Data Scientists Can Unlock New Retail Customers

Harnessing location intelligence: How data scientists can drive customer growth in retail

• Unlock New Customers with Location Intelligence – Learn how data scientists use geospatial data, foot-traffic analysis and demographic insights to identify high-potential customer segments in retail.
• Optimize Store Locations and Marketing Strategies – Discover how predictive analytics and mobility data help retailers choose ideal store locations and create targeted marketing campaigns.
• Leverage Cutting-Edge Tools and Techniques – Explore GIS software, geospatial APIs and machine-learning models that empower data scientists to make data-driven decisions for retail growth.

What Is Location Intelligence?

Location intelligence refers to the practice of using geospatial data to better understand the proximity relationships between people, places, and things. This can include the analysis of property information, demographic trends, traffic patterns, foot traffic, weather conditions, and much more. By combining geographic data with traditional business data, companies can gain valuable insights into how customers interact with physical locations. They can also more quickly answer valuable questions like:

• Where are my existing customers coming from?
• What neighborhoods are underserved or untapped?
• Which locations have the highest potential for new customers?
• What are the purchasing patterns and preferences of the areas where my best customers live?

7 Ways Data Scientists Can Use Location Intelligence in Retail

1. See the Potential

The first step in uncovering new customers is identifying areas where your brand has potential for growth. Location intelligence allows you to map out customer demographics, competitor locations, and areas of high foot traffic to pinpoint neighborhoods that are underserved by your brand or may be visiting a competitor. By analyzing geographic and demographic data, you can identify:

• Areas with a high concentration of potential customers but few competing retailers.
• Regions where your current customer base is growing, suggesting expansion opportunities.
• Locations with high traffic volume but low brand awareness.

For example, if you run a retail chain selling athletic apparel, you might notice a surge in interest from areas with a high concentration of fitness enthusiasts but no nearby competitors. This could be an ideal spot for a new store or pop-up location.

Understanding how people move through physical spaces can help retailers predict where they will be able to attract new customers. Data scientists can analyze foot-traffic patterns through mobile location data, GPS signals, and other location intelligence technologies. These insights can help determine:

• The best times to target potential customers.
• How far people are willing to travel to visit your store.
• The impact of surrounding businesses or events on foot traffic.

For instance, if you operate in a shopping mall, understanding the busiest times for foot traffic in different sections of the mall can help you place your store in the most lucrative location.

3. Enhance Targeted Marketing Campaigns

Location intelligence allows you to segment potential customers more effectively and deliver highly targeted marketing campaigns. By combining geospatial data with customer behavior insights, you can create targeted offers and promotions that resonate with specific neighborhoods or communities.

Data scientists can use geospatial analysis to identify:

• Areas with high concentrations of potential customers who fit specific demographic or behavioral profiles.
• ZIP codes or neighborhoods where your brand has low visibility but high potential.
• Locations near competitors where people are more likely to cross-shop.

This approach can be used to serve personalized advertising to consumers based on their geographic location, whether they are within a certain radius of a store or in areas where your brand has high growth potential.

4. Optimize Store Locations and Real Estate Decisions

For brick-and-mortar retailers, selecting the right location is a critical decision. Location intelligence can help retailers identify the most strategic sites for new stores based on a variety of factors:

• Demographics (age, income, family size)
• Proximity to competitors or complementary businesses
• Accessibility (parking availability and public-transit options)
• Nearby population density and foot traffic

Data scientists can combine datasets from commercial real estate platforms, traffic analysis tool, and census data to create heat maps and predictive models that guide site location decisions. This ensures that the retailer invests in the right locations with the highest likelihood of attracting new customers.

5. Understand Consumer Behavior Through Mobility Data

With the rise of smartphones and GPS-enabled devices, mobility data has become an invaluable resource for understanding consumer behavior. By analyzing this data, data scientists can uncover patterns that reveal potential customer hotspots, preferred shopping routes, brand affinity, and behavior at specific times of day or during special events.

Mobility data can provide insights like:

• Areas where people spend significant time, which could suggest interest in your product.
• Popular routes or clusters of activity that your brand could tap into with targeted marketing or localized store openings.
• The behaviors of potential customers as they move between locations, allowing for personalized recommendations and promotions.

This type of analysis can also be helpful in predicting the success of new store locations by understanding where people are traveling from and whether they’re likely to visit your store based on convenience and finding the right merchandise.

6. Predict Trends and Consumer Shifts

Location intelligence not only helps uncover current customer segments, but it can also predict where new customer segments will emerge. By analyzing long-term demographic trends and historical geographic information, data scientists can forecast changes in consumer behavior and identify emerging market opportunities.

For instance, if you’re targeting a younger, urban demographic, location intelligence can help you spot areas where this group is moving and growing, allowing you to stay ahead of market shifts.

7. Key Tools and Technologies for Location Intelligence

To take advantage of location intelligence, data scientists need to become proficient in a variety of tools and technologies. Some key platforms and techniques include:

• GIS Software (Geographic Information Systems): Tools like Esri’s ArcGIS and CARTO’s mapping solutions, both of which are CoreLogic partners, allow users to visualize and analyze geospatial data.
• Geospatial Data APIs: CoreLogic’s location-intelligence solution offers detailed location data and APIs to help analyze foot-traffic patterns and other insights.
• Big Data Analytics Platforms: Tools like CoreLogic’s Araya can process large-scale mobility data to reveal patterns and insights.
• Machine Learning: Predictive analytics and machine-learning models can help forecast customer behavior and identify new opportunities.

The Power of Location Intelligence

For data scientists, the potential of location intelligence in retail is enormous. By analyzing geographic, demographic, and behavioral data, data scientists can uncover new opportunities to attract customers, optimize store placements, and tailor marketing campaigns to the right audience at the right time. The key is to combine your technical skills with a deep understanding of spatial patterns and market trends.

In a world where customer expectations always evolve, using location intelligence can give retailers the edge they need to stay competitive, reach new audiences, and drive growth. So, whether you’re analyzing foot traffic, identifying potential store locations, or optimizing marketing campaigns, location intelligence offers a wealth of opportunities for data scientists to make a meaningful impact in retail.

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Written by: Data Team

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