Experience Data Products and Market Signals

10. Experience Data Products and Economics

10.1 From raw telemetry to experience SKUs

Because Spaxiom enforces schemas and allows licensing/metadata tags at the language level (Phase 2 and 3 design), it becomes natural to treat certain event streams as data products:

• "Anonymous Retail Dwell Patterns v1.2 (CC-BY-NC)"
• "Industrial Near-Miss Events (commercial license)"
• "Hospital Gait Instability Episodes (de-identified, research-only license)"

Each product can document:

• Schema: event types, fields, units.
• Aggregation level: per-event, per-zone/day, etc.
• Privacy guarantees: k-anonymity, minimum aggregation horizons.
• Licensing: permissive vs commercial.

10.2 Revenue sharing and valuation

If a data product aggregates events from M contributing sites, we can define a revenue-sharing scheme where revenue R is partitioned via weights w_i:

where V_i might be a function of:

• volume (event count),
• diversity (number of distinct patterns),
• quality (coverage, completeness, low noise).

Then site i receives revenue:

Spaxiom can help compute V_i automatically from event logs.

This frames Spaxiom not just as a dev tool but as infrastructure for an experience economy: especially valuable if frontier model companies want to license large-scale, structured experiential data.

11. Spaxiom-Derived Market Signals and Public Equities

Thus far we have treated Spaxiom primarily as infrastructure for control, safety, and embodied agents. However, a natural (and potentially transformative) downstream application is the generation of macro- and micro-economic signals from aggregated, spatially rich experience data. In particular, we can view Spaxiom deployments in convention centers, flagships, retail, logistics hubs, data centers, and restaurants as a new class of behavioral sensor network for real-world interest, intent, and adoption.

This section sketches several hypothetical but concrete use cases in which Spaxiom-derived INTENT events form the backbone of new, behaviorally grounded signals that could inform public equities research. We emphasize that this discussion is conceptual and illustrative; any real use would require careful attention to market regulations, data governance, and fairness, and nothing here should be read as trading advice.

11.1 Convention centers to retail: from expo engagement to demand proxies

Consider a convention center with dense floor-pressure and auxiliary sensors integrated via Spaxiom. Each booth k (e.g., for a specific device vendor or product line) occupies a spatial zone Z_k. Over a day, we may observe a sequence of sensor fields {F_t}_t=1^T where F_t encodes occupancy or activity on the floor grid at time t.

Spaxiom and INTENT can compress these raw fields into a small set of behavioral features per booth and time window, such as:

• v_k,t: visitor count or dwell-time-weighted visit volume,
• e_k,t: engagement intensity (e.g., fraction of visitors who linger beyond a threshold),
• c_k,t: conversion proxies (e.g., movements consistent with interactions at a checkout or pre-order desk),
• σ_k,t: crowding or hype measures (spatial density and its variance around the booth).

For each discrete window (e.g., 15 minutes), Spaxiom can emit an INTENT event of type BoothEngagement:

{
  "type": "BoothEngagement",
  "site_id": "expo-2027-ces",
  "zone": "hall-b/booth-217",
  "vendor_ticker": "EXMP",   # optional, if mapped
  "timestamp": "2027-01-09T14:15:00Z",
  "visitor_count": 432,
  "avg_dwell_s": 210.3,
  "engagement_score": 0.81,
  "conversion_proxy": 0.14
}

Aggregated over the expo, these features form a time series f_k,t summarizing how much attention and engagement a particular category or issuer receives, with much finer granularity than traditional survey-based or anecdotal reports.

Lead–lag structure: expos to retail

Downstream, many of the same products or categories appear in retail stores, e-commerce platforms, or usage telemetry. Suppose for a given issuer or category we can observe:

• f_t: an expo-derived feature (e.g., normalized engagement index during industry shows),
• R_t: realized revenue or unit sales in retail at a later time,
• P_t: stock price or factor-adjusted return series.

