About Fintech SME Credit Platform
the fintech lender is one of the UK's leading fintech lenders, providing fast, flexible credit facilities to small and medium-sized businesses. Founded in 2012, it has extended credit to over 100,000 businesses across the UK and Europe, offering revolving credit facilities and term loans designed to operate at the speed and simplicity that traditional banks structurally cannot match. Its underwriting model leverages open banking data and real-time business performance signals rather than lagged credit file data alone.
The UK SME lending market remains significantly underserved by high-street banks, with post-2008 regulatory constraints and legacy infrastructure continuing to limit their appetite for small-ticket business lending. Fintech lenders have captured meaningful market share by competing on speed, decisioning transparency, and applicant experience — but the majority of this volume has been won through broker networks and inbound search, leaving direct outbound to founder and finance director audiences largely untapped as an acquisition channel.
the fintech lender's growth had been built on strong broker relationships and high-intent paid search — channels that efficiently captured businesses already in the market for credit. But a structural gap existed in the funnel: businesses approaching a working capital need but not yet searching for solutions were invisible to these channels. Reaching founders and finance directors before need crystallised into an active search — and before they defaulted to their bank relationship or a competitor — required a direct outbound capability that the fintech lender had not previously built.
Executive Summary
Over eight months, the programme generated 106 direct SME founder and finance director conversations, producing 38 direct credit applications at a cost-per-application 44% below the comparable PPC acquisition cost. LinkedIn acceptance rates reached 34% and cold email reply rates held at 16%, both materially above sector benchmarks. The programme also delivered a strategically valuable secondary outcome — refined ICP credit appetite criteria based on real application and conversion data from directly sourced SMEs.
What needed
to change.
the fintech lender's growth was constrained by broker referral networks and PPC budgets with no direct outbound reaching SME founders before they turned to traditional banks or competing fintech lenders.
Growth was limited to self-identified inbound leads — founders who were already actively searching for credit — missing the larger opportunity of SMEs who weren't yet in market but were approaching the working capital need.
A direct outbound channel to SME founders and finance directors would reach a segment that was underserved by existing channels and highly responsive to the positioning of working capital as a growth tool rather than a crisis response.
How we built the solution.
Every LVRA engagement runs through four structured phases — each one feeding the next.
Discovery & Audit
Phase 01
We began by analysing the fintech lender's existing acquisition channel mix — mapping broker referral volume, PPC conversion rates by keyword category, and organic search performance — to establish a baseline cost and conversion model that a direct outbound channel would need to match or improve on. PPC cost-per-application data by sector and business size provided the benchmark against which outbound economics were ultimately measured.
We interviewed the fintech lender BDM and underwriting staff to understand the profile of the highest-quality, highest-LTV customers acquired through existing channels — identifying the business characteristics, growth signals, and timing indicators that correlated with fast decisioning, full drawdown, and repeat facility usage. This data shaped the ICP definition for the outbound programme more precisely than firmographic targeting alone could achieve.
We audited the fintech lender's existing content assets and messaging across broker-facing and direct SME channels, assessing how the working capital proposition was framed relative to competitive alternatives. The audit confirmed that most existing messaging addressed credit need as a crisis response rather than a growth enabler — a framing that was factually accurate but commercially limiting, attracting distressed borrowers rather than growth-oriented businesses with stronger credit profiles.
Market Intelligence
Phase 02
We defined the target ICP as UK businesses between two and seven years old with turnover between £200K and £3M, prioritising sectors with predictable working capital cycles — wholesale trade, manufacturing, professional services, and construction. Growth signals including hiring activity, premises expansion posts, new contract announcements, and trade fair participation were identified as the most reliable LinkedIn-detectable proxies for approaching working capital requirement.
Research into founder and finance director decision-making dynamics revealed a consistent pattern: founders evaluate credit speed and simplicity against relationship continuity with their bank, while finance directors evaluate total cost of facility, drawdown flexibility, and reporting burden against finance committee approval thresholds. This distinction required completely separate messaging architectures for each audience — a finding that became a structural principle of the outreach programme design.
We benchmarked the fintech lender's direct-to-SME proposition against three fintech competitors and high-street SME lending products, identifying the specific decision dimensions where the fintech lender held demonstrable advantages — decisioning speed, open banking underwriting accessibility, and revolving facility flexibility. These became the core message pillars for cold email and LinkedIn outreach, with each pillar supported by specific performance claims rather than generic faster-simpler-better positioning.
Strategy Design
Phase 03
The outreach programme was structured across two parallel tracks: a founder-focused LinkedIn authority and direct message programme, and a finance director cold email cadence. Founder messaging framed working capital as a growth acceleration tool — specifically the ability to move faster on supplier terms, inventory investment, and project mobilisation than would be possible with existing cash flow alone. Finance director messaging led with cost of facility transparency, drawdown speed versus bank overdraft renewal timelines, and FD-defensible underwriting documentation.
LinkedIn targeting parameters were set to identify founders of businesses matching the ICP definition who had posted growth signals in the preceding 30 days. Connection notes referenced specific business context drawn from their recent activity — new contract congratulations, hiring post acknowledgement, or sector-specific working capital commentary — to demonstrate relevance before any product reference was made. A separate LinkedIn sequence for finance directors at the same target businesses followed a more functional, ROI-led format.
