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Driver Analysis Research for Construction
Stated importance lies. Ask a contractor what matters for an adhesive and price tops the list, but run regression on actual brand preference and training, technical confidence, and tube ergonomics often outrank stated price sensitivity. The same gap appears in roofing, facade, insulation, and windows, and pitches built on stated reasons miss the real lever. We use derived importance via regression, Shapley value decomposition for correlated attributes, MaxDiff for forced trade-offs, and importance-performance analysis to find what really drives specification, brand preference, satisfaction, and loyalty. Stated and derived run side by side, and the gap between them is usually the most valuable finding. In short: we identify the drivers your customers won't tell you about, and rank them by real impact, not survey noise.
30+
years exclusive construction sector focus
13
construction subsectors covered by statistical driver analysis
5 methods
derived importance, MaxDiff, regression, Shapley, and IPA
50+
countries of CATI fieldwork with architects, contractors, and installers
What We Measure
Examples
Drivers of Brand Choice & Preference
What makes a roofer put your underlay on the truck rather than the competitor's. Why an architect specifies your insulation board over an alternative. How stated reasons (price, availability, habit) differ from derived reasons (technical confidence, brand promise, training, warranty terms).
Drivers of Specification
Which product, service, brand, and support attributes predict appearing in the spec. Role of certification, reference projects, and aesthetic factors among architects and specifiers. Influence of technical support and BIM library availability.
Drivers of Satisfaction & NPS
Which product, technical service, logistics, and commercial elements move the score. Separation of mentioned attributes from those that statistically drive it. Driver gaps against key competitors.
Drivers of Loyalty & Repeat Purchase
What causes a contractor to buy again rather than switch. Driver differences between consumables (adhesives, sealants, fasteners) and re-buy categories (windows, doors, paint).
Drivers of Channel Preference
Why a roofer buys direct, why a tiler stays with one merchant, why an installer chooses an online specialist. Trade-offs between price, availability, technical support, credit, and delivery.
Drivers of Premium Acceptance
Willingness to pay premium for green building certifications, energy performance, fire ratings, and acoustic performance. Conditions under which the premium holds and where it collapses.
Construction Subsectors Covered
Subsector
Adhesives and Sealants
drivers of brand preference among tilers, flooring fitters, glaziers. Common finding: technical support and tube ergonomics outrank stated price sensitivity.
Subsector
Roofing
drivers of system specification by architect and brand purchase by roofer. Drivers differ between flat and pitched, and between residential and commercial.
Subsector
Facade
drivers of architect preference for ventilated, ETICS, and rendered systems. Role of fire rating, certification, and reference projects.
Subsector
Insulation
drivers of installer brand preference and specifier choice. Trade-off between thermal performance, handling, and price per square metre.
Subsector
Doors and Windows
drivers of brand choice among installers and specifiers. Often dominated by availability, delivery accuracy, and warranty terms rather than headline product spec.
Subsector
Paint and Coatings
drivers of brand preference among professional decorators and applicators.
Subsector
Tools and Consumables
drivers of brand preference including the role of tool platform and merchant relationship.
Subsector
Walls and Ceilings, Civil and Infrastructure
drivers of contractor brand choice. Often regulated and certification-driven.
Note: this is a portion of the subsectors we cover.
What We Measure
How Construction Driver Analysis Works
Example Project Scenario: a facade systems manufacturer wants to know what really drives architect preference for ventilated facade systems in BE, FR, and DE. Brand awareness is fine but consideration is below target, and the team suspects the value proposition is misaligned with how architects decide. Design: CATI with 400 architects across the three countries, capturing awareness, consideration, preference, performance on 18 attributes, and project context. Two parallel analyses run: stated importance from direct ratings and derived importance from regression of attributes on preference, with MaxDiff on technical and service attributes to address the flat-scale problem common in B2B. Output: the 18 attributes ranked by real impact, a stated versus derived gap analysis, brand and competitor ownership on each driver, and three named investment priorities, with per-country views of where drivers diverge. Note: this is a typical project design, not a fixed process.