A simple hypothesis is that f_t contains leading information about future R_t+ℓ for some lag ℓ > 0, which in turn may correlate with earnings surprises and eventually with P_t+ℓ'.

At a purely statistical level, one might model:

where X_t captures known macro and seasonal effects, and ε_t is noise. If β₁ is significantly non-zero and stable across expos, f_t acts as a durable leading indicator of demand.

Similarly, we can define an earnings or fundamental surprise S_t+ℓ'' and ask whether:

with γ₁ ≠ 0. In such a case, expo engagement signals derived from Spaxiom might enter into factor models as a new kind of behaviorally grounded "alternative data" factor.

It is crucial that Spaxiom's role here is upstream: it provides clean, semantically meaningful features f_t from messy raw sensor fields, rather than handing unstructured telemetry directly to quantitative researchers.

Cross-site causal chains: expos → flagships → retail

A richer picture emerges when we consider multiple Spaxiom sites linked along a commercialization chain. For a given product category c (e.g., consumer AR headsets), we can define:

• Expo engagement time series: f^expo_c,t from convention centers.
• Flagship experience time series: f^flag_c,t from brand-owned stores and demo labs (e.g., in-store dwell around experience zones).
• Retail adoption time series: f^retail_c,t from multi-brand stores (e.g., dwell around the category, footfall to the shelf, trial-to-purchase proxies).

We can model the propagation of interest as a simple linear–time-invariant system, or more generally using vector autoregressions:

where A encodes propagation and decay of interest, and u_t captures interventions and shocks (marketing campaigns, product recalls, macro events).

If specific entries of A (e.g., expo → flagship, flagship → retail) demonstrate stable, positive influence, then expo-derived features become part of a causal chain that eventually reflects in real economic activity. Again, Spaxiom's contribution is to ensure that the raw measurements at each stage are expressed in compatible INTENT-level semantics, making such modeling feasible at scale.

Example INTENT pattern

As a concrete (simplified) example, consider a Spaxiom INTENT pattern CategoryAggregator that rolls up booth-level engagement into category-level indices in real time:

from spaxiom.intent import CategoryAggregator
from spaxiom.logic  import on, Condition
from spaxiom.temporal import within

# Suppose booth_engagement_stream yields BoothEngagement events
aggregator = CategoryAggregator(source="BoothEngagement")

def category_index(category: str) -> float:
    data = aggregator.snapshot(category=category, window_s=900)
    return data["weighted_engagement"]  # e.g., dwell * visitor_count

# Create a condition that fires once per 15 min window
new_window = within(900, Condition(lambda: True))

@on(new_window)
def publish_category_signals():
    for cat in ["consumer_ar", "gaming_laptops", "ai_pcs"]:
        idx = category_index(cat)
        event = {
            "type": "ExpoCategoryEngagement",
            "category": cat,
            "timestamp": now_iso(),
            "engagement_index": idx
        }
        # Write to an internal bus; a separate, policy-checked
        # process may aggregate & delay-release this as a data product.
        bus.publish("internal.expo.signals", event)

11.2 Contrast with traditional alternative data

It is helpful to contrast Spaxiom-derived signals with existing classes of alternative data commonly used in public equities research:

• Transaction exhaust (credit/debit card panels, bank account aggregates) provides direct but lagged views of consumer spend, often with limited category granularity and complex sampling biases.
• Web and app telemetry (web-scraped prices, app usage metrics, clickstreams) captures digital intent and engagement, but generally lacks fine-grained physical context; the same click may correspond to wildly different in-store realities.
• Remote sensing (satellite imagery, aerial photos, parking lot counts) gives coarse physical signals (e.g., cars at big-box stores, oil tank levels) but at relatively low spatial and temporal resolution and with limited behavioral semantics.
• Survey and panel data (brand trackers, NPS, ad recall) encode sentiment and self-reported behavior, but are expensive to scale, subject to response biases, and typically low-frequency.

