to model analytically ANSWERMarch 3, 2013 ©Arup Guha - Indian Institute of Foreign Trade - New Delhi, India
  19. 19. SOCIAL NETWORK DATA HAS GIVEN BIRTH TONETWORK ANALYSIS WHICH ANALYSES INTERACTIONStore redesign for a major retailer was left to individual departments tocoordinate and execute. It was taking too long Heterogeneous AGENTS, Complex EXPECTED ACTUAL interactions, Dynamics Enter the past interaction data between agents and see the network formed and uncover mavens, masterminds, strength of connection Software: Gephi EVERYONE WAS THEY WERE EXPECTED TO INTERACTING ANSWER INTERACT WITH ONLY THROUGH AGENT BASED MODELS EVERYONE ELSE THEIR ANSWER MANAGERSMarch 3, 2013 ©Arup Guha - Indian Institute of Foreign Trade - New Delhi, India
  20. 20. BIG DATA AND ANALYTICAL SOFTWARE R is open source, freely downloadable, rapidly updated software with cutting age techniques Between them, SAS and SPSS has 90% of the world analytical software market share MATLAB is best suited to financial analysis but it can also be used for other data analyses For cutting edge work, most people still write C++ codes Hadoop uses others computers to process huge data for freeMarch 3, 2013 ©Arup Guha - Indian Institute of Foreign Trade - New Delhi, India
  21. 21. THANK YOUMarch 3, 2013 ©Arup Guha - Indian Institute of Foreign Trade - New Delhi, India

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