Der Blick der Wissenschaft auf Digitalisierung, Vernetzung und disruptive Geschäftsmodelle Prof. Dr. Bernd Skiera

Transcription

1 Der Blick der Wissenschaft auf Digitalisierung, Vernetzung und disruptive Geschäftsmodelle Prof. Dr. Bernd Skiera 70. Deutscher Betriebswirtschafter-Tag Düsseldorf

2 Disruptive Business Models The world s largest taxi company, owns no vehicles. The world s most popular media owner, creates no content. The most valuable retailer, has no inventory. The world s largest accommodation provider, owns no real estate. 1

3 Global Internet Public Market Leaders Meeker, Mary (2016), Report Internet Trends 2016, 2

4 Most Successful German Internet Company Founded: 2008 Founder: Robert Genz, David Schneider Headquarter: Berlin, Germany IPO: 2014 Market cap: 9 Billion Euro ( ) Third largest online shop in Germany (2015) Zalando (2016), Facts and Figures, 3

5 Is the Business Model of Zalando Disruptive? 4

6 Comparison of Available Information in Online and Offline Shopping Offline Shopping Online Shopping Profit per Product Number of Products Revenue per Customer Profit per Customer Loyalty per Customer Acquisition Channel (and Acquisition Cost) Number of Customers 5

7 Tendency to collect more Information about Customers Loyalty programs Smartphones: precise information usage consumption Connected cars: information about each driver Wearables: precise information about user 6

8 Impact of More Data about Customers Deliver more value to customers Marketing Sales Strategy Learn more about value of customers Reporting Accounting Finance 7

9 Customer-Based Firm Valuation 8

10 My Previous Work on Customer-Based Firm Valuation 9

11 Information needed in Customer-Based Valuation (Numerical Example) Non-customer metrics (i.e., financial metrics) Number of customers Long-term profit per customer (Customer lifetime value) 10

12 Information needed in Customer-Based Valuation (Numerical Example) Non-customer metrics (i.e., financial metrics) Fixed costs per period: $5,000 Debt: $10,000 No taxes Discount rate: 10% Number of customers Long-term profit per customer (Customer lifetime value) 11

13 Information needed in Customer-Based Valuation (Numerical Example) Non-customer metrics (i.e., financial metrics) Number of customers Customers at beginning: 50 Customers at end of period: New customers per period: 20 Lost customers: 20% Long-term profit per customer (Customer lifetime value) 12

14 Number of Customers Existing customers 20% 80% Leaving customers New customers 60 20% 80% Leaving customers New customers 68 20% 80% 13

15 Growth of Number of Customers Maximum Existing customers at beginning 50 Retention rate per period New customers per period 80% 20 14

16 Information needed in Customer-Based Valuation (Numerical Example) Non-customer metrics (i.e., financial metrics) Number of customers Long-term profit per customer (Customer lifetime value) Profit per customer and period: $80 Number of periods in which customer is active ( retention rate ): 80% Discount rate: 10% Acquisition cost: $30 15

17 Profit Contribution per Customer $ $80 $60 $40 $20 $0 Profit contribution: $50 $80 $80 $80 $80 $80 Margin Acquisition costs Skiera, Bernd / Schulze, Christian (2014), "Customer-Based Valuation: Similarities and Differences to Traditional Discounted Cash Flow Models", in: Rust, Roland T. / Ming-Hui Huang (eds.), "Handbook of Service Marketing Research", Cheltenham, U.K.: Edward Elgar,

18 Likelihood of Retaining Customer Retention rate: 80% 100% % 60% 40% 20% 0% 100% 80% 64% 51.20% 40.96% 32.77% Likelihood that customer is still active Skiera, Bernd / Schulze, Christian (2014), "Customer-Based Valuation: Similarities and Differences to Traditional Discounted Cash Flow Models", in: Rust, Roland T. / Ming-Hui Huang (eds.), "Handbook of Service Marketing Research", Cheltenham, U.K.: Edward Elgar,

19 Expected Profit Contribution per Customer $ $80 $60 $40 $20 $0 Expected profit contribution: $50 $64 $51.20 $40.96 $32.77 $26.22 Margin * likelihood Acquisition costs Skiera, Bernd / Schulze, Christian (2014), "Customer-Based Valuation: Similarities and Differences to Traditional Discounted Cash Flow Models", in: Rust, Roland T. / Ming-Hui Huang (eds.), "Handbook of Service Marketing Research", Cheltenham, U.K.: Edward Elgar,

20 Calculation of Customer-Lifetime Value $ $80 $60 $40 $20 $0 Discounted expected profit contribution: $50 $58.18 $42.31 $30.77 $22.38 $16.28 Discounted margin * likelihood Acquisition costs CLV current = $80 + $ $ = $ CLV future = $30 + $80 + $ $ = $ Skiera, Bernd / Schulze, Christian (2014), "Customer-Based Valuation: Similarities and Differences to Traditional Discounted Cash Flow Models", in: Rust, Roland T. / Ming-Hui Huang (eds.), "Handbook of Service Marketing Research", Cheltenham, U.K.: Edward Elgar,

