Driver Analysis Research for Home Improvement
Ask a DIY consumer why they picked a paint brand and you get colour and price. Model their actual choice behaviour and a different picture emerges: prior brand experience, shelf visibility, and confidence that the job will work out on the first attempt carry more weight than stated price sensitivity. Ask a professional painter the same question and you get quality. Run regression on what actually predicts their loyalty and it is one-coat coverage, merchant availability, and the tinting service at the counter. Home improvement is two markets in one. The DIY consumer and the trade professional buy the same categories through different channels for different reasons. We run separate driver models for each, in the same study, using derived importance via regression, Shapley value decomposition for correlated attributes, MaxDiff for forced trade-offs, and importance-performance analysis. Stated and derived run side by side. The gap between them is usually the most valuable finding. In short: we identify what really drives brand choice at the shelf and loyalty in the trade, and rank it by real impact, not survey noise.
30+
years exclusive focus on home improvement, construction, and installation
Driver analysis across 7 home improvement subsectors, from paint to power tools
DIY consumer (CAWI) and trade professional (CATI) driver models in one integrated study
Four
proprietary syndicated monitors covering home improvement audiences
What We Measure
Drivers of Brand Choice at the Shelf
What makes a DIY consumer pick your paint, sealant, or drill off the shelf rather than the private label next to it. How stated reasons (price, colour range) differ from derived reasons (prior experience, packaging clarity, fear of redoing the job). The real weight of in-store visibility and promotion versus brand equity built before the store visit.
Drivers of Trade Brand Loyalty
Why a professional painter stays with one paint brand for fifteen years and what would make them switch. The role of merchant availability, tinting service, technical reliability, and account terms among painters, handymen, and fitters. For power tools: how battery platform lock-in outranks almost every stated attribute once a professional owns three tools on one system.
Drivers of Channel Choice
Why a consumer buys flooring at a DIY superstore, a specialist, or online, and what each channel decision does to brand outcomes. Why a handyman buys through a trade counter rather than a retail store. Trade-offs between price, stock, credit, and opening hours. Drivers of online versus in-store purchase by category, since they differ sharply between paint, tools, and bathroom products.
Drivers of the DIY versus DIFM Decision
What pushes a homeowner to do the job themselves versus hire a professional, by category and by project type. Which product attributes (ease of application, perceived risk, finish quality) move that boundary, because the boundary defines who your real customer is.
Drivers of Satisfaction & NPS
Which product, range, service, and channel elements actually move satisfaction scores among consumers and trade customers. Separation of mentioned attributes from those that statistically drive the score. Driver gaps versus key competitors and versus private label.
Drivers of Premium & Sustainability Acceptance
Willingness to pay for low-VOC paint, FSC-certified products, durable tool ranges, and water-saving bathroom products. Conditions under which the premium holds for consumers, and the very different conditions under which it holds for professionals.
Drivers of Recommendation
What makes a painter, fitter, or handyman recommend a brand to the homeowner who pays for it. How much of the final brand decision the professional actually controls, by category.
Subsectors Covered
Subsector
Paint
Drivers of brand choice among DIY consumers and brand loyalty among professional decorators. The two models rarely agree. Tinting service and merchant stock drive the trade, prior experience and colour confidence drive the consumer.
Subsector
Hand & Power Tools
Drivers of brand preference across DIY and professional segments. Battery platform lock-in is the dominant hidden driver for professionals.
Subsector
Flooring
Drivers of category choice (LVT versus laminate versus solid wood) and brand choice within category. Online research now shapes drivers long before the store visit.
Subsector
Bathroom Products
Drivers in a three-way decision between homeowner, installer, and showroom. Whose preference actually wins varies by product and price tier.
Subsector
Kitchen Products
Drivers of brand and retailer choice in a high-consideration purchase with strong fitter and designer influence.
Subsector
Outdoor & Gardening
Drivers of brand and channel choice in a seasonal, weather-driven category. Garden centre versus DIY superstore dynamics shape what drives the purchase.
Subsector
Decorative Sundries
Drivers of brand choice for brushes, rollers, fillers, and tape, where trade habit and counter availability dominate.
Note: This is a portion of the subsectors and product categories we cover within home improvement research.
