Typical engagement length, scaled to the transformation
Trusted by
KKR
Bain Capital
FountainVest
LDC
Paragon Partners
Normec
Aurelius
KKR
Bain Capital
FountainVest
LDC
Paragon Partners
Normec
Aurelius
What we do
Ways we create commercial value
Commercial value creation strategy, GTM acceleration, sales operations, transaction pricing, price architecture, and custom pricing tools. Each built around delivery inside Private Equity-backed businesses.
We map the commercial value creation opportunity in the first weeks of entry. Quick wins identified, VCP agreed, programme structured around your hold period.
02
Embedded transformation
Our partners work inside the business, alongside management. We design and run the programme, removing blockers in real time and keeping EBITDA targets in sight.
03
Exit-ready capability
We build the commercial capability into the organisation so the EBITDA improvement is P&L transparent at exit. The team continues to perform after we leave.
The PE context
Why commercial transformation cannot wait
Financial engineering is no longer sufficient. In the current environment, EBITDA growth has to come from the business, and the commercial function is where the fastest, most defensible gains are found.
~80%
of PE returns must now come from EBITDA improvement, not multiple expansion or leverage. Operational value creation is no longer optional.
1:1
Commercial levers convert directly to EBITDA. Pricing and margin recovery carry no incremental cost — unlike volume growth, which always comes with additional cost of goods and sales.
100
days. The commercial value creation plan must be agreed and moving in the first hundred days — giving sufficient runway to capture the full commercial opportunity across the hold period.
McKinsey research consistently links sustained revenue growth throughout the hold period to materially higher exit valuations. The businesses that capture that premium are the ones running a commercial programme continuously — not starting one when the process begins.
Physical presence
Pan-European
In-country partners operating in local language across five European capitals.
🏢 HQ
London
England
Cécile George
📍 Partner
Rome
Italy
Renata Vancini
📍 Partner
Munich
Germany
Gregor Cohnen
📍 Senior Expert
Copenhagen
Denmark
📍 Head of AI & Analytics
Zagreb
Croatia
Our team
Operators, not advisors
View team profiles →
Cécile George
Founder · London
20+ years delivering rapid revenue and margin growth. Former Operating Partner. Ex McKinsey, INSEAD.
A 30-minute call with a Hirondl partner. No pitch, no slides. Just a conversation about where value is being left on the table.
Our capabilities
We build the commercial engines that Private Equity-backed businesses run on.
Built for businesses where growth through commercial transformation is a priority.
Six capabilities
Six ways we create commercial value
We design, implement, and stay until the results are visible. Each capability is built around delivery.
Sales & GTM
Pricing
01
Commercial Value Creation Strategy & Implementation
Most businesses start a growth programme without a clear picture of where commercial value actually sits, or in what order to go after it.
We map it, prioritise it, build the plan, and stay to implement it. The overarching strategic foundation linking commercial opportunity to fund-level value creation — delivered, not just designed. Typical time-to-insight: 4–6 weeks.
02
GTM (Go-to-Market) Enhancement & Acceleration
Growth rarely comes from direct sales alone. Most businesses haven't built the full Go-to-Market infrastructure — the right channels, partners, and markets — to capture available opportunity.
Direct sales acceleration, channel management, ecosystem-led and partner-led growth, and international expansion.
03
Sales Operations & Org Design
Dashboards, CRM, MIS, salesforce structure, and incentive design — built to last after we leave.
04
Transaction & Value Pricing
Rep discounting erodes margins no one can see on a P&L. Value pricing helps to identify underpriced products & services.
We recover structural margin leakage — from quick wins to full transformation. Typical uplift: 2–5 percentage points, sustained after engagement close.
05
Price Setting & Architecture
Prices set by gut or history, not by value delivered. No clear logic linking product, customer, and price point — and for SaaS businesses, legacy per-seat models that leave significant revenue on the table as usage scales.
Target price setting, price architecture, and segmented pricing models that hold under commercial pressure. For SaaS, we design the shift from user-based to volume- or outcome-based models that capture value as customers grow.
06
Pricing Tools & Custom Tooling
Off-the-shelf solutions create workarounds. Price communication breaks down without the right infrastructure.
Custom pricing tools, Power BI dashboards, and CRM configurations that embed change permanently.
Sector strengths
B2B agnostic. Sector-proven.
We operate across industries. Our deepest delivery track record sits in the sectors below.
IndustryB2B ServicesSaaS
What this looks like in practice
Results we hold ourselves to
150+
engagements delivered across Europe since 2014
2–5pp
revenue growth improvement
80%+
client re-engagement rate
3–12mo
typical engagement length, scaled to the transformation
How we deliver
Why we succeed in transformation
Impact-first scoping
We focus on the levers that will actually move the number, not a 50-slide diagnostic.
Hands-on with the sales team
We coach reps individually on their own data, not from a classroom.
Flexible team model
From a single expert to a full delivery team, scaled to what the business needs.
Local language, local culture
We deliver in-market across the UK, France, Germany, Italy, Scandinavia and Eastern Europe.
Custom tooling, not slide decks
We build the dashboards, models and pricing tools that stay in use after we leave.
Built to hand over
We work alongside internal leaders so the change sustains without us.
Proof of delivery
See these capabilities in practice
Our case studies show exactly how each service line has been applied inside Private Equity-backed and industrial businesses across Europe.
Our team
OPERATORS, NOT ADVISORS
View team profiles →
Cécile George
Founder · London
20+ years delivering rapid revenue and margin growth. Former Operating Partner. Ex McKinsey, INSEAD.
Built a potential-based segmentation across 125+ agents in 50+ markets. Coached agents to target high-potential accounts, generating €7m in new sales within six months.
Asian GTM Strategy — Private Equity-Owned Premium Paper Supplier
2
New subsidiaries opened
2×
Regional sales growth
Designed and executed a market entry strategy across 50 Asian markets, leading to two new subsidiaries and sustained double-digit growth.
B2B Services
Germany
Sales Strategy · Homecare · B2B Services
Sales Transformation & Targeting Redesign — National Homecare Provider
22%
Commercial margin growth
1st
Time hitting target in 2 years
Redesigned sales targeting and built nurse-level dashboards to stop customer losses and redirect effort to high-margin segments. Hit commercial target for the first time in two years.
