# Lifted Financial Model Tech-enabled home care agency providing hourly, overnight, and live-in care for elderly and dementia patients in the UK. - Canonical: https://finamodel.com/startups/lifted - Excel download: https://finamodel.com/startup-models/lifted.xlsx - Category: Health-tech - Model type: SaaS ARR / Valuation - Funding round: Seed - Funding: $2M - Founded: 2020 - Geography: UK (London focus implied by London Living Wage reference and London N1 address in product demo). - Customer: B2B2C ## About the company Lifted is a tech-enabled UK home-care agency for elderly and dementia patients. It delivers care through employed carers rather than marketplace contractors, combining operational technology with a direct service relationship for families needing support at home. Families pay directly for hourly, overnight, and live-in care. Economics therefore hinge on care hours, realised pricing, carer recruitment and retention, utilisation, local branch density, scheduling efficiency, and the ability to maintain service quality across a vulnerable client population. The model forecasts care hours by service type, realised hourly rate, carer wages, utilisation, branch overhead, and client retention. It includes recruitment, training, scheduling, marketing, working capital, gross margin, operating cash flow, cash burn, and runway. ## What's included - 5-year monthly revenue build with stage-appropriate growth assumptions - Full P&L, headcount plan, and operating-expense schedule - Cash-flow statement, runway, and burn-rate tracking - Valuation via exit multiple with a DCF cross-check - Returns analysis with MOIC and IRR - Unit economics including CAC, LTV, payback, and cohort retention ## Product & value proposition Three-tier care offering delivered by employed, trained carers using a proprietary digital platform: - **Hourly care** from £19/hr (min 1 hr) - **Overnight care** from £100/night - **Live-in care** from £950/week Platform components: Client App (visit summaries, wellness data, carer ratings), Carer App (geo-location check-in, task lists, health observations), Admin Console (scheduling, events, incident management). Differentiation: carers paid London Living Wage; structured daily wellness data collection (mood, sleep, pain, appetite, bowel movements) as backbone for future predictive analytics. ## Market - UK home care market: £9B, doubles by 2035 - 55% of older people living alone - Care home beds declining -10% - Market highly fragmented: ~10,000 agencies, none >2% market share - No explicit SAM or SOM figures in deck. ## Revenue model - **Fee-for-service**: families pay Lifted directly for care hours at published rates. - Hourly: from £19/hr - Overnight: from £100/night - Live-in: from £950/week - Carers are employed (not marketplace/gig) - cost structure is a direct labour model with COGS = carer wages + travel. - No subscription, platform licensing, or insurance billing mentioned; pure private-pay B2C. - Future product stages (Prolonged Independence, Constant Care) flagged as commercially sensitive / withheld. ## Traction & metrics - Care hours delivered: >6,000 cumulative - MRR: withheld ("£x*", commercially sensitive) - Revenue to date: withheld ("£x*", commercially sensitive) - MRR bar chart shows 7 months of data (April–October 2019) with consistent month-on-month growth; October bar is the highest, roughly 5–6x the April bar - but no axis labels are visible; exact values redacted. - Note: one large client removed from MRR figures to avoid data skew - Trustpilot: rated "Excellent", 10 reviews, all 5 stars ## Unit economics - Published price floor: £19/hr (hourly care) - Carers paid London Living Wage (£10.55/hr as of Oct 2019) - No CAC, LTV, payback period, or gross margin data in deck. - Gross margin directionally: revenue per hour minus carer wage, travel, platform cost - not quantified. ## Competition / moat - Competitive framing: incumbent agencies described as paper-based, fragmented, with high carer churn (40% annual) and poor carer conditions. - Moat claims: proprietary tech platform, employed (not gig) workforce with LLW pay, structured wellness data collection as a long-term data asset for predictive health analytics. - No explicit competitor comparison table in deck. ## Team & funding ask / use of funds - **Rachael** (CEO & Co-Founder): ex-McKinsey consultant. - **Sam** (COO & Co-Founder): personal motivation from family care experience. --- ## Recommended financial model - **Archetype + why**: Direct-labour home care P&L model (care-hours-delivered driver). This is a staffed services business - revenue is hours × rate, COGS is hours × carer wage (with overhead). Not SaaS (no subscription), not marketplace (carers are employed). Closest archetype: DTC services / labour-intensive P&L, similar to a staffing agency with three SKUs (hourly, overnight, live-in). A 3-statement model is appropriate given the capital-intensity of hiring employed carers. - **Forecast horizon & granularity**: 3 years monthly (Year 1–2) then annual (Year 3). Monthly granularity needed to model carer headcount ramp and MRR growth trajectory visible in the deck. - **Key drivers & assumptions**: - Active clients (monthly): >6,000 hours delivered total by Oct 2019 - implies ~low hundreds of active client-months; exact client count not stated. Start model at ~50 active hourly clients in Month 1, growing 10% MoM. - Care hours per client per month: ~20 hrs/month for hourly clients (3-4 visits/week × ~1.5 hrs); overnight and live-in modelled separately. - Blended revenue mix: 70% hourly, 20% overnight, 10% live-in by revenue (live-in skews revenue heavily; mix may shift over time). - Hourly rate: £19/hr; £100/night overnight; £950/week live-in. - Carer COGS: London Living Wage (~£10.55/hr at time of deck) + ~20% employer NI/pension = ~£12.70/hr direct cost. Gross margin on hourly care ~33% at £19 rate. - Carer:client ratio: 1 carer covers ~5–6 active hourly clients (part-time employed model). - Carer churn / attrition: 20% annual (target significantly below industry 40% due to LLW and better tools). - Platform cost (tech/hosting): low fixed cost, scale as % of revenue (~3–5%). - CAC: £150–£300 per acquired client (no data; home care typically referral + digital ads). - Churn (client): 5–8% monthly (care relationships can be long but end-of-life events drive turnover). - Gross margin target: 30–40% (labour-heavy services; benchmark against UK domiciliary care agencies). - Opex: Tech/product team, ops, sales/marketing - seed-stage headcount of ~10–15 FTE in Year 1 growing to 30+ by Year 3. - **Scenarios (Base / Bull / Bear - which variables flex)**: - **Base**: 10% MoM client growth, blended GM ~33%, carer churn 20%. - **Bull**: 15% MoM growth (digital marketing scales), live-in mix increases (higher ASP), platform licensing revenue to third-party agencies introduced in Year 3. - **Bear**: 5% MoM growth, carer wage pressure (National Living Wage rises), client acquisition costs higher than assumed, longer sales cycle. - **Required sheets / outputs**: 1. Assumptions dashboard (all drivers in one place) 2. Revenue build (client cohorts × hours × rate, by SKU: hourly / overnight / live-in) 3. Carer headcount & payroll model (direct COGS) 4. P&L (monthly Year 1–2, annual Year 3): Revenue, COGS, Gross Profit, Opex by function, EBITDA 5. Cash flow & runway (burn rate vs. funding) 6. KPI summary: MRR, care hours delivered, active clients, carers employed, gross margin % 7. Scenario toggle (Base / Bull / Bear) ## Frequently asked questions ### Is the Lifted financial model free? Yes. The Lifted model is a free Excel download with live formulas. ### Can I change the assumptions? Yes. The workbook is editable and its live formulas recalculate when assumptions change.