Why retailers cluster

People often prefer to go to multiple shops, for example when trying out clothes, and therefore may prefer to go to concentrations of similar shops. Agglomeration may therefore imply higher retail profits and also higher rents. Counteracting this effect, however, is the competition effect , which means that locating close to competitors leads to greater price competition and therefore lower profits and rents.

For example, gasoline stations offer extremely homogeneous products and therefore compete intensely on prices, whereas a shoe shop may offer a wide array of different products and therefore price competition is less strong. In the thesis it is is found that the effect that finally dominates is the agglomeration effect, or in other words, the effect of being located close to similar shops is on average positive for an average retailer.

However, the effect of being located close to competitors is expected to differ between different types of retailers. For this reason, this effect was estimated for some selected sectors separately.

The net effect of being located close to competitors was negative for supermarkets while for clothing and shoe shops the effect was positive. This result is in line with the idea that supermarkets offer a generally homogenous basket of products. Moreover, people usually do not go to multiple supermarkets, so the benefits of clustering are expected to be lower. Skip to search form Skip to main content You are currently offline.

Some features of the site may not work correctly. DOI: Levinson Published 26 January Business Environment and Planning B: Planning and Design This paper investigates the emergence of retail clusters on supply chains comprised of suppliers, retailers, and consumers. An agent-based model is employed to study retail location choice in a market of homogeneous goods and a market of complementary goods.

On a circle comprised of discrete locales, retailers play a noncooperative game by choosing locales to maximize profits which are impacted by their distance to consumers and to suppliers. Our findings disclose that in a market of… Expand. View on SAGE. Save to Library Save. Create Alert Alert. Share This Paper. Methods Citations.

Citation Type. Has PDF. Publication Type. More Filters. This paper presents a game theoretical model of price setting strategies under different consumer mobility and retail unit capacity constrictions. The theoretical model is based on a consumer grid … Expand.

View 2 excerpts, cites background. View 1 excerpt, cites background. Multi-level two-way cross-chain inventory coordination model that combines the same level with different levels in multi-supply chains. A cross-chain inventory emergency replenishment model that comprises the enterprises of manufacturers, wholesalers, and retailers at the same level and at different levels based on a co-opetition … Expand. As an example, a broad-line mass merchandiser may choose to beef up their assortment of hunting, fishing, and camping gear if they compete in a market against an outdoor specialty superstore.

Many retailers face a distinct set of competitors for their ecommerce channel, and may elect to use this approach to offer special or extended assortments. This approach does not provide merchants and assortment planners any information about the types of products they should add to or edit from their assortments. Instead, competitive shopping and other forms of research must be used to help determine the optimal product mix.

Sales outlets are segmented based on statistical data about the characteristics of the shopping population. This approach has some benefit, particularly if products within an assortment have a clear appeal to a particular demographic group, such as with ethnic foods or specialized products for the aged.

Clusters are created based on characteristics that might include average age, ethnicity, income level, population density, educational level, and others.

There are several challenges with the demographics-based technique, however. The first is that the demographic data associated with any particular store may not actually represent the actual shoppers. Most demographic data that is available to retailers is based on U. Census data. It represents the characteristics of the population of a certain radius around the store, typically 5 miles. Unfortunately, the population that shops at a particular store is not necessarily representative of the population surrounding the physical address of the store.

The demographics-based clustering approach would suggest that the population shopping that store would resemble that of Manhattan. Sales outlets are grouped based on sales history of meaningful product attributes of the assortment. In our opinion, this is one of the most valuable retail clustering approaches and demands a lengthier discussion. This approach has the benefit of the clusters being explicitly tied to the makeup of the assortment.

It removes the guesswork on the part of the merchant about which products satisfy the customers in which clusters. Outlets are clustered based on their relative sales of products exhibiting these attributes.

Should a store exhibit an affinity for silver jewelry perhaps because it is based in the Southwestern United States , then the merchant can simply assign more silver items to the assortment for that store. The attributes with the most impact should be the ones used for clustering. Stores were clustered based on the penetration of each of these attributes in their sales history.

Below are graphical representations of the penetration of each of those attributes values in two of the resulting clusters. As you can see, customers in the stores that make up Cluster 1 have a clear preference for new age, teas, vitamin, and energy drinks. These same customers are not as interested in traditional beverages, such as still water, sparkling water, and juice.

Cluster 2 seems much more oriented toward thirst quenching, over-indexing on still water, soda, and isotonic such as Gatorade , at the expense of new age, vitamin, and energy. The strength of those preferences can even be gauged by the magnitude of the index number.

In Cluster 1, customers have bought 1. This kind of precise preference data can directly inform the number of choices assigned to each cluster that bear each attribute. Once attribute clusters are formed, demographic data for each cluster can be analyzed to determine if there are any significant relationships between population characteristics and cluster membership.

If such relationships exist, there is now some compelling insight into the makeup of the customer base of that cluster. If demographics reveal no significant population characteristics for the cluster, all of the necessary information still exists to make intelligent assortment decisions. Demographic cluster characteristics can also be used to create a model to predict which cluster a new store might fit into.

One significant drawback of this approach is that it demands the use of different clusters for each product category, which tends to increase the complexity of creating and maintaining store clusters — especially when faced with a rapidly changing store base. Another potential source of complexity comes with categories that have many different seasonal assortments, for example in apparel.

If an apparel retailer has six seasons or collections a year and drops six distinct assortments with different attributes, then the clustering and assortment planning processes may have to be performed six different times. Another shortcoming of this method is that it does not take into account the display capacity of the stores within each cluster.

To overcome this problem, a hybrid method could be employed that includes both the penetration of product attributes and the capacity of the store. As we have illustrated, clustering for assortment planning is a complex undertaking with many different factors to take into account.

Simpler, more straightforward methods tend to have significant shortcomings for creating meaningful differentiated assortments. More sophisticated approaches bring with them increased complexity, which may require more manpower or systems resources to successfully employ. Yet, the financial rewards for getting targeted assortments right are significant. After all, every markdown dollar saved goes directly to the bottom line.

It is seductive to gloss over clustering capability when creating an assortment planning strategy or selecting and implementing an assortment planning solution, as retail clustering is commonplace, highly analytical, and too often taken for granted. Start by clearly defining the purpose and objectives of your assortment planning efforts. Once these are established, the best clustering approach can be identified and operationalized.

The Parker Avery Group is a leading retail and consumer goods consulting firm that specializes in transforming organizations and optimizing operational execution through the development of competitive strategies, business process design, deep analytics expertise, change management leadership, and implementation of solutions that enable key capabilities. View our most recent newsletter. Clustering Defined. Assortment Clustering vs. Customer Segmentation.

Retail Clustering Tools. Retail Clustering Approaches. Single Assortment. Each sales outlet receives the exact same selection of items in the assortment. Channel-Based Clusters.