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 and prior experience, shelf visibility, and confidence the job will work first time outweigh stated price. Ask a professional painter and you get quality, but loyalty actually rides on one-coat coverage, merchant availability, and the tinting service at the counter. Home improvement is two markets in one, bought through different channels for different reasons, so we run separate driver models for consumer and trade in one study, using regression, Shapley, MaxDiff, and importance-performance analysis. Stated and derived run side by side, and the gap is usually the most valuable finding.
30+
years of exclusive sector focus
7 subsectors
from paint to power tools
Dual-Audience
consumer and trade driver models
4
syndicated home improvement monitors
Our clients
What We Measure
Drivers of Brand Choice at the Shelf
What makes a DIY consumer pick your paint, sealant, or drill over the private label beside it. How stated reasons (price, colour) differ from derived (prior experience, packaging clarity, fear of redoing the job).
Drivers of Trade Brand Loyalty
Why a painter stays with one brand for years and what would make them switch: merchant availability, tinting service, reliability, account terms. For tools, battery platform lock-in outranks almost every stated attribute.
Drivers of Channel Choice
Why a consumer buys flooring at a superstore, a specialist, or online, and what each does to brand outcomes. Trade-offs between price, stock, credit, and hours, differing by category.
Drivers of the DIY versus DIFM Decision
What pushes a homeowner to do the job or hire a professional, and which attributes (ease of application, perceived risk, finish) 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 among consumers and trade, separating mentioned attributes from real drivers. Driver gaps versus competitors and private label.
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
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. Marketing wants to know whether to defend on price or invest elsewhere, suspecting the two audiences buy for entirely different reasons but lacking a model to prove it.
Design: CAWI with 300 DIY consumers per country who bought a power tool in the past 12 months, with category and behaviour checks, plus CATI with 100 trade professionals per country recruited from our own frames. Both rate performance on the same 16 attributes and complete a MaxDiff. Two parallel analyses per audience: stated importance from direct rating, derived from regression on brand preference.
Output: separate ranked driver models for DIY and trade. For professionals, battery platform compatibility and dealer service top the derived list despite mid-table stated importance; for consumers, price matters but prior experience and durability outrank it. Recommendation: hold price, invest in platform breadth and retail demonstration. Per-country views show where Germany 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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