Gross-to-Net Optimisation — Consumer Healthcare Business
5–7pp
Gross margin improvement
>50%
Revenue absorbed by trade investments pre-programme
Delivered a full Gross-to-Net transformation for a consumer healthcare business, restructuring commercial conditions across pharmacy, wholesale, and e-commerce to recover 5–7pp of gross margin.
B2B Services
Munich, Germany
Pricing · Private Equity Portfolio · B2B Services
Value-Based Pricing Redesign — PE-Backed Professional Services Firm
Ecosystem-Led Growth Transformation — PE-Backed Global Talent Assessment Platform
2.5×
ACV increase ($10K → $25K)
~$1M
Partner-influenced revenue
Built an integration partner ecosystem that grew ACV 2.5×, generated ~$1M in partner-influenced revenue at zero incremental headcount, and reduced churn 15% on integrated accounts.
London, UK · Private Equity Portfolio · Industrial Manufacturing
Pricing Improvement Program — Private Equity-Owned Welding Supply Manufacturer
2–3pp
Gross margin improvement per market
4
Rollout waves over 12 months
35%
Max price variation found for same product
100%
Sales team adoption by program close
Situation
Three years of transaction data showed the same product being sold to comparable customers at prices varying by up to thirty-five percent. The variation was structural, consistent across geographies, and entirely the result of autonomous rep decisions made without any shared pricing framework.
Approach
Built rep-level price opportunity capture sheets. Set rep-specific improvement targets. Wave-based rollout with individual coaching. Power BI impact tracking dashboard. Revised distribution price book.
Impact
2 to 3 percentage point gross margin improvement sustained after engagement close. Sales teams began using the tool voluntarily in client meetings. Resistant markets became the strongest advocates.
Read the complete case study
Full methodology, results visualisation, Power BI dashboard breakdown, and implementation learnings from all four deployment waves.
Results visualised
Gross margin recovery across four engagement waves
Each wave represents approximately three months of implementation, starting with highest-alignment markets and using early results to bring resistant markets along.
Obstacles we overcame
Raw material costs had risen sharply but prices had not kept pace, with high variation in profitability across comparable accounts and cost leakages from freight, returns, and payment terms invisible in reported margins.
The recoverable opportunity was larger than the original investment thesis assumed. But recovering it required changing how the entire sales organisation thought about pricing, not just correcting a spreadsheet.
Initial group workshops met significant resistance in two markets. We shifted to individual coaching sessions working through each rep's own account data. Personal data proved far harder to dismiss than aggregate patterns. The wave-based rollout let early adopter markets serve as proof of concept for resistant ones.
Custom Tooling Built
Tools that stayed in use after we left
Rep-level price opportunity capture sheets, personalised per sales territory
Power BI gross margin tracking dashboard with wave-by-wave progress view
Revised distribution price book with floor pricing and discount logic
AI-Ready Use Cases
How AI applies to this type of engagement
AI-assisted transaction clustering can identify structural discount patterns across years of data in days rather than weeks of manual analysis
Automated flagging of accounts with the highest recoverable margin surfaces the 20% of accounts driving 80% of the opportunity
Rep-specific insight packs can be built at scale, one per territory, without adding analyst headcount
Facing a pricing challenge in a portfolio company?
We have delivered pricing transformations across manufacturing, distribution, and B2B services. References available on request.
← All case studies
Global · Industrial Manufacturing · Casting Equipment
A global industrial casting equipment manufacturer had seen revenues decline at minus two percent per year over the prior decade. The business relied on a network of 125+ independent agents across 50 markets, but agents had no incentive to prospect new accounts. Sales activity was concentrated on existing relationships, and high-potential customers were systematically under-served.
Approach
Built a potential-based customer segmentation across every market. Identified must-win target accounts per agent territory. Introduced structured agent coaching through regular jour fixe sessions using each agent's own data. Developed value-selling tools quantifying total cost of ownership for key products. Built a custom agent management platform to track pipeline, activity, and performance.
Impact
Seven million euros in additional sales within the first six months from agents targeting high-potential accounts for the first time. Pipeline from target customers tripled versus baseline. Over twenty-one million euros in quotations sent to target customers in the first full year. Agent coaching adoption reached critical mass across the network.
Read the complete case study
Full methodology, results data, agent management platform build, and implementation learnings from across 50+ global markets.
Results visualised
Pipeline value and agent activity shift
Left: pipeline from target customers before and after the segmentation programme. Right: agent effort reallocation from existing accounts to high-potential prospects.
Target customer pipeline (€m)
Agent effort allocation
Obstacles we overcame
Agents had operated autonomously for years with no structured performance management or prospecting expectations. Many viewed the new framework as head-office interference rather than a tool to grow their own commissions. Data quality across 50 markets was inconsistent, requiring significant enrichment before the segmentation model could run.
The breakthrough came when early-adopting agents started closing deals with target customers they had never approached before. Their results became the proof that brought resistant agents along — peer evidence proved far more persuasive than any training deck.
Some agents required escalation to a formal watchlist with defined improvement milestones. The phased rollout across business units gave each division time to calibrate the framework to its specific market dynamics before full adoption was expected.
Custom Tooling Built
Tools that stayed in use after we left
Customer Intelligence System (CIS) tracking potential, strategy, and activity across 125+ agents and 50+ markets
Agent management platform with pipeline tracking, jour fixe scheduling, and performance scoring
PowerBI dashboard with real-time KPI visibility for sales managers and leadership
Total cost of ownership calculators for key product lines, used by agents in customer meetings
AI Use Case
How AI accelerated this engagement
AI-assisted market potential scoring across 50+ countries identified high-value targets that agents had never approached
Automated data enrichment filled CIS gaps using company size, industry codes, and public filings across diverse geographies
AI-generated agent performance summaries surfaced coaching priorities ahead of each jour fixe session
Are your agents focused on the right customers?
We build potential-based segmentation frameworks and agent coaching programmes that redirect commercial effort where it generates the highest return.
← All case studies
DACH Region · Private Equity Portfolio · Industrial Manufacturing
Pricing Transformation & New Customer Growth — PE-Backed Specialty Films Manufacturer
€3.9m
Pricing opportunity identified across all teams
93%
Team commitment against capture target
14
Regional sales teams onboarded
46k t
New customer volume opportunity identified
Situation
A PE-backed specialty films manufacturer operating across the DACH region had significant structural margin leakage. Transaction analysis revealed wide pricing variation for comparable products and volumes, driven by autonomous rep discounting with no shared pricing framework. The business had no systematic approach to new customer acquisition, relying entirely on inbound demand and existing relationships.
