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15 ways to use xenscope property data in practice

Reusable targeting playbooks for installers, local authorities, analysts, and programme teams.

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1 Find homes likely to need loft insulation upgrades

Search intent fit: Useful for installer and marketing teams seeking loft-insulation targeting logic, and for local authority teams planning area-level interventions.

Expanded use case: Build a prospect list of homes where loft insulation is likely to deliver meaningful comfort and bill improvements, then prioritise by install viability.

Suggested search filter / criteria

  • EPC band in D, E, F, G
  • Roof description contains: no insulation, limited insulation, unknown
  • Property type: detached, semi-detached, terraced, bungalow
  • Exclude flats where loft access is less likely

Why these criteria work

  • Lower EPC bands increase probability of improvement need.
  • Roof insulation indicators are the strongest direct loft signal.
  • House-type filtering reduces wasted outreach.

2 Identify properties with cavity wall insulation potential

Search intent fit: Designed for insulation specialists and retrofit programme teams looking for cavity-wall targeting rules.

Expanded use case: Target homes where wall construction and age profile indicate likely cavity presence and retrofit opportunity.

Suggested search filter / criteria

  • Wall description contains: cavity wall or as built cavity
  • Wall insulation status: poor, unknown, partial
  • Construction age band aligned to cavity-era stock
  • EPC recommendation text includes wall-insulation actions

Why these criteria work

  • Cavity descriptor is the core technical-fit signal.
  • Unknown/poor insulation status flags upgrade opportunity.
  • Age band narrows stock to relevant archetypes.

3 Target solid-wall homes for external/internal insulation campaigns

Search intent fit: Built for hard-to-treat property workflows where teams need to separate solid-wall stock from cavity stock.

Expanded use case: Identify homes where external or internal wall insulation is likely to be a primary route to efficiency gain.

Suggested search filter / criteria

  • Wall description contains: solid brick, solid stone, solid wall
  • EPC band in D–G
  • Property age band in older stock (often pre-1930)
  • Exclude properties already marked as wall insulated

Why these criteria work

  • Solid-wall types require a different retrofit pathway and messaging.
  • Lower EPC bands indicate stronger potential uplift.
  • Excluding already-insulated stock improves conversion efficiency.

4 Segment homes by EPC band for upgrade prioritisation

Search intent fit: Fits operational planning where teams need a repeatable triage model for sequencing outreach.

Expanded use case: Create campaign tiers to target highest-need homes first while matching effort and budget to opportunity.

Suggested search filter / criteria

  • Tier 1: EPC F/G
  • Tier 2: EPC E
  • Tier 3: EPC D with high potential uplift
  • Use potential-vs-current score gap threshold (e.g. +10)

Why these criteria work

  • Band segmentation creates clear operational priority.
  • Potential uplift identifies where interventions can materially improve outcomes.

5 Find homes with low insulation performance signals

Search intent fit: For teams searching broad insulation opportunity detection methods using multiple signals.

Expanded use case: Build a broad insulation-opportunity audience combining roof, wall, and recommendation indicators.

Suggested search filter / criteria

  • Any of: poor roof insulation, poor wall insulation, low fabric-energy score
  • EPC recommendation includes loft/wall insulation
  • Exclude records with strong recent efficiency indicators where known

Why these criteria work

  • Multi-signal matching catches opportunities missed by single-field filters.
  • Recommendation text provides action-ready intent signals.

6 Select homes suitable for heat pump upgrades

Search intent fit: Supports low-carbon-heating targeting for ASHP conversion candidates using EPC and property context.

Expanded use case: Identify housing stock with likely technical and economic alignment for ASHP campaigns and installer lead generation.

Suggested search filter / criteria

  • Main heating fuel/system currently gas/oil/LPG
  • EPC recommendation mentions heat pump or low-carbon heating
  • Property type and age aligned to viable install patterns
  • EPC potential improvement above threshold

Why these criteria work

  • Baseline heating identifies conversion opportunities.
  • Recommendation and uplift signals improve fit and response.

7 Locate areas with high concentrations of EPC improvement potential

Search intent fit: Targets geo-strategy intent where users need postcode or district concentration analysis before allocating resources.

Expanded use case: Prioritise postcodes or districts where campaign density and upgrade need are both high.

Suggested search filter / criteria

  • Group by postcode district/sector
  • Score by count of D–G homes and average potential uplift
  • Apply minimum volume threshold per area (e.g. 300+ homes)

Why these criteria work

  • Area-level concentration improves route efficiency and CAC.
  • Volume thresholds avoid under-scaled campaign zones.

8 Build postcode-first prospect lists for retrofit campaigns

Search intent fit: Addresses postcode-led execution where campaign planning starts with territory and then ranks addresses.

Expanded use case: Create local campaign packs with ranked homes for territory-by-territory execution.

Suggested search filter / criteria

  • Input postcode sets (district/sector/outcode)
  • Rank by EPC band, insulation need, and recommendation match
  • Include property type and tenure proxies where available

Why these criteria work

  • Geography-first execution aligns with installer and council operations.
  • Ranking within postcode maximises early conversion impact.

9 Prioritise homes likely to benefit from glazing upgrades

Search intent fit: Supports glazing and fabric-upgrade intent with criteria for materially beneficial window interventions.

Expanded use case: Target properties where glazing improvements are likely to improve thermal efficiency and comfort.

