Pricing Online Banking amidst Network Effects Baba Prasad

Pricing Online Banking amidst Network Effects Baba Prasad Assistant Professor Carlson School of Management University of Minnesota, Minneapolis, MN 55455 bprasad@csom. umn. edu

Network Effects n When the value of a product is affected by how many people buy/adopt it n n Example: Phone System Types of Network Effects n n n Direct Indirect Post-purchase 2

Direct Network Effects n n The number of users directly impacts the value of the system Based on interaction between members of a network n n E. g. Telephone service Metcalfe’s Law: Network of size N has value O(N 2) 3

Indirect Network Effects n n n Do not directly affect the value of the product Indirect influence Credit cards: n More adopters of the card more merchants accept it higher value for the card 4

Post-Purchase Network Effects n n Mostly support related Examples n n Software user groups (LINUX Users Group) Consumer networks 5

The Model n Value of a product in a market with network effects is given by: Zt is the size of the network at time t, a represents the value without network effects g represents value from network effects. 6

Pricing with Network Effects n Value ascribed to system by customer: Willingness to Pay (WTP) n n n Most optimal policy: price at WTP Difficulty: How to determine WTP? High collective switching costs n Leads to default standards (e. g. QWERTY; VHS, Java…) 7

Monopoly pricing n What happens to Coase conjecture? n Coase (1937): prices drop to marginal costs over a long period of time even in monopolistic settings 8

Standard Assumptions n Product purchase decision n Positive network effects n Constant parameter to represent network effect 9

Services and Network Effects n Assumptions in product purchase model are not valid n n Not a one-time purchasing decision Possibility of reneging and resubscribing: Market is not depleting over time 10

Services and Network Effects (contd. ) n n Network effects can be negative Service systems have fixed capacities Fixed capacities deterioration in service User notices negative network effects: n Random sampling; or word-of-mouth value Number of users 11

Are network effects constant? n n n Previous discussion implies timedependency Thus, not really constant, but random (how many users are already on the system, when an additional user enters? ) Thus, stochastic network effects 12

E-Banking and Network Effects

Network Externalities in Retail Banking n Checking facility and exchange of checks n n n Credit cards and indirect network effects n n E-Checks (Kezan 1995, Stavins 1997, Ouren et al 1998) Critical mass theory and Internet-banking users Introduced in 1930 s, “re-introduced” in the 60 s, did not take off till the mid-80 s Econometric Studies n n Gowrisankaran and Stavins (1999) find strong evidence of network effects in electronic payments (ACH) Kauffman et al have observed network effects in ATM networks 14

Why Electronic Banking? n n Tremendous reduction in transaction costs Coordination of Delivery Supply Chain 15

Cost of Transactions Across Delivery Channel Teller ATM Internet Cost of Transaction $1. 09 $0. 27 $0. 01 Source: Jupiter Communications 1997 Home Banking Report 16

Coordination of Delivery Supply Chain n Electronic transactions facilitate : n n Exchange of information with other banks through Electronic Clearing Houses (ECH) Flow of information within the firm n Workflow management; process control 17

Banking Some Online Banking Options and Prices (March 2002) Bank Price Nations Bank $5. 95/month Bank One $4. 95/month Chase $5. 00/month (Free if average account balance > $5000) Wells Fargo Free with checking account (Checking account balance > $1500) Huntington Free first 3 months, then $4. 95/month Wachovia Free first 3 months, then $5. 95/month 18

Bank Problem n n Choose a price vector (P) over the two periods at the outset Choose the optimal price vector assuming that the customer will switch in such a way as to optimize value over the two periods 19

Reservation Prices n n Each customer indexed by h [0, 1] Customer strategies n (0, 0): (0, 1): (1, 0): n (1, 1): n n n Do not choose online banking in either period Choose online banking only in period 2 Choose online banking in period 1 and renege in period 2 Choose online banking in both periods Let h 00 (= 0), h 01, h 10, and h 11 denote the minimal reservation prices that will allow each of these strategies 20

Some propositions n n h 11 h 10 h 01 0 There exists threshold network effects gu and gl such that n n when g > gu, consumers who choose online banking in period 1, do not renege in period 2, and when g < gl , consumers who do not choose in period 1 will not do so in period 2 also. 21

Consumer Surplus Equations n First period V 1(h, p 1) = n h – p 1, = 0, for online banking for non-online banking Second Period d(h – p 2+ g qi-1) for online banking -d. Cjk, for non-online banking d is the one-period discount factor (0 < d < 1) V 1(h, p 2, qi-1) = = 22

Demand Equations n n D 1 = 1 – h 10 and D 2 = 1 – h 01 Using indifference equations between choices in the 2 periods, we arrive at: D 1 = {1 – d – p 1 + dp 2 + d(C 01 – C 10)} (1 – d + dg) D 2 = {(1– d + dg) (1– p 2) – gp 1 + dp 2(1+ g) + dg(C 01 – C 10) – d. C 01 – dg 2} (1–d+ dg)} 23

Optimal Pricing Problem P = max (p 1, p 2) p 1 D 1 + dp 2 D 2 s. t. 0 ≤ h 01 ≤ h 10 ≤ h 11≤ 1 24

Optimal Prices p 1* = {(1 – d) (2 – dg + d 2 +2 dg. C 10 + 2 d. C 01) – 6 d} {(d – 2)2 + d 2 g(g-2)} p 2* = {(2 p 1* + d(C 10 +C 01) – (1 – d)} {d (1 – g)} 25

