Are You Expanding Into Markets Where You’re Doomed to Lose?

Companies often invest in new markets with high hopes—only to find themselves outpaced by stronger, more established competitors. Without clear insights into consumer behavior and competitor brand affinity, you could be pouring money into regions where your brand simply won’t stick.

📊 McKinsey reports that businesses using data-driven growth strategies experience 15–25% EBITDA growth. And Adobe says 51% of companies cite “identifying new market opportunities” as the top benefit of data-driven strategy.

It’s time to put your expansion plans on solid footing.

Why External Data Gives You the Edge

By combining third-party audience and competitive data with your internal sales and customer behavior insights, you can:

✅ Pinpoint white space opportunities where your brand can win
✅ Avoid costly investments in saturated or high-affinity competitor markets
✅ Reduce customer acquisition costs and increase brand stickiness
✅ Drive more efficient market penetration and sustainable growth

How It Works

🔹 Conduct white space analysis using competitor brand affinity & purchasing data
🔹 Use cluster analysis to segment markets based on loyalty, opportunity, and growth potential
🔹 Run simulations to prioritize the most profitable market expansion scenarios
🔹 Analyze intent signals and behavioral data to align messaging with unmet demand

Real-World Impact

L’Oréal used competitive audience intelligence to stay ahead of beauty trends and identify new product markets. By mining data across 3,500+ sources, they accelerated time-to-market and continuously gained ground in white space categories before competitors caught on.

📩 Want to stop guessing and start expanding with confidence?

Let’s talk about how external data can uncover your next best market.

👉 Talk to a Data Expert

Live Webinar

Is “Quality” Killing Your AI? Defining Data Fit for Strategic Success

February 18th, 2026 / 1:00 PM EST

Every data investment carries risk unless you know how to measure its “fit” for the mission. Many organizations assume that “high-quality” data is sufficient for AI and analytics, only to discover too late that data fit is the real determinant of success. In this live session from Blue Street Data’s Building with Better Data series, Andy Hannah and Malcolm Hawker unpack why data that works for BI can be dangerous for AI, leading to model failure, wasted spend, and lost trust. You’ll learn how to define, measure, and validate data fit so your models deliver reliable, business-aligned outcomes. Reserve your spot today!