21 Customer Equity Calculation Present values Current customers cohort $ = $14,667 Future customers cohort $ ,1 1 = $4,788 Future customers cohort $ ,1 2 = $4,353 Future customers cohort $ ,1 3 = $3,957 Future customers cohort $ ,1 4 = $3,597 Future customers cohort n Fixed costs $ ,1 n = $ $5, /0.1 = $55,000 Number of existing customers Number of new customers Total present value = $17,600 Skiera, Bernd / Schulze, Christian (2014), "Customer-Based Valuation: Similarities and Differences to Traditional Discounted Cash Flow Models", in: Rust, Roland T. / Ming-Hui Huang (eds.), "Handbook of Service Marketing Research", Cheltenham, U.K.: Edward Elgar,

22 Derivation of Shareholder Value $57,933 $72,600 $55,000 $14,667 $17,600 $0 $17,600 $-10,000 $7,600 Customer equity of current customers Customer equity of future customers Customer equity before fixed costs Fixed costs (terminal value) Customer equity after fixed costs Nonoperating assets Firm value Debt Shareholder value Skiera, Bernd / Schulze, Christian (2014), "Customer-Based Valuation: Similarities and Differences to Traditional Discounted Cash Flow Models", in: Rust, Roland T. / Ming-Hui Huang (eds.), "Handbook of Service Marketing Research", Cheltenham, U.K.: Edward Elgar,

23 Empirical Results for Netflix 2008 [$'000,000] ,418 2,976 3,707 3,664 3,816 3,545 2,127 2, ,054 CE 1 (current cust.) CE 1 (future cust.) Customer Equity 1 (CE1) Indirect Cost Customer Equity 2 (CE2) Nonoperating Assets Firm Value Debt / Nonequity Claims Shareholder Value (pre-tax) Tax Shareholder Value Market Valuation (min) Market Valuation (max) Schulze, C. / Skiera, B. / Wiesel, T. (2012), "Linking Customer and Financial Metrics to Shareholder Value: The Leverage Effect in Customer-Based Valuation", Journal of Marketing, Vol. 76 (March),

24 Estimated versus Observed SHV (Netflix) Schulze, C. / Skiera, B. / Wiesel, T. (2012), "Linking Customer and Financial Metrics to Shareholder Value: The Leverage Effect in Customer-Based Valuation", Journal of Marketing, Vol. 76 (March),

25 So, what is new? 24

26 Cohort-based view Difference between Cohort-Based and Period- Based Valuation Models Current customers cohort Future customers cohort 1 Future customers cohort 2 Future customers cohort 3 Future customers cohort 4 Future customers cohort n Fixed costs Present value of current customers Present value of cohort 1 Present value of cohort 2 Present value of cohort 3 Present value of cohort 4 Present value of cohort 5 Present value of fixed costs Present value of period 0 Present value of period 1 Present value of period 2 Present value of period 3 Present value of period 4 Present value of period 5 Total Present Value Period-based view Skiera, Bernd / Schulze, Christian (2014), "Customer-Based Valuation: Similarities and Differences to Traditional Discounted Cash Flow Models", in: Rust, Roland T. / Ming-Hui Huang (eds.), "Handbook of Service Marketing Research", Cheltenham, U.K.: Edward Elgar,

27 Examples of Customer-Oriented Reporting 26

28 Example: Zalando 27

29 Customer Lifetime Value (CLV) at Zalando IPO-Prospect of Zalando, p

30 Example: Baremetrics 29

31 Dashboard at Baremetrics 30

32 Tracking of Customer Retention at Baremetrics 31

33 Example: Zynga 32

34 Development of Stock Price of Zynga Market cap.: 1.92 Billion Euro ( ) 33

35 Distribution of Retention Rates across Online Casual Games Histogram of 1-day retention rate across 379 games Histogram of 7-day retention rate across 379 games Avg. 1-day retention rate across games = 59% Avg. 7-day retention rate across games = 10.5% Hui, Sam K. (2014), "Understanding Gamer Retention in Social Games using Aggregate DAU and MAU Data: A Bayesian Data Augmentation Approach", Working Paper 34

36 Final Remarks on Disruption 35

37 Who Delivers Baremetrics Data? 36

38 Other Integrations into Stripe 37

39 Overview of Analytics Providers via Stripe 38

40 Contact Prof. Dr. Bernd Skiera Phone: +49 (69) Chair of Electronic Commerce Goethe University Frankfurt Theodor-Adorno-Platz Frankfurt am Main 39