Assessing the Utility of Retailer Based on Generalized Costs of End-Consumers

Businesses are paying a lot of attention to studying their customers, to increase their utility and comfort, minimize fatigue, and to attract choosing needed retailer. Generally, the utility function of end-consumers depends on the price (Nakaya, et al., 2000; Scott, 2017) and non-price factors (Birkin, et al., 2017; MacInnis, et al., 2014; Oruc and Tihi, 2012). At the same time, such methods do not fully reflect modern market conditions. Certain parameters of existing models and methods do not provide a systematic approach to determining retailer visiting probability according to the generalized costs of end-consumer factors (Galkin, Dolia and Davidich, 2018).

The generalized costs approach is an important factor influencing the ability of purchases. Therefore, in modern logistics system, the maximum effect will come from a technology that can assess and manage the costs of consumers. Different ratios and sizes of these costs could affect consumer’s behavior utility (Galkin, et al., 2019a). The generalized costs consist of monetary expression of the purchase of goods, monetary costs estimate of the travel time spent on purchases, monetary expression of the energy value spent on purchases, monetary expression of expenses of other non-price factors, cost related to delivery, and the values based on the elements of purchase process: time spent on the way to the retailer and back; time spent searching, waiting, getting acquainted with the goods in the store and their selection, cash accounting, and so on. In a particular case, the time consuming costs associated with the level of service offered: expectations of service, the degree of compliance with the timetable or schedule, the presence of goods, the organization of shopping services, the level of organization of the work of personnel, and so on.

The generalized costs of visit by residents of the ω-th neighborhood of the z zone that visits the j-retailer (Galkin, et al., 2018) are given as: (1)Θtotalwj=Θ1ωj+Θ2ωj+Θ3ωj+Θ4ωj+Θ5ωj→min\Theta _{{\rm{total}}}^{{\rm{wj}}} = \Theta _1^{\omega {\rm{j}}} + \Theta _2^{\omega {\rm{j}}} + \Theta _3^{\omega {\rm{j}}} + \Theta _4^{\omega {\rm{j}}} + \Theta _5^{\omega {\rm{j}}} \to \min where:

ω – number of consumer’s zones, ω = 1, 2, …, I;

The research hypothesis is based on the choice of the retailer by a certain group of customers (the consumer group), based on the generalized costs that they would incur as a result of their choice. Each consumer choice leads to costs generation. The purchase method determines the option and parameters of the purchase process. In the traditional shopping through a retailer, the consumer chooses a way of moving: by public or individual, if it is available, transport. Depending on the transport mode, the end-consumer determines the routes, the parameters of which affect the origin–destination transit time and the fatigue that is caused. After arriving at the retailer, the end-consumer spends time and calories there (shopping service). After that, the end-consumer needs to return to his destination (home, work, etc.) and deliver his/her goods. When choosing an online shopping method, these stages are missing. The final stage is after-sales, which includes assessment of the results of the purchase, consumption, and deprivation of the used goods.

The particular purchase process elements are described by two groups of parameters: inside and outside of retailer (Galkin, et al., 2019a). The first group includes the parameters that affect the buyer on the way to the shop; some of them are the distance to the shop (Bucklin, 1967), the speed and time spent to get to the store and way back (Paroli and Maraschin, 2018), age of the consumer (Bradski, 1998), and so on. The second group of factors directly affects the consumer in the shop. These include: the area (Kotler and Armstrong, 2010), the range of products (Nerlove, 1995), the number of sellers (Dąbrowska, 2011), lighting, the number of people in the queue (Erhun and Tayur, 2003), the quantity of cashboxes (Ibrahim and McGoldrick, 2017), commercial service (Noseworthy, et al., 2014), the amount of purchases (Hernandez and Biasiotto, 2001), and the quality of service (Strizhakova, et al., 2008). Following methods do not evaluate generalized costs of the consumers. Methods for assessing utility do not provide assessment of retailer on possible generalized costs according to all possible options.

The time spent by a buyer on purchases is a cost-generating measurement. In general, human time can be divided into sleep (rest), work, and leisure time. Sleeping is a biological need, work is necessary for existence in society, and leisure time is the time of self-actualization, self-development of a person, and so on (Lemon and Verhoef, 2016). Shopping is a part of the leisure time, and the purchasing process is a specific product – the time resource that is produced by the transport system or shopping services. Classic purchase process (Kotler and Dubois, 2009) can be described as: (2)tωjpurch=tωj'walking+tjω''walking+tjTS{\rm{t}}_{\omega {\rm{j}}}^{{\rm{purch}}} = {\rm{t}}_{\omega {\rm{j}}}'^{{\rm{walking}}} + {\rm{t}}_{{\rm{j}}\omega }''^{{\rm{walking}}} + {\rm{t}}_{\rm{j}}^{{\rm{TS}}} where:

tωj'walking{\rm{t}}_{\omega {\rm{j}}}'^{{\rm{walking}}} – travel time from zone ω to a retailer j (h),

