How Allica Bank is Modernizing Growth with HockeyStack

The Who

Allica Bank is a UK-based B2B fintech bank focused on serving established SMEs. With over 750 employees and back-to-back Deloitte Fast 50 wins in 2023 and 2024, it is one of the fastest-growing fintechs in Europe.

Prajval Dsilva, VP of Growth and RevOps at Allica Bank, leads a cross-functional growth team that spans growth marketing, growth analytics, GTM AI engineering, sales enablement, and revenue operations. Formed in late 2023, the growth function is responsible for GTM, customer acquisition through non-sales channels, activation, growth-related revenue operations including performance metrics, and analytics.

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The Problem

As Allica scaled acquisition in 2024, our early-stage analytics setup built around last-touch attribution stopped being decision grade. Last-touch was useful at the start, but at scale it introduced systematic bias: it over-credited “closer” touchpoints and under-credited assist channels that create demand upstream. With multiple GTM motions running in parallel (paid, ABM, organic, and sales-assisted inbound), this made it harder to confidently allocate budget and identify true inefficiencies.

The team needed:

• A shift to account-level, multi-touch attribution to reduce bias and support smarter investment decisions.
• Clearer visibility into how marketing influences inbound pipeline that is ultimately sourced/converted through sales activity in HubSpot.
• Insights that go beyond outcomes to drive input-level optimisation e.g., touches-to-convert, time to convert  and journey patterns so we can improve CAC efficiency over time.

“We’re not trying to eliminate channels. We’re trying to make smarter allocation decisions based on influence and efficiency.”

—Prajval Dsilva, VP Growth , Allica Bank"

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The Requirements

To build a more efficient growth operating model, Allica needed a platform that could:

• Integrate cleanly with HubSpot as the system of record, and work with their existing martech stack
• Provide account-level journey visibility and multi-touch attribution aligned to CRM stages.
• Separate performance by GTM motion (e.g., paid, organic, ABM) to support practical allocation decisions (beyond just reporting)
• Deliver fast time to value without heavy engineering, while still allowing deeper instrumentation as they matured
• Include strong QA and governance capabilities.

HockeyStack fit these constraints: it connected digital touchpoints to HubSpot outcomes at the account level and its AI assistant (Odin) supported faster insight synthesis .

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The Alternatives

Allica evaluated a number of B2B attribution options before choosing HockeyStack. Each had strengths, but they prioritised a HubSpot-first setup that could quickly produce decision-grade insights without heavy engineering. HockeyStack stood out because it offered:

• A proven HubSpot-native integration that aligned with our CRM stages and sales-assisted motion
• Faster time-to-value for a lean team moving from implementation to usable reporting quickly.
• Account-level journey mapping and pre-built B2B reporting that matched the decisions we needed to make (allocation, assists, pathing).
• An AI workflow (Odin) that supported summarisation + validation, helping us spot anomalies

“We saw HockeyStack running with HubSpot and the account-level journey reporting clicked, especially for understanding influence across a B2B buying journey.”

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The Insights

Allica surfaced actionable patterns that weren’t visible under last-touch early in implementation:

• Journey patterns by motion and segment: we’re analysing touches to convert and common journey archetypes .
• Assist value is real: A channel we had invested minimally in appeared weak on last-touch, but showed meaningful assist presence in successful journeys supporting a more nuanced allocation decision. 
• Organic often reflects a combination of influences: a portion of conversions labelled organic were downstream of earlier digital touches, giving clearer visibility into marketing’s role in sales-assisted pipeline.

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Furthermore, Odin supports the team’s reporting cadence by enabling a “trust-but-verify” loop:

• Flagging anomalies and prompting validation 
• Summarising what changed and what matters without spending hours in dashboards.
• Surfacing overlooked assist signals from long-tail sources (e.g., DuckDuckGo, Ecosia) that would otherwise be ignored.

“Odin helps summarize what matters most when we’re moving fast. And when something looks off, it becomes a loop: question, validate, fix.”

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The Results

Early wins for Allica include:

• Clearer decision making on allocation: improved visibility into cross channel assist and influence so we can make more defensible budget choices.
• Better attribution of sales-assisted inbound: account-level journeys now show where pipeline was influenced upstream, even when the final conversion happened through sales activity in HubSpot.
• More credible planning inputs for 2026: we’re using journey patterns to inform budget planning across a fast-scaling GTM motion.
• A repeatable trust and QA loop: anomalies are now part of the workflow (identify → validate identity/tracking → fix), improving confidence in what we see.

“These learnings are already being embedded into our operating cadence and experimentation priorities so we can improve efficiency over time."