Approach
Built a pocket margin waterfall decomposing every cost layer from sales discount through freight, payment terms, and returns. Created quantity-bucket pricing targets by product group and industry segment. Developed rep-level pricing capture sheets for fourteen regional teams with individual margin targets. In parallel, built a new customer prospecting engine identifying 46,000 tonnes of addressable volume across target segments.
Impact
Identified 3.9 million euros in recoverable pricing opportunity. Sales teams committed to capturing 1.56 million euros, representing ninety-three percent of the phased target. Top-performing regions exceeded their targets within the first month. A new customer pipeline of over four hundred qualified leads was built and integrated into the CRM for ongoing prospecting.
Read the complete case study
Full pocket margin waterfall methodology, rep-level pricing tooling, and results across 14 regional teams.
Results visualised
Team pricing commitment and regional performance
Left: pricing capture commitment building across submission rounds. Right: regional team performance against individual capture targets.
Capture commitment (€m)
Regional performance vs target
Obstacles we overcame
Raw material costs surged during the implementation period, creating a headwind that dampened first-month pricing capture to forty percent of the monthly run rate. Industrial customers on quarterly fixed-price contracts could not be repriced mid-quarter, requiring a phased approach that prioritised spot and short-cycle accounts first.
The teams that engaged earliest became the proof case for the rest. Europa West hit 143% of target in the first round — once that result circulated internally, resistant teams shifted from scepticism to competition.
Adoption was uneven across fourteen teams, with some regions at over 140% of target while others lagged at under 50%. Individual coaching sessions using each team's own scatterplot data proved far more effective than group workshops at closing the gap.
Custom Tooling Built
Tools that stayed in use after we left
Rep-level pricing opportunity capture sheets with target margins by product-volume class, deployed across 14 teams
Automated weekly pricing performance dashboard tracking GK1 margin vs baseline per customer and product group
New customer prospecting engine with 400+ qualified leads across target segments, integrated into Salesforce
Quantity-bucket pricing model setting floor margins by order size and product group
AI-Ready Use Cases
How AI applies to this type of engagement
AI-assisted transaction clustering can identify structural discount patterns by product-volume-industry combination in days rather than weeks
Automated scatterplot generation flags pricing outliers per team, enabling targeted coaching rather than blanket training
AI-generated prospect longlists in target segments match industry profiles to volume potential at scale
Leaving margin on the table?
We identify structural pricing leakage and build the tools that recover it — with team-level commitment, not just a recommendation.
Asian Go-to-Market Strategy — Private Equity-Owned Premium Paper Supplier
2
New subsidiaries opened in Asia
2×
Sales growth sustained across the region
50
Asian markets assessed bottom-up
3
Distinct GTM archetypes deployed
Situation
The business had operated across Asia for years through a distributor network that had grown incrementally. Each relationship had made sense in isolation. Collectively, they had created a commercial structure that bore almost no relationship to where the actual market opportunity sat.
Approach
Bottom-up potential mapping for every Asian market. Market clustering into three strategic archetypes. Tailored GTM recommendation per cluster. Resource plan and subsidiary launch support for priority markets.
Impact
Two new subsidiaries opened within eighteen months. Double-digit regional sales growth achieved and sustained. Commercial team left with a repeatable framework for assessing new markets independently.
Read the complete case study
Full market assessment methodology, archetype framework, subsidiary launch playbook, and results from the Asian expansion.
Results visualised
Market potential vs resource coverage — before and after
Illustrative view across 50 assessed markets, grouped by GTM archetype. The misalignment between potential and coverage was the core finding that drove the new strategy.
Obstacles we overcame
As-is GTM strategy was not in line with growth ambition in Asia. No rigorous view of potential by market. Insufficient resource available in priority markets. Existing distributor network built on relationships, not strategy.
The most common mistake in Asian market strategy is optimising for the markets you understand rather than the markets where the real potential sits. Getting to the right answer requires separating familiarity from opportunity.
Our initial brief was to recommend a single updated go-to-market model for the region. As the potential data came in, it became clear that a single model was the wrong answer. We shifted to a clustering methodology, grouping markets into archetypes based on potential, distributor reliability, competitive dynamics, and barriers to direct entry. Each archetype received a distinct GTM recommendation.
Custom Tooling Built
Tools that stayed in use after we left
Market entry scoring model ranking 50 Asian markets by potential, competitive intensity, and barrier to entry
Distributor evaluation framework with weighted scorecard used to rationalise and select partners per archetype
Year 1 P&L template for subsidiary launches, pre-built for two new markets
AI-Ready Use Cases
How AI applies to this type of engagement
AI synthesis of competitive intelligence across 50+ markets can compress a 3-month research phase to 3 weeks
Automated regulatory summary generation flags key market entry restrictions by country before field validation
AI-assisted distributor profiling scans public data to pre-screen hundreds of potential partners down to a qualified shortlist
Facing a similar challenge in a portfolio company?
We work directly with Private Equity teams and portfolio companies to turn value creation plans into measurable outcomes.
← All case studies
Germany · B2B Healthcare Services · Homecare
Sales Transformation & Targeting Redesign — National Homecare Provider
22%
Commercial margin growth projected
1st
Time hitting target in 2 years
8
Regional markets redesigned
€1.1m
Annual margin uplift identified
Situation
A national homecare provider serving wound care, nutrition, stoma, and incontinence patients across Germany had missed its commercial targets for two consecutive years. Sales nurses were spread too thin across low-value accounts, with no data-driven targeting. The company was losing patients to smaller, more agile regional competitors who were cherry-picking high-margin segments.
Approach
Mapped commercial margin by nurse, institution, and therapy segment across every region. Identified the fifty lowest-performing discharge sources and care homes consuming disproportionate capacity. Redirected freed nurse time to high-margin segments including parenteral nutrition and IV antibiotics. Built nurse-level performance dashboards and introduced structured KPI-driven coaching for regional managers.
Impact
The business hit its commercial target for the first time in two years. Customer losses stopped and growth resumed with target accounts. Commercial margin projected to grow twenty-two percent from the baseline, with over one million euros in annual uplift identified across eight regional markets. Blueprint region scaled nationally.
