Fig. Classification of assessment location explanatory variables
Previously we have talked about what elements to look at when evaluating a retail site in a macro perspective. (Click to read more) Now we will formulate a location strategy of prospective sites into micro analysis.
Micro problem is the other main component of assessing a site location. Retailers have always understood location as paramount, but understanding all aspects of store performance, site potential in addition to consumer behavior demands a great amount of information. This involves an understanding of the geographical, demographic, socioeconomic, and competition data in the area. The figure above suggests a possible classification of assessment location explanatory variables. In opposition to trade zone evaluation (demand and competition), the site and store variables intend to evaluate internal factors or the new store offerings.
The retail sector is going through a restructuring phase, driven by factors such as increasing consumer mobility, increasing electronic commerce, changing household size, concentration of market power, market saturation, and changes in planning legislation. These changes require retail groups to invest strongly in stores of smaller dimension while focusing on a strategy that prioritizes proximity to the target customer and quality of goods and services. This investment has a long – run as well as smaller economies of scale, which forces careful decision-making.
ForbesChina has announced a ranking of best cities for business in China. Business development has expanded to tier2, tier 3 cities in addition to Beijing, Shanghai, and Guangzhou. Below are the top 20 cities.
From the outward growth of business focus we can find that either local or foreign enterprises have altered their network planning. How brand owners to prioritize their city expansion strategy in terms of business requirements is crucial to the future success of the firms. Benchmark your brand to competitors and prioritize cities with most potential to help you develop best market plan.
Crucial aspects for the success of smaller retail stores include location, dimension, services offered, and the targeting of specific market segments. Here we consider a phased decision methodology where we separate decisions about choosing geographic regions from choosing store locations within the region.
In the first phase, define the Macro Problem based on: strategic policy for a network, zone, or region where new stores will be installed; the number of units to be built; along with implementation timing.
In the second phase, make the final store-site choice by narrowing down a selection of available locations, usually with the help of real estate agencies. For this decision level, the number of commercial and academically published models suggests that researchers feel a need for rational and formal use of information.
The third decision level, concerning store and services design is the level most linked to the concept of service quality and customer satisfaction. Focus on models that take into account store location (level 2) and the physical design of the facility. Furthermore, some application can be extended in the store characteristics and services definition (level 3).
Tab. Decision levels involved in the expansion strategy of a retail network
China Resources Enterprise (CRE) has finalized acquisition with Tesco to create the largest food retailer in China by the end of May, 2014. The joint venture will combine Tesco’s 135 outlets in the country with CRE’s almost 1,000 stores, called Vanguard. Vanguard owns various business types such as hyper-marts, convenience stores, supermarkets and others. Among these outlets there are more than 250 hyper-marts. It is crucial for CRE to effectively carry out integration and store optimization plan after the acquisition.
To better execute store network optimization, brand owners need to fully understand market potential and penetration of targeted city. Reviewing performance of current stores and comparing actual performances against potential’s to get a clear picture.
Steps of SNO (Store Network Optimization)
Scorecard: to assess each branch and analyze competitor distribution
Channel optimization: overall planning of current stores (keep, improve, move or close)
Market expansion: site selection of new stores in terms of untapped high potential areas
You have heard that over than 80 percent of enterprise data has spatial components based on IDC market. But what is Location Intelligence got to do with it? and how can it transform your data from an underutilized asset into a source of competitive advantage?
By mapping locations, agencies and businesses can find new insights about business operations and processes, and improve services for customers. For organizations, the use of business location data and GIS increases collaboration across the agency, along with improving business functions. In many instances, GIS software can be layered on top of CRM or ERP systems, making implementation very easy, and leveraging resources in new ways.
Location analytics is a critical business asset that will provide a competitive advantage by adding geographic and location context to information enables organizations to understand more about their customers, whether they are other businesses or consumers. It provides critical business insights, enables better decisions and improves processes and performance. Location awareness can benefit efforts in marketing, sales, and customer acquisition and retention; logistics and supply chain management; and financial and operational decisions, not least among them where to place retail outlets, business assets and people in various functions. It also can increase the value of technological innovations such as business and social collaboration, and even mobile technology that is used by the business and customers along with the analysis of social media commentary and other expressions of customer sentiment that could be part of big data efforts.
Here are a few examples of how location intelligence is being used today in a variety of different industries.
Site Selection: The decision about where to locate a new store or facility, is probably the most common application of location intelligence today. When location data is combined with available real estate, demographic data, current customers characteristics, and information on the most likely prospective customers, the resulting Location Intelligence can help identify a site location with maximum revenue potential.
Store Performance Management: Detect and categorize poorly performing stores and assess the strategies to uncover the relationship between stores, products and customer types that affect sales performance. In addition, location intelligence can be used to forecast and develop store-specific budgets and expectations based on the size of surrounding demographics and other location specific information.
Risk Assessment: Location is extremely important in the insurance industry, where customers and natural disasters are both tied to a location. When a natural disaster occurs, insurance companies have the capability to instantly understand their claims exposure by visually plotting their customer data and the affected area on a map. This also allows them to more accurately estimate the number of resources they will need to service claims in an affected area.
Customer Preference: Understand who your consumers are and where and when they want to engage with you. Optimize retail stores for maximum customer engagement increasing profits and brand loyalty.
Healthcare: a significant percentage of disease is caused by environment. There is a tremendous amount of knowledge to be gained and disease to be reduced or eradicated by understanding these effects. Health records tied to user home and work location can be a massive benefit for researchers.
Government Public Affairs: Monitor and identify pollution violators. Determine deteriorating areas of a city and revitalize. Gain more input from citizen reporters and be able to analyze and take action. Create transparency to stimulate self-regulation at scale.