The Influence of Shopping Path Length on Purchase

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Wandering Degree demonstrates the extent of wandering in the store, therefore, iden- tifying two types of ... in this store are females of 40-60 years old. ... The Influence of Shopping Path Length on Purchase Behavior in Grocery Store. 275.

The Influence of Shopping Path Length on Purchase Behavior in Grocery Store Marina Kholod1, Takanobu Nakahara2, Haruka Azuma2, and Katsutoshi Yada2 1

Data Mining Laboratory, Research Institute for Socionetwork Strategies, Kansai University 2 Faculty of Commerce, Kansai University 3-3-35 Yamate, Suita, Osaka, 564-8680 Japan {r098057,nakapara,da60016,yada}

Abstract. In this paper we analyze the new type of information, namely RFID (Radio Frequency Identification) data, collected from the experiment in one of the supermarkets in Japan in 2009. This new type of data allows us to capture different aspects of actual in-store behavior of a customer, e. g. the length of her shopping path. The purpose of this paper is to examine more closely the effect of shopping path length on sales volume, which is one of the established ideas in RFID research as well as in retailing industry. In this paper we developed a simple framework, based on criteria of Wandering Degree and Purchase Sensitivity, in order to see how the relationship between distance* walked within the store and sales volume interacts with walking behavior of customers. As a result, in this paper we came up with some useful suggestions for more efficient in-store area management. Keywords: RFID (Radio Frequency Identification) data, Wandering Degree, Purchase Sensitivity, In-Store Area Management.

1 Introduction Analysis of RFID data has become an attention-getting topic in the research field of in-store customer behavior. The experiments on tracking actual purchasing behavior by using RFID tags have been conducted in Europe, the US and Japan (see details in Larson et al. 2005, Hui et al. 2009, Yada 2009). In our paper we use the unique dataset, obtained from the experiment carried out in one of the supermarkets in Japan. One of the main advantages of RFID data is that it allows us to capture the exact shopping path of each particular customer, which is different from one another, as customers are different e.g. those, who are in hurry or elderly customers, etc. Thus, Larson et al. (2005) discovered 14 path types and found out that shoppers make short trips into the aisles instead of crossing areas in their full size. Their research treats exclusively the path itself, without taking into consideration the purchasing activity of the customer. However, it is natural that there is a difference in actual purchasing activity depending on the path walked. While Hui et al. (2009) observe the deviations from the optimal shopping path and find the positive relationship with purchased quantity, in our paper we investigate further into the relationship between the length R. Setchi et al. (Eds.): KES 2010, Part III, LNAI 6278, pp. 273–280, 2010. © Springer-Verlag Berlin Heidelberg 2010


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of the shopping route and sales volume with the purpose to make concrete suggestions for the efficient in-store area management, resulting in sales growth. This paper develops a simple framework which allows the use of the relationship between the length of shopping path and sales with the purpose to improve the latter. We base our analysis on two criteria – Wandering Degree and Purchase Sensitivity. Wandering Degree demonstrates the extent of wandering in the store, therefore, identifying two types of customers–those who wander around while shopping, as they do not have clear shopping plans and need to think what to buy, and those who don’t wander, as they come to the store with clear shopping goals. We call such behavioral patterns wandering and decisive, respectively. Purchase Sensitivity is the correlation of Wandering Degree with purchased quantity, so that it indicates areas with different strength of positive relationship between two variables. Finally, we classify areas in the WD-PS Matrix, which helps us spot the areas for the improvement by changing the behavioral pattern of their customers. From the matrix it becomes clear that in the case of high Purchase Sensitivity, it is preferable to have wandering customers, and in the case of low Purchase Sensitivity – decisive customers. The remainder of this paper is organized as follows. In the second section, we describe the data obtained from the experiment in more detail, validate the relationship between shopping path length and sales, and postulate the necessity of taking size of supermarket areas into account, when analyzing the length of customers shopping route. In the third section, we introduce the criterion of Wandering Degree with the purpose to overcome the area size difference problem, identifying different types of shopping behavior in different areas, and then we compute Purchase Sensitivity of each area. In the fourth section, we present the WD-PS Matrix and give some area improvement suggestions. The fifth section summarizes main results and brings in the future tasks.

2 Does Longer Shopping Path Result in Sales Growth? 2.1 Basic Analysis of RFID and POS Data RFID experiment, which data we analyze in this paper, was conducted in one of the supermarkets in Japan during almost 6 weeks on May 11-June 15, 2009. RFID tags were attached to shopping carts in order to track the trajectory of a customer within the store, to record her departure from the entrance, her paths from one area to another, stationary visits to different areas until she reaches the checkout register, where her shopping trip completes and POS (Point-of-Sale) transaction data on products, prices and quantity purchased is generated. As a result, we have two main types of information – on what was bought and how those purchases were made. Our dataset has 6997 customers (110492 purchases) and the store, where the experiment was conducted, has sales of 45-60 million yen per day. Majority of customers in this store are females of 40-60 years old. An average customer in our dataset spends 3525 yen and buys 20 items per one shopping trip. The supermarket has 2 entrances, 25 areas, Central Aisle and Checkout Register as seen in Figure 1. This layout, which is typical for a supermarket in Japan, is reproduced from x and y coordinates, registered by RFID sensors placed around the perimeter of the store.