Read the complete case study
Full margin mapping methodology, nurse-level dashboard build, coaching framework, and results across 8 regional markets.
Results visualised
Commercial margin trajectory and capacity reallocation
Left: projected commercial margin growth across the programme. Right: nurse capacity shift from low-value to high-margin segments.
Commercial margin (€m)
Nurse capacity allocation
Obstacles we overcame
The business had no centralised commercial data when we started. Nurse performance was tracked on paper, and regional managers had no visibility into which institutions were generating margin and which were consuming capacity without return. Building the data foundation was a prerequisite before any targeting decisions could be made.
The biggest shift was cultural. Sales nurses had always been measured on visit frequency, not commercial margin. Moving to margin-per-nurse as the lead KPI changed how every regional team thought about their day.
A major competitor's recent restructuring had created market instability, with experienced nurses and account managers leaving to join smaller regional players. We had to move fast to capture the displaced customers before competitors consolidated them.
Custom Tooling Built
Tools that stayed in use after we left
Nurse-level commercial dashboard tracking margin, patient count, and segment mix per nurse per region
Institution scoring model ranking discharge sources and care homes by margin contribution and growth potential
Regional manager coaching framework with structured KPI review cadence
Capacity planning tool mapping nurse territory coverage against high-value institution density
AI Use Case
How AI accelerated this engagement
AI-assisted analysis of patient and institution data identified the highest-margin therapy segments per region in days rather than weeks
Automated institution profiling matched care homes and discharge sources to margin potential using publicly available facility data
AI-generated regional performance summaries gave managers a coaching-ready view of each nurse ahead of review sessions
Is your sales team focused on the right accounts?
We redesign commercial targeting and build the dashboards that make the change stick.
← All case studies
Operator Track Record
London, UK · Private Equity Portfolio · B2B SaaS
Ecosystem-Led Growth Transformation — PE-Backed Global Talent Assessment Platform
2.5×
Average contract value increase ($10K → $25K)
80%
Growth in partner-sourced pipeline
~$1M
Partner-influenced revenue generated
15%
Reduction in customer churn on integrated accounts
Situation
A PE-backed global talent assessment provider undergoing a service-to-SaaS transformation, serving customers across 60+ countries. The commercial problem had two layers: 1) a sales enablement gap: no consistent way to scope, price, or close integration-attached deals; 2) an untapped ecosystem opportunity, with ATS/HRIS vendors as potential co-sell partners rather than just technical integration dependencies. No structure existed to activate either.
Approach
Mapped the full ATS ecosystem to identify highest-value integration partnerships by deal size, win-rate correlation, and customer demand signal. Built a centralised, self-serve integration knowledge hub converting fragmented tribal knowledge into a dynamic sales asset. Standardised scoping and pricing by ATS tier (global enterprise, regional, emerging). Launched co-sell motions with priority ATS partners — joint collateral, shared deal registration, and integration demonstration environments.
Impact
ACV grew from $10K to $25K through integration-attached deal structures. Partner-influenced revenue reached ~$1M without incremental headcount. Win rates on integration-driven opportunities rose from 35% to 50%. Integration activations doubled. Customer churn on integrated accounts fell 15%. Sales adoption reached 90% within six months.
Read the complete case study
Full ecosystem strategy, integration hub build, co-sell motion design, and results from the full ELG programme.
Results visualised
ACV growth and win rate improvement
Left: average contract value before and after integration-attached deal structures. Right: win rate on integration-driven opportunities before and after the ecosystem programme.
Average contract value ($K)
Win rate on integration-driven deals
Obstacles we overcame
The platform operated within a highly fragmented HR Tech ecosystem. Customers demanded seamless integrations with their existing Applicant Tracking Systems, but with 30+ global ATS providers and hundreds of regional ones, the business had no scalable way to deliver. Integration was simultaneously the most requested capability and the biggest commercial bottleneck — costing an estimated $2M annually in competitive losses. Deals that did close were underpriced. The partner ecosystem was untapped as a commercial channel, with no structured co-sell motion and no visibility into partner-sourced pipeline.
The partner co-sell motion required a reversal: instead of formal channel programme outreach, we started with integration delivery on shared customers, then formalised the partnership after proving value jointly.
The initial plan was a static documentation project. Two weeks in, it became clear this would replicate the problem. We shifted to a Confluence-based dynamic knowledge hub with keyword-linked search, co-created with AEs around real deal scenarios. Early adopter involvement turned sceptics into advocates — the tool spread through the sales team on its own momentum.
Enterprise value connection
Lever
Mechanism
Impact
Revenue expansion
ACV uplift from integration-attached deal structures
2.5× ACV ($10K → $25K)
New revenue channel
Partner co-sell at zero incremental headcount
80% pipeline growth · ~$1M
Net revenue retention
Lower churn on stickier, integrated accounts
15% churn reduction
Sales efficiency
Higher win rates, faster scoping
Win rate +15pp · scoping −40%
Margin improvement
Engineering redirected from support to product
Velocity +30% · support −20%
EBITDA uplift
ACV expansion + retention with no additional headcount growth
80% net new pipeline growth · $1M partner-sourced pipeline · 15% churn reduction
Exit multiple defence
Ecosystem lock-in strengthens competitive moat
Activations 2× · 90% adoption
Custom Tooling Built
Tools that stayed in use after the programme
Dynamic integration sales knowledge hub with keyword search, linked to live pricing and scoping data
Standardised integration scoping and pricing templates by ATS tier (global enterprise, regional, emerging)
Routing workflow for custom integration requests with defined SLAs
Partner co-sell playbook with joint value propositions, deal registration, and shared pipeline tracking
No single owner of integration sales — solved with a cross-functional team and defined SOPs
Sales team scepticism about "another internal tool" — overcome through early adopter co-creation
Partner engagement too formal — reversed the sequence to start with integration delivery on shared customers
Integration Product Manager temporarily reassigned — took direct ownership reporting to commercial leadership
AI Use Case
How AI could accelerate this today
Automated partner scoring by customer overlap and deal-size correlation
Predictive pipeline modelling to forecast partner-sourced revenue contribution
Building a B2B SaaS platform without an ecosystem strategy?
