Customer Incentive, Demand Referral and Service Cooperation in Internet Supply Chain

Yuangao Chen School of Information

Zhejiang University of Finance and Economics Hangzhou, China

chenyg@zufe.edu.cn

Abstract—Considering an infomediary provides demand

referral service to retailer and incentive rebate to customer in Internet supply chain, a horizontal cooperation model with stochastic demand and rebate cost was established, and then the optimal policies of infomediary and retailer in centralized and decentralized supply chain were analyzed. Moreover, a horizontal cooperation contract based on incentive rebate cost as well as unit referral service price was developed to coordinate the supply chain. The results revealed that the contract contributed to profit optimization of Internet supply chain and effective win-win cooperation.

Keywords—Internet supply chain; infomediary; customer incentive; demand referral; cooperation; coordination

I. INTRODUCTION The rapid development of Information technology and Internet presents the retailers with the opportunities for expansion to new online segments. According to the recent report of China Internet Network Information Center (CNNIC), the number of online shopping users reached 242 million and utilization ratio of online shopping rose to 42.9% by the end of December 2012. The increase of users’ purchase power and the combination of online consumption habit and the forms of mobile and social online shopping promote the growth of online retail market. The Internet market structure is in a rapid optimization stage while the online shopping users rapidly increase in number. Firms are increasingly embracing integrated Internet-based supply chains because such chains are believed to enhance efficiency and competitiveness. The Internet has opened up opportunities for firms to share information and efficiently collaborate their activities with other entities in the supply chain[1]. E-commerce has given rise to a new breed of intermediaries, so-called information intermediaries or infomediaries [2, 3]. An infomediary plays an expanded role in brokering prospect information in product categories, such as automobiles, insurance, and real estate, where the purchase decisions that consumers face are complex and frequently require direct interactions with salespeople [4]. The lowering of search costs with the advent of the Internet has changed the shopping way of consumers. With the help of online infomediary, consumers

can get the information they need to make more informed choices [5]. For example, Shopping.com collects the consumers’ comments and offers online comparison shopping, and today is currently one of the fastest growing shopping destinations for a comprehensive set of products. It recommends interested customers to some trusted online stores, such as Ebay.com, Amazon.com and Buy.com.

Several recent studies consider referral service, price of segments and other marketing problems. Reference [6] analyze the effect of referral infomediary on retail markets and examines the contractual arrangements that they should use in selling their services. The infomediary helps consumers to costlessly get an additional retail price quote before purchase and endows enrolled retailers with a price discrimination mechanism[7, 8]. Reference [4] investigate the role of online infomediaries in market segmentation and price discrimination in the automotive retailing context based on three online buying service(OBS) pattern. The result shows that consumers who obtain price information pay lower prices (for the same product), whereas consumers who obtain product information pay higher prices. Furthermore, the quality and quantity of information about companies, products, brands or services via online media, in the form of scores or comments, becomes the core competition advantage of infomediary [9]. Recent evidence suggests that consumer reviews have become very important for consumer purchase decisions and product sales [10]. So the infomediary provide many kinds of incentives, such as price discount, coupon, cash rebate et al, to encourage customers to share their shopping experience and record their comments. Our primary interests of this research focus on the effect of customer incentive mechanism and the demand referral service cooperation between infomediary and retailer. We will characterize supply chain demands, equilibrium prices, incentive commission paid by infomediary. The coordination mechanisms of the E-supply chain are also to be investigated.

II. MODELS AND ASSUMPTIONS Considering an Internet supply chain, a retailer sells the

product through self-owned channel as well as through an infomediary website. The retailer pays the infomediary for the referral service at t per unit. The infomediary only provides the customers with product and price information and recommends the potential customers to the retailer. Thus, the retailer orders

978-1-4673-4843-0/13/$31.00 © 2013 IEEE

the products from upstream supplier, sells to his direct customers from self marketing effort and indirect customers from the demand referral (Figure 1). It is reasonable to expect that the infomediary gets the compensation from the retailer for demand referral service. Furthermore a rebate r will give the customers an incentive to record his experience and comment on the product and service in detail. We suppose the retailer declares the same price p to both two customer segments. Assume that all the potential customers influenced by the price and the referral customers are also dependent on the amount of rebate.

Fig. 1. Supply chain framework based on referral service

We consider the retailer and infomediary both facing independent stochastic demand. We assume the retailer’s demand is Dr with a probability density function ( )h y and a cumulative distribution function ( )H y , and the demand of infomediary is Di, ( )f x and ( )F x respectively. Assuming the infomediary can exert customer rebate r to stimulate customer demand, the total demand of infomediary is ( )iD d r+ , where

( )d r is the demand share decided by the effect of the incentive mechanism and ( )C r is the rebate cost function. The total demand of dual-channel is ( )i rD D d r+ + following the probability density function ( )φ i and the cumulative distribution function ( )Φ i . Furthermore, the manufacturer offers the wholesale price w and the order quantity Q. Without generality ( )d r satisfies ( ) 0d r′ ≥ and ( ) 0d r′′ ≤ , and ( )C r follows ( ) 0C r′ ≥ and ( ) 0C r′′ ≥ [11]. It is obvious that the marginal effect of customer incentive decrease with the increasing amount of the rebate. The sales cost, transportation cost of retailer is zero and unfulfilled demand is lost.