Spaxiom-derived experience signals are different along several axes:

1. Spatial and behavioral granularity. By instrumenting floors, zones, and paths inside buildings, Spaxiom can distinguish, for example, "quick pass-by traffic" from "deep engagement dwell" within the same store, and can localize interest to specific fixtures, booths, or demo areas.
2. Real-time, pre-transactional visibility. Many traditional alt-data sources observe behavior after a transaction (card swipes, shipments, bookings). Spaxiom can observe shifts in intent and exploration upstream of purchase: for example, intense engagement with a new category at an industry expo before distribution has ramped.
3. Schema-level semantics. Rather than raw images or ad-hoc features, Spaxiom produces INTENT events with explicit meaning (e.g., BoothEngagement, ProductTrial, AbandonedQueue). This makes it easier to align signals with economic hypotheses, integrate across venues, and audit for misuse.
4. Built-in privacy and policy hooks. Because primitives are defined at the language level, aggregation, anonymization, and latency policies can be expressed as part of the Spaxiom configuration (for example, enforcing minimum crowd sizes or time windows before any signal can leave a site) rather than being bolted on post hoc.
5. Embodied context. Spaxiom is inherently tied to embodied behavior in physical spaces: how people move, queue, linger, and interact with objects. This is complementary to purely digital attention data and potentially more robust to shifts in digital channels or interface design.

Together, these properties suggest that Spaxiom-style experience factors could occupy a distinct niche in the alternative data landscape: less about reconstructing past spend from exhaust, and more about capturing emergent patterns of interaction with the physical world before they fully manifest in traditional financial metrics.

11.3 Logistics corridors and port facilities → global trade and shipping

Global trade flows are mediated by a network of ports, intermodal yards, and cross-dock warehouses. Traditional indicators of trade health (e.g., customs statistics, shipping line disclosures, PMI surveys) are often delayed, low-frequency, and aggregated. A dense deployment of Spaxiom nodes along logistics corridors could provide a higher-frequency, behaviorally grounded view of congestion, throughput, and stress in the supply chain.

Consider a container terminal instrumented with Spaxiom-integrated sensors across:

• inbound and outbound gate queues,
• chassis staging areas,
• yard blocks and stacking rows,
• rail interchange tracks and cross-dock facilities.

From floor sensors, gate loop detectors, RFID/RF beacons, and environmental sensors, INTENT patterns can synthesize events such as:

• GateQueue (queue length, wait time distribution),
• YardCongestion (effective density of containers per zone),
• IdleChassis (underutilized asset pools),
• BerthTurnaround (time from berthing to completion).

For a given facility d and day t, define a congestion index g_d,t ∈ [0,1] derived from normalized queue lengths and dwell times, and a throughput measure q_d,t (e.g., TEUs handled). At a regional or global level, we can aggregate:

where w_d captures facility capacity or strategic weight. G_t then represents a Spaxiom-derived real-time logistics stress index for a trade lane.

We can examine lead–lag relationships between (G_t, Q_t) and traditional macro indicators, such as export volumes, shipping line revenues, or freight rate indices. For example:

where ΔR^shipping_t+ℓ is a sector-level revenue or earnings change at horizon ℓ.

Example INTENT pattern

A simplified pattern for gate queues:

from spaxiom.intent import GateQueueMonitor
from spaxiom.logic  import on, Condition
from spaxiom.temporal import within

gate = GateQueueMonitor(
    entry_sensor=gate_loop_entry,
    exit_sensor=gate_loop_exit,
)

# fires once per 15 minutes
tick_15m = within(900, Condition(lambda: True))

@on(tick_15m)
def emit_gate_state():
    state = gate.snapshot(window_s=900)
    event = {
        "type": "GateQueue",
        "site_id": "port-alpha",
        "timestamp": now_iso(),
        "avg_wait_s": state["avg_wait_s"],
        "p95_wait_s": state["p95_wait_s"],
        "queue_length": state["queue_length"],
        "stress_score": state["stress_score"],
    }
    bus.publish("internal.logistics.events", event)