The Influence of Shopping Path Length on Purchase Behavior in Grocery Store

Meat (M)

Food 6 (B6)

Food 5 (B5)

Food 4 (B4)

Food 3 (B3)

Food 2 (B2)

Food 1 (B1)

Household Goods 1(A1)


Seafood2 (F2)

Prepared Food (G)


Frozen Foods (K)

Entrance (E)

Register (R)

Liquor 2 (D2)

Household Goods 2(A2) Household Goods 3(A3) Snacks&Sweets 1 (C1) Snacks&Sweets 2 (C2) Snacks&Sweets 3 (C3) Liquor 1 (D1)

Fresh Produce 2 (V2)

Central Aisle (H)

Fresh Produce 1 (V1)


Western Deli (I)

Drinks (L)

Japanese Deli (J) Event Space (S)

Entrance (E)

Fig. 1. Grocery Store Layout

2.2 Relationship between Length of Shopping Path and Sales As mentioned in the introduction, Hui et al. (2009) showed positive relationship between the distance customers walk during shopping trips and the quantity they buy. This gives us a strong motivation to investigate further into the relationship between length of shopping paths and sales volume. This relationship holds for our data with Pearson Correlation coefficient equal to 0.8457, which can be confirmed from Figure 2. The chart shows average sales (in yen) per one shopping trip for each interval of distance, computed from x and y coordinates contained in RFID data by using Euclid's algorithm. As seen from the graph, there is stable growth in sales as shopping path length increases. Thus, a supermarket manager might think to “make” a customer walk longer distances in order to improve sales. However, he should be careful and check the validity of the same relationship at the area level. In our data the correlation between average shopping path and average sales on an area level was found out to be poor, which can be seen from Figure 3.To demonstrate why the relationship does not hold, let’s have a look at the area V1 (vegetables) located at the extreme right end of the shopping path axis. As we can see customers do walk long distances in this area but average sales are relatively low. We relate this poor correlation to the fact that the plot in Figure 3 does not take into consideration the difference in area sizes, e.g. V1 is the second biggest area, which is obvious from Figure 1. In this case it would be useful to have a criterion based on the size of the area and distance walked in it.


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Fig. 2. Average Sales per Shopping Trip vs. Total Shopping Path

Fig. 3. Average Shopping Path vs. Average Sales per Area

3 Wandering Degree and Its Influence on Sales 3.1 The Definition of Wandering Degree and Its Interpretation In order to take area size into consideration, as mentioned above, we present Wandering Degree (WD), the ratio of distance and area size, which shows the extent of wandering for customer c (c=1,…,n) in area a (a=1,…,m), and is computed by the following equation.

The Influence of Shopping Path Length on Purchase Behavior in Grocery Store

WD =

Dca Asize


, where WD - Wandering Degree of customer c in area a , Dca - distance walked by customer c in area a, Asize - size of area a

If ratio WD is equal to 1, this means that the length of the side of squareapproximated area is equal to the distance walked within that area. If the ratio is less than 1, then it means that distance walked by a customer within that area is shorter than the side of the squared area, thus a customer probably simply made a short trip into the area, instead of crossing it in full size. If the ratio is greater than 1 than it means that distance walked within that area is longer than the length of one side, thus demonstrating the fact that customer was wandering around the area. 3.2 Wandering Degree and Three Types of Purchasing Behavior Furthermore, when looking at each area, we found out that the distributions of WD among purchasers can be grouped into three different shapes as in Figure 5, reflecting three types of purchasing behavior: wandering, decisive and mixed. a)

The peak of the distribution curve of WD corresponds to the value greater than 1, which shows that customers do wander around, while thinking what to purchase. The areas with wandering customers are colored dark grey in Figure 1. b) The peak of the distribution curve corresponds to the value less than 1, meaning that customers do not walk a lot within the area, as they come to the store with clear shopping goals. The areas with these customers are colored grey in Figure 1. c) The distribution has two peaks, lying on both sides from value of 1, corresponding to the presence of both above-mentioned types of customers. These areas are light grey in Figure 1. a)

Mountain-shaped Distribution (Wandering Customers)


Hill-shaped Distribution (Decisive Customers)


N-shaped Distribution (Mixed Customers)

Fig. 4. Three Types of WD Distributions

3.3 Wandering Degree and Purchase Sensitivity In order to shed light on the strength of the relationship between Wandering Degree and quantity purchased in each area, we check for their correlation, computed by Pearson Correlation coefficient, and call this correlation Purchase Sensitivity (PS). In


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Figure 5areas are divided into four groups and colored according their PS. High correlation implies that the longer distance a customer walks in a corresponding area the more she buys, and low PS corresponds to the situation when even if a customer walks longer distance, she won’t buy more. Thus using such criterion as PS can help us judge whether it is appropriate or not to extend the shopping route of a customer for each particular area.