How Home Improvement Driver Analysis Works - Example Project
Scenario: A power tool manufacturer holds strong share among professionals but is losing DIY consumers to a cheaper competitor in DE, FR, and UK. The marketing team wants to know whether to defend on price or invest elsewhere. They suspect the two audiences buy for entirely different reasons but have no model to prove it. Design: CAWI with 300 DIY consumers per country who bought a power tool in the past 12 months, screened with category engagement and behaviour verification checks. In parallel, CATI with 100 trade professionals per country recruited by phone from our own frames. Both audiences rate performance on the same 16 attributes and complete a MaxDiff exercise. Two parallel analyses per audience: stated importance from direct rating, derived importance from regression of attributes on brand preference. Output: Separate ranked driver models for DIY and trade. For professionals, battery platform compatibility and dealer service emerge as the top derived drivers despite ranking mid-table on stated importance. For DIY consumers, price matters but prior brand experience and perceived durability outrank it. The recommendation: hold price, invest in platform breadth and retail demonstration. Per-country views show where the German DIY market diverges from the UK. Note: This is an example of a typical project design, not a fixed process.
Target Audiences
Note: Audience mix is tailored to each project.
DIY Consumers & Homeowners [CAWI]
Purchase and channel drivers. Screened on category purchase in the past 12 to 24 months, quotas on demographics and buyer type.
Professional Painters & Decorators [CATI]
Brand loyalty and switching drivers. Recruited by phone, not panels.
Handymen & General Contractors [CATI]
Multi-category purchase drivers and recommendation drivers toward the homeowner.
Kitchen & Bathroom Fitters [CATI]
Brand and wholesaler drivers in installation-led categories.
Interior Designers [IDI / video call]
Specification and recommendation drivers in kitchen, bathroom, and flooring.
DIY & Specialist Retailers [IDI / CATI]
Ranging drivers that determine which brands consumers encounter at all.
Our Advantage
A generalist will run a stated importance exercise on a consumer panel and call it driver analysis. We always run both stated and derived, because the gap is the point. And we never pool consumers and professionals into one model. They buy the same categories for different reasons, and a blended driver ranking misleads both your retail team and your trade team.
We know which attributes belong on the list before the questionnaire starts. After 30 years of home improvement work we know that battery platform beats stated price sensitivity for professional tool buyers, that tinting service quietly anchors painter loyalty, and that fear of redoing the job drives more DIY paint choices than colour range. 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 per audience with named priorities, supported by the statistical detail for your insight team. The CMO can act on the first page, the category and brand teams can dig into the rest.
Project Examples
A power tool manufacturer commissioned research on purchasing criteria among professionals and DIY consumers across seven countries. Parallel driver models per audience identified which product and service attributes really move brand choice, separating professional platform drivers from consumer price perceptions.
DK, FR, DE, IT, PL, ES, SE
A painting tools manufacturer measured brand strength and satisfaction among professional users. Driver analysis ranked which product and distribution attributes move satisfaction and repeat purchase, feeding range and channel decisions.
DE, UK
A manufacturer exploring the wood care and repair segment commissioned a study covering market size, channels, pricing, and satisfaction drivers. The driver layer identified which product attributes move user satisfaction and where competitors under-deliver.
UK, DE
An adhesives and sealants brand measured brand health among installers, handymen, and pool builders. Derived importance modelling ranked what drives brand preference among trade users, separating habit from genuine performance drivers.
NL, FR
Deliverables
- Ranked driver list per audience with derived importance values and confidence intervals
- Stated versus derived importance gap analysis with named misalignments
- Separate importance-performance grids for DIY consumers and trade professionals, per country
- Driver model by sub-group where sample allows (heavy versus light DIYers, DIY versus DIFM, trade segments)
- MaxDiff utilities on the attribute set, with simulator if the brief includes scenario testing
- Top-three investment priorities per audience with supporting rationale
- Full data tables and cross-tab database (SPSS or Excel)
- Workshop session to translate findings into marketing, retail, and trade priorities
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Why is derived importance different from what respondents say is important?
Consumers over-state price and rational attributes and under-state habit, experience, and emotional confidence when asked directly. Professionals over-rate technical attributes and under-rate service and availability. Regression on actual brand preference exposes the real drivers. The gap is usually the most valuable finding.
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Can you cover DIY consumers and trade professionals in one study?
Yes, and in home improvement you usually should. We run CAWI for consumers and CATI for professionals in parallel with a shared attribute core, then model each audience separately. You get two driver rankings and a comparison, not a blended average that describes nobody.
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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 consumers. Regression when you have a clean outcome variable (preference, satisfaction, switching) 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 driver model?
For consumers, 200 to 300 per country gives a stable model. For trade audiences, minimum 100 to 150 per audience per country with 12 to 18 attributes. Below that, coefficients become unreliable when attributes are correlated.
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Can you run this across 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 home improvement they regularly do, especially on channel and private label attributes.
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At what stage should driver analysis run?
Before a brand message refresh, before committing range or packaging investment, before a price defence decision, and as an annual layer on satisfaction or brand tracking to keep the action grid current.
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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. Paint and tool attribute lists are heavily correlated, so this matters more in home improvement than most sectors.
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