The purchasing process consists of consecutive elements: time of the retailer, time of the shopping service, and travel time from the retailer to the destination. The cost of an hour of leisure time by the buyer is determined by the formula (10): (3)Cthour=S¯pcΦctf−(Φwtf+Φftf){\rm{C}}_{\rm{t}}^{{\rm{hour}}} = {{{{{\rm{\bar S}}}_{{\rm{pc}}}}} \over {{\Phi _{{\rm{ctf}}}} - ({\Phi _{{\rm{wtf}}}} + {\Phi _{{\rm{ftf}}}})}} where:

S¯pc{{\rm{\bar S}}_{{\rm{pc}}}} – the average salary of people in the area of research (UAH),

Part of monthly leisure time is the time for purchase, cinema, theatres, and so on. Recently, in countries with a developed market economy, all forms of development of various forms of after-sales services to customers, such as the delivery of purchased goods to home, are increasingly being developed. Thus, in the traditional sense, for a buyer, the full price of a product includes the travel costs from the place of residence or work to the retailers and the cost of its time for the purchase of goods, the cost of time.

The monetary expression of costs estimate of the travel time spent on purchases is as follows: (4)Θ1ωj=CпΓ⋅tpurch\Theta _1^{\omega {\rm{j}}} = {{\rm{C}}_{\Pi \Gamma }} \cdot {{\rm{t}}^{{\rm{purch}}}} where:

t^purch – purchase time (h), and

Analysis of the technological process of purchases indicates dependence on the group of factors affecting the consumer during the road to the trading venue and during the shopping service. The travel time will depend on the following parameters: (5)tij'walking=f(δωjbefore; Rωj; Lωj){\rm{t}}_{{\rm{ij}}}'^{{\rm{walking}}} = {\rm{f}}(\delta _{\omega {\rm{j}}}^{{\rm{before}}};\,{R^{\omega {\rm j}}};\,{{\rm{L}}_{\omega {\rm{j}}}})(6)tji''walking=f(δjωafter; Rωj; Ljω){\rm{t}}_{{\rm{ji}}}''^{{\rm{walking}}} = {\rm{f}}(\delta _{{\rm{j}}\omega }^{{\rm{after}}};\,{R^{\omega {\rm j}}};\,{{\rm{L}}_{{\rm{j}}\omega }}) where:

δωibefore\delta _{\omega {\rm{i}}}^{{\rm{before}}} – the slope factor on the road from the zone ω and to the retailer j,

The shopping time at the retailer depends on its area: (7)tjTS=f(Sjshop){\rm{t}}_{\rm{j}}^{{\rm{TS}}} = {\rm{f}}({\rm{S}}_{\rm{j}}^{shop}) where:

Sjshop{\rm{S}}_{\rm{j}}^{\rm shop} – size of the retailer j (m²).

The described regression models have shown simulation of the purchase process. These models can be used for macrosimulation in residential area.

Estimation of fatigue is carried out with the indicator energy cost of movement. This value characterizes the direction of the energy regulation channels in the human body (Drewnowski, 2011). For any consumer activity, energy is consumed (Kidwell, et al., 2008). The amount of energy expended depends on the amount of effort spent on one or another element of the consumption process. Estimation of the amount of energy expended is carried out in various ways, among which one can distinguish the cost of action (Keytel, et al., 2005), pulse based on built-in sensors (Baevskii, 2002), and so on.

Determining the cost estimate of the energy costs of the human body in the purchasing process is based on the analysis of the required amount of calories for human existence (Postanova, 2000). According to the normative documents, the daily consumption of calories for normal functioning is 2790.8 kcal (Monsivais, et al., 2013). But this value may depend on gender, age, and the physical activity of a person. Difficulty valuation calorie value is 1 kcal different due to different levels of income, the cost structure of the budget and human households, as well as different values of the products and their usefulness to humans (Halkin, et al., 2019).

The cost of human energy is proposed to be determined by dependence: (8)SKCalcп=∑1M(Qω⋅PцiHa)ALF⋅Tcons{\rm{S}}_{{\rm{KCal}}}^{{\rm{c}}} = {{\sum\limits_1^{\rm{M}} {({{\rm{Q}}_\omega } \cdot {{\rm{P}}_{{\rm{iHa}}}})} } \over {{\rm{ALF}} \cdot {{\rm{T}}_{{\rm{cons}}}}}} where:

Q_ω – an individual need for a material flow of one resident for a period of time living in the ω-th zone for the analyzed period for normal functioning of the organism, kg/inhabitant (unit/inhabitant),

