Retail Is Being Repriced by Data
Retail real estate is no longer constrained by demand. It is constrained by visibility and execution.
Across the United States, retail fundamentals have stabilized. Vacancy in high-performing centers is compressing. Leasing activity has returned. Capital is selectively re-entering the sector. Yet performance dispersion between assets has widened materially.
The underlying issue is structural. Retail remains one of the only institutional asset classes where the primary driver of value, tenant sales, is not directly observable. Leasing decisions continue to be driven by fragmented data, broker relationships, and lagging indicators.
This is beginning to change.
A new category of proptech platforms is emerging, focused on two core problems: understanding tenant performance in real time and executing leasing decisions with speed and precision. At the center of this shift are CenterCheck, which provides transaction-level retail intelligence, and Dan AI, which systematizes leasing workflows through AI-driven matching and execution.
Together, they represent a broader transition in retail, from intuition-led decision-making to data-driven operating models.
The Market Context: A Large Asset Class Operating with Limited Transparency
Retail real estate represents approximately $4–5 trillion in asset value in the United States alone. Despite its scale, the sector remains operationally fragmented.
Owners and operators face four persistent constraints:
Limited visibility into tenant-level revenue performance
Reliance on proxy data such as foot traffic and demographic estimates
Manual, broker-led leasing processes
Extended deal cycles with inconsistent outcomes
This lack of transparency creates inefficiencies across the value chain. Leasing decisions are made without direct insight into tenant productivity. Acquisitions are underwritten using assumptions rather than verified performance. Asset management strategies rely on historical reporting rather than forward-looking indicators.
As capital costs increase and margins compress, these inefficiencies are becoming more difficult to absorb.
A Structural Shift: From Proxy Metrics to Transaction-Level Insight
The first major shift underway in retail is the move from proxy-based analytics to transaction-based truth.
For the past decade, location intelligence platforms have dominated retail analytics. Foot traffic became the industry standard for evaluating asset performance. While useful, it remains an indirect measure.
CenterCheck is built on a different foundation. By leveraging anonymized credit and debit card transaction data, the platform estimates store-level sales performance across the U.S. This allows landlords, brokers, and investors to directly assess tenant productivity.
The implications are immediate.
Leasing teams can identify underperforming tenants before lease expiration. Acquisition teams can validate underwriting assumptions with real data. Portfolio managers can benchmark assets based on revenue generation rather than occupancy alone.
This shift effectively reframes retail as a measurable asset class. Instead of asking whether a center is active, owners can quantify how much economic value it is producing.
In an environment where NOI growth is increasingly constrained, this level of precision becomes a competitive differentiator.
Execution Is the Bottleneck: Leasing Moves from Relationships to Systems
Improved visibility alone does not solve the problem. The second constraint in retail is execution.
Leasing remains one of the most manual and relationship-driven processes in commercial real estate. Tenant discovery, matching, and deal progression are fragmented across brokers, spreadsheets, and internal systems. Time to lease remains extended, particularly in mid-market and secondary locations.
Dan AI is positioned at this point of friction.
The platform combines data and workflow automation to help leasing teams identify tenants, match them to available spaces, and move transactions forward more efficiently. Rather than replacing brokers, it introduces structure and repeatability into the leasing process.
This reflects a broader pattern seen across industries. Markets that were historically relationship-driven, including capital markets and logistics, have transitioned toward system-based execution.
Retail leasing is now following a similar trajectory.
The impact is measurable. Reduced vacancy downtime, improved tenant selection, and faster deal cycles directly translate into higher asset performance.
The Emerging Stack: Decision Intelligence and Execution Infrastructure
The evolution of retail proptech can be understood as the development of a two-layer stack.
The first layer is decision intelligence. Platforms like CenterCheck provide visibility into what is happening at the asset and tenant level. They enable better decisions by improving the quality and timeliness of data.
The second layer is execution infrastructure. Platforms like Dan AI operationalize those decisions by enabling leasing teams to act quickly and efficiently.
This mirrors the development of other data-intensive industries. Financial markets combined real-time data platforms with execution systems. Residential real estate paired listing data with transaction platforms.
Retail is now building its equivalent.
The convergence of these layers creates a feedback loop. Better data leads to better decisions. Better execution reinforces performance. Over time, this compounds into a structural advantage for early adopters.
Adjacent Players: Expanding but Not Replacing the Core Shift
A number of established and emerging platforms are adjacent to this transformation.
Placer.ai remains a leader in foot traffic analytics, providing valuable insights into visitation patterns. Buxton continues to serve as a key player in customer analytics and site selection. CoStar Group has expanded its retail data coverage, while VTS is extending its leasing and asset management capabilities into retail.
These platforms contribute to the broader ecosystem. However, they do not fully address the two core gaps: transaction-level revenue visibility and systematized leasing execution.
CenterCheck and Dan AI are differentiated by their focus on these specific constraints.
Retail is a behavior-driven asset class. It requires data models that capture consumer spend and systems that enable rapid execution. Horizontal platforms have historically struggled to address this level of specificity.
Why Now: Convergence of Economic and Technological Forces
The timing of this shift is not coincidental. Three factors are converging.
First, margin compression is forcing owners to focus on operational efficiency. Higher interest rates and rising expenses have reduced the margin for error.
Second, data availability has improved materially. Transaction data, once difficult to access, is now being aggregated and anonymized at scale.
Third, AI adoption is accelerating across commercial real estate. Owners and operators are increasingly willing to deploy AI tools that deliver measurable outcomes, particularly in areas such as leasing, underwriting, and asset management.
Together, these forces create a clear inflection point. The industry is moving beyond experimentation and toward implementation.
Market Size: A Large and Underpenetrated Opportunity
The total addressable market for retail-focused data and leasing platforms spans multiple categories.
Retail analytics and data platforms represent an estimated $5–8 billion global market, driven by demand for location intelligence, performance benchmarking, and site selection tools.
Leasing and brokerage technology represents a larger segment, estimated at $10–15 billion, encompassing CRM systems, deal management platforms, and workflow tools.
Beyond these categories, a broader market is emerging around AI-driven decision-making in commercial real estate. This includes underwriting automation, portfolio optimization, and predictive analytics, and is expected to exceed $20 billion over time.
CenterCheck and Dan AI operate at the intersection of these segments. As their capabilities expand, they are likely to capture value across multiple layers of the stack.
Implications for Owners, Operators, and Investors
Retail real estate is transitioning from a static asset class to a dynamic operating business.
Performance will increasingly be determined by the ability to:
Access accurate, real-time data on tenant performance
Translate that data into actionable leasing decisions
Execute transactions with speed and consistency
This has direct implications for capital allocation. Assets that are actively managed using data-driven systems are likely to outperform those that rely on traditional approaches.
It also has implications for organizational structure. Leasing teams will need to integrate technology into their workflows. Asset managers will need to interpret and act on real-time data. Investment teams will need to incorporate new forms of intelligence into underwriting.
The gap between leading and lagging operators is likely to widen.
What this means for proptech
Retail real estate is not being disrupted by e-commerce. It is being restructured by data.
The emergence of platforms like CenterCheck and Dan AI signals a broader shift in how the industry operates. Visibility into tenant performance and systematization of leasing are no longer optional capabilities. They are becoming foundational.
For decades, retail value was inferred through indirect signals. That model is no longer sufficient.
The next phase of the market will be defined by those who can measure performance directly and act on it quickly.
That is where the advantage will be created.