Fig. 5. Purchase Sensitivity of Supermarket Areas

4 Implications for In-Store Area Management In this section, we present a matrix in Table 1, basing on criteria of WD and PS, from which we can draw conclusions regarding the effect of walking distance on purchased quantity in each area and make some implications for more efficient in-store area management. This matrix allocates each supermarket area in a corresponding cell according to its WD and PS. The rows of the matrix correspond to behavioral patterns of customers (wandering, decisive and mixed), implied by the distribution of WD among purchasers in the area. The columns correspond to PS of the area, which shows the strength of relationship between the degree of wandering of customers and quantity purchased. Each cell contains areas similar to each other according their behavioral pattern and PS, thus helping us to understand how PS interacts with the behavioral pattern of the area. According to the meaning of PS, it is profitable to have wandering shoppers as they buy more while thinking and wandering around. If PS of the area is high, it means that customers who come to such areas buy more as they wander around. Thus it is profitable to have wandering customers in these areas. As we can see from the matrix, areas V1 and G reflect this idea. Furthermore, if PS of the area is low, it means that customers who come to such areas already have clear shopping goals and just pick up the items they need, without wandering around. In this case it is profitable to have decisive customers. From the matrix it is clear that this is exactly the case for areas I, B5, H, R and S. The cells which contain two above-mentioned types of areas present the ideal situations which do not require any improvement.

The Influence of Shopping Path Length on Purchase Behavior in Grocery Store


Table 1. The WD-PS Matrix

PS Middle

PS Low

Wandering Customers

PS High V1, G

Decisive Customers


I, B5,H,R,S

Mixed Customers


L,B2,B4,K,A2, A3 F2,V2, D2,A2

However, as we can see from the matrix, for such areas as M, F1, J and C1, it is important to make some improvements, because in spite of high PS value they have decisive customers. Hence it is necessary to change their behavioral pattern into wandering e.g. by capturing their attention with in-store media.

5 Conclusion In this paper we performed the analysis of new type of data, namely RFID data, recently available as a result of RFID technology development. This type of data allows to study the actual in-store behavior of customers. Combined with the purchase data, it gave us a powerful source of information on how a customer made her purchasing decisions. One of the main aspects of in-store behavior is a path that the customer takes during her shopping trip. In this paper we explore the relationship between the length of the shopping route and the purchased quantity. Instead of path length, we use the standardized measure, Wandering Degree, which demonstrates the extent of area wandering, bringing out three types of areas with wandering, decisive and both types of customers. Then we compute Purchase Sensitivity of each area by correlation between WD and purchased quantity and summarize in the WD-PS Matrix. This Matrix helps us understand the necessity of improvements for each particular area. Guided by the value of PS we identify which type of customer behavior makes the area profitable. E.g. if PS of the area is high then it is desirable to have wandering customers in this area, and on the contrary, if PS is low, then – decisive customers. Thus we made some concrete suggestions about the areas which needed to be improved by changing customer behavior from decisive into wandering. The further hypothesis about the effects of proposed measures on purchasing behavior, as well as verification of the proposed criteria in other settings except for supermarkets, is left as a future task. Acknowledgements. This work was partially supported by MEXT.KAKENHI 22243033 and “Strategic Project to Support the Formation of Research Bases at Private Universities”: Matching Fund Subsidy from MEXT (Ministry of Education, Culture, Sports, Science and Technology), 2009-2013.


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References 1. Larson, J.S., Bradlow, E.T., Fader, P.S.: An Exploratory Look at Supermarket Shopping Paths. International Journal of Research in Marketing 22(4), 395–414 (2005) 2. Hui, S.K., Fader, P.S., Bradlow, E.T.: Path Data in Marketing: An Integrative Framework and Prospectus for Model Building. Marketing Science 28(2), 320–335 (2009) 3. Hui, S.K., Fader, P.S., Bradlow, E.T.: The Travelling Salesman Goes Shopping: The Systematic Deviations of Grocery Paths from TSP Optimality. Marketing Science 28(3), 566– 572 (2009) 4. Hui, S.K., Bradlow, E.T., Fader, P.S.: Testing Behavioral Hypotheses using An Integrated Model of Grocery Store Shopping Path and Purchase Behavior. Journal of Consumer Research 36(3), 478–493 (2009) 5. Yada, K.: String Analysis Technique for Shopping Path in a Supermarket. Journal of Intelligent Information Systems (2009) (Online publication)

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