Breaking down data silos to generate retail insights

The commercial real estate (CRE) sector has long thrived on gut instinct and historical experience. But today, the rules have changed – data is taking centre stage.

According to Daniel Lorenzo, International Director of Specialty Leasing & Marketing at Nhood, data and retail insights have evolved from a back-office reporting tool into a strategic weapon.

“Data is becoming more and more crucial for companies like us…It helps us advise clients, anticipate tenant needs, and make decisions with confidence.”

This shift isn’t just about improving operations, it’s about mitigating risk and forecasting the future of retail, mixed-use, and office spaces across geographies. But data modelling and forecasting are by no means a new concepts, these processes have been in place for years, resulting in ecosystems with floods of data sources and a torrent of dashboards.

Perhaps it’s time to review the data we capture and re-visit the business case to ask; which data sources have real world application?

To address this, Nhood is building a global ecosystem of digital solutions that can unify retail insights across borders. A move that’s crucial not just for streamlining operations, but for surfacing macro-level market trends.

“Thanks to data, we can learn from one market and apply those insights elsewhere,” Lorenzo says. “It becomes a common language.”

This also raises an interesting point. Yes, there is an issue of technical connectivity and system fragmentation, but there’s also the issue of human connectivity and human integration. In a global business that spans 11 countries the cultural differences will be nuanced, which can present challenges. Perhaps, in this case, data can be the unifying language that breakdown barriers. A common language.

ESG, consumer trends, and the power of real-time retail data

Lorenzo highlights that not all data is created equal. When it comes to what matters most, it’s the consumer who sits at the centre.

“If we can understand the consumer, we can foresee new habits and trends,” he says. “That gives us the key to make the best decisions—not only for landlords, but also for tenants.”

Brands are already capitalizing on this with in-store loyalty programs and digital touchpoints. Shopping centres, however, often lag behind due to fragmented data ownership and lack of integration with tenant systems.

“The shopping mall and the brands should be working together on loyalty,” Lorenzo says. “But there’s a commercial tug-of-war. The brands want to own the data—and the opportunity.”

This tension underscores a deeper issue: data silos and a reluctance to share information across stakeholders. “It’s not about privacy concerns,” Lorenzo clarifies. “It’s about ownership. Everyone wants to hold onto their piece of the pie.”

This strategy isn’t accidental. With less focus on in-store inventory, retailers are doubling down on immersive brand storytelling, showroom-style layouts, and high-touch service. All aimed at data capture, generating owned retail insights and customer loyalty.

“It seems like the strategy of many retailers is to push online sales through these flagship locations,” says Lorenzo. “They want the data, they want the engagement, and they want to control the narrative.”

 This shift presents both a challenge and an opportunity for landlords and shopping centre operators. To stay relevant, they must evolve from passive hosts to active collaborators, integrating their own data strategies with those of their tenants to create a seamless, insight-rich environment. A win win for all stakeholders.

AI data analysis and forecasting

For Nhood, the future of data isn’t just about collecting more, it’s about doing more with what’s collected. That’s where artificial intelligence comes in and true retail insights are developed.

As part of their transformation, Nhood is looking to new technologies that unify data across departments and provides a single source of truth.

“We want to increase efficiency and be more customer-oriented,” Lorenzo says. “Less time spent looking for information, more time using it.”

While AI’s immediate impact lies in data analysis and reporting, Lorenzo sees the future in predictive intelligence.

“Data is the foundation. But thanks to AI, we can take it to the next level—forecasting trends, identifying risks, and unlocking opportunities before they arrive.”

One tech ecosystem, many stakeholders, shared data access

So what’s the missing piece of the CRE data puzzle? It’s not the technology, Lorenzo suggests – it’s the people.

“We need internal alignment. We need buy-in from decision-makers. And we need stronger collaboration between landlords, tenants, and retailers.”

For now, Nhood is doubling down on partnerships to help them interpret external market data, beyond their internal performance. “It’s difficult to do alone,” Lorenzo admits.

“But with the right alliances, we can better understand the market evolution.”

See also

• eBook: Commercial real estate data – Are you operating in data darkness?
• Podcast: CRE Corner Ep#2 | Mark Bruce – The role of retail data

FAQ

What are the different types of retail analytics?

Retail analytics includes various data analysis types to improve decision-making. Descriptive analytics reveals what has happened, like sales trends. Diagnostic analytics explains why something occurred, such as a drop in foot traffic. Predictive analytics forecasts future outcomes like demand or customer churn. Prescriptive analytics suggests actions like pricing or staffing changes. Other key areas include customer analytics (behavior and loyalty), sales/product analytics (SKU performance), operational analytics (inventory and efficiency), and location/footfall analytics (in-store behavior). Together, these insights help retailers optimize everything from marketing and merchandising to store layout and supply chain.

What are the 4Rs in retail?

The 4 R’s in retail refer to four critical pillars that drive retail success:

Right Product – Offering products that meet customer demand and preferences.

Right Place – Making products available at the right locations, whether in-store or online.

Right Time – Ensuring product availability when customers want them, aligned with seasons, trends, or promotions.

Right Price – Setting competitive, value-driven prices that attract customers while maintaining margins.

Together, these ensure efficient operations, satisfied customers, and strong sales performance.