How Can we Profit from Data Analytics? [B4] Uetliberg,

Transcription

1 [B4] Uetliberg,

2 OUTLINE Data science in business environment o o o o o Why? Areas People Process Tools and algorithms Applications o o o o Forecasting Production planning Geo-Fingerprint Geo-Select Demonstration with SAP Predictive Analytics In-house products: real data, applied to clients Slide 2

3 DATA SCIENCE IN BUSINESS ENVIRONMENT Why data science? o Discover success, failure reasons o Understand customers, you o Plan future o Design products o Experiment The more data-driven a firm is, the more productive it is firms that adopt data driven decision-making have output and productivity that is 4-6% higher than what would be expected given their other investments and information technology usage. Brynjolfsson, Hitt, Kim, MIT & Penn s Wharton School, Slide 3

4 DATA SCIENCE IN BUSINESS ENVIRONMENT Areas implicated to a data science business project Data Analytics Big Data Data Wrangling Machine learning Data mining Descriptive statistics Complex data Complex problem Parallelization technologies Data organization Format / Restructure data Change data Slide 4

5 DATA SCIENCE PROJECT: PEOPLE Domain expert Data expert Data scientist Slide 5

6 DATA SCIENCE PROJECT: PROCESS Change business Understand and change data Build models, discover patterns Slide 6

7 DATA SCIENCE PROJECT: TOOLS Chart from "2014 Data Science Salary Survey" (ISBN ) 2015 O'Reilly Media, Slide 7

8 DATA SCIENCE PROJECT: ALGORITHMS Clustering Classification Regression Associators Optimizers Discover natural groupings of cases Predict what class case belongs to Predict numerical outcomes Explore associations, links, and between cases Maximize / minimize Slide 8

9 APPLICATIONS

10 DDD APPROACH: APPLICATIONS Domain Financial services Insurance Retail Manufacturing Common big data scenarios Modeling true risk, Portfolio optimization, Threat analysis and fraud detection Customer retention and expansion, fraud/error detection Improve labor scheduling, Customer churn analysis Trade surveillance and forecasting Credit scoring and analysis Personalized policies, Increase cross-selling and upselling Understand customers and products, Sentiment analysis Cost-effective production planning, Identification of market gaps Government Perform controls, Assess variability in performance across sectors Environmental monitoring, Offer citizen satisfaction Healthcare Genomics & Cancer research, Air quality monitoring Improve clinical efficiency, Reduce expenses Slide 10

11 DDD APPROACH: APPLICATIONS Domain Financial services Insurance Retail Common big data scenarios Modeling true risk, Portfolio optimization, Threat analysis and fraud detection Customer retention and expansion, fraud/error detection Improve labor scheduling, Customer churn analysis Trade surveillance and forecasting Credit scoring and analysis Personalized policies, Increase cross-selling and upselling Understand customers and products, Sentiment analysis Manufacturing Forecasting Cost-effective production planning, Identification of market gaps Government Perform controls, Assess variability in performance across sectors Environmental monitoring, Offer citizen satisfaction Healthcare Genomics & Cancer research, Air quality monitoring Improve clinical efficiency, Reduce expenses Slide 11

12 DDD APPROACH: APPLICATIONS Domain Financial services Insurance Retail Manufacturing Common big data scenarios Modeling true risk, Portfolio optimization, Threat analysis and fraud detection Customer retention and expansion, fraud/error detection Improve labor scheduling, Customer churn analysis Trade surveillance and forecasting Credit scoring and analysis Personalized policies, Increase cross-selling and upselling Understand customers and products, Sentiment analysis Cost-effective production planning, Identification of market gaps Government Perform controls, Assess variability in performance across sectors Environmental monitoring, Offer citizen satisfaction Healthcare Genomics & Cancer research, Air quality monitoring Improve clinical efficiency, Reduce expenses Slide 12

13 FORECASTING

14 FORECASTING We want to predict the demand of our product for the next 9 months We have data on: sales of previous years, and other factors that potentially affect sales Slide 14

15 FORECASTING Models based only on past sales may have large error margins Including other factors increases the model s forecasting power Slide 15

16 FORECASTING Forecasting Validation Only time information + other factors Slide 16

17 PRODUCTION PLANNING

18 PRODUCTION PLANNING We want to generate a 12-month production schedule of a product We have data on: the monthly product demand (di), the monthly production costs per piece (ci), the inventory cost (h), and the production capacity (m) Description Inventory Cost (h) per month per piece Production Capacity (m) per month Value 0.1 CHF pieces How we should set the production level of the product for each month (x) in order to minimize the total cost (production & inventory costs)? Slide 18

19 PRODUCTION PLANNING Mathematical formulation of the problem level of the product for each month (xi) Over all months the monthly production costs per piece (ci) Slide 19

20 PRODUCTION PLANNING Mathematical formulation of the problem Over all months Remaining product for each month the monthly inventory costs per piece (hi) Slide 20

