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Retail site selection using an AI Neural Net model

Client: Canadian auto, rural, & home improvement goods retailer

Retail site selection using an AI Neural Net model

1. Introduction

This case study unveils a pioneering approach to retail expansion, undertaken by a notable Canadian retail chain specializing in a diverse array of products for farm, industrial, and garage needs. Although well-established across Canada, the retailer faced a unique challenge: Quebec was an untapped market with no existing store presence. This project was not just about expanding into a new province; it was about strategically breaking into an entirely new territory without the advantage of existing market data. Tasked with this ambitious endeavor, the retailer turned to predictive analytics to identify the most lucrative locations for their new stores in Quebec, aiming to maximize revenue potential in a market ripe with opportunities.



2. The Challenge


Entering a new market is a venture filled with uncertainties, more so when the market is as diverse and uncharted as Quebec for this leading Canadian retail chain. The challenge was not just geographical expansion but understanding and predicting the market dynamics in a region with no prior footprint. This presented a two-fold problem:


  • Navigating the Unknown: With no existing stores in Quebec, the retailer faced the daunting task of estimating market potential in a province where they had no customer base or sales history. This lack of local market data meant venturing into the unknown, making strategic decisions without the guidance of historical insights.


  • Objective - Precision in Prediction: The core objective was to develop an advanced predictive model to estimate potential store trade area revenue. The model needed to not only identify viable locations but also ensure these locations aligned with the retailer’s unique market – areas with high potential for their specific product range, like near farms and industrial hubs. The stakes were high; the model had to be precise, as each location choice carried significant investment and risk.


The retailer was at a crucial juncture – to expand into Quebec successfully, they needed more than just data; they needed insights that could predict the future of their business in a completely new market.



3. Methodology and Implementation


The methodology adopted for this project represented a blend of advanced analytics and strategic ingenuity. It was a meticulous process that required both precision and creative thinking:


  • Advanced Machine Learning Application: The core of our approach involved developing a deep learning model using TensorFlow. This advanced technology functions by identifying and learning patterns from extensive datasets. For this project, it meant analyzing diverse factors to pinpoint what influences the success of a retail location.


  • Comprehensive Data Analysis: The strategy hinged on the intelligent use of data. We gathered a wealth of demographic, geographic, and spatial data, along with information about drive distances from the retailer's existing stores. This data, reflective of proven success in other provinces, was then carefully applied to the Quebec context. It was a task of adapting tried-and-tested formulas to a new, unexplored market.


  • Predictive Simulation for Revenue Potential: The model was tasked with simulating various scenarios across all Quebec postal codes. This involved predicting which locations would yield the highest revenue, considering factors like customer accessibility and area demographics. It was a process similar to forecasting market trends, where the goal was to identify the most promising areas for new store locations.


This methodology was more than just sophisticated number crunching; it was about strategically translating data into actionable business insights. By bridging the gap between data science and practical application, the project aimed to chart a course for successful market expansion.


Sample code of what a typical AI Neural Network model would be in Python that was used for this project:


import tensorflow as tf
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense
from sklearn.model_selection import train_test_split
import pandas as pd

# Sample code for illustrative purposes
# Load and preprocess data
data = pd.read_csv('data.csv')  
X = data.drop('target_variable', axis=1)
y = data['target_variable']

# Split data into training and testing sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# Building the neural network model
model = Sequential([
    Dense(64, activation='relu', input_shape=(X_train.shape[1],)),
    Dense(64, activation='relu'),
    Dense(1)
])

model.compile(optimizer='adam', loss='mean_squared_error')

# Training the model
model.fit(X_train, y_train, epochs=10, batch_size=32)

# Evaluate the model
loss = model.evaluate(X_test, y_test)
print(f'Model Loss on Test Data: {loss}')

4. Results and Achievements


The culmination of this project was not just in its completion, but in the tangible, impactful results it delivered. The meticulous methodology and innovative approach translated into significant achievements for the retailer:


  • High Accuracy and Predictive Power: The model, a sophisticated blend of various data points, achieved an impressive 92% accuracy between the training and test sets. This high level of precision indicated the model's strong predictive capability, providing the retailer with a reliable tool for informed decision-making.


  • Successful Market Entry Strategy: Guided by the model’s insights, the retailer made a strategic entry into the Quebec market. The first store opened in a location identified by the model as a high potential area. This was not just a new store opening; it was a calculated move into a new market, backed by data-driven confidence.


  • Quantifiable Impact on Business Decisions: The results went beyond theoretical predictions; they had a direct and quantifiable impact on the retailer's expansion strategy. The choice of location, influenced by the model's predictions, was a decisive step in maximizing revenue potential and market reach in Quebec.


  • Visualization as a Decision-Making Tool: The use of a choropleth map provided a clear visual representation of potential high-revenue areas. This was not just a map; it was a strategic tool that transformed complex data into an easily interpretable format, aiding the retailer’s leadership in visualizing the market landscape.


These achievements marked a significant milestone for the retailer, showcasing the power of data science in transforming business strategies. The project stood as a testament to the potential of leveraging advanced analytics for strategic market expansion, setting a new benchmark in the industry.



5. Conclusion and Key Takeaways


This case study highlights the crucial role of predictive analytics in retail expansion. The successful entry into a new market exemplifies how data-driven strategies can effectively guide business decisions and market exploration, setting a new standard in the retail industry.

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