Decoding what changes after $25M

At a glance

JLL's UK marketing team supports capital markets initiatives by helping uncover who the right buyers are, how they behave, and what differentiates high-value transactions from the rest.

They had a hypothesis: deals above $25M are fundamentally different. Different buyers. Different behaviors. Different signals. But they couldn't prove it.

With 18 months of closed deals sitting in their CRM, they had data — but not the structure to analyze it. Jeeva enriched 700 leads across 66 data columns, scored them by relevance, and gave the team what they'd never had before: evidence-based segmentation.

"We stopped guessing which deals were different. Now we can see exactly what changes when you cross the $25M line."

—Emma Richardson, Head of Marketing Analytics, JLL UK

The challenge: assumptions without evidence

As deal values scale, assumptions creep in. Everyone on the team had theories about what made bigger deals different. But no one could prove them.

"We'd sit in pipeline reviews and say things like 'this feels like a $30M buyer,'" recalls James Park, a senior marketing strategist on the team. "But what does that even mean? We had no framework for it."

"We had 18 months of deals in the CRM. But when I tried to compare the $20M transactions to the $40M ones, I couldn't find any meaningful patterns. The data was too shallow."

James Park, Senior Marketing Strategist, JLL UK

The team wanted to analyze closed deals from the past 18 months to understand whether transactions above $25M show materially different characteristics. But the challenge wasn't a lack of data — it was structure and depth.

Without this, any conclusions about deal behavior would remain speculative.

Why Jeeva

The team needed more than a data vendor. They needed enrichment that went deep enough to reveal behavioral patterns — not just fill in missing fields.

"We talked to a few providers," says Richardson. "Most of them could give us emails and phone numbers. But we needed investment history. Net worth signals. Affiliation data. The kind of depth that actually tells you something about how a buyer thinks."

"We didn't need more contact data. We needed to understand how $40M buyers behave differently than $20M buyers. That requires a completely different level of enrichment."

Emma Richardson, Head of Marketing Analytics

Jeeva's approach stood out for three reasons:

1. 66 columns of depth. Not just contact info — investment profiles, financial signals, affiliations, property histories, and behavioral patterns.

2. Scoring and ICP extraction. Every lead scored on relevance and profile strength, making it possible to compare apples to apples across segments.

3. Analysis-ready output. The data came back structured for segmentation, not just stored in a spreadsheet.

Going live

Jeeva enriched all 700 leads across six categories:

Contact & Personal Information: Name, age range, email, phone, LinkedIn

Corporate & Professional Profile: Company, type, size, sector, roles, partners

Real Estate Investment Profile: Holdings, property types, geography, acquisition/sale history, LLCs, permits, 1031 activity

Financial & Asset Profile: Net worth tier, asset ownership, SEC filings

Affiliations & Public Engagement: Associations, memberships, philanthropy, publications, litigation

Lead Scoring: Relevance score based on ICP alignment and profile strength

With enrichment complete, the team could finally run the analysis they'd been planning for months.

The "aha" moment

Park remembers the first time he opened the enriched dataset.

"I filtered by deal value and started looking at the patterns. Within an hour, I found three things we'd never seen before."

"Buyers above $25M were three times more likely to have multiple LLCs. That's not a coincidence — it's a signal. And we never would have found it without this level of enrichment."

—James Park, Senior Marketing Strategist

The analysis revealed clear differences between segments:

"We finally had the evidence," says Richardson. "Not theories. Not gut feel. Actual patterns we could build strategy around."

The outcome

The analysis changed how JLL UK's marketing team approaches campaign strategy.

Instead of generic outreach, they now segment by deal-value profile — using the signals Jeeva uncovered to tailor messaging, timing, and channel mix.

"We used to treat every lead the same. Now we know: if they have multiple LLCs and 1031 history, they're probably a $30M+ buyer. We market to them differently."

James Park, Senior Marketing Strategist

The team has already applied these insights to two major campaigns:

• High-value buyer nurture: A dedicated track for leads showing $25M+ signals, with longer-form content and direct broker introductions.

• Mid-market acceleration: A faster-moving track for sub-$25M profiles, optimized for speed and volume.

"The ROI isn't just in better targeting," Richardson adds. "It's in not wasting time on leads that were never going to close at the price point we needed."

What's next

The team is now planning a follow-up analysis focused on deal velocity — understanding whether higher-value deals also take longer to close, and what signals predict faster movement.

"This was just the first question," says Park. "Now that we have the data infrastructure, we can ask a hundred more. That's the real unlock."

"The difference between a $20M deal and a $40M deal isn't just size. It's who's involved, how they invest, and what patterns they follow. Now we can see all of it."

—Emma Richardson, Head of Marketing Analytics