Determining the Predictive Revenue of Your Future Locations

Predictive Revenue: A Given for Retailers

AI AT THE SERVICE OF RETAIL NETWORK DEVELOPMENT

As consumer expectations evolve rapidly, Artificial Intelligence is helping retail brands build their network development plans.

It is no secret. Today's world is uncertain, constantly evolving and accelerating. And consumer expectations are changing at an unprecedented pace. Every investment decision addressing them must be carefully thought through.

For retailers, opening a new point of sale represents more than ever a significant investment that must be justified, explained, and made as reliable as possible. The choice of a commercial location can result in rapid profitability, just as it can lead to losses of hundreds of thousands of euros. Whether you are considering opening a company-owned or franchise location, it is essential to reassure management, the project leader, and the bank.

That is why today, it is essential to quickly evaluate multiple locations to systematically find the best opportunity for your target market. And it is also in this demanding environment, where speed and accuracy are essential, and where every square meter counts, that modern technologies such as artificial intelligence and predictive models prove invaluable, helping to reduce the risks associated with decision-making.

"AI is becoming a true assistant for development teams, enabling them to save time in their studies and strengthen their reliability."

Discover how to determine the best locations for your next openings using revenue forecasting models

IN WHAT CONTEXTS SHOULD YOU USE THESE PREDICTIVE MODELS?

Selecting the best location for your future openings

Predictive models allow you to both determine the best opportunity between two or more locations for an opening project, and to identify and display the areas with the highest potential to host your next openings.

As a complement to your studies

Predictive models, and more specifically those that calculate future revenue, allow you to enhance your reports and local market studies, adding greater depth and security.

Auditing the effectiveness of an existing point of sale

Beyond testing the potential of an opening project, these models also allow you to audit the effectiveness of your existing points of sale by comparing their actual revenue with the revenue calculated by the model. If the model indicates revenue higher than the actual revenue of the point of sale, it may indicate that you are not exploiting 100% of your point of sale's potential.

What If You Could Predict the Revenue of Your Points of Sale?

Between new consumer expectations and an uncertain context, opening a new point of sale often feels like an uphill battle. However, current artificial intelligence technologies and predictive models make it possible to move forward while securing future investments, by helping project leaders choose the right location, predict the future revenue of the point of sale, or even estimate the achievable market share. An analysis of these disruptive technologies in our white paper.

3 Innovative Revenue Forecasting Models

Our consultants specializing in retail and geomarketing have collaborated with our data scientists to design reliable and operational models dedicated to revenue forecasting.

These models are directly referenced and available in our tool for our users.

Custom Models

Our revenue forecasting models, developed by our experts and accessible in just a few clicks on our platform, are recognized for their effectiveness and precision. However, we understand that each network has specific needs. That is why our experts can customize these models to perfectly adapt to your network.

Here are the 3 innovative revenue forecasting models ranked by accuracy:

Standard Simulation (+/- 80% reliability)

These ready-to-use models primarily integrate data related to the market, the trade area, and more broadly the site environment.

Data used in this model:

• Sociodemographic data

• Competition

• Market data

This method provides a revenue estimate.

Advanced Simulation (+/- 90% reliability)

Customization of models by adding more extensive data such as foot traffic and data from your existing points of sale. Additionally, projects are qualified by the discriminating factors of revenue capture (surface area, store type, etc.)

Additional data used in the model:

• Foot traffic

• Your points of sale (surface area, typology, location type, etc.)

This method provides a more reliable model than the previous one, which will further secure your next openings.

Expert Simulation (+/- 95% reliability)

Configuration of a custom model dedicated to the specific characteristics of each retail network. This model adds even greater depth by analyzing distribution methods, transaction receipts, and many other customer data points.

This model requires an in-depth exchange with the network teams to optimize the model.

Additional data used in the model:

• Transaction receipts

• Distribution types

• Your customers

• Performance indicators

The best method to secure your next openings.

This article will allow you to learn more about revenue forecasting models.

The Future of Predictive Analytics for Retail Networks

The Next Predictive Models for Store Networks

At Galigeo, several years ago, we decided to take a strategic turn in favor of predictive analytics and AI to best support store network managers in their strategic decisions. We have already made significant strides in this direction by developing several operational predictive models, including our point-of-sale revenue forecasting model, which you are already familiar with at this point in the article.

However, our pursuit of excellence does not stop there. We are already working with our experts and clients on new innovative, reliable, and operational models to facilitate decision-making.

Here is a non-exhaustive list of predictive models we are working on:

A predictive model to determine key success factors: Based on your business data, data from your existing points of sale, and competition data, our model will allow you to identify your key success factors and subsequently search for locations that possess these characteristics to ensure the success of your future openings.

A predictive model to determine your growth zones: This model will allow you to identify the areas most conducive to your future development based on a multitude of data such as competition data, your network data, performance indicators from your points of sale, your business data, and much more. Once your growth zones are determined, all you need to do is find a location within these zones for your next openings.

A predictive model to determine the best local advertising strategies: This model will indicate, based on your business data, targeting, foot traffic, and budgets, the best local advertising spots where you should display your advertising to maximize visits to your points of sale.

AI FOR RETAILERS WITH LSA

Discover our article on AI and revenue forecasting dedicated to the retail world.

Refine your development plan with AI.

DISCOVER THE BEST PREDICTIVE SOLUTIONS TO SUPPORT YOU IN MANAGING YOUR NETWORK

Integrate Your Predictive Reports Into Leading BI Tools

Using leading business intelligence tools such as Power BI, SAP BI, IBM Cognos, Tableau, or Qlik is of considerable value for store networks. These tools offer advanced capabilities for collecting, analyzing, and visualizing data, enabling retail networks to make informed decisions.

It is possible to directly integrate revenue forecasting models into these tools and visualize the results.

These tools offer easy integration with various point-of-sale data sources. They allow collecting data from POS systems, loyalty programs, websites, management and logistics software, social media, and much more.

The State of the Art of Geomarketing for Retail Networks

Geomarketing is essential for store networks because it enables strategic decisions based on trade area analysis, external information such as market and sociodemographic data, foot traffic, competition, and internal information from your points of sale and customers, to optimize the effectiveness of your current network and determine the best locations for your next openings.

Geomarketing for Network Management