IR Report Q1 2026:
Our Unit Economics at 5,000 Users; And What They Look Like at 50,000

January 25, 2026

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DestinyTrackers - Scenario-Based Scaling Model (Illustrative)

This report shows how DestinyTrackers’ economics behave at two scale points—5,000 and 50,000 active users—under a one-time purchase model with AI included by default and optional upgrades for power users.

All figures below are illustrative and designed to demonstrate the logic and discipline of the model. They are not guidance.

1) Definitions and core assumptions

What we mean by “user”

An active user is someone who uses DestinyTrackers in a given month (planner or app). This matters because variable costs (hosting, support, AI) track activity, not total historical customers.

Pricing anchors

Planner (current): €34.95
App (planned one-time): €99.95
Optional AI Power (opt-in): €9.99/month or €99/year (illustrative)

The business model reality

We don’t get “subscription compounding” by default. We get run-rate stability from:

consistent new customer acquisition
a product ladder (planner → app → optional upgrades)
strong referral and repeat behavior

2) Cost structure assumptions (illustrative but realistic)

Variable costs (scale with active users)

We model variable costs per active user per month as:

Infrastructure (sync + storage + compute):

at 5k users: €0.35/user/month

at 50k users: €0.20/user/month
(volume pricing + utilization optimization)

Support load allocation (CS time, tooling):

at 5k: €0.45/user/month

at 50k: €0.20/user/month
(better docs, fewer “how-to” tickets, deflection, automation)

Included AI (base tier):
We assume “base AI” is guided and rate-limited by design (workflows, caching, fair-use).

at 5k: €0.60/user/month

at 50k: €0.35/user/month
(routing, prompt efficiency, caching, better model selection)

Total variable cost/user/month

5k users: €0.35 + €0.45 + €0.60 = €1.40
50k users: €0.20 + €0.20 + €0.35 = €0.75

3) Revenue assumptions per active user (run-rate lens)

Because the app is one-time, the cleanest way to show scaling is to model annual run-rate as:

(A) New app sales per year (drives cash)

We model new app sales as a percentage of the active base per year:

Conservative: 15% of active users buy per year
Base case: 25% per year
Aggressive: 35% per year

This reflects a world where:

planners continue feeding the funnel
app conversion improves as product matures
referral loops strengthen

(B) Optional AI Power upgrades (only for heavy users)

We assume an opt-in upgrade rate:

at 5k users: 4% take upgrades
at 50k users: 6% take upgrades
(more awareness + more power users at scale)

Upgrade price (illustrative): €99/year

4) Scenario results at 5,000 active users

4.1 Annual variable cost (run-rate)

€1.40/user/month × 5,000 × 12
= €84,000/year

4.2 Annual revenue run-rate (three cases)

App revenue (one-time sales)

Conservative (15%): 750 sales × €99.95 ≈ €74,963
Base (25%): 1,250 sales × €99.95 ≈ €124,938
Aggressive (35%): 1,750 sales × €99.95 ≈ €174,913

Optional AI Power upgrades

4% of 5,000 = 200 upgrades × €99 ≈ €19,800

Total revenue run-rate

Conservative: €74,963 + €19,800 = €94,763
Base: €124,938 + €19,800 = €144,738
Aggressive: €174,913 + €19,800 = €194,713

4.3 Contribution after variable costs (before payroll + marketing)

Conservative: €94,763 − €84,000 = €10,763
Base: €144,738 − €84,000 = €60,738
Aggressive: €194,713 − €84,000 = €110,713

Interpretation at 5,000 users:

This scale point is typically where you’re still funding:

core team
feature completeness
onboarding and documentation
conversion optimization

So the goal here is not maximal profitability. The goal is proving the cost curve and conversion curve behave.

5) Scenario results at 50,000 active users

5.1 Annual variable cost (run-rate)

€0.75/user/month × 50,000 × 12
= €450,000/year

5.2 Annual revenue run-rate (three cases)

App revenue (one-time sales)

Conservative (15%): 7,500 × €99.95 ≈ €749,625
Base (25%): 12,500 × €99.95 ≈ €1,249,375
Aggressive (35%): 17,500 × €99.95 ≈ €1,749,125

Optional AI Power upgrades

6% of 50,000 = 3,000 upgrades × €99 ≈ €297,000

Total revenue run-rate

Conservative: €749,625 + €297,000 = €1,046,625
Base: €1,249,375 + €297,000 = €1,546,375
Aggressive: €1,749,125 + €297,000 = €2,046,125

5.3 Contribution after variable costs (before payroll + marketing)

Conservative: €1,046,625 − €450,000 = €596,625
Base: €1,546,375 − €450,000 = €1,096,375
Aggressive: €2,046,125 − €450,000 = €1,596,125

Interpretation at 50,000 users:

At this level, the model supports a serious company structure:

a larger engineering team
full customer success and QA
expanded content + creator partnerships
and still has room for reinvestment

6) What changes between 5,000 and 50,000 users

6.1 Variable costs get cheaper per user

Two reasons:

hosting contracts improve and systems become more efficient
support scales with documentation + product maturity (not linearly)

6.2 Revenue becomes more predictable without subscriptions

Not because users keep paying, but because:

you have enough top-of-funnel volume
conversion rates stabilize
upgrades become a modest but meaningful stabilizer (only opt-in)

7) The two biggest risks in the model (and how we design around them)

Risk A: AI cost runaway

If you let AI usage become unlimited “chat for everything,” costs can explode.

Design response:

workflow-based AI (reports, plans, summaries, audits)
caching and context reuse
fair-use limits that protect the base tier
optional upgrades for heavy users

Risk B: CAC inflation in paid channels

One-time products cannot tolerate careless acquisition.

Design response:

focus on owned channels (SEO, community, referral loops)
influencer ROI measured like performance marketing
conversion-rate optimization to increase revenue per visitor

8) The headline takeaway

At 5,000 active users, the model is in “prove the curve” territory—validation, not peak margins.
At 50,000 active users, even conservative conversion assumptions can produce €1M+ annual run-rate while keeping AI included for base users, because variable costs per user fall and optional upgrades absorb heavy usage demand.

Overview