Shopping centres and shopping malls. How to analyse location, traffic, profile and revenues

Objective

To assess locations for retail outlets, or shopping centres, their attractiveness for specific types of business, calculate the potential traffic of a shopping centre or a tenant's point.

Context

Expert evaluation of locations and retail area options for business is based on accumulated private experience, which is good for analysis, but limited to familiar options and the physical ability to evaluate only about 10-20 factors. Here is an account of how technology enhances expert capabilities.

Solution

One of the most popular requests from shopping centres and malls is to perform an assessment in terms of rent collection and predictive analysis: who will leave, who will attract more traffic, and how to form a pool of brands for the food court or in a certain product category. Over the years, the shopping centres have accumulated extensive expertise of their own, as well as a lot of their own data. Here is how we analyse and enrich them to increase the prediction accuracy. We usually start with the area of the shopping centre location potential. We have basic layers for population, wealth, traffic, the concentration of business centres, shopping centres, and road and transport infrastructure. To these layers, we add aggregation layers and extensive geographical analysis.

Let's start with the simple and obvious. We use the above data to calculate the number of actually available shopping centres per person in the city. The deficit and surplus areas are highlighted, and for convenience, the data is displayed in a colour scheme. At this point, everyone can see at a glance why a particular shopping centre in a deficit zone, for example, has a higher utilisation rate, a higher rental price and a higher demand for it.

Socio-demographic factors: it is important who lives within a 500 m and a two-kilometre radius.

We at Marketing Logic have accumulated a lot of our own data over time, and it can be very useful. For example, retro data on the lifespan of an organisation in a particular location will, at the very least, make you take a closer look at it and the various factors affecting the business. We analyse the profile and format of the companies so we can make our conclusions and recommendations more precise. We also determine the shopping centre's opening and closing rate and the floor space utilisation rate. In addition, we derive the optimum mix of business types within a shopping centre: how much space should be allocated to clothing shops, how much to food stores, food courts, entertainment, cinemas, and different types of shops – of course, we do this together with the customer's experts. We calculate such coefficients for all the key areas and types of activity. At this stage, it is not a strong recommendation to look for another shopping centre or another tenant, but at least a reason to take a closer look at the business profile. If there was already a shoe shop here, and it moved out six months later, and three other shoe retailers had moved out before it, this is an obvious reason to get yourself thinking and do further analysis on other variables. And it will probably show that it is better not to enter this territory with a shoe shop. The coefficient distribution makes it possible to identify and describe the profile of each shopping centre. It only seems that they are all the same. In fact, this is not the case at all: each one is unique, and many have a distinct profile. Some have more space for home and office products, some for gardening, some for cosmetics and a high proportion of beauty salons, etc. The profile of a shopping centre, if it is successful, says a lot about the target audience, which ensures the centre's popularity and, as a consequence, its economic performance.

Layers with advanced characteristics help us to assess Cash Flow in the next stage

A full-fledged project undergoes several stages of “filtering”. At the stage of joint expert decisions, we draw up a set of rules according to which the objects are to be evaluated. A pool of potentially interesting objects is compiled: for example, all shopping centres and shopping and entertainment centres in Russia or individual regions and cities, and then this list is assessed with regard to key, most important parameters. After we eliminate those that do not meet the important criteria, the remaining objects or locations are assessed based on additional parameters. These can vary from socio-demographic to economic or to a specific type of activity – it all depends on what the ultimate goal of the analysis is. If we are looking for a site for a particular business, we conduct a kind of "scoring" of shopping centres which meet these parameters. If we search for a location to build a shopping centre, we perform a "scoring" of construction locations. It is a common task to define the type of business to fill the shopping centre so that it can be as successful as possible while taking into account local socio-demographic factors and desired financial results, as well as the sustainability of the model. Often the administration and management of a shopping centre are 100% confident in their flagship brands, the so-called "anchor" tenants, but the profile of a shopping centre is not comprised of them alone. It is important to distribute the proportions correctly so that everyone feels good and comfortable: the shopping centre itself, the tenants, their businesses, and, of course, the customers, who will find what they need and generate consistent traffic.

Our geo-analytical system helps to free up the business experts' time for more detailed expert analysis or other tasks. At the same time, the expert usually accompanies the project, “steering” it from the initial basic layers to machine-learning-based models, i.e., up to date on all calculations and rules of their design.

Result

The result of comprehensive and detailed calculations, analysis and assessment of shopping centres, areas and locations based on a large amount and variety of data is the equally comprehensive and detailed assessment of any property. It is also a universal set of generated and calibrated models for a particular business and its objectives. We can run an unlimited number of objects through these models to assess their potential and projected financial outcomes. Such solutions save months of manual expert labour and significantly improve accuracy by analysing terabytes of data.