Driver Analysis Research for Installation
Stated importance lies. Ask an HVAC installer what matters in a heat pump brand and price tops the list, but run regression on actual preference and technical hotline quality, commissioning support, training, and wholesaler stock often outrank it. The same gap appears with plumbers choosing press fittings and electricians choosing wiring devices, and pitches built on stated reasons miss the real lever. We use derived importance via regression, Shapley value decomposition, MaxDiff, and importance-performance analysis to find what really drives preference, satisfaction, loyalty, and recommendation. Stated and derived run side by side, and the gap is usually the most valuable finding: the drivers your installers won't tell you about, ranked by real impact, not survey noise.
30+
years of exclusive sector focus
3 subsectors
HVAC, electrical, waste water
5 methods
derived importance, MaxDiff, regression, Shapley, IPA
20+
countries of CATI fieldwork
Our clients
What We Measure
Drivers of Brand Choice & Preference
What makes a plumber load your press fittings rather than the competitor's, or an electrician default to one wiring brand. How stated reasons (price, habit) differ from derived (technical confidence, hotline quality, training, warranty).
Drivers of Energy Transition Adoption
What moves an installer to recommend a heat pump over a gas boiler beyond the subsidy: commissioning support, refrigerant handling, margin per job. Drivers of EV charging and solar uptake among electrical installers.
Drivers of Satisfaction & NPS
Which product, technical service, logistics, and commercial elements actually move installer and wholesaler scores. Separation of mentioned attributes from those that statistically drive the score. Driver gaps versus key competitors, per audience and per country.
Drivers of Loyalty & Switching
What makes an installer buy the same brand again or switch. Replacement behaviour: when a circulator pump fails, what determines like-for-like versus upgrade versus brand switch. Consumables versus considered purchases.
Drivers of Wholesaler & Channel Choice
Why an installer stays with one wholesaler, splits spend, or moves online. Trade-offs between price, stock, counter service, credit, and delivery, plus what drives the wholesaler's own counter advocacy.
Subsectors Covered
Subsector
HVAC & Plumbing
Drivers of brand choice among heating installers and plumbers. Energy transition reshapes the driver set: commissioning support and refrigerant training now move heat pump brand choice in ways they never moved boiler choice. Press-fit system drivers differ by country, with adoption strong in DE and NL and weaker in FR and UK.
Subsector
Electrical Installation
Drivers of brand preference among electricians and panel builders across wiring devices, cable management, enclosures, EV charging, and smart building products. Wholesaler stock and time-saving in installation are recurring hidden drivers.
Subsector
Sewer & Waste Water
Drivers of product and brand choice in drainage and waste water, where specifier influence, replacement cycles for ageing infrastructure, and regional drainage norms shape who decides and on what basis.
Note: This is a portion of the subsectors and product categories we cover within installation research.
How Installation Driver Analysis Works - Example Project
Example project
Scenario: a heat pump manufacturer has strong awareness among HVAC installers in DE, NL, and UK but recommendation lags a key competitor. The brand team believes price is the problem; the technical director suspects service. Nobody has a model to settle it.
Design: CATI with 450 HVAC installers across the three countries, recruited from our own frames with quotas on company size and heat pump activity. Brand funnel, recommendation behaviour, and ratings on 16 product, service, and commercial attributes captured. Two parallel analyses: stated importance from direct rating, derived from regression on recommendation, with MaxDiff on service and support attributes.
Output: 16 attributes ranked by real impact. Price ranks third on stated importance and seventh on derived; commissioning support and technical hotline response rank first and second on derived impact, and the competitor owns both. Three named investment priorities, with per-country views showing the UK diverges on training because of certification.
Note: This is an example of a typical project design, not a fixed process.
Target Audiences
Note: Audience mix is tailored to each project.
HVAC Installers [CATI]
Brand, loyalty, and recommendation drivers for heating, cooling, and ventilation products. Quotas on company size and activity profile.
Plumbers [CATI]
Purchase drivers for water supply, drainage, and hot water products. Consumable versus considered purchase dynamics.
Electrical Installers & Panel Builders [CATI]
Brand and channel drivers across domestic, commercial, and industrial work.
M&E Engineers [CATI / IDI]
Specification drivers on commercial and industrial projects. Certification and documentation drivers.
HVAC & Electrical Wholesalers [CATI / IDI]
Stocking and advocacy drivers. Senior decision-makers, since their counter recommendation moves installer choice.
Energy Companies & Utilities [CATI / IDI]
Adoption and partnership drivers as energy transition programmes shift who influences product choice.
Homeowners [CAWI]
Where the category has a consumer decision layer, such as thermostats, heat pumps, and visible sanitary products.
Our Advantage
A generalist will run a stated importance exercise and call it driver analysis. We always run both stated and derived, because the gap is the point. Without it you do not know where your sales pitch and your installer's actual decision diverge.
We know which attributes belong on the list before the questionnaire starts. After 30 years of installation work we know that hydronic balancing is accepted in Germany and resisted in France, that press-fit adoption follows different logic in the Netherlands than in the UK, and that hotline response time quietly drives heat pump brand recommendation more than headline efficiency figures. A generalist agency learns this on your money.
We deliver an action grid, not a regression table. The output is a one-page IPA plot with three named priorities, supported by the statistical detail for your technical team. The CCO can act on the first page, the R&D and brand teams can dig into the rest.
Project Examples
An HVAC manufacturer ran brand health tracking covering funnel, NPS, price positioning, and perception across seven markets. Derived importance modelling layered on the tracking ranked which attributes move installer preference, turning the tracker into an investment prioritisation tool.
DE, AT, CH, UK, ES, AE, US
An electrical manufacturer measured wholesaler satisfaction and NPS against competitors across four countries. Driver analysis identified which service, logistics, and commercial attributes statistically move the score, producing improvement priorities per market.
FR, ES, PT, IT
A wire connection manufacturer used qualitative work to map the competitive attribute set, then quantitative driver analysis to identify which factors actually move installer brand decisions between connection technologies.
US, PT, ES, IT
An HVAC supplier established NPS and Customer Effort Score tracking for retention. Key driver analysis ranked which interactions and service elements move effort and loyalty scores, focusing retention investment on the few moments that matter.
DE, CH, UK, BE, NL
Deliverables
- Ranked driver list with derived importance values and confidence intervals
- Stated versus derived importance 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, adopters versus loyalists)
- 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, sales, and R&D priorities
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Why is derived importance different from what installers say is important?
Installers over-rate price and product specifications and under-rate service, support, and availability when asked directly. Regression on actual brand preference or recommendation 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, which is common with installers. Regression when you have a clean outcome variable (preference, satisfaction, recommendation) and want to model how attributes move it. Often we use both in the same study.
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How many interviews do you need for a credible regression?
Minimum 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, which in installation they regularly do because of regulation and subsidy differences.
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At what stage of brand or product strategy should driver analysis run?
Before a message refresh, before R&D or service investment commits, before deciding how to respond to a competitor’s price move. Also as an annual layer on NPS or brand tracking to keep the action grid current.
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Can you separate drivers by installer type or company size?
Yes, where sample supports it. We commonly split residential versus commercial, heating versus ventilation specialists, SME versus large installation companies, and heat pump adopters versus boiler loyalists.
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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. Common in HVAC and electrical, where quality and reliability attributes overlap heavily.
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