21 PRODUCTION PLANNING Mathematical formulation of the problem You can never exceed the production capacity Slide 21

22 PRODUCTION PLANNING Mathematical formulation of the problem Always meet the demand Slide 22

23 PRODUCTION PLANNING Mathematical formulation of the problem How to find the xi s? Linear Programming more than 30 years of experience! Slide 23

24 PRODUCTION PLANNING Use Linear Programming (LP) Schedule optimally the production level of the product Perform efficiently and create value Save more than 2.5% of the overall product costs (producion & inventory) Slide 24

25 GEO-FINGERPRINT

26 GEO-FINGERPRINT & GEO-SELECT Characterization of regions Level State (Kanton) District (Bezirk) Community (Gemeinde) Available Data Employees per sector Censuses Gross wages Bruttolohn Rental prices Healtcare costs Building Costruction costs Educational level Commuters (several hundred available) Hectare (100m x 100m) Censuses, Hausdeholds Buildings and apartments Land use, land cover Slide 26

27 GEO-FINGERPRINT Identify groups Combine with company data Customer Segment High in education High in income High in commuting Speaking DE Ederswiler Quota of people between Sales per Person 3% 20 CHF Reitheilm Quota of people between Sales per Person 10% 46 CHF Slide 27

28 dependent independent GEO-FINGERPRINT APPLICATIONS Understand Products, Customer-/Member-base Variable Reg. 1 Reg Reg Age Quota Regression analysis Frac. Fam Education Urban Lev Language DE FR IT Who are the important factors and how they influence the output... Sales Members Slide 28

29 Products Education Sales GEO-FINGERPRINT APPLICATIONS Identify problematic parts of your company / organization cluster analysis Income Identify market gaps / under-represented segments Features Comparative analysis Slide 29

30 GEO-FINGERPRINT & ORIOR: MARKET ANALYSIS OF HAUS PASTETE 2X55G

31 ORIOR: INTRODUCTION About ORIOR AG o o A traditional Swiss food group specializing in fresh convenience food and meat processing Build on well established companies and brands including Rapelli, Ticinella, Albert Spiess, Möfag, Fredag, Pastinella and Le Patron o Employs more than1,200 people, handles more than products and generated sales of 522 million CHF in 2014 o Being a B2B company, has no information on end-customers of products Slide 31

32 OVERVIEW 36% less in families, 21% less in IT-part, 14% more for 65+ people Strong sales inefficiency in 18.3% of the regions More than 25% decrease of the anticipated sales performance Losses of 9.44% of the total annual revenue Slide 32

33 16K PRODUCT UNDERSTANDING Data integration o Orior Data (Coop+Migros / ) Store Name Store Address Date Sales (Kg) Coop BE Murtenstrasse 4, Aarberg Migros Ostschweiz Gäuggelistrasse, Chur o Demographic Data Region ID Name DE Density FR Density Quota of 1-p houses 1006 Bezirk Sense District de la 1002 Glâne Slide 33

34 141 PRODUCT UNDERSTANDING Regression analysis 16 Region ID Name Demographics Sales 1006 Bezirk Sense District de la Glâne Bezirk Uster Bezirk Winterthur Bezirk Reiat Bezirk Lebern Bezirk Schaffhausen Slide 34

35 PRODUCT UNDERSTANDING Demographic Feature Correlation p-value Fingerprint Total Population >0.05 Quota of People between <0.05 Quota of People between >0.05 Quota of People between >0.05 positive neutral negative Quota of People 65 und mehr <0.05 fraction of Foreigners >0.05 Quota of 1-p Houses <0.05 Quota of family-houses <0.05 Income pro Jahr pro Person >0.05 Low Educational Fraction >0.05 High Educational Fraction >0.05 Degree of Urbanization >0.05 Social Status Index >0.05 DE-Sprache Density >0.05 FR-Sprache Density >0.05 IT-Sprache Density < Slide 35

36 PRODUCT UNDERSTANDING Sales & demographic factors Slide 36

37 PRODUCT UNDERSTANDING Sales & demographic factors Slide 37

38 SALES EFFICIENCY Name of Region Annual Sales per Person (gr) Bezirk Horgen District de Nyon Slide 38

39 SALES EFFICIENCY Name of Region Annual Sales per Person (gr) Quota of Family Houses (%) Bezirk Horgen District de Nyon Slide 39

40 SALES EFFICIENCY Name of Region Annual Sales per Person (gr) Quota of Family Houses (%) Anticipated Annual Sales per Person (in gr) Sales Performance Deviation (%) Bezirk Horgen District de Nyon Slide 40

41 SALES EFFICIENCY Name of Region Annual Sales per Person (gr) Quota of Family Houses (%) Anticipated Annual Sales per Person (in gr) Sales Performance Deviation (%) Bezirk Horgen District de Nyon Description Absolute Number Percentage (%) Sales Decrease > 50% % > Sales Decrease > 50% % to 25 % % > Sales Increase > 50% Sales Increase > 50% Slide 41