CAC is up 30–40% since 2022. AI has compressed feature parity from years to quarters. Traditional GTM levers burn capital that a PE-owned structure will not forgive. Ecosystem-Led Growth scales revenue without proportional cost — using integrations and relationships that make the product harder to displace, not just easier to sell. We help PE-backed software businesses build and activate it.
← All case studies
Operator Track Record
Southern Europe · Consumer Healthcare · Commercial Transformation
Gross-to-Net Optimisation — Consumer Healthcare Business
>50%
Revenue absorbed by trade investments pre-programme
A consumer healthcare business was facing declining profitability despite sustained top-line growth. The organisation lacked full transparency on its Gross-to-Net structure, with over half of gross revenues absorbed by discounts, bonuses, and trade investments before net revenue was recognised. Commercial conditions had grown highly complex and inconsistent across channels, brands, and customers, with incentives primarily tied to sell-in volumes rather than sell-out performance.
Approach
We delivered an end-to-end GtN transformation covering transparency creation, commercial model redesign, and implementation support. This included full price waterfall analysis across channels and customers, benchmarking against best-practice models, and identifying structural inefficiencies in discounting and bonus design. We redesigned the commercial model to shift from sell-in to sell-out performance metrics, and built a partnership framework linking trade investment to measurable outcomes for key customers. A fundamental element of the redesign was rebalancing the weight of each channel: we recommended reducing dependence on wholesale in favour of direct and cooperative channels to improve margin retention and strengthen commercial control over pricing and investment allocation.
Impact
5–7pp gross margin improvement through GtN optimisation and commercial model redesign. Reduction in misaligned trade spend across all major channels. Improved alignment between commercial investment and financial outcomes. A scalable governance model and commercial policy framework built to support future rollout across European markets.
Read the complete case study
Full GtN waterfall analysis, commercial model redesign, channel rebalancing strategy, and implementation results.
Results visualised
Gross-to-Net structure before and after
Left: net revenue as a share of gross revenue, before and after the programme. Right: breakdown of trade investment components addressed through the redesign.
Net revenue retention (% of gross)
Trade investment reduction (achievable range)
Component
Reduction range
Discounts
20 – 25%
Bonuses
10 – 15%
Ranges reflect outcomes across engagements. Actual results depend on baseline complexity and market conditions.
Obstacles we overcame
Data on off-invoice investments was fragmented and incomplete. Before any analysis could begin, we needed to reconstruct a full view of the GtN waterfall — combining system exports, finance records, and commercial team inputs across multiple channels and brands.
The margin leakage was not the result of poor commercial intent — it was the result of complexity that had grown faster than the systems and governance designed to manage it.
IT system misalignment between CRM and order planning created significant gaps in the baseline picture. Established commercial practices carried deep organisational roots, making change management as important as the technical redesign. Managing conflicting incentive structures across pharmacies, wholesalers, and e-commerce simultaneously — without disrupting key customer relationships — required careful sequencing throughout the implementation.
Delivered by
Delivered by Luca Pelamatti during his engagement as lead commercial transformation consultant. Luca brings over ten years of commercial excellence and Revenue Growth Management experience across B2B2C environments in Europe.
Custom Tooling Built
Tools that stayed in use after we left
RGM function blueprint — a structured playbook to establish a Revenue Growth Management capability, covering team design, governance structure, data requirements, and process integration
Interactive GtN performance dashboard for ongoing margin monitoring across channels and brands
KPI framework tracking commercial effectiveness and implementation progress against the roadmap
Governance model for trade investment decisions — defining approval levels, review cadence, and accountability
AI-assisted trade spend analysis to identify discount and bonus structures that erode margin without driving incremental sell-out performance
AI-assisted invoice detection to identify off-invoice leakage patterns, reducing reliance on manual periodic reviews
Facing margin pressure from trade spend complexity?
We design and implement Gross-to-Net frameworks that close the gap between commercial investment and financial outcomes. References available on request.
The team
Operators, not advisors
We combine Private Equity operating partner experience with top-tier management consulting backgrounds, with in-country partners across five European capitals.
London
Cécile George
Founder
20+ years delivering rapid revenue and margin growth. Former Operating Partner at a Private Equity house. Held both generalist consulting and independent expert roles for McKinsey and Company. Built and led global sales teams in B2B chemicals. Extensive experience operating businesses directly.
15+ years of hands-on business building experience from Fortune 500s down to startups. Top-tier sales and marketing boutique consultancy background. Deep expertise in sales and pricing transformations across FMCG, industrial goods, and luxury.
20+ years value creation leadership in private equity and industry as Operating Partner, Portfolio Director and C-Level Executive. Focus on design and execution of complex commercial transformations in B2B. Held both consulting and operational roles at BASF, Aurelius, Paragon Partners, and Private Equity portfolio companies across Europe.
13+ years international B2B SaaS experience spanning commercial leadership, partnerships, and GTM strategy across the UK, Europe, and China. Former senior commercial roles at Deel (pre-IPO Decacorn) and Thomas International (Private Equity-backed). Specialises in ecosystem-led growth for Private Equity-backed software businesses.
Former McKinsey and Company consultant. 20+ years of experience in business building, commercial transformation, and consulting. Passionate about delivering impact from next generation technology across industrial and B2B markets.
Former Bain consultant. 10+ years driving top-line growth for multinationals and SMEs. Focus on commercial excellence and Revenue Growth Management in B2B2C environments across Europe.
Short pieces from our team on commercial transformation, pricing, and value creation in Private Equity-backed businesses.
Pricing · 3 min read
When the Margin Wasn't Where the Model Said It Was
By Cécile George · Hirondl
A Private Equity-backed welding manufacturer. Three years of transaction data. A structural pricing problem nobody had looked for. Here is what we found and how we fixed it.
AI & Commercial Transformation · 5 min read
AI in the commercial function: what we have learned, and what usually goes wrong
By Cécile George · Hirondl
Every PE-backed business is asking which AI tools to use in the commercial function. It is the wrong question — and answering it first tends to produce the same outcome. Here is what we do instead.
AI & Commercial Transformation · 3 min read
AI Boosts Market Segmentation Precision in B2B
By Cécile George · Hirondl · May 2026
One of the key impact levers of AI in commercial functions isn't efficiency. It's how much further we can take things we were already doing — starting with market segmentation.
Follow us on LinkedIn
Fresh insights on pricing, sales transformation and commercial value creation in PE-backed businesses.
