Location Analytics in Retail: How to Utilize Geospatial Data for Informed Business Decisions

Benefits of Location Analytics

Location analytics is the process of analyzing and interpreting geospatial data to understand patterns, trends, and relationships associated with specific geographic locations. With geomarketing, location-specific success criteria of shops, sales areas, target groups, or branch and distribution networks can be objectively evaluated with GIS applications.

A geographic information system (GIS) collects, stores, checks, modifies, integrates, analyzes, and presents geoinformation. The predefined geo-analytical tools can evaluate the following common criteria meaningful for retail stores:

• Demographics: Population density, age distribution, income levels, household size, education levels, and cultural diversity of the area surrounding the location.
• Foot traffic: Pedestrian count, proximity to transportation hubs, busy intersections, and other high-traffic areas.
• Competitive landscape: Presence of competitors, their proximity, market saturation, and market share.
• Accessibility: Ease of access by car, public transportation, walking, and cycling. Availability of parking spaces and traffic flow.
• Costs and rent: Affordability of rent, lease terms, and other overhead costs associated with the location, such as prices on electricity, water, internet connectivity, and telecommunications.
• Safety and security: Crime rates, presence of security measures, and overall safety perceptions of the area.
• Market potential: Growth trends, market demand for specific products or services, and economic indicators of the area.

Spatial data analysis helps businesses make informed decisions about where to establish their retail presence to maximize profitability and meet the needs of their target market. Moreover, they can anticipate any local infrastructure projects or changes in the area that may impact the retail store’s success. Below you will find the benefits most retailers noted after more specific geospatial data utilization.

1. Geospatial technology aids decision-making for retailers through two key steps:

• Data collection and analysis: Geospatial technology gathers vast amounts of data related to customer movement, demographic information, and purchasing behavior from various sources such as mobile devices, GPS trackers, and social media. This data is then analyzed using geographic information systems (GIS) to identify patterns, trends, and correlations relevant to retail operations.
• Insight generation and actionable intelligence: Once the data is analyzed, retailers gain valuable insights into foot traffic patterns, customer preferences, and market trends. These insights empower retailers to make informed decisions, such as selecting optimal locations for new stores, adjusting inventory levels to meet demand fluctuations, and designing targeted marketing campaigns to attract specific customer segments. 

2. Location analytics enhances operational efficiency and customer experience through the following steps:

• Data integration and analysis: Location analytics integrates geographic data with other operational data sources, such as sales transactions, inventory levels, and customer demographics. Through spatial analysis techniques, retailers can identify inefficiencies in delivery routes, pinpoint areas with high demand, and analyze customer movement patterns within stores.
• Strategic decision-making and implementation: With insights from location analytics, retailers can make data-driven decisions to optimize operations and improve customer experience. This may involve restructuring delivery routes to reduce transportation costs and delivery times, adjusting inventory levels to prevent stockouts or overstocking, and redesigning store layouts to enhance customer flow and product visibility. By implementing these strategic changes, retailers can provide customers with a more personalized shopping experience.

3. Geo-analytics refines narrow-targeted marketing through the following steps:

• Location-based segmentation: Location data analytics allows retailers to segment their customer base, including proximity to store locations, frequency of visits, and purchase history. By analyzing this data, retailers can identify specific customer segments with similar shopping behaviors and preferences within targeted geographic areas.
• Personalized campaigns and messaging: Leveraging insights from geo-analytics, retailers can create highly personalized marketing campaigns tailored to the preferences and behaviors of specific customer segments. By sending relevant promotions, discounts, and offers to customers based on their location, retailers can effectively engage with their target audience, increase brand loyalty, and drive sales performance. This personalized approach enhances the effectiveness of marketing efforts and fosters stronger connections between retailers and their customers.

Location Analytics in Use

To better understand the reasons why more and more retailers rely on location-driven decision-making, let’s delve into real-life use cases from the retail industry.

Use case 1: Declining sales in a large retail chain

A large retail chain faced stagnant sales and struggled to identify the most lucrative locations for new stores amidst fierce competition.
Before the location analytics implementation:	After the location analytics implementation:
The retail chain relied on traditional market research and intuition to select store locations. However, this approach often led to suboptimal decisions and missed opportunities.	The company gained access to comprehensive demographic data, foot traffic patterns, and competitor analysis. Armed with geospatial insights, the retail chain strategically positioned new stores in high-growth areas with significant customer demand. As a result, the company experienced a substantial increase in sales revenue, expanded market share, and strengthened its competitive position in the retail landscape.

Use case 2: The challenge of a clothing boutique

A boutique clothing retailer struggled to attract foot traffic and increase sales despite offering high-quality products and personalized customer service.
Before the location analytics implementation:	After the location analytics implementation:
The boutique relied heavily on word-of-mouth marketing and local advertising efforts to drive store traffic. These strategies failed to effectively target the retailer’s ideal customer demographic.	The brand gained deeper insights into customer behavior, preferences, and purchasing patterns within specific geographic areas. Leveraging location-based insights, the retailer optimized its marketing campaigns, refined its product offerings, and adjusted pricing strategies to better align with local market trends. Consequently, the boutique experienced higher conversion rates and increased sales volume.