Suggested search filter / criteria

  • Windows description indicates single glazing
  • Include properties with double glazing installed before 2002 (where glazing age/era is available)
  • EPC recommendation includes glazing/window actions
  • EPC band C–G with additional fabric weakness signals

Why these criteria work

  • Window-related fields directly capture glazing opportunity.
  • Recommendation + EPC context reduces false positives.

10 Identify homes likely to benefit from wall insulation by construction type

Search intent fit: For technically-led teams needing construction-aware segmentation to match offers to building archetypes.

Expanded use case: Segment homes by build form so insulation offer type (cavity/internal/external) matches construction reality.

Suggested search filter / criteria

  • Build groups: cavity-era, solid-wall-era, mixed/unknown
  • Use wall description + construction age band + property type
  • Add EPC recommendation relevance where available

Why these criteria work

  • Construction-led segmentation improves technical fit and quote quality.
  • Better-fit messaging increases lead quality.

11 Find properties where EPC recommendations indicate insulation action

Search intent fit: Maps to recommendation-led intent for teams wanting actionable, audit-friendly logic linked to EPC guidance.

Expanded use case: Build actionable recommendation-led lists for targeted outreach and CRM workflows.

Suggested search filter / criteria

  • EPC recommendation contains: loft, cavity wall, solid wall insulation
  • Current EPC below target threshold (e.g. below C)
  • Optional: minimum recommendation confidence/priority where available

Why these criteria work

  • Recommendation-led filtering aligns campaign with assessed advice.
  • EPC thresholding avoids low-need inventory.

12 Compare local authorities by domestic energy efficiency opportunity

Search intent fit: Supports public-sector and partnership planning intent requiring LAD-level benchmarking.

Expanded use case: Rank LADs on retrofit need, addressable stock, and potential improvement to guide programme allocation.

Suggested search filter / criteria

  • Aggregate by LAD: count of D–G homes
  • Mean current EPC and mean potential EPC
  • Insulation recommendation incidence rate
  • Postcode coverage and data completeness score

Why these criteria work

  • Authority benchmarking helps allocate budget and delivery partnerships.
  • Completeness checks increase confidence in strategic decisions.

13 Create installer-ready lead lists by technology (solar, insulation, heat pumps)

Search intent fit: Serves commercial lead-generation intent where each installer type needs different qualification logic.

Expanded use case: Produce specialist lead packs so each installer type gets technically relevant opportunities only, including solar-PV-specific targeting for high-electricity homes.

Suggested search filter / criteria

  • Solar: EPC recommendation text contains solar photovoltaic panels / solar water heating
  • Solar: where EPC says solar in recommendation fields, prioritise first
  • Solar: EPC field `Current energy consumption` (kWh/year) = current usage baseline
  • Solar: EPC field `Potential energy consumption` (kWh/year) = potential usage benchmark
  • Solar: prioritise homes with high `Current energy consumption` and a strong current-vs-potential gap
  • Solar: roof/solar potential proxies + owner-occupied proxies where available
  • Insulation: wall/roof weakness + recommendation match
  • Heat pump: heating conversion opportunity + EPC uplift potential
  • Territory constraints by postcode/LAD

Why these criteria work

  • Solar recommendation text is a direct intent signal from EPC guidance.
  • Using `Current energy consumption` and `Potential energy consumption` gives a clear before/after usage view.
  • High-usage homes with a large usage gap are often stronger-value solar conversations.
  • Technology segmentation improves relevance and close rates.
  • Territory constraints map to real sales and delivery workflows.

14 Track retrofit-ready housing segments over time

Search intent fit: Supports analytics intent for recurring campaigns needing trend tracking and refresh-cycle visibility.

Expanded use case: Monitor market movement to identify where opportunity is rising or saturating, then adapt strategy.

Suggested search filter / criteria

  • Snapshot by month/quarter for key segments
  • Track counts by EPC movement and recommendation incidence
  • Compare area-level trend deltas vs baseline

Why these criteria work

  • Time-series view supports better planning than one-off extracts.
  • Trend deltas highlight emerging opportunity pockets.

15 Combine EPC and property signals to score upgrade propensity

Search intent fit: Targets model-driven prioritisation for teams ranking likely responders to improve campaign ROI.

Expanded use case: Build ranked propensity scores to prioritise likely adopters for outreach and partner delivery.

Suggested search filter / criteria

  • Inputs: current EPC, potential uplift, recommendation signals, property archetype, area context
  • Score bands: high, medium, low propensity
  • Validate with campaign response and iteratively tune

Why these criteria work

  • Composite scoring outperforms single-metric targeting.
  • Feedback loops improve model precision over time.

How to use this page in practice

  1. 1 Start with the use case closest to your immediate objective.
  2. 2 Copy the suggested criteria into your query logic.
  3. 3 Run a pilot in one geography first.
  4. 4 Validate response rates and conversion quality.
  5. 5 Refine thresholds and scale.

This process helps turn broad property data into specific, measurable campaign outputs.

Who this is for

  • Installers and lead-generation teams: Build higher-quality prospect lists for insulation, heat pumps, and solar workflows.
  • Local authorities and programme teams: Benchmark need, target interventions, and track delivery impact.
  • Retrofit and data analysts: Build repeatable segmentation logic and monitor trend movement over time.
  • Partnership and strategy teams: Align geography, stock profile, and likely outcomes before allocating budget.

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