MARKETING. DATA (BIG or SMALL): Sources, uses, integration, and best practices. PAID ADVERTISING: Working together for efficiency and effectiveness

Transcription

1 MARKETING Session 1 THE UNDERPINNING: Analysis Strategy Execution Measurement Session 2 POSITIONING: The art and science of building your brand house Session 3 DATA (BIG or SMALL): Sources, uses, integration, and best practices Session 4 PAID ADVERTISING: Working together for efficiency and effectiveness Strategy Positioning Data Media

2 Session 1: STRATEGY 101 Marketing Defined Going to market with: Offering Marketing Mix = The 4 P s of Product, Price, Promotion, and Place Controllables Performance Strategic Marketing A system and process of management 3 C s #1 = Customer(s), Competitors, Corporation 3 C s #2 = Competition, Complexity, Change The Process: Research Analysis Planning Implementation Control

3 Session 2: POSITIONING 202 Segmentation Breaking a heterogeneous market down into smaller homogeneous markets Identification, creation and prioritization of segments Targeting The strategic choice of which segments to serve Different targeting strategies to be assessed and selected Positioning The value proposition by which we desire our target segments to perceive us The positioning process Identify value drivers Map target segment perceptions Select positioning strategy Select grand position

4 MARKETING Session 1 STRATEGY 101 Session 2 POSITIONING 202 Session 3 BIG DATA 303 Session 4 GOING TO MARKET 404 Strategy Positioning Data To Market

5 Eric Schwartz Reduced to a Word Cloud

6 Big Data 303: Digging Deeper Efficiently Using Information to Optimize Performance Professor Eric Schwartz Ross School of Business University of Michigan

7 Topics introduced Big data, better science and engineering Be customer-centric Celebrate customer heterogeneity More is not better: use the right data Begin to do customer-base analysis Always be testing Be scientists: form hypotheses and experiment 2

8 Customer centricity Who are your customers? What are their needs, attitudes, and behaviors? From beginning to end, describe the customer life cycle Acquisition, retention, and development are key actions 3

9 Conversion funnel Customer life cycle Life does not end with conversion Retention (and development) Churn/ Attrition/ Death? Acquisition 4

10 Balancing act If you had an extra dollar to spend, which of these activities would you allocate it to? Acquisition Retention Development How much should you spend to acquire a customer? Cost per acquisition (CPA)? No! Big mistake! Value per acquisition (VPA) (or ROI = VPA/CPA) 5

11 Celebrate customer heterogeneity How do customers differ? Celebrate customer heterogeneity: distinguish the profitable customers from the less profitable ones How much are they worth? Metrics such as customer lifetime value (CLV) make this possible 6

12 Attrition rate (quarterly annualized %) Vodafone attrition rate Source: Vodafone Germany Analyst & Investor Day presentation ( ) 7

13 % customers Celebrate heterogeneity! Attrition rate There is no average customer Source: Vodafone Achievement and Challenges in Italy presentation ( ) 8

14 Tell me about your data! 9

15 More is not better: use the right data 10

16 SCIENCE, VOL 343, 14 MARCH

17 Big data hubris 'Big data hubris' is the often implicit assumption that big data are a substitute for, rather than a supplement to, traditional data collection and analysis.... However, the quantity of data does not mean that one can ignore foundational issues of measurement and construct validity and reliability and dependencies among data. The core challenge is that most big data that have received popular attention are not the output of instruments designed to produce valid and reliable data amenable for scientific analysis. Lazer et al SCIENCE, VOL 343, 14 MARCH

18 More is not better: use the right data 13

19 More is not better: use the right data 14

20 More is not better: use the right data 15

21 More is not better: use the right data 16

22 More is not better: use the right data Customer-base analysis Frequency histograms Recency and Frequency Trial (penetration) vs. Repeat (frequency) Cumulative vs. incremental transactions Types of data Event logs Individual-by-time, Individual-by-variable Marketing activity-by-time 17

23 Always be testing Run marketing experiments Do not forget to be scientists! Ask important questions Form hypotheses Set up measurement instruments Analyze Repeat 18

24 A/B (/C/D/E ) testing common in interactive marketing Randomly divide customers into two (or more) groups Expose each group to different ads / website elements / offers Measure outcomes for each group Broadly implement feature that led to best outcome in experiment 19

25 (or direct mail) is one of the easiest communications channels for experimentation 20

26 Testing on marketing Testable features Subject line Discounts and promotions Almost endless features of the creative: images, fonts, colors Time and day sent Measurable outcomes Open and click through Products viewed Sales and subscriptions Time on site Unsubscribing 21

27 Testing on online advertising Always be testing and learning Always be earning while learning 22

28 Typical analytics for A/B tests or MVT 23

29 Testing on websites Online stores, online brochures (sometimes called owned media) Testable features Headlines Calls to action Almost endless features of the creative: images, fonts, colors Measurable outcomes Sales or subscriptions Intermediate steps in shopping Time-on-site Churn (unsubscribing) 24

30 Topics introduced Big data, better science and engineering Be customer-centric Celebrate customer heterogeneity More is not better: use the right data Begin to do customer-base analysis Always be testing Be scientists: form hypotheses and experiment 25

31 Thank you! Questions? Professor Eric Schwartz 26