When the margin wasn't where the model said it was
By Cécile George · Hirondl · Published 2025
Context
The welding supply industry is built on volume, relationships, and tight margins. When a private equity firm acquires a business in this space, the value creation thesis almost always includes a pricing improvement component. Not because the business is badly managed, but because pricing discipline tends to erode quietly over time, one discount at a time, until the gap between what the business could charge and what it actually charges becomes structural rather than tactical.
The problem
When we pulled three years of transaction history, we found something more significant than a pricing gap. For the same product, sold to comparable customers, prices varied by up to thirty-five percent. The variation had nothing to do with volume, strategic value, or competitive pressure. It was the result of individual sales reps making discount decisions autonomously, without shared guidelines, over many years.
Then we built the pocket margin waterfall. Freight costs were being silently absorbed on low-volume routes. Extended payment terms were being offered as relationship courtesies with no adjustment to the effective price. Return credits were sitting against accounts that looked profitable on gross revenue alone.
The recoverable opportunity was larger than the original investment thesis had assumed. But recovering it required changing how the entire sales organisation thought about pricing, not just correcting a spreadsheet.
The solution
We built each sales rep a price opportunity capture tool tailored to their own book of business. Each rep could see their own discount patterns, the suggested target price, and the revenue impact of correcting specific accounts. We ran the program across four waves over twelve months, starting in markets where alignment was strongest and using those results to bring resistant markets along progressively.
The outcome was a two to three percentage point improvement in gross margin depending on the market, sustained after the engagement closed. The more meaningful signal was behavioural. The teams that had been most resistant at the start became the most vocal advocates for the tool.
What this means
A pricing intervention changes what people charge today. A pricing transformation changes how a commercial organisation thinks about pricing permanently. The gap between those two outcomes is almost never a data problem. It is a capability and change management problem.
Working through a similar situation?
contact@hirondl.com
← All insights
AI & Commercial Transformation · 5 min read
AI in the commercial function: what we have learned, and what usually goes wrong
By Cécile George · Hirondl · April 2026
The question most boards are asking
Every PE-backed business I speak to is asking some version of the same question: which AI tools should we be using in the commercial function? It is a reasonable impulse. The technology is moving fast, the pressure to modernise is real, and the fear of falling behind competitors who are deploying it is genuine. But it is the wrong starting point — and answering it first tends to produce a predictable outcome: a set of expensive subscriptions, low adoption, and a leadership team that has not moved any commercial KPI.
The better question is this: where in your commercial process does poor-quality information cause the most expensive decisions? Answer that first. Then AI has a precise job to do. Without that answer, you are introducing a powerful accelerant into a system you do not fully understand.
The businesses that get the most from AI in commercial functions are not the ones that deploy the most tools. They are the ones that are most precise about the problem they are solving before they introduce any technology.
Where we always start
Before any AI tool enters a Hirondl engagement, we run a data diagnostic. Three questions: What commercial decisions is this business making every week? What information are those decisions based on? And how reliable is that information?
In most cases, the answer is uncomfortable. Transaction data is incomplete. CRM adoption is patchy. Pricing decisions are made by individual reps using instinct rather than data. Customer segmentation reflects history, not potential. AI introduced into that environment will not fix it. It will amplify it — and faster than a team of analysts would have.
Where AI creates value in the commercial function
01
Pricing diagnostics
Most B2B businesses do not know which customers are profitable at the transaction level. AI can scan years of invoice data to surface structural discount patterns, price-cost gaps by customer, and margin variation by product group that manual analysis would take months to find — and that rep intuition consistently misses. The output is a ranked list of recoverable margin by account and product group, ready to act on within the first sixty days.
→ Pricing Transformation case study
02
Customer profitability by account
Profitability calculated at the invoice level routinely overstates margin. An account that looks healthy on revenue can be destroying value once freight, credit terms, customisation, and support costs are allocated against it. AI-assisted cost-to-serve modelling builds a true profitability view across the entire customer portfolio — segmented by the levers that are actually controllable. That changes the conversation in every commercial review and every pricing negotiation.
→ Pricing Transformation case study
03
Prospecting and territory design
AI-assisted ICP scoring and firmographic matching can build a qualified prospect pipeline at a scale no sales team could sustain manually. Combined with territory design — matching rep capacity to addressable opportunity — the result is a prospecting infrastructure the commercial team can run continuously, not a one-off list that goes stale. The insight that a business is sitting on 46,000 tonnes of untapped opportunity is only actionable if you can identify who to call.
→ Specialty Films case study
04
Pipeline health and sales performance
Pattern recognition across historical deal data produces scoring that flags at-risk opportunities before they slip and forecasts that are data-driven rather than rep-reported. At the rep level, AI-generated coaching summaries surface the specific behaviours — objection handling, follow-up cadence, deal velocity — that separate high performers from the rest. The insight already exists in the CRM and call data most commercial teams are sitting on. The question is whether you are extracting it.
05
Market intelligence and competitive positioning
Synthesising competitor pricing signals, market share proxies, and product positioning across multiple geographies is a task that previously required months of desk research. AI compresses the synthesis phase to days: regulatory summaries, competitive intensity proxies, and distributor landscape pre-screening, all structured and ready for strategic interpretation. The decisions remain human. The time to reach them is cut significantly — which, inside a PE hold period, is often the constraint that matters most.
→ Asian GTM Strategy case study
2–4pp
gross margin improvement typical from AI-assisted pricing diagnostics in the first twelve months
60 days
from data to ranked customer profitability view — the foundation of every commercial transformation
400+
qualified prospects built in one engagement using AI-assisted ICP scoring and firmographic matching
What bad implementation looks like
AI introduced into a broken commercial process does not fix it. It amplifies it — and faster than a team of analysts would have.
We have seen conversational intelligence platforms deployed before anyone had defined what a good sales call looks like. Prospecting AI tools activated before the business knew which customers were actually profitable. Outbound copy generated at scale before a validated message existed.
In each case, the result was the same. Volume went up. Quality went down. The commercial function became harder to manage, not easier — because AI had removed friction from an activity that was already pointed in the wrong direction. The failure mode is always predictable: treating AI as a solution to a problem you have not yet defined.
There is also a softer failure mode, harder to see in the moment. When a commercial team adopts AI tools as a productivity layer before the underlying commercial process is sound, they often lose the discipline of doing the diagnostic work manually. They skip the step of understanding the data because the AI generates an output quickly. The speed creates confidence. The confidence is often misplaced.
