Key Insights For Retail Real Estate Strategy In 2026

I got to speak with Gregg Katz, VP at ESRI and a well known player in the retail real estate industry. I’ve known Gregg for years, working with him at TSCG & RetailStat. I knew that Gregg gets retail and data. In this episode I found out why. His experience owning and operating his own restaurant chain gave him a unique viewpoint.

You are going to love this one. Insights from our conversation below:

The Past Rhymes - Insights From Nostalgic Retail Series: Gregg includes in his series The Corner Of Main & Main a nostalgic and defunct retailer. It is both a retro graveyard and a treasure trove of retail lessons learned. I asked Gregg, whom we might call a sort of retail mortician, what are the most common causes of death?

1. Over Expansion: Often death does not occur because demand dries up. It's because they expanded faster than their systems or unit economics could handle. Investment amplifies cracks before it creates scale.
2. Drifting from the core: Innovation is great - but abandoning what made customers choose you in the first place is fatal. The inverse example of this is Chili’s who’s success came from cutting 25% of their menu and centering around their $10.99 meal before they hit scale with the viral Chili’s cheese pull.
3. Category thinking blinds you from competition: Retailers obsess over direct competitors while not realizing when share erodes from “outside category”. Bringing in Chili’s again - McDonalds and Wendy’s were likely paying more attention to each other while Chili’s launched their similarly priced Big Smasher burger.

Aging Is The Most Underrated Retail Trend Of 2026: Retail is chasing sexy instead of solvent. Gen X is overlooked. They spend both up (parents) and down (kids) - blending the needs of multiple generations under one spender. And by the way - most of the wealth in the country is in the boomer and Gen X households. Look for retailers and landlords building places that work for multiple generations at once.

Write the question before you buy the data: As someone who was early to mobile, transaction, and segmentation Gregg has learned how to separate the signal from the noise in buying data for retail decisions. And it all centers around one idea: name the question it answers before you buy the data. This helps you separate the “interesting” from “useful”, and most importantly replaces FOMO as a data strategy. As Gregg says “FOMO is a terrible data strategy.”

Triangulation Beats Silver Bullets: Gregg describes the real edge in retail analytics as a recipe, like combining mobility, segmentation, transactions, and local broker knowledge. Instead of touting a singular metric, successful teams are honest about what each ingredient can and can’t do - and analyze accordingly. He in many ways pioneered frontline use of mobile data for retail analysis back when people were skeptical. Now he has to play defense, reminding people not to rely on mobile data alone.

Regulatory changes in mobile data: Privacy regulation in Maryland and Oregon have prohibited the sales of precise geolocation data within 1,750 feet. We don’t know how this will play out nationally yet, but in the short term if mobile data providers brush questions about this pay attention. However, even less granular mobile data will still be more accurate than radius rings.

Transaction data is ripe for misuse: Gregg and I touch on this. There is a lot of potential in credit card transaction data. It gets at the real purchase, not just the visit right? Gregg warns on using it out of context with other datasets. And now these are my words here - I can already see where I have to be more diligent in educating around transaction data. Unlike mobile data, transaction data is very hard to tag to a specific retail store. Some panels are nicely demographically balanced, but you have to watch closely for geographic balance of specific participating banks. And just because a segment of the population has a high ticket size for a brand does not mean it is the ideal segment for a brand - they might only be a small slice of the brands sales - revenue share is a much more trusty metric.

You can’t outsource the question - even to AI: Today AI and data can accelerate analysis, but it can’t decide what matters. If you outsource your decision to an estimated number, or your questions to a model, you are letting the tool define your future. Retailers and CRE folks who win will not outsource questions to AI.

The Consumer Code Conclusion

I’ll never forget the ICSC research conference where Gregg promised me that he would be one of our toughest customers and biggest advocates. He delivered on both.

He applies that same rigor to his thinking in real estate. If you’d like to hear more from Gregg, he is very active on Linkedin - you can find his newsletter here: https://www.linkedin.com/newsletters/7262906814252437504/