Price behavior n Note that as g 1, p 2* n n Increased network effects cause higher second period benefit. Contrast with Coase conjecture; common with other studies with positive network effects Note also that as g increases, p 1* decreases n Increased network effect leads to low introductory pricing If d = 0, p 1* = ½ and p 2* 26

Optimal Prices over 2 Periods 27

Demands at Optimal Prices Figure 1. 2. Demands at Optimal Prices in the Two Periods 28

Intermediate Conclusions n n n Should banks have chosen low introductory pricing to promote online banking? Positive switching costs lead to initial reluctance How are issues of security and convenience perceived by consumers? 29

Negative word-of-mouth effects "Banking is founded on trust. We want an ecommerce service we can feel safe with, because if even one customer somewhere gets hacked - well that's bad for the customer but we suffer the impact to a greater extent because of the damage to customer trust. " (Bob Lounsbury of Scotiabank, 1999) Scotia Bank was the 1998 Net. Commerce Award winner in the Canada Information Productivity Association competition 30

Online Banking Adoption Decision n Kennikel and Kwast (1997) n n 33% of respondents reported that friends and relatives who already used online banking affected decision to adopt online banking Highest reported source of information in adoption decision n (next is financial consultants/brokers at 26. 8%) 31

Word-of-mouth Effects n Stochastic Can be positive or negative n Mean and variance n Affects adoption of online banking in future periods n 32

How to model W-o-M effects? Positive 1 - m p m t 1 - p - q t+1 No Effect q Negative Figure 1. Transition probabilities and outcomes 33

Modeling the Stochastic Network Effect n n n The trinomial tree can be shown to follow a Geometric Brownian motion process Apart from “customer-talk”, expert opinion also influences demand We model word-of-mouth effect, g, as a jump-diffusion process 34

Word-of-Mouth as jumpdiffusion process d. Z is the increment of a Wiener process, Z, with mean, mg, and s. d. sg d. Q(t) is the increment of a Poisson process with mean arrival rate, l k is a draw from a normal distribution, or in other words, k ~ N(mp, sp 2). 35

Demand function where, D(t) is demand at time t S(t) is size of network at time t p(t) is the price at time t, and g(t) is the stochastic network effect 36

Maximizing profit n n Stochastic Optimal control Bellman’s equation 37

Effects of Word-of-Mouth on prices 38

Pricing Strategy Positive Price to Build Network Negative Price Myopically Price to Penetrate Market mg Low Price to Recover Sunk Costs s 2 High Table 1. Pricing Strategy with Word-of-mouth Effects 39

Stochastic Cross-Product Network Effects 40

Consumer Surplus Equations n First period V 1(h, p 1) = h – p 1, = 0, n for online banking for non-online banking Period i, i ≥ 2 V 1(h, p 1, qi-1) = (h – p 2+ g Di-1+ hi Di-1), for online banking = - Cjk, for non-online banking 41

Customer’s Problem n n At the beginning of each period, the customer chooses a mode (online or not). The problem is to choose an optimal sequence of modes that will maximize value To switch from mode j to mode k costs Cjk 42

Dynamic Program Formulation n n Customer chooses to maximize value over the T periods Consider the last period, period T: Choose that mode that will give maximum value after discounting switching cost n Suppose customer arrives in this period in mode M n VT = Max(Vm. T – CMm), m {Online, Non-online} 43

Dynamic Program Formulation (contd. ) n For any period, j (0 < j) Vj-1 = Max (Vmj – C(j-1)m + d. E[Vj]) E[] being the expectation, and d being the one-period discount factor 44

The Model n Value of a product exhibiting both types of network effects is given by: where Zt is the size of the network at time t, the parameter a represents the value without network effects, g represents network externality effects, and h represents word-of-mouth effects. t is the time delay for the word-of-mouth effects. For simplicity, we assume t = 1 45

Variation of Optimal prices with Mean of Word-of-mouth Effect 6 p 2_w_o_m 4 price ----> 2 0 -0. 9 -2 p 1_w_o_m p 2 p 1 -0. 8 -0. 7 -0. 6 -0. 5 -0. 4 -0. 3 -0. 2 -0. 1 0. 2 0. 3 0. 4 0. 5 0. 6 0. 7 0. 8 0. 9 mean w_o_m ------> -4 -6 -8 46

Optimal Prices with Variance of Word -of-mouth Effect 47

Multiple Periods: Prices with Increasing Lag in Word-of-mouth Effects 1. 2 1 0. 8 0. 6 price_1 D _2 D price_3 D 0. 4 0. 2 h 0 1 2 3 4 5 6 7 -0. 2 period 48

Multiple Periods: Demands versus Prices with Increasing Lag in Word-of-mouth Effects n n n Not much variation in optimal demand Optimal prices increase about 5% for every additional period of lag Banks seem to be more sensitive to word-of-mouth than customers n Back to Lounsbury’s comment 49

Anecdotal Evidence: Bank Strategies Some Online Banking Options and Prices (March 2002) Bank Price Wachovia Free first 3 months, then $5. 95/month Huntington Free first 3 months, then $4. 95/month 50

Bank Strategies (contd. ) n Wells Fargo n n Charged for online banking in 1998 Now, it is free 51