42 SALES EFFICIENCY Strongly under-, and over-performing regions Name of Region (Bezirk) Annual Sales per Person (in gr) Sales Performance Deviation (%) Description Bezirk March Under-performing Bezirk Waldenburg Under-performing District de Sierre Under-performing Bezirk Mittelland Under-performing Kanton Appenzell Innerrhoden Under-performing Bezirk Gäu Over-performing Wahlkreis See-Gaster Over-performing District de la Broye-Vully Over-performing Knowledge transfer Slide 42

43 REVENUE LOSSES Computation Anticipated Annual Sales per Person (in gr) Annual Sales per Person (gr) Total Population Average Product Price in CHF per gr CHF Overall losses Product Name Total Annual Revenue (CHF) Absolute Annual Revenue Loss (CHF) Percentage of Annual Revenue Loss (%) Haus Pastete 2x55g x xxx xxx xxx xxx 9.44 Segmentation of losses Name of Region (Bezirk) Sales Performance Deviation (%) Percentage of Annual Revenue Loss (%) Bezirk Zürich Distretto di Lugano Bezirk Dielsdorf Bezirk Dietikon Wahlkreis St. Gallen Slide 43

44 GEO-SELECT

45 GEO-SELECT Choosing optimal points at the national level or city level Slide 45

46 GEO-SELECT APPLICATIONS Advertising your products at the city level Choosing locations for new stores and shops Slide 46

47 GEO-SELECT & YOUNG SWISS (YS): OPTIMAL PROMOTION STRATEGY

48 YOUNG SWISS: INTRODUCTION About Young Swiss o o o o The biggest trilingual youth organization in CH Over memberships of young people Radical restructuring of the process to acquire memberships Change its member-base to a new direction target a specific segment of young people Which CH-locations are best for promotion events, given budget-constraints? Slide 48

49 YOUNG SWISS: OVERVIEW What part of the segment can a promotion event attract in each region? Which combination of regions is the best for promotion events? How to optimally allocate your budget in order to get 8% more members and CHF? Slide 49

50 IMPACT OF PROMOTION EVENTS Compute impact of promotion events in each region o Local characteristics Memberships and demographics o Connectivity characteristics Travel distance from surrounding regions Urbanization level of the region Number of people commuting for work Attracting Capacity of a region = Total number of persons attracted from a promotion event in a region Slide 50

51 IMPACT OF PROMOTION EVENTS Compute Attracting Capacity of promotion events for each region 5K 10K 10K 50K over 50K Rank Name Attracting Capacity 1 Zürich Winterthur Genève Bern Basel Lausanne Luzern Adliswil Dübendorf Kloten Slide 51

52 COMPUTE BEST COMBINATIONS Find best combined attracting capacity of regions that maximizes the national promotion impact o Which are the best 2 regions to hold promotion events? Rank Name Attracting Capacity 1 Zürich Winterthur Slide 52

53 COMPUTE BEST COMBINATIONS Find best combined attracting capacity of regions that maximizes the national promotion impact o Which are the best 2 regions to hold promotion events? Rank Name Attracting Capacity 1 Zürich Winterthur -75% Large overlap! Not a good choice! Slide 53

54 COMPUTE BEST COMBINATIONS Find best combined attracting capacity of regions that maximizes the national promotion impact o Which are the best 2 regions to hold promotion events? Promotion Points Name Cumulative Coverage 1 Zürich 15.5% 2 Genève 20.9% Any other selection of 2 regions achieves less than 20.9% Slide 54

55 COMPUTE BEST COMBINATIONS Find best combined attracting capacity of regions that maximizes the national promotion impact o Which are the best 3 regions to hold promotion events? Promotion Points Name Cumulative Coverage 1 Zürich 15.5% 2 Genève 20.9% 3 Bern 25.9% Any other selection of 3 regions achieves less than 25.9% Slide 55

56 COMPUTE BEST COMBINATIONS Find best combination of regions that maximize promotion impact o Which are the best 15 regions to hold promotion events? Promotion Cumulative Name Points Coverage 1 Zürich 15.5% 2 Genève 20.9% 3 Bern 25.9% 4 Basel 30.6% 5 Lausanne 35% 6 St. Gallen 37.4% 7 Luzern 39.1% 8 Winterthur 40.4% 9 Lugano 41.4% 10 Neuchâtel 42.1% 11 Fribourg 42.8% 12 La Chauxde-Fonds 43.4% 13 Sion 43.9% 14 Chur 44.4% 15 Thun 44.8% Slide 56

57 OPTIMAL BUDGET ALLOCATION Allocate budget proportional to the attracting capacity o Example: 7 regions, total budget of CHF Optimal budget allocation Population-based budget allocation Value of up to: members (2%) CHF Slide 57

58 OPTIMAL BUDGET ALLOCATION Allocate budget proportional to the attracting capacity o Example: 7 regions, total budget of CHF Optimal budget allocation Same budget allocation Value of up to: members (8%) CHF Slide 58

59 CONCLUSION Data Analytics is the next natural step after the establishment of a BI framework The effort for data wrangling is substantially decreased. Data analytics project can be run fast Data-Driven Decision-making can offer substantial value to the company Slide 59

60 Wir freuen uns auf angeregte Gespräche mit Ihnen Dr. Sotiris Dimopoulos Data Scientist Follow