Target Audiences for Driver Analysis Research in Construction
Architects [CATI]
specification drivers. Quotas by practice size and project type.
Specifiers and Structural Engineers [CATI]
technical drivers, certification drivers.
Main Contractors and Subcontractors [CATI]
purchase and loyalty drivers. Quotas on category usage.
Trade Specialists [CATI / face-to-face]
roofers, facade fitters, glaziers, tilers, dryliners, painters. Hands-on follow-up where the brief needs it.
Wholesalers and Merchants [CATI]
channel-level drivers, recommendation drivers. Senior decision-makers.
Building Owners and Real Estate Developers [CATI / IDI]
capex and lifecycle drivers.
DIY Consumers [CAWI]
where the category sells through retail.
Our Advantage
Why Driver Analysis Research for Construction?
A generalist runs a stated importance exercise and calls it driver analysis. We always run both stated and derived, because the gap is the point. Without it you cannot see where your pitch and your customer's real decision diverge.
We know which attributes belong on the list before the questionnaire starts. After 30 years we know delivery accuracy is a hidden driver for windows, bond-line visibility moves adhesive choice, and warranty terms beat headline R-value for insulation contractors. A generalist learns this on your money.
We deliver an action grid, not a regression table: a one-page IPA plot with three named priorities, backed by the statistical detail for your technical team. The CCO acts on the first page; R&D and brand dig into the rest.
Project Examples
Aluminium Systems Loyalty Drivers
An aluminium systems manufacturer ran architect and fabricator satisfaction tracking with derived importance modelling of which service, product, and commercial attributes move loyalty and NPS. Used to set service investment priorities by country.
US, CA, NL, RO, BG
NPS Driver Benchmarking
A building products manufacturer ran satisfaction and NPS benchmarking among installers, wholesalers, and architects, with driver analysis ranking service, product, and commercial drivers of NPS and action grids per audience.
IT, PL, FR, DE, BE
Roof Window Recommendation Drivers
A roof window manufacturer used IDIs to define the attribute set, then quantitative driver work to find which value propositions shift installer recommendation.
DE, FR, UK
Facade Architect Preference Drivers
A ventilated facade brand ran architect tracking with brand funnel and derived importance to rank what drives architect preference between competing brands.
BE, FR, DE
Deliverables
- Ranked driver list with derived importance values and confidence intervals
- Stated versus derived gap analysis with named misalignments
- Importance-performance grid per audience and per country
- Driver model by sub-group where sample allows (residential versus commercial, SME versus large)
- MaxDiff utilities on the attribute set, with simulator if the brief includes scenario testing
- Top-three investment priorities with supporting rationale
- Full data tables and cross-tab database (SPSS or Excel)
- Workshop session to translate findings into marketing and R&D priorities
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Why is derived importance different from what respondents say is important?
B2B respondents over-rate technical attributes and under-rate relational and service attributes when asked directly. Regression on actual brand preference exposes the real drivers. The gap is usually the most valuable finding.
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When do you use MaxDiff versus regression?
MaxDiff when the brief is to rank attributes against each other and direct scales are flat, common with architects. Regression when you have a clean outcome (preference, satisfaction, loyalty) and want to model how attributes move it. Often both in one study.
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How many interviews do you need for a credible regression?
A minimum of 150 to 200 per audience per country for a stable model with 12 to 18 attributes. Below that, coefficients become unreliable when attributes are correlated.
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Can you do this in multiple countries with one model?
Yes. Standard practice is a pooled model with country dummies plus per-country models where sample allows. We compare to flag where drivers diverge.
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At what stage of brand or product strategy should driver analysis run?
Before message refresh, before R&D investment commits, before service redesign. Also as an annual layer on NPS tracking to keep the action grid current.
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Can you separate drivers by company size or project profile?
Yes, where sample supports it. We commonly split residential versus commercial, SME versus large contractor, and renovation versus new build.
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How do you handle attributes that are correlated?
Shapley value decomposition. It allocates explained variance fairly across correlated drivers, where OLS would assign it arbitrarily to whichever variable enters first.
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