Use case 3: Underperforming supermarket store locations

A regional supermarket chain grappled with declining profitability and underperforming store locations amidst shifting consumer preferences and evolving market dynamics.
Before the location analytics implementation:	After the location analytics implementation:
The supermarket chain relied on historical sales data and gut instinct to manage its store network. However, this reactive approach often resulted in inefficient operations and missed growth opportunities.	GIS applications allowed the supermarket to gain actionable insights into customer demographics, shopping behaviors, and competitive landscape across its various locations. Armed with location intelligence, the chain optimized its store layouts using geospatial visualization, adjusted product assortments, and tailored promotional strategies to better meet the needs of local communities. As a result, the supermarket chain regained its position as a preferred shopping destination in its target markets.

Gathering, integrating, and processing diverse data sources is considered the most labor-intensive part of any successful use case. To access and utilize location data seamlessly across various business functions, retailers apply for data engineering services that help optimize data collection. processing, storage, and retrieval mechanisms, from inventory management to customer segmentation.

Implementation of Location Analytics

Successful examples of using spatial analysis inspire many companies to take a similar step. Solutions can be standard or customized; the principal factor in choosing one for many businesses is a long-term perspective and reliability.

The biggest players in the retail market, such as Amazon, Walmart, and Target, prefer custom GIS systems over off-the-shelf solutions, as they provide enhanced functionality, reliability, and scalability by tailoring the system to specific organizational requirements. They enable organizations to leverage unique data sources, customize analysis workflows, and stay ahead in today’s competitive retail landscape.

Where to start when implementing location mapping at your retail business? Lightpoint experts follow a step-by-step GIS implementation process:

1. Requirements gathering: The team collects your organization’s specific needs and objectives. Stakeholders from various departments are engaged to identify key requirements, including geospatial data sources, analysis capabilities, integration needs, and user preferences.
2. System design and planning: The team develops a comprehensive system design and implementation plan based on the gathered requirements. The scope of the GIS project is defined, as well as timelines and needed resources, including hardware, software, and data infrastructure.
3. Customization and development: Dedicated experts follow the requirements and design specifications to customize and develop the GIS solution tailored to your organization’s unique needs. This may involve developing custom functionalities, integrating existing IT systems and geospatial data analytics platforms, and ensuring compatibility with other business applications.
4. Testing and quality assurance: The team conducts thorough testing and quality assurance processes to validate the custom GIS solution’s functionality, performance, and reliability. Geospatial data integrity and system stability are tested. Experts check the integration with existing systems to ensure a seamless user experience and accurate results.
5. Deployment and integration: Finally, the team deploys the custom GIS solution into your existing IT and data analytics ecosystem following a well-defined rollout plan. The GIS system is integrated with existing databases, applications, and analytical tools to enable seamless data sharing, collaboration, and analysis across the organization.

2. Variety of sources and formats: Many businesses lack comprehensive data governance framework which automatically ensures data quality and standardization. 

Mitigation tip: Utilize data integration platforms that support various formats and provide tools for data validation, ensuring accuracy while handling diverse geospatial data sources.

3. Data privacy: Ethical guidelines for handling geospatial data containing individual identifiers limits many initiatives related to data tracking.

Mitigation tip: Implement anonymization techniques and encryption methods to protect sensitive information, and conduct regular audits to ensure compliance with data privacy regulations.

4. Intellectual property and licensing: Many companies do not clearly understand the terms of intellectual property rights and licenses associated with geospatial data sources.

Mitigation tip: Establish a comprehensive legal review process, and negotiate agreements with data providers to ensure compliance with licensing terms, avoiding legal pitfalls related to intellectual property.

Skilled experts from data analytics solution companies help handle data conflicts, rights, and licensing while extracting value from the data. Opt for an interactive platform with strong governance and flexibility to utilize multiple sources and custom filters for geospatial analytics.

Conclusion

Geographic data interpretation in the retail industry helps identify potential opportunities and challenges, providing valuable insights when combining location data analytics with other information. By harnessing the extended geospatial insights, retailers can:

• Identify optimal locations for new stores based on demographic trends, foot traffic patterns, and competitor proximity;
• Target their marketing campaigns more effectively by segmenting customers based on geographic preferences;
• Strategically identify better transportation routes and storage facilities to minimize transportation costs, reduce stockouts, improve inventory turnover rates, and more.

Custom GIS solutions empower organizations to gain deeper insights and drive innovation by leveraging the full potential of geospatial data within their operational workflows. To ensure the correct location data interpretation at your organization, schedule a consultation with a Lightpoint expert.