As a Retail Manager, you were used to deal with all the aspects of a store management, from the supply to the merchandising, from the internal team management to the stores performance analysis, etc. Now you are mostly in charge of a store portfolio management. This article does not pretend to focus on all the aspects of your job, only on those related to this latest issue.
Concerning the store portfolio management, you usually need to know perfectly how your stores are runned in order to understand their strengths and weaknesses and adapt the portfolio to that. Indeed, there are things that are inherently different among stores and that is why you normally need to go regularly to the stores. Checking how everything is going, gathering feedback from the ground and looking around how the dynamics of the area are evolving will help you to adapt your store portfolio management, product mix or even sometimes suggesting stores closings. But, some issues remain hard to solve over and over: how to compare your points of sale between them and understand their performance in relation with the area they are in? How to maximise the Return on Investment of store openings or closings and how to forecast sales?
1. Comparing your stores between them and balancing their performances
Actually, things are inherently different among stores because of the impact and influence of their location: a store in a high pedestrian traffic street, on a very dynamic commercial area and with few competitors around, cannot be compared to a similar store, but in an area with less traffic, many competitors and few attractors.
That is why your stores’ results can be very different from one store to another. Nevertheless, the point of sales with the best results is not necessarily a top performer compared to its potential. Indeed, a store getting good results could obtain them just because of its exceptional location and positive impactful external factors (high pedestrian traffic, huge amount of target profiles in the area, etc.) without performing at its full potential. On the other hand, a point of sales with lower results is maybe making the most of its location and external factors. Then, how to compare objectively the stores within your portfolio?
2. Being a forecaster
As a Retail Manager, you need to be a master in forecasting: supply, product mix needs, trends, sales, etc. With your experience, you turn out to be an expert in all operational aspects of your stores, such as supplying your stores for example. Speaking with your team within the stores, benchmarking what your competitors are doing and attending congresses around the future trends in your industry, helps you know pretty much how trends are going to evolve and thus how you need to adapt to them. It’s not a precise science but your experience and gut feelings help you figure out what is going to work out in your points of sale, when and how. Nevertheless, what you are not sure about is how your new initiative is going to be impacted by the location of each store. For example, a product mix should be adapted to each point of sales and to their audience.
Your top management is also expecting you to anticipate the result of each store within your portfolio, even the brand new ones for which you don’t have any historic data yet. Definitely you have a lot experience and know-how, but how to forecast results if you need hard data? and where to find it?
3. Managing closing and new openings
Sometimes, your store portfolio is performing so well that several new openings are made during the year. On the other hand, it does not prevent some stores from your portfolio to be underperforming and thus need to be closed. How then, should you choose which one is worth opening and which one would be better closing? Your objective is to maximise the profits while minimising the costs. Opening or closing another one is an expensive process, hence the need to manage it in the best way to have the best Return on Investment.
In these two different dynamics, how to help the expansion team from your company on the location selection? Advising them on factors that will definitely make a difference!
Then, making several detailed analysis would be necessary in order to find answers to all the issues mentioned before. Studies could be conducted, but with which data? And by who? Would they be objective? And in the end, who else than you would have in mind the whole picture of your business in order to understand all the peculiar relations between a product mix, a store, a location and the consumers? That is why you need a tool able to gather for you objective data, and help you understand the hidden dynamics within your store portfolio. This tool could be a Location Intelligence solution! Why?
Because those kind of software are built with a lot of geo-located databases, providing a deep understanding of an area with variables such as sociodemographic, socioeconomic, commercial assets, and also others as pedestrian traffic data, for example. Crossing those external data with internal data stores’ growth, sales/product or sales/point of sales for example, will enable you to understand what external factors have more impact on your results and then to adapt your stores to them. Thus, once the external success factors are known, the idea would be to replicate them for the openings to come. This will also help you gain powerful insights: for example, if a store is performing pretty well while being in an area with low amount of successful external factors, its success can be certainly explained because of great management. These best management practices can then be replicated in the rest of the store portfolio!
Furthermore, using internal information about the performance of your stores with the Location Intelligence solution, will help you following and control them. This will allow you to find underperforming stores and understand why: is it because of a bad location or because of a wrong management? What is more, it will also help you follow granular indicators of your choice, like for instance: what is the population structure in a 10 minutes walking area around your stores. Thus, in the future, you would be able to choose the same kind of product for a specific store and adapt your product mix according to the population profile, while selecting others in different points of sale.
Once you have understood the different surroundings’ typologies present in your network, you will be able to segment your stores into clusters according to the area they are in. For example, a “Residential” cluster with a medium level of pedestrian traffic, high income and no competitors in the area is different from a “Seasonal” one, that has a very high pedestrian traffic, high touristic flow and seasonal consumption. According to those clusters, you will be able to adapt the business management to each store.
The next step would be then to manage the sales forecasting of the whole portfolio, for existing points of sale as for hypothetical new openings. Thanks to Geoblink this is possible, backed by objective data. Thanks to the cluster the store belongs to and the noticeable distinctive internal and external variables, sales forecastings can be made out of the store future performances. For new openings, it works the same way: the external characteristics of an area will allow you to classify it within one specific cluster and, due to internal characteristics (store size, product mixt, number of employees etc.), to make sales forecasting if opening there.
Then, due to all those insights, you will be able to estimate the potential captured by your network and what best practices to implement. You will also be in position to give feedback to the expansion team, explaining them what leads you to ask for a relocation or store closing and what external factors they should look after to maximise a store performances.
As a Retail Manager, a Location Intelligence tool would thus definitively be for you a game changer in your portfolio management, allowing you to understand the external factors that have real influence on your stores, adapting their management, clustering them, making sales forecast and extracting the most of each store.
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