FinTech Competitor Conquesting: Capturing Market Share via Service-Line Bidding Architecture
Broad-match bidding drained a B2B FinTech's ad spend on unqualified retail traffic. We restructured the paid search architecture around service-line segmentation. Aggressive negative keyword governance then powered highly targeted competitor conquesting campaigns.
Executive Summary
Context
A $50M+ Enterprise FinTech SaaS provider wanted to take market share from legacy competitors. High-intent search channels were the route.
What We Built
A restructured Paid Media architecture featuring service-line segmentation, competitor conquesting tracks, and rigorous negative keyword governance.
Tech Stack
- Google Ads
- LinkedIn Ads
- Salesforce (for offline conversion tracking)
- SEMRush
Not a fit if your product keywords do not overlap with an unrelated consumer market; the negative keyword governance built here exists specifically to filter retail investors and job seekers out of a B2B FinTech funnel, and without that overlap there is little junk traffic to remove. It also assumes multiple distinct service lines to segment bidding around: a single, unsegmented offering gets no benefit from a service-line account hierarchy or competitor-specific bidding tiers.
The Challenge
The paid media structure leaned on broad category keywords, and junk clicks from unqualified personas piled up. High-traffic terms with no commercial intent drained ad spend, while specific competitor keywords went neglected. The account also lacked service-line segmentation, so the client couldn't accurately allocate budget to its most profitable software modules, and that missing granularity made it impossible to attribute pipeline value back to specific ad groups. The spend model prioritized click-through rate over contract value, with no visibility into which clicks actually drove pipeline.
Our Approach
We gave the account a total overhaul, starting with a deep-dive audit of competitor search volume and legacy platform weaknesses. The single-bucket budget approach gave way to a segmented model that treated competitor conquesting as a high priority. We built dedicated ad groups for every major incumbent in the broker-dealer space, backed by aggressive negative keyword lists that exclude non-commercial traffic, then restructured the account around the client's internal service lines. Bidding now varies with the specific LTV of each software module.
The account had been one national search campaign with no ad groups for its main service lines, which made it an overly broad container with no visibility into which line drove conversions. It became service-line ad groups (document management, client onboarding, straight-through processing) plus a separate conquesting campaign, and click-through rates on the restructured groups reached 3.26%. Two landing paths were tested on purpose: awareness traffic to an educational page, where average time on page ran above three minutes, and conversion traffic straight to the demo request. LinkedIn carried the persona layer, targeting operations and compliance titles by job function and industry while search caught the high-intent generic terms.
This engagement is part of our growth programs work for finance & insurance organisations.
Impact
Market Share Capture
The campaigns intercept high-intent traffic searching for legacy competitors. The client's modern SaaS alternative stands as the primary upgrade path.
Improved Lead Fidelity
Rigorous negative keyword governance cut ad spend waste. Retail investors and job seekers get filtered out of the B2B procurement funnel.
Granular Budget Control
Budget can now be moved between service lines on each line's own conversion data.
A tiered bidding structure isolates "Conquesting" terms from "Category" terms. We can bid aggressively on competitor-branded searches without inflating the CPCs for broader industry keywords.
The Google Ads account now mirrors the client's product architecture. Each service line runs as its own ad group with its own conversion goal, and conquesting runs as a separate campaign. The ROI of individual software modules is plainly visible.
A centralized negative keyword library was built specifically for the FinTech vertical. It holds thousands of excluded terms covering retail trading, career searches, and unrelated consumer financial services. The enterprise budget stays protected.
Broad category keywords used to drain spend on unqualified retail traffic. Without service-line segmentation, the client couldn't tie pipeline value back to specific ad groups. Now the account is built around the client's service lines. Each major incumbent in the broker-dealer space has its own ad group, and negative keyword lists exclude non-commercial traffic. Bidding varies with the LTV of each software module.
FAQ
We use a "Quality over Volume" filter. Competitor keywords only get bids when paired with high-intent modifiers, e.g., "alternatives to [Competitor]" or "[Competitor] pricing." That rule keeps us from paying for "informational" searches where the user just wants a competitor's login page or support docs.
Salesforce tracks the lead at the account level, but the paid media segmentation lets us identify the "entry point" product. That data is critical. It shows which software module has the shortest sales cycle or the highest initial conversion rate. We adjust front-end spend accordingly.
