How to Adopt a Data-Driven Approach to Site Evaluation

In today's dynamic world, deciding where to open your next branch may feel overwhelming, with many factors in play. The frequently asked questions we receive from retailers revolve around assessing the potential of specific locations. In this article, we'll demonstrate how Location Intelligence can provide precise answers to your queries, offering assurance that your decisions are data-driven and grounded in facts rather than feelings.

Start the site evaluation with data, not a feeling

In retail site analysis, people tend to choose a different approach. Some believe their employees have enough knowledge to visit a location and estimate straightaway, whether it has potential or not, others look at already functioning branches and are searching for a similar spot. However, until you incorporate data into your decision-making, all those approaches can be merely considered educated guessing. While effective for some, imagine transferring such knowledge to a new employee.

There are some questions though, that can be difficult to answer even for very experienced managers. Let’s take a look at one example. Our customer sought to open a new branch in Brno, Czech Republic, ideally near an exposed street. They faced multiple rental options, close in proximity but with vastly different prices. Their dilemma boiled down to whether doubling the rent equated to double the potential compared to other locations, or if there were potentially more lucrative alternatives nearby. How would you decide?

Site Evaluation in retail

Two close locations  space-wise. Data-wise not that much.

We believe that you don’t need a genius expansion manager or a long list of functioning branches to make the right decision. Instead, you can choose a modern approach and put your trust in data.

What data is key for location planning


There is a wide variety of datasets that can help you with “scoring” a location, but the first one that comes to mind for every expansion manager is mobility. How many people will pass around my store? That is the key question. Since the introduction of GDPR in Europe, data from mobile apps have become less reliable. Instead, CleverMaps has partnered with a few mobile operators to provide the customers with data from the cell service towers. All major cities are currently very well covered with a wireless network and because of that, an analysis on a near street-level detail can be conducted. Incorporate the market share of each operator into the analysis, and a very precise estimate of human mobility is at your disposal.

Transaction data

Another very commonly requested item is the transactions dataset, telling you how much and for what people spend money in the area. Since the transactions are made in specific shops, this data can be delivered in even higher detail than the human mobility data. Seeing where exactly the people are spending for food, goods, or accommodation can give you a hint about where the probability of spending money on your product is the highest.

Points of interest

Last but not least, expansion managers care about the location of their competitors and the location of other stores, which is involved in the points of interest dataset. CleverMaps enables you to filter points of interest (POIs) by type, subtype, and even the brand itself.

Demography, traffic, and parking

Apart from the POIs, other datasets such as demography, traffic stats, or parking locations are widely used and can help you determine whether your selected location fulfils all your needs.

How to do location analysis

1. Choose a Location Intelligence tool

We have the data sorted out but that’s only a part of the success. Half the battle requires putting the data to work and gaining valuable insights from it. Ideally, you need a Location Intelligence tool that enables you to prepare the right metrics over the data and query it together. There are, of course, different ways to achieve this, but CleverMaps offers you all the necessary tools and know-how to have the result ready in a flash. You can either let our Professional Services team take care of it, or you can dive deep into our documentation and create the project yourself.

2. Prepare a data model

To start building a project on our platform, you will need to create your account and then your project. CleverMaps allows you to use a widely used Git environment like Bitbucket or GitHub for project development. You will need to prepare a data model that will allow you to query all datasets on a single granularity. In this case, a point granularity containing the points from all the resources is ideal.

3. Start building metrics

After that, you will start building metrics over the created data model. To create the metrics the correct way, you can follow our documentation. In the case of site evaluation, a simple metric like the sum of the population will be enough to get the needed insights. Since you will query the data on an identical granularity, you can build a more advanced metric combining the input datasets like number of people per transaction. It’s upon your fantasy and needs to create metrics explaining the location the way you like.

Site Evaluation analysis using metrics in CleverMaps Semantic Layer

Example of a turnover metric in the CleverMaps Semantic Layer.

How to show the results of location analysis

When the project with the metrics is ready, we can finally get to the most exciting part - evaluating locations! There are three ways of the deployment and your preferred one will probably be very much dependent on your role in the company and your skillset. You may even be implementing all three of them and have everyone inside the company use it their own way. Let’s have a look at your options.

1. Querying the data

The first one includes querying the data directly from a code without any UI. For that, you can use the CleverMaps PythonSDK and simply send locations directly against the data model. The app will return enhanced input data with location information. This way of querying the data can be simply wrapped into some sort of data app with tools like Streamlit and can be built solely in Python with the output being a plain CSV. You can even send the results to a project in our UI to see the locations on a map and understand their strengths and weaknesses.

2. Playing with an interactive map

The second use case includes querying the data over a map. To prepare this analysis, you need to continue building your project in Git or you can switch to our UI called CleverMaps Studio, where you can add indicators and charts to a dashboard. After that, you can simply click on any location on the map, and the dashboard will show you the values of the metrics straight away. This works very well for people who need to evaluate multiple locations every day and compare them.

3. Data storytelling

If you are not that much into the process and you care more about the result, you will be going with the third option - storytelling. CleverMaps offers this through a presentation layer called Stories Builder. This way you won’t need to care about the analysis process. You will simply see and can easily share the evaluated locations described by data on a map, allowing you to understand the advantages and disadvantages of choosing one evaluated location over the other and making the right decision.

Site Evaluation analysis using Location Intelligence

Example of a location analysis in CleverMaps Studio (location is hidden for security reasons).

Spatial Data Analysis maximizes your profits

Similar to investing, where the aim is to maximize yield while minimizing risk, when considering opening a store, you aim to choose an excellent location. While no one can guarantee that your new store will become a flagship, you can ensure that you open in a location with significant potential. This is precisely what Location Intelligence offers. Stay one step ahead of your competition and harness the power of spatial data analysis for your next site evaluation.

Not sure where to start? Contact us.

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