III. THE OPTIMAL REFERRAL SERVICE AND SALES POLICIES

A. Centralized supply chain When the supply chain system has a centralized decision

maker, the profit of the whole system t∏ is

min( , ( ) ) ( )t c i c r c cE Q D d r D p wQ C r⎡ ⎤⎣ ⎦∏ = + + − − (1)

Subscript “c” represents centralized supply chain. Let cQ be the order quantity of retailer and cr be the rebate incentive of infomediary. To maximize the system profit, considering the first derivatives we can get

( ) ( )

0 0( ) ( ) ( )c c c cQ d r Q d r yt

c

p w p f x h y dxdyQ

− − −∂ ∏= − −

∂ ∫ ∫ (2)

( ) ( )

0 0( ) ( ) ( ) ( )c c c cQ d r Q d r yt

c cc

pd r f x h y dxdy C rr

− − −∂ ∏ ′ ′= −∂ ∫ ∫ (3)

To achieve the optimal results, it must satisfy 0t

cQ∂ ∏

=∂

and

0t

cr∂ ∏

=∂

. Solving (2) and (3), we can obtain the following

newsvendor type solution for Qr [12, 13]

1( ) ( )c cp wQ d r

p− −= + Φ (4)

and the solution for rc

( ) ( ) ( )c cp w d r C r′ ′− = (5) The Hessian matrix of (1) is

[ ]

2 2

2

2 2

2

( ) ( )2

0 0

( )

( ) ( ) ( )

( ) ( ) ( ) ( ) ( ) ( ) ( ) ( )c c c c

t t

c cct

t t

c c c

c c cQ d r Q d r y

c c c c c c

Q rQH

r Q r

p Q p Q d r

p Q d r p Q d r pd r f x h y dxdy C r

φ φ

φ φ− − −

⎡ ⎤∂ ∏ ∂ ∏⎢ ⎥∂ ∂∂⎢ ⎥∏ = ⎢ ⎥∂ ∏ ∂ ∏⎢ ⎥∂ ∂ ∂⎢ ⎥⎣ ⎦

′−⎡ ⎤⎢ ⎥=

′ ′ ′′ ′′− + −⎢ ⎥⎣ ⎦∫ ∫ (6)

Considering ( ) 0d r′ ≥ , ( ) 0d r′′ ≤ and ( ) 0cC r′′ ≥ , it is easy to prove that ( )tH Π is negative definite matrix and the objective function tΠ is jointly concave in ( rQ , r ). (2) and (3) are the optimal solutions to the maximization of system profit. Therefore the profit of centralized supply chain is maximized when the order quantity and incentive rebate are chosen according to (2) and (3).

B. Decentralized supply chain When the Internet supply chain is a decentralized system,

then retailer and infomediary are independent decision makers. Assuming the retailer is the leader of the game, and the infomediary chooses the optimal marketing effort with consideration of the given referral service fee, the profit of infomediary and retailer is

[ ]( ( )) ( )i iE t D d e C eΠ = + − (7)

[ ]min( , ( ) ) ( ( ))r r i r r iE Q D d e D p wQ t D d eΠ = + + − − + (8) Solving the first derivate of equation (7) according to r

( ) ( )i td r C rr

∂ ∏ ′ ′= −∂

(9)

The infomediary can achieve the maximized profit when its rebate plan *r satisfies * *( ) ( )td r C r′ ′= . Additionally the second derivative is

2

2 ( ) ( )i td r C rr

∂ ∏ ′′ ′′= −∂

(10)

Because d(r) is concave in r and C(r) is convex in r, it is easy to prove the profit function of infomediary i∏ is concave in r.

When the infomediary decides its optimal rebate *r , we can solve the first derivative of (7) according to t

* **

* ** * *

( ) ( )( )

( ) ( ) ( )

ii

i

d r C rED d r tt t t

r rED d r td r C rt t

∂ ∏ ∂ ∂= + + −∂ ∂ ∂

∂ ∂′ ′= + + −∂ ∂

(11)

Substituting * *( ) ( )td r C r′ ′= into (10) we can obtain

*( )iiED d r

t∂ ∏

= +∂

(12)

It is not difficult to verify *( ) 0iED d r+ ≥ . Obviously the optimal profit of infomediary is an increasing function on referral price t. Thus for a given referral service price t, the infomediary chooses the optimal customer rebate *r according to * *( ) ( )td r C r′ ′= . The optimal profit of infomediary is increasing in referral service price. Because d(r) is concave in r higher referral service price will activate the infomediary to increase his incentive rebate plan.