The PE timing constraint changes everything
Most AI implementation advice assumes you have eighteen months to run pilots, iterate, and find what works. PE-backed businesses rarely do. When you have a thirty-six-month hold period and EBITDA improvement is expected in year one, the sequencing of AI deployment is not a technology question — it is a programme management question.
In our experience, AI delivers the highest return in the diagnostic and data structuring phase — the first sixty days of an engagement. This is where slow analysis is most expensive, where speed has the most direct commercial consequence, and where AI has the least risk of amplifying a broken process. Because at that stage, the process is being rebuilt from the data up.
Later in an engagement, AI supports sustainment: coaching summaries that update automatically before manager sessions, pipeline dashboards that flag outliers in real time, prospecting engines that run continuously rather than in quarterly bursts. But the strategic diagnosis has to come first. And the strategic diagnosis has to be human.
How we sequence it
01
Diagnose — identify where poor-quality information causes the most expensive commercial decisions
02
Build the foundation — clean CRM data, consistent lifecycle definitions, baseline commercial metrics
03
Deploy precisely — apply AI specifically to the identified gap, not as a general productivity layer
04
Embed into workflow — build AI outputs directly into the daily process so adoption is automatic, not effortful
05
Validate before expanding — confirm commercial KPIs are moving before broadening the AI scope
The short version
AI is genuinely useful in commercial transformation. We use it in every engagement we run. The pattern it follows in good implementations is consistent: it compresses the time between raw data and actionable insight, it creates tools the commercial team continues to use after we leave, and it lets our partners spend their hours on judgement rather than on information gathering.
The pattern in poor implementations is equally consistent: a tool was introduced before the problem was defined. The question you need to answer first is not which AI tools to buy. It is where in your commercial process you are flying blind — and what it costs you every quarter that you are.
Working through an AI deployment challenge in a portfolio company?
contact@hirondl.com
← All insights
AI & Commercial Transformation · 3 min read
AI Boosts Market Segmentation Precision in B2B
By Cécile George · Hirondl · May 2026
A step change, not a new concept
One of the key impact levers of AI in commercial functions isn't efficiency. It's how much further we can take things we were already doing.
B2B market segmentation — combining external data with internal customer information to identify white space — has been valuable for years. The problem was always execution. Despite delivering real impact, these efforts were built on approximations, partial data, and workarounds. The analysis was only as good as the data you could practically access and reconcile, which placed a ceiling on how precise and how useful the output could be.
"One of the key impact levers of AI in commercial functions isn't efficiency. It's how much further we can take things we were already doing."
What changes with AI
AI removes that ceiling. Segmentation can now be built with much greater precision and — crucially — continuously updated. Not a one-off exercise, but a living model that evolves with the data.
For a PE-backed B2B business, this matters because white space identification is one of the highest-leverage inputs to a commercial value creation programme. When you know precisely which customer segments are underserved, which products are cross-sell candidates for which accounts, and which geographies represent genuine expansion opportunity, the commercial programme becomes targeted rather than broad.
The implication for value creation
The segmentation work doesn't change in concept. The same logic applies: understand who your best customers are, find more of them, and build the commercial motion around that insight. What AI changes is how well and how sustainably that work can be executed — and how quickly the output becomes stale without it.
For management teams under hold period pressure, that distinction matters.
Hirondl embeds into your portfolio companies and drives commercial value from entry through to exit, measurable in EBITDA, not slide count.
Why Private Equity funds choose Hirondl
We work the way Private Equity works
Most consultants hand over a report. We stay in the business, manage the programme, and leave only once the numbers move.
01
Entry & 100-day plan
We map the commercial value creation opportunity in the first 90 days. AI-assisted data analysis helps us identify pricing and segmentation levers faster. Quick wins identified, VCP agreed, programme structured around your hold period.
02
Embedded transformation
Our partners work inside the business, not from a client service centre. We design and run the programme alongside management, removing blockers in real time.
03
Exit-ready capability
We build the capability into the organisation so the EBITDA improvement is defensible at exit. The team continues to perform after we leave.
Typical mandates
What we get called in for
We run six core service lines, all built around measurable commercial impact in Private Equity-backed businesses.
Commercial Maturity Assessment
Pre-deal assessment of commercial value creation levers, covering pricing, sales effectiveness, and go-to-market opportunity before close.
Pricing transformation
Identifying and recovering structural margin leakage. Typically 2–4pp gross margin improvement in the first twelve months.
Sales force effectiveness
Redeploying rep time from admin to selling. Building performance management systems that surface outliers and drive accountability.
Go-to-market strategy
Opening new geographies or channels. From market assessment through to hiring plan, distributor selection, and first-year P&L.
Revenue growth management
Designing and embedding RGM frameworks across B2B2C businesses, including category, pack, and price architecture built for long-term margin.
Commercial leadership hiring
We define the headcount plan and build detailed role profiles for senior commercial hires. We then work alongside specialist headhunters to run the search, ensuring the brief is precise and the right candidate is secured.
Track record
Numbers which matter in Private Equity
150+
Engagements delivered across Europe since 2014
2–5pp
Revenue growth improvement
80%+
Client re-engagement rate
3–12mo
Typical engagement length, scaled to the transformation
The partners
Private Equity experience in the room
Our partners have held Operating Partner, portfolio company MD, and in-house Private Equity consulting roles, not just advisory mandates.
Cécile George
Founder
Former Operating Partner at a Private Equity house. McKinsey independent expert. Built and led global sales teams in B2B chemicals. 20+ years of commercial transformation delivery.
15+ years running commercial transformation projects across Private Equity-owned assets. Deep expertise in sales and pricing across FMCG, industrial goods, and luxury. Former top-tier sales and marketing boutique consultant.
20+ years value creation leadership. In-house consulting and operational roles at Aurelius and Paragon. Designed and executed complex B2B commercial transformations across Private Equity-backed portfolio companies.
A 30-minute introductory call with a Hirondl partner. No slides, no pitch. Just a conversation about where value is being left on the table.
◷
30 minutes · Video call
✉
contact@hirondl.com
☏
+44 7583 165409
124 Wigmore St, London W1U 3RY
You will be speaking with
Cécile George
Founder · ex McKinsey · Private Equity Operating Partner
Gregor Cohnen
Partner · 20+ years Private Equity value creation
Your details are used solely to confirm and manage your booking. We do not share your information with third parties or send unsolicited marketing communications.
