1. Offer Shaping
A fully automated parcel‑claim platform that (1) detects claim‑worthy events using AI trained on >1 bn shipments, (2) auto‑generates carrier‑specific evidence packs, (3) files and enforces claims end‑to‑end, and (4) charges a low fixed fee plus a success‑only fee on recovered refunds.
Why it’s compelling
Prospects instantly reduce manual claim handling time from hours to minutes, recover refunds they would otherwise lose, and only pay when the claim succeeds, aligning cost with value.
The primary problem
Save Money
Differentiation
claim.me combines a proprietary LLM trained on billions of shipments, 100+ carrier integrations, AI‑agent AiME for 24/7 consumer communication, and a success‑based pricing model—features not jointly offered by generic ticketing or logistics software.
Evidence and gaps
Evidence: The website states AI trained on 1 bn+ shipments, 100+ carrier coverage, and customer quotes citing 30% more refunds and 85% less manual effort (source 1). Gaps: No independent third‑party benchmarks, no disclosed average refund amounts, and no public data on claim success rates beyond the quoted 70%+ within 2 weeks.
2. Campaign Hypotheses
H1 E‑commerce CS leaders using Zendesk
If we target heads of CS at e‑commerce firms that use Zendesk and tell them claim.me can auto‑file parcel claims and recover up to 30% more refunds, they will reply because they are already frustrated with manual claim processes and see a clear ROI.
- Audience
- Head of Customer Support at mid‑size e‑commerce
- Awareness
- Problem Aware
- Problem
- Save Money
- Tone
- Warm
List criteria
- Company ships >5,000 parcels/month
- Uses Zendesk as primary CS platform
- Based in EU or North America
- Revenue between $5M‑$50M
Why it could work
- Direct integration reduces friction – prospect sees immediate workflow fit.
- Success‑based pricing aligns cost with recovered refunds, lowering perceived risk.
- AI claim detection promises time savings that CS leaders value.
Why it might not
- Prospects may already have in‑house claim processes they are reluctant to replace.
- Integration effort, even if low, may be seen as a project they cannot prioritize.
High confidenceWebsite explicitly lists a Zendesk app and provides a testimonial about 30% more refunds (source 1).
H2 Logistics managers at D2C brands
If we target logistics managers at D2C brands and say claim.me can automatically recover lost‑parcel refunds without any IT work, they may reply because they are unaware of automated claim solutions but lose money on each failed delivery.
- Audience
- Logistics Manager at direct‑to‑consumer brand
- Awareness
- Unaware
- Problem
- Save Money
- Tone
- Cool
List criteria
- Ships own‑brand products directly to consumers
- Handles >2,000 parcels/month
- No existing claim automation tool
- Operates in EU
Why it could work
- Financial loss from unclaimed refunds is a clear pain point for logistics teams.
- Low‑effort API promise may appeal to non‑technical managers.
Why it might not
- Unaware audience may not prioritize claim recovery over core logistics tasks.
- Perceived complexity of API integration could deter non‑technical prospects.
Medium confidenceWebsite mentions “no win, no pay” and easy API integration (source 2), but no data on adoption among D2C firms.
H3 Customer experience directors at large retailers
If we target CX directors at retailers with >50,000 parcels/month and highlight claim.me’s AI agent AiME that handles consumer‑facing claim communication 24/7, they may reply because they seek to reduce CS workload and improve customer satisfaction.
- Audience
- Director of Customer Experience at large retailer
- Awareness
- Solution Aware
- Problem
- Save Time
- Tone
- Warm
List criteria
- Annual parcel volume >50,000
- Uses any of the listed CS apps (Zendesk, Freshdesk, Gorgias)
- Has a dedicated CX or CS department
- Operates in multiple EU countries
Why it could work
- AI‑driven consumer interaction directly addresses CS staffing constraints.
- Multi‑carrier coverage matches large retailers’ complex shipping footprints.
Why it might not
- Large retailers may have legacy claim processes tied to internal teams.
- Data privacy concerns around AI handling customer communications.
Medium confidenceThe site describes AiME as a 24/7 CS colleague (source 1) and cites efficiency gains, but no quantitative data on time saved.
H4 Operations heads at marketplace platforms
If we target marketplace ops heads and state that claim.me can increase recovered refunds by 20‑30% and feed claim data into BI tools for loss‑pattern analytics, they may reply because they aim to boost net revenue and gain operational insights.
- Audience
- Head of Operations at online marketplace
- Awareness
- Problem Aware
- Problem
- Make Money
- Tone
- Warm
List criteria
- Runs a multi‑seller marketplace
- Processes >10,000 parcels/month
- Uses BI/dashboard tools (e.g., Tableau, PowerBI)
- Has a documented loss‑reduction initiative
Why it could work
- Revenue impact of recovered refunds aligns with marketplace profit goals.
- Analytics can uncover systemic carrier issues, a strategic benefit.
Why it might not
- Marketplace sellers may handle claims individually, limiting centralization.
- Complex seller‑carrier relationships could reduce claim eligibility.
Low confidenceWebsite mentions analytics and BI feed (source 2) but no marketplace‑specific case studies.
3. Priority Order
- H1 Highest confidence, clear integration fit, and existing testimonial evidence.
- H3 Strong time‑saving angle for CX leaders; medium confidence with clear AI differentiation.
- H2 Broad market of D2C brands; moderate risk due to awareness level.
- H4 Potential high upside but low confidence and limited evidence.
4. Testing Plan
Test angles first
Test Save Money (refund recovery) versus Save Time (automation) angles across H1 and H3 to see which pain triggers replies.
Within a winning angle
For the winning pain, iterate on hook variations (e.g., % refund uplift vs. minutes saved per claim) and test different temperature messaging (Warm vs. Direct) while keeping the same list criteria.
When to iterate
When prospects ask for proof of refund uplift, request for a pilot or case‑study data, or when they express interest but raise integration concerns.
When to stop
If after 30 outreach attempts per hypothesis no reply or only negative replies (e.g., “not interested”) are received, discontinue that hypothesis.
When to continue
If at least 10% of contacts reply with a request for a demo, pricing details, or a pilot, continue scaling the hypothesis.
What to measure
Success is measured by reply signals: explicit interest (demo request, pricing inquiry), qualified objections (integration cost, data privacy), and silence. Open rates are ignored.