For the retailer, the profit in decentralized decision is

( ) ( )

0 0

( ) [ ( )]

( ) [ ( ) ] ( )r r

r r iQ d r Q d r y

r

p w Q tE D d r

p h y dy Q d r x y f x dx− − −

∏ = − − + −

− − −∫ ∫ (13)

Solving the first order condition of (13) according to Qr

( ) ( )

0 0( ) ( ) ( )r rQ d r Q d r yr

r

p w p f x h y dxdyQ

− − −∂ ∏= − −

∂ ∫ ∫ (14)

It is easy to verify 2

2 0r

rQ∂ Π

≤∂

and r∏ is concave in Qr. The

optimal order quantity of retailer must satisfy 0r

rQ∂ ∏

=∂

, then

we can obtain

* 1( ) ( )rp wQ d r

p− −= + Φ (15)

Thus for a given retailing price p and wholesale price w, the retailer chooses the optimal order quantity

* 1( ) ( )rp wQ d r

p− −= + Φ .

Especially when the referral price t equals p w− , the optimal rebate of infomediary in decentralized supply chain is

equal to that in centralized system. And the retailer’s benefit from the referral demand share *( )iD d r+ is zero ( *( ) [ ( )] 0ir ip w t E D d r∏ = − − + = ). Consequently rational retailer will have no incentive to promise this price-only service contract. The separation of marketing and sales operation will result in inefficiency in the infomediary supply chain.

IV. SERVICE COOPERATION BASED ON INCENTIVE COST SHARING

In this supply chain, the incentive marketing plan of infomediary has impact on the retailer’s profit as well as its own. But the incentive activities result in considerable costs. When demand is affected by sales and marketing effort, such as buy-back, rebate and returns contract alone can not achieve channel coordination [11, 14]. But the marketing cost sharing mechanism can help supply chain attain a win–win goal[15, 16].

We assume the retailer provides γ ( [0,1]γ ∈ ) percent of incentive cost C(r) as well as referral price t. Superscript “ ” denotes the variables in this scenario with cost sharing contract.

Let iΠ be the profit of infomediary and rΠ the profit of retailer. We can get

[ ]( ( )) (1 ) ( )i iE t D d r C rγΠ = + − − (16)

min( , ( ) )( ( )) ( )

r i rr

r i

Q D d r D pE

wQ t D d r C rγ+ +⎡ ⎤

Π = ⎢ ⎥− − + −⎣ ⎦ (17)

From (16) we get the first order condition of iΠ according to e

( ) (1 ) ( ) 0i td r C rr

γ∂Π ′ ′= − − =∂

(18)

Then

( ) (1 )( )

d rC r t

γ′ −=′

(19)

It is easy to verify iΠ is concave in r. If the supply channels achieve coordination, the incentive rebate of infomediary in decentralized scenario equals to that in centralized scenario ( cr r= ) . Substituting (5) into (19) we can obtain

1 tp w

γ = −−

(20)

Let *rQ denote the optimal order quantity of retailer.

Similarly according to first order condition 0i

rQ∂Π

=∂

we can

get *r cQ Q= when cr r= . So when the online infomediary

provides demand referral service, the retailer must share

1 tp w

γ = −−

percent of the rebate cost of infomediary in

addition to unit t service fee. This incentive cost sharing

contract can achieve coordination of demand referral supply chain.

V. CONCLUSIONS We elaborate on the problem of demand referral service of

infomediary, customer incentive effect and coordination contract in this article. With the sharing of customers’ comments and experiences the infomediary helps to meet and match the common needs between the numerous and rapidly growing consumers and suppliers. The Internet supply chain benefits from the horizontal referral service. The higher referral service price paid by the retailer will activate the infomediary to exert its customer incentive. But the service price contract alone can not achieve supply chain coordination. An incentive cost sharing contract besides service price is proved to coordinate theInternet supply chain.

The next step in future research is to investigate the various products information provided by different suppliers. Competition will have a significant impact on the referral strategy of infomediary. In the current model, the sole product is mainly recommended by the infomediary in deciding on the service fee. However facing competing products with identical incentive efforts, retailers will still make their price decisions based largely on market share and cost factors. A rigorous study is necessary to confirm this and we leave that for future work.

ACKNOWLEDGMENT This work is supported by the Natural Science Foundation

of Zhejiang Province (Grant No. LY12G02006), Research Project of Education Department of Zhejiang Province (Grant No. Y201224328) and the National Natural Science Foundation (Grant No. 71271188).

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