How Hirondelle & Co Ltd collects, uses, and protects your personal information when you visit hirondl.com or get in touch with us.
Last updated: 6 May 2026 · UK GDPR & Data Protection Act 2018
1. Who we are
This website is operated by Hirondelle & Co Ltd (trading as Hirondl & Co), a company registered in England & Wales under company number 09006684, with its registered office at 124 Wigmore Street, London W1U 3RY, United Kingdom.
For the purposes of UK GDPR and the Data Protection Act 2018, Hirondelle & Co Ltd is the data controller of personal information collected through this website.
If you have any questions about this policy or how we handle your information, please contact us at contact@hirondl.com.
2. The information we collect
We collect personal information directly from you when you choose to share it with us, and a limited amount of technical information automatically when you use our website.
Information you give us
Contact and case study enquiries: name, work email, company, job title, and any message you choose to send.
Booking a call: name, email, and any details you provide via Calendly when scheduling a meeting.
Information collected automatically
Analytics data (only with your consent): if you accept analytics cookies, Google Analytics 4 records anonymised information about how you use the site, such as pages visited, approximate location (country/city level), device type, and referring source.
Server logs: our hosting provider (Netlify) records standard request logs, including IP address and user-agent, for security and abuse prevention. These are retained for a short period and are not used for marketing.
We do not collect special category data (such as health, ethnicity, or political views), and we do not knowingly collect information from anyone under the age of 16.
3. How we use your information
We only use your personal information for the purposes set out below, and only where we have a lawful basis to do so under UK GDPR.
Purpose
Lawful basis
Responding to enquiries you send via the contact form, case study form, or email
Legitimate interests (responding to a request you have made)
Scheduling and confirming meetings you book through Calendly
Performance of a contract / your request
Granting access to gated case study content you request
Legitimate interests (delivering the content you asked for)
Measuring website usage via Google Analytics
Your consent (you may withdraw it at any time via the cookie banner)
Maintaining client records and complying with legal, tax and accounting obligations
Legal obligation
Protecting the website from abuse and securing our systems
Legitimate interests (network and information security)
We do not sell your personal information, and we do not send unsolicited marketing communications. We will not use information you submit to enquire about our services for any unrelated marketing without first asking you.
4. Cookies and similar technologies
A cookie is a small text file stored on your device by your browser. We use the smallest possible number of cookies and only set non-essential cookies after you give consent through our cookie banner.
Cookie / storage
Purpose
Type
hirondl-cookie-consent (localStorage)
Remembers your cookie choice so we don't ask you again on every visit
Strictly necessary
Google Analytics (_ga, _ga_*)
Anonymised website analytics — only set if you click Accept
Analytics (consent-based)
You can change your cookie preferences at any time by clicking Manage cookies in the footer of any page. You can also clear cookies through your browser settings.
Embedded services such as Calendly (when you book a call) and HubSpot (when you submit a case study form) may set their own cookies once you actively interact with them. Please refer to their privacy policies for further details.
5. Who we share your information with
We share your information only with carefully selected service providers who help us run our website and respond to you. They process information on our instructions and under written data-processing agreements.
Netlify — website hosting and request logs.
Google Analytics (Google Ireland Ltd / Google LLC) — anonymised website analytics, only with your consent.
HubSpot — handles case study form submissions and stores contact details so we can respond.
Calendly — handles meeting bookings and the information you provide when scheduling.
Microsoft 365 — our business email provider, used to communicate with you.
We may also disclose your information where required to do so by law, by a court order, or to protect our legal rights.
6. International transfers
Some of our service providers are based outside the UK, including in the European Economic Area and the United States. Where personal data is transferred outside the UK, we rely on appropriate safeguards recognised under UK GDPR, such as the UK International Data Transfer Agreement, the UK Addendum to the EU Standard Contractual Clauses, or the UK Extension to the EU-US Data Privacy Framework.
7. How long we keep your information
Enquiry and case study form submissions: up to 24 months from your last interaction with us, unless you become a client.
Client records and correspondence: up to 7 years from the end of the engagement, in line with our legal, tax and professional obligations.
Analytics data: retained by Google Analytics for the period set in our GA4 configuration (currently the GA4 default).
Server logs: retained by our hosting provider for a short period for security purposes only.
You can ask us to delete your information at any time, subject to any legal retention obligations we are required to meet.
8. How we protect your information
We use HTTPS across the entire site, modern security headers (HSTS, CSP, X-Frame-Options), and reputable hosting and CRM providers with their own security certifications. Access to personal information inside Hirondl is restricted to staff who genuinely need it to do their job.
9. Your rights under UK GDPR
You have the following rights in relation to the personal information we hold about you:
Access — ask for a copy of the personal information we hold about you.
Rectification — ask us to correct information that is inaccurate or incomplete.
Erasure — ask us to delete your information where we no longer need it.
Restriction — ask us to limit how we use your information in certain circumstances.
Portability — ask to receive your information in a structured, machine-readable format.
Objection — object to processing based on our legitimate interests.
Withdraw consent — where we rely on consent (such as for analytics cookies), you can withdraw it at any time without affecting prior processing.
To exercise any of these rights, please email contact@hirondl.com. We will respond within one month of receiving a valid request.
10. Complaints
If you are unhappy with how we have handled your personal information, please contact us first so we can try to put things right. You also have the right to complain to the UK's data protection regulator:
Information Commissioner's Office (ICO)
Wycliffe House, Water Lane, Wilmslow, Cheshire SK9 5AF
Helpline: 0303 123 1113
Website: ico.org.uk
11. Changes to this policy
We may update this Privacy Policy from time to time to reflect changes in our practices or in the law. The "Last updated" date at the top of this page will tell you when the policy was last revised. We encourage you to review it periodically.
Cookies on hirondl.com
We use a small number of cookies to keep the site running and, if you agree, to understand how visitors use it via Google Analytics. We do not use advertising or tracking cookies. Read our Privacy Policy.
Full Case Study
Access the complete analysis
Leave your details below to read the full methodology, results visualisation, and commercial tooling built on this engagement.
Please complete all fields with a valid work email.
Your details are used solely to grant access to this case study. We do not share your information with third parties or send unsolicited marketing communications.
LDC