Cold email sequences were six-touch, 22-day cadences with subject line and opening personalised to sector and business stage. Each sequence included a mid-cadence case study touchpoint — a two-paragraph account of how a comparable business had used an the fintech lender facility to capture a time-sensitive growth opportunity — designed to shift the frame from credit product to business outcome. A direct application CTA was reserved for touch four onwards, following two credential-building touches.
Launch & Optimise
Phase 04
The programme launched with a 200-contact pilot targeting founders in wholesale trade and professional services — sectors where working capital cycle patterns were most predictable and growth signal detection was most reliable on LinkedIn. Pilot results at week four confirmed message resonance: a 16% cold email reply rate and 34% LinkedIn acceptance rate among signal-identified founders provided sufficient statistical confidence to expand to the full ICP database across all five target sectors.
Weekly optimisation cycles reviewed reply rate, application rate, and facility approval rate by sector, business age band, and message variant. The insight that businesses in years three to five had materially higher application-to-approval rates than those in years one to two led to a mid-programme ICP refinement — shifting outreach weighting toward the more established segment and improving overall cost-per-approved-application metrics.
At month five, a finance director re-engagement sequence was introduced for founder-contacted businesses where the founder had engaged but not applied — routing follow-up to the finance director contact at the same business using a different message architecture focused on facility cost modelling and drawdown process documentation. This cross-contact sequencing produced an incremental 14 applications from businesses that had not converted through founder outreach alone.
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How it was built, channel by channel.
ICP Definition & Growth Signal Detection
The target ICP was defined across five firmographic dimensions — business age, turnover band, sector, headcount trajectory, and geography — and enriched with a sixth behavioural dimension: the presence of detectable growth signals on LinkedIn within the preceding 30 days. Signal categories included hiring announcements, premises expansion references, new contract wins, export market entries, and trade fair participation.
Growth signal detection was operationalised through a daily LinkedIn monitoring workflow, with identified prospects entering the outreach sequence within 48 hours of signal detection. The time-bounded approach to signal-triggered outreach was critical — working capital relevance diminishes rapidly once a growth event has been announced and early financing decisions made, making fast follow-up a structural requirement rather than a best-practice recommendation.
Dual-Track Founder and FD Outreach
Founder outreach was designed as a LinkedIn-led programme with email as the secondary channel — reflecting founders' higher LinkedIn engagement and lower tolerance for unsolicited email. Finance director outreach was structured as email-led with LinkedIn as the secondary channel — reflecting FDs' preference for detailed, documented communication over social platform engagement.
Message content was architecturally separate for each audience. Founder messaging used outcome-led language centred on commercial opportunity acceleration. Finance director messaging used process-led language centred on facility mechanics, drawdown documentation, and cost modelling. Testing confirmed that cross-applying either message architecture to the wrong audience produced significantly lower engagement — validating the investment in separate content development for each track.
Working Capital as Opportunity Framing
A core creative and strategic decision was to reframe the the fintech lender proposition from credit facility to commercial velocity tool — specifically the ability to act faster on growth opportunities than internally-funded cash flow would allow. Case study content was produced to illustrate this frame across four sectors: a wholesale trade business capturing a bulk-buy margin opportunity, a professional services firm mobilising a new client project ahead of invoice receipt, a manufacturer bridging a component supply lead-time, and a construction company managing subcontractor payment timing.
This reframing produced measurably different conversation quality in initial meetings — prospects who had engaged with the opportunity-framing content entered conversations discussing specific near-term business decisions rather than generic credit need, enabling faster qualification and higher application intent from the first touchpoint.
PPC Benchmark & Channel Economics Reporting
A channel economics model was constructed at programme outset, using the fintech lender's PPC cost-per-application data as the performance benchmark against which direct outbound would be measured. The model accounted for all programme costs — research, database build, content production, outreach execution, and meeting coordination — to produce a fully-loaded cost-per-application figure comparable to the PPC metric on equivalent terms.
At programme close, direct outbound delivered a 44% lower cost-per-application than PPC across equivalent volume. The model was reviewed monthly to track trajectory and identify optimisation levers — database enrichment cost, reply-to-meeting conversion rate, and meeting-to-application conversion rate each had meaningful impact on the overall cost metric and were actively managed throughout the programme.
3 pillars. One integrated system.
Each strategic pillar was designed to feed the next — creating compounding returns across every channel activated.
The numbers
that matter.
Every metric comes from verified campaign data — attributable to specific strategic decisions made during this engagement. No projections. No vanity numbers.
What this engagement taught us.
These principles carry forward into every engagement that follows — applicable well beyond Fintech SME Credit Platform's specific context.
Industry
Fintech Lending / SME Credit
Market
United Kingdom
Duration
Ongoing engagement
Positioning working capital as opportunity rather than crisis changes the conversation entirely.
SME founders who associate working capital facilities with financial difficulty are reluctant to engage. Positioning the fintech lender as growth fuel — the financial flexibility to hire, expand, and launch ahead of revenue — attracts a higher-quality, lower-risk conversation.
Direct outbound to SMEs costs 44% less than PPC at equivalent application volume.
PPC for SME lending is highly competitive and expensive. Direct outbound reaches SMEs who aren't actively searching, at significantly lower cost-per-application — and with higher quality conversations because the ICP is defined before contact.
Founder and finance director messaging must be completely different.
A founder thinks about opportunity and speed. A finance director thinks about compliance, reporting, and facility terms. Sending the same message to both converts neither. The distinction between these personas is the difference between 16% reply rate and 3%.
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