What Are Retail Sales Estimates?
Retail sales estimates combine mobile, transaction, and demographic data to approximate store-level sales when direct data isn’t available.
They help retailers, investors, real estate professionals, and civic planners understand performance, benchmark locations, and identify new opportunities – all without needing access to proprietary sales figures.
When Do You Need Retail Sales Estimates?
Sales estimates are essential when:
- A retailer wants to assess its own performance against competitors.
- A property owner or lender needs insight into tenant strength.
- An investor or financial analyst is modeling cash flows, margins, or credit risk.
- A civic organization is evaluating anchor businesses and local economic activity.
When actual sales data isn’t accessible, alternative datasets – such as location analytics and aggregated transaction data – can fill the gap to produce reliable, near real-time estimates.
How Different Stakeholders Use Retail Sales Estimates
1. Retailers – Benchmark and Identify Market Potential
- Competitive Benchmarking: Compare store performance against peers or industry averages using sales estimates.
Example: Walmart location benchmarks performance compared to other superstores.
- Market Potential: Spot stores with above-average sales productivity or room for optimization.
Example: A grocery chain compares a location’s estimated sales per square foot to regional and nationwide averages in order to prioritize investment in high-performing markets.
2. CRE Professionals – Strengthen Leasing and Portfolio Strategy
- Void Analysis & Tenant Mix Optimization: Combine void analysis with sales estimates to identify high-performing tenants.
Example: A shopping center compares sales estimates across home improvement retailers to attract the right anchor tenant.
- Lease Negotiations: Gain transparency into tenants’ revenue potential to support rent pricing and term discussions.
Example: A shopping center looks at its tenants’ sales metrics to understand performance and drive lease negotiations
3. Franchisors – Support Operations and Performance Management
- Franchise Visibility: Evaluate location-level performance to inform operational support, marketing strategies, and contract renewals.
4. Finance Professionals – Enhance Forecasting and Valuation
- Sales Forecasting: Feed estimated sales into models for revenue and margin prediction.
- Creditworthiness: Use estimated sales to assess repayment capacity for loans or financing.
- Market Valuation: Evaluate private or pre-IPO retailers’ financial health prior to investment.
5. Civic and Economic Development Teams – Strengthen Local Economies
- Anchor Identification: Identify top-performing retailers driving foot traffic and community spending.
- Regional Monitoring: Track shifts in consumer demand and retail vitality across neighborhoods or cities.
Example: A local development office uses sales estimates to gauge the performance of local retailers, identify anchor businesses within their communities, and strengthen economic development planning.
How Are Retail Sales Estimates Calculated?
There are multiple methods for estimating sales, from traditional observation to AI-driven analytics:
- Observation-Based Counts – Manual traffic counts multiplied by average transaction size.
- Market Research & Surveys – Consumer spending self-reports.
- Financial & Public Data – Segmenting company-reported revenue by geography or category.
- Benchmarking – Comparing known competitors’ performance in similar trade areas.
- Supplier & Distributor Insights – Estimating product flow through supply partners.
- Credit Card Data – Aggregating anonymized transactions by merchant and region.
- Location Intelligence (AI-Enhanced) – Combining foot traffic analytics with transaction data to model sales volume, basket size, and revenue per visit over time.
Modern AI and machine learning models integrate multiple datasets to deliver store-level, category-level, or region-level sales estimates, enabling accurate, scalable insights across markets.
Placer.ai’s Retail Sales Report
Placer.ai’s Retail Sales Report merges visitation analytics with third-party transactional data to generate precise sales estimates for retailers and properties across categories – including grocery, restaurants, home improvement, and more.
Users can:
- View sales estimates by location, square footage, or ticket size.
- Identify trends in customer spending over time.
- Assess performance at the store, chain, or market level.
Case Study: How Sales Estimates Drove Financing Confidence
A grocery-anchored retail center in Staten Island sought to refinance $22M but lacked tenant sales data. By leveraging Placer.ai’s Retail Sales Report, Northmarq Capital demonstrated strong tenant performance, enabling the client to secure the loan.
Key Takeaways
Retail sales estimates provide visibility where direct data is limited, empowering:
- Retailers to benchmark performance.
- Landlords and CRE teams to attract high-performing tenants.
- Investors and lenders to model returns and risk.
- Civic leaders to track economic health.
By combining location analytics and alternative data, solutions like Placer.ai deliver actionable insights that drive better investment, leasing, and operational decisions across the retail ecosystem.
Discover how Placer.ai’s Retail Sales Report can enhance your retail intelligence here.