The process of consuming the goods is closely linked to the concept of the process of consumption. The cost of action in the process of consumption of goods is given by: (9)Ecп=∑p=1PEp=Eωprep+Eωj'wovm+EjTS+Ejω''movm+Eωaftsell{{\rm{E}}^{{\rm{c\Pi }}}} = \sum\limits_{{\rm{p = 1}}}^{\rm{P}} {{{\rm{E}}_{\rm{p}}}} = {\rm{E}}_{\rm{\omega }}^{{\rm{prep}}} + {\rm{E}}_{{\rm{\omega j}}}'^{{\rm{wovm}}} + {\rm{E}}_{\rm{j}}^{{\rm{TS}}} + {\rm{E}}_{{\rm{j\omega }}}''^{{\rm{movm}}} + {\rm{E}}_{\rm{\omega }}^{{\rm{aftsell}}} where:

Eωprep{\rm{E}}_{\rm{\omega }}^{{\rm{prep}}} – the value of energy consumption of the end-consumer at the preparatory stage of consumption, preceding the purchase, kJ (kcal),

The choice of product depends on the amount of time spent on the purchase. Energy value of consumer actions in the process: (10)ΔE=Ep−E0\Delta {\rm{E}} = {{\rm{E}}_{\rm{p}}} - {{\rm{E}}_0} where:

Е_p – energy consumption in performance of the p-th element of the process of consumption, kJ (kcal),

Estimation of the monetary value of energy costs can be done by the formula: (11)Θ3ij=Eωjcп⋅SKCalcп\Theta _3^{{\rm{ij}}} = {\rm E}_{\omega {\rm j}}^{{\rm{c}}\Pi } \cdot {\rm S}_{\rm KCal}^{{\rm{c}}\Pi } where:

Eωjcп{\rm E}_{\omega {\rm j}}^{{\rm{c}}\Pi } – energy consumption of the consumer in the process of development of goods (kcal) and

The amount of energy consumption of the consumer during the road to the j-th retailer and back to the ω-th zone (delivery by the consumer himself/herself) is represented as follows: (12)Eωj'movn=f(δωjbefore; Rωj; Lωj){\rm{E}}_{\omega {\rm{j}}}'^{{\rm{movn}}} = {\rm{f}}(\delta _{\omega {\rm{j}}}^{{\rm{before}}};\,{R_{\omega {\rm{j}}}};\,{{\rm{L}}_{\omega {\rm{j}}}})(13)Ejω''movm=f(δjωafter; Rjω; Ljω){\rm{E}}_{{\rm{j}}\omega }''^{{\rm{movm}}} = {\rm{f}}(\delta _{{\rm{j}}\omega }^{{\rm{after}}};\,{R_{{\rm{j}}\omega }};\,{{\rm{L}}_{{\rm{j}}\omega }})

The value of energy costs of the human body during the shopping service in the j retailer is given as: (14)EjTS=f(Sjshop){\rm{E}}_{\rm{j}}^{{\rm{TS}}} = {\rm{f}}({\rm{S}}_{\rm{j}}^{\rm shop})

Described theoretical notes are shown the possible to assess the utility of retailers for end-consumers, using generalized costs according to the parameters of the purchase process.

A probability is assigned to every retailer using the model: (15)Pwj=Uwj∑j=1JUwj{{\rm{P}}_{{\rm{wj}}}} = {{{{\rm{U}}_{{\rm{wj}}}}} \over {\sum\limits_{{\rm{j}} = 1}^{\rm{J}} {{{\rm{U}}_{{\rm{wj}}}}} }} where:

P_wj – probability of choosing the j-th retailer by the w-th end-consumer, and

The utility of choosing a retailer is possible based on an analysis of the generalized costs of end-consumer. The lower the costs incurred by the end-consumer as a result of his/her choice, the greater the utility of choosing a distribution channel and it is given as: (16)Uij=−Θtotalwj{{\rm{U}}_{{\rm{ij}}}} = - \Theta _{{\rm{total}}}^{{\rm{wj}}}

From dependence (15), it can be seen that the attractiveness of choosing a retailer depends on the usefulness of one or another option for end-consumers. Simultaneously, from dependence (16) it can be seen that attractiveness has an inversely proportional effect on the possible costs when choosing an option.

The lower the costs incurred by end-consumers as a result of his/her choice, the more benefit (positive effect) the choice will bring him/her, and as a result, the greater will be the likelihood of retailer choice.

The probability of choosing a retailer we suggested is found as follows: (17)Pij=(−Θtotalwj)⋅Ni∑j=1J(−Θtotalwj)⋅Nw{{\rm{P}}_{{\rm{ij}}}} = {{(- \Theta _{{\rm{total}}}^{{\rm{wj}}}) \cdot {{\rm{N}}_{\rm{i}}}} \over {\sum\limits_{{\rm{j}} = 1}^{\rm{J}} {(- \Theta _{{\rm{total}}}^{{\rm{wj}}}) \cdot {{\rm{N}}_{\rm{w}}}} }} where:

N_w is the capacity of consumers in w-zone.

Dependence (17) shows that the attractiveness depends on the utility. At the same time, according to dependency (3), the utility has an inverse of the potential generalized cost choosing various options. The lower the costs incurred by an end-consumer after his or her choice, the greater utility this choice will bring to him/her and the higher will be the likelihood of visiting such a retailer.