DataOx

We help real estate teams build external data pipelines that actually feed their analytics stack. DM to see what that looks like for your market. data-ox.com/?utm_source=X&…

The global PropTech market is expected to grow from $34.4B in 2025 to $40.4B in 2026. But 67% of PropTech implementations fail to deliver expected ROI — due to poor planning and execution. Why? Read in thread ↓ #techdata #marketdata #scrapedata #datanews

Ask yourself: → What external data sources feed your current PropTech stack? → Is that data updated weekly — or whenever someone remembers to export it? → Are your decisions data-driven — or data-decorated?

Properties using AI-driven lease administration report 14% improvement in rent collection rates and 31% reduction in lease-related disputes. That's not the AI doing the work. That's what happens when AI has complete, accurate data to work with.

The firms getting ROI do this first: → Map which external data sources actually drive decisions → Build structured feeds from listing platforms, permit databases, and market indices → Then layer AI and analytics on top of clean, fresh inputs

Most PropTech implementations fail for the same reason: → Buy the platform → Connect existing internal data → Wait for insights

DataOx collects property and listing data from Zillow, Redfin, MLS, and public records. DM if you want to see what that looks like. data-ox.com/?utm_source=X&…

PwC & ULI Emerging Trends 2026: 89% of real estate firms analyze less than 20% of their available data. Business owners using advanced PropTech analytics report 34% improvement in investment decision accuracy and 41% faster deal closure times. Read in thread ↓ #PwC #realestate

Ask yourself: → Are permit filings in your target market part of your weekly data feed? → Do you know which property types major players are quietly exiting right now? → Is your market intelligence automated — or dependent on someone manually checking?

The number of commercial real estate companies running AI pilots went from 5% to 92% in only three years. The race isn't about having AI. It's about what data you feed it.

The firms closing faster do this: → Monitor permit filings weekly by zip code → Track days-on-market shifts before they move prices → Watch competitor portfolio exits — quietly, in public data

Most real estate investors approach market intelligence like this: → Review MLS data when evaluating a deal → Check Zillow estimates before making an offer → Read market reports published last quarter

We collect competitive data for AI SaaS teams — changelogs, job signals, reviews. DM if relevant. data-ox.com/?utm_source=X&…

McKinsey: 78% of organizations now use AI in at least one business function. That means AI is no longer your differentiator. The data your AI runs on - is. Read in thread ↓ #McKinsey #researchdata #AI #scrapedata #aidata #businessdata #B2B

Ask yourself: → When did you last collect structured data on competitor feature releases? → Do you know which integrations competitors added in the last 90 days? → Is your training data reflecting the market as it is — or as it was 6 months ago?

Over half of HR leaders report AI has increased efficiency — and 46% say it's contributed to more innovation. But AI products trained on stale competitive data produce stale strategic decisions. Freshness isn't a nice-to-have. It's the product.

The teams building defensible products do this: → Scrape competitor changelogs and docs monthly → Track job posting clusters to predict roadmap shifts → Collect unfiltered user feedback from competitor app store reviews

Most AI SaaS teams build product intelligence like this: → Read competitor blog posts → Check G2 reviews quarterly → Monitor Product Hunt launches

DM us to see what daily price and product monitoring looks like. data-ox.com/?utm_source=X&…

Global retail e-commerce is forecast to reach $6.88 trillion in 2026. At that scale - retailers fully embedding AI into their customer journey report 15–25% revenue increases. Read in thread ↓ #ecommerce #researchdata #scrapedata #Aidata #retail

Ask yourself: → Do you know if a competitor changed their pricing in the last 48 hours? → Are you tracking which SKUs they're quietly removing? → Is your product intelligence updated daily or whenever someone checks manually?