Hot Drop

I’m exploring HOT DROP for hockey and football because the game-day experience includes more than the action on the field or ice. I want to understand what fans actually give up to buy something, and what service teams can realistically deliver. MacEwan and University of Alberta sports are possible discovery settings, not claimed partners.

Test whether a seat-aware service model can fit fans’ needs and authorized venue operations.

The reviewed hockey checkout explicitly simulates orders and charges. Football and hockey are demonstration contexts; no team, venue, campus or concession relationship is implied.

Labels separate facts about the build, my decisions, assumptions to test and unanswered questions. Product facts describe reviewed source, not independently verified live behaviour. No supported market signal or proven outcome is asserted in this draft.

Problem

HYPOTHESISSome fans may miss action or abandon purchases; operators may face peak queues and fulfilment constraints.

Customer segments

HYPOTHESISFans; concession operators; venue/event decision-makers; runners and service staff. Each supplies different evidence.

Existing alternatives

UNKNOWNConcession queues, preorders, pickup, roaming sellers and existing venue systems. Their actual local availability is unknown.

Unique value proposition

HYPOTHESISA way to buy during an event that preserves the experience and remains workable for the operator.

High-level concept

HYPOTHESISSeat-aware event service, adapted to venue conditions.

Solution

VERIFIED PRODUCT FACTReviewed checkout implements sample cart, seat, fulfilment choice and local order simulation; no real payment or dispatch is established.

Channels

OWNER DECISIONAuthorized operator interviews and fan discovery across hockey and football; campus sports are an exploratory segment.

Revenue streams

UNKNOWNOperator service contract, permitted service fee or partner-funded model are hypotheses. Buyer, terms and margins unknown.

Cost structure

UNKNOWNRunner labour, picking, integrations, support, refunds, connectivity and operations. Peak-load economics unknown.

Key metrics

OWNER DECISIONAbandoned purchases, time away from play, order accuracy, fulfilment time, incremental contribution and operational burden.

Unfair advantage

UNKNOWNNot yet demonstrated. A demo and configurable branding do not establish access or defensibility.

Early adopters

HYPOTHESISAn authorized operator with a specific service problem and a feasible limited test area; fans with recent costly workarounds.

The assumptions that could change my direction

1. Fans have an unmet purchasing problem rather than preferring current options.
2. The venue and concession operator can authorize and economically fulfil a limited service.
3. Peak demand, connectivity and labour constraints allow reliable operation.

Founder context is a draft for review. Revenue, adoption and outcomes remain unproven.

I’ll ask about recent events, current alternatives and real effort or spending before showing the product. These questions are a first instrument to pretest, not a scored assessment or a sales script.

Only after those answers are recorded

My short concept introduction: A seat-aware event-service concept for purchasing and fulfilment, with the delivery or pickup model determined by what an authorized operator can support.

• Where would this fit, if anywhere?
• What would it replace, and what would make it not worth using?
• Who would need to approve it, and what evidence would they require?
• What should it never do?

I’ll keep these reactions separate from the earlier answers. I won’t introduce a feature to counter every objection or treat a compliment as demand.

Invitation and participant handling

I’m researching how people currently handle this situation and would value a 15–25 minute conversation about what I have misunderstood. This is discovery, not a sales pitch; criticism and examples of existing approaches that work are welcome.

Participation is voluntary. I’ll use participant codes and manual notes, ask for no identifying information about other people, and request separate permission before any recording. Before recruiting, I’ll confirm where notes are stored, who can access them and when they will be deleted. Public updates will use anonymized findings, not raw notes.

The sequence

1. Recruit the relevant role and confirm participation and note handling.
2. Ask about current behaviour without the product name, screenshots or mechanics.
3. Record the answers before a short concept introduction.
4. Record concept reactions separately and ask permission for follow-up.
5. Complete coded notes immediately; record the question version and any wording changes.
6. Stop after the first 3–5 conversations in each planned pretest and review before expanding outreach.

What I’ll record

Participant code, role, date, instrument version, last real example, current alternative, frequency and consequence, actual time or money committed, barriers, buyer or approver, contradictions, and concrete follow-up. Every finding will say which assumption it supports or challenges and whether the concept had already been shown.

What counts as evidence

A detailed recent example, repeated unprompted difficulty, costly workaround or concrete commitment is more useful than “interesting” or “I would try it.” I’ll preserve negative evidence and compare segments separately. A few conversations test the questions and direction; they do not prove market size, payment, retention or effectiveness.

My next action

Research recent purchasing behaviour and operator constraints before demonstrating HOT DROP or proposing a partnership.

Next evidence gate

Review fan and operator pretests independently. Select one sport, one permitted area and one narrow service only if a problem, authority and plausible economics align; do not launch commerce from interview enthusiasm.

What could make me change or stop

Pause if current systems suffice, authorization is unavailable, or added labour and peak-load failures outweigh value. Campus sports may require a different model.

After review, I’ll update the canvas, date the change and choose a smaller segment, revised questions, a specific next cohort, a bounded test or a pause. New features will follow a specific finding rather than every suggestion.