Marketing & Big Data

Transcription

1 Marketing & Big Data Surat Teerakapibal, Ph.D. Lecturer in Marketing Director, Doctor of Philosophy Program in Business Administration Thammasat Business School

2 What is Marketing?

3 Anti-Marketing

4 Marketing The process by which companies create value for customers and build strong customer relationships in order to capture value from customers in return. Reference: Kotler, P. and Armstrong, G. (2014). Principles of Marketing. Pearson: London.

5

6 The Big Challenge

7 The Evolution of Marketing Science Pre-1980s Aggregate data Time delayed data E.g., warehouse withdrawals 1980s The first data revolution Behavior of individual consumers making purchases over many shopping trips and for many product categories E.g., scanner data

8 The Marketing Information System Reference: Kotler, P. and Armstrong, G. (2014). Principles of Marketing. Pearson: London.

9 Internal Databases Marketing department Customer characteristics Sales transactions Website visits Customer service department Customer satisfaction Service problems Accounting department Sales Costs Cash flows

10 Internal Databases (cont.) Operations department Production Shipments Inventories Salesforce reports Competitor activities Reseller reactions Marketing channel partners Point-of-sale transaction

11 Marketing Intelligence Observe consumers Quizzing company s own employees Benchmarking competitors productions Researching the Internet Monitoring the Internet buzz

12 Marketing Intelligence Service Sample

13 Marketing Research Exploratory research preliminary information to help define problems Observational research Ethnographic research Depth interview Focus groups Causal research test hypotheses about cause-andeffect relationships Experimental research

14 Marketing Research (cont.) Descriptive research describe marketing problems, situations, or markets, such as the market potential for a product or the demographics and attitudes of consumers Survey research Secondary data Consumer panel data

15

16

17

18 Purchase data Store data Customer data Customer ID Store ID Customer ID Brand bought Products available Income Quantity bought Price Household size Place of purchase (Store ID) Feature

19

20

21 Synergy of Data Store data Purchase data Consumer data

22 Advancements of Data Analysis Descriptive Statistics Mean, standard deviation, standard errors, etc. E.g., Which type of consumers respond to coupons and deals? Behavioral Models Originate from Transportation Science and Economics Inclusion of time-dependence terms, consumer perceptions, context dependence, etc. E.g., Which consumer segments are more loyal than others?

23 Random Utility Model (RUM)

24 What is Big Data? Large amount of data Emerge from decreasing storage costs Soaring interests in collecting data Increase at an unprecedented rate From firms and specialized data suppliers Social network data Consumer shopping and purchase behaviors

25 What is Big Data? Different data sources Internal External Multiple data types Structured: fixed format Semi-structured Unstructured: undefined format

26 What is Big Data? Many forms Text Web data Tweets Sensor data Audio Video Click streams

27

28 What is Big Data? Data in motion Speed continues to accelerate Data creation Data processing Data analysis

29 What is Big Data? Real-time data access Enabling models to customize marketing instruments to consumers as consumers search for information, compare prices or make purchases

30 The Age of Big Data

31 Video-Based Automated Recommender (VAR) System Capturing shoppers behavior Facial expression Body positions Identification of similar customers Database of shoppers with known: preferences purchasing consideration decisions Making recommendations Suggest preferred garments

32 Video-Based Automated Recommender (VAR) System

33 Forecasting TV Program Demand Implications for broadcasting companies and advertisers Prediction of TV ratings Show adjustments Pricing of advertising: dynamic, real-time, etc. Challenges Unstructured data: texts, video, audio, and images Large volume of data Solution

34 Forecasting TV Program Demand

35 Forecasting TV Program Demand Twitter is a crucial indicator of TV rating 3 measures Volume Sentiment (positive, neutral, negative) Principal Component Analysis ( tonight, can t wait, etc.)

36 Visualization of Competitive Market Structures Who are your competitors? Brand switching matrix Consideration sets Perceptual map What if you have 1,000 products in the category? Survey will not work (Cognitive capability is an issue) Solution Clickstream data

37 Visualization of Competitive Market Structures

38 Visualization of Competitive Market Structures

39 Visualization of Competitive Market Structures

40 Visualization of Competitive Market Structures

41 Visualization of Competitive Market Structures

42 Data Science

43 The Shopper

44 The Consumer Black Box Product Price Place Promotion... CRM Situation Influencers Choice

45 Tree of Marketing Reference: Department of Marketing, Thammasat Business School

46

47 Econometrics. What is it? Econometrics is the branch of economics that aims to give empirical content to economic relations. The most basic tool for econometrics is the linear regression model

48 Sales (Million) 120 Advertising Expenditure vs. Sales Advertising Expenditure (Million)

49 Sales (Million) 120 Advertising Expenditure vs. Sales Sales = Advertising Expenditure Advertising Expenditure (Million)

50 Implications Capability to determine existence of relationship On average, increasing the advertising expenditure by 1 baht will result in 2.58 baht increase in sales. (More careful statistical analysis could be conducted to test for statistical significance.)

51 Implications (cont.) Future sales can be forecasted based on knowledge about advertisement expenditure For instance, if the firm spends 10million baht on advertising next month, what would be the expected sales?

52 Software Packages

53

54

55

56 An Example: Car or Bus?

57 Random Utility Models (RUMs) A decision maker, n, faces a choice among J alternatives. The utility that decision maker n obtains from alternative j is: known to the researcher unknown to the researcher

58 Implications We need 2 equations for our case: 1 for car and 1 for bus. We need to know marketing theories in order to identify variables to be included in the model. We need to find the data for each of the variables for both car and bus We need large amount of data for accuracy Existence of relationship Predication

59 Based on Traditional Logit Framework For car: For bus:

60 Based on Traditional Logit Framework (cont.) For car: For bus: known to the researcher

61 How Consumers Choose?

62 What Do We Have to Do?

63 Likelihood Function

64 Software Packages

65 Nested Logit Alternatives can be partitioned into subsets, called nest Some information regarding decision making process can be revealed

66 Implications We can find the maximum value of likelihood for different choice structures to determine which resembles consumers behavior best Black Color?? Pepsi Coke Fanta Mirinda Pepsi Mirinda Coke Fanta

67 Price Elasticity A measure of responsiveness of the quantity of a product or service demanded to changes in its price:

68

69

70

71 Reference Price Replace price by reference price Price Previous Price Price Advertised Price Price Price that Friends Bought

72

73 Machine Learning Reproduce known patterns and knowledge in order to apply results to decision making and actions Automatically Quickly Sample algorithms Classification Regression Clustering Dimensionality reduction

74 Implications Businesses should consider transforming themselves to be more data-driven Culture Personnel Structure Speed and automation is the key Data is only the raw material. Success lies in Analytics Incorporation of findings into decisions Never stop improving!