Independent · Algorithm-driven · No sponsored placements
How we score, in full.
This page is our work. Every product on the site has been screened, weighted, and matched by the rules below. If you find a problem, we want to hear about it.
By the Decision Widget editorial team · Last updated April 22, 2026
What is DecisionScore?
A scoring engine, not a black box.
DecisionScore is our proprietary product scoring methodology. It evaluates every product across weighted criteria specific to each category — covering performance, value, build quality, and real-world usability — then matches those scores to your stated needs.
The system is deterministic, not probabilistic: the same answers always produce the same results. There is no randomness, no editorial override, and no model that “learns” to prefer certain products. DecisionScore currently powers 350+ product categories with 18+ criteria evaluated per category.
Every score is reproducible and transparent. You can see exactly which criteria influenced your recommendation and how each product performed against them.
Deterministic
Given the same answers and the same product catalog, the same recommendation is produced every time. There is no randomness, no A/B variation in results, and no editorial override.
Transparent
Scoring rules are defined per category with explicit weights. Each product earns points based on how well its attributes match your stated needs.
No sponsored rankings
No brand or product pays for placement. Affiliate commissions are earned after a recommendation is made — they never influence which product is recommended.
Curated catalog
Each category starts with a hand-curated product universe. Only products that meet baseline quality and availability thresholds are included in scoring.
§ 1 · Quality screen
What earns a product a spot in the catalog.
Every product clears the same four gates before it can be matched to anyone. The screen is binary: pass or out. There is no partial credit.
| Criterion | Definition | Threshold |
|---|---|---|
| User rating | Average across verified-purchase reviews | ≥ 4.5 |
| Review volume | Minimum verified reviews to count | ≥ 200 |
| Expert validation | Cross-referenced with independent reviewers | ≥ 1 source |
| Availability | Listed and in stock from major retailers | ≥ 2 |
§ 2 · Matching engine
How we score a product against you.
Each of your answers raises or lowers the weight on a set of measurable product attributes. The final score is a weighted sum, normalized to 100. The formula and weights are public.
// fit score, normalized 0–100 fit(p, u) = Σ w_i(u) · a_i(p) where w_i = your weight on attribute i (from quiz) a_i = product score on attribute i (from spec data)
No machine learning, no opaque ranking. You can replicate any match by hand from the published weights.
The DecisionScore pipeline
- 1Filter. Products that violate hard constraints (e.g., exceeding your budget) are removed from the candidate set via allow-list rules.
- 2Score. Each remaining product is evaluated against every DecisionScore rule. If a rule’s conditions match (question answer + feature value), the rule’s weight is added to the product’s total score.
- 3Rank. Products are sorted by total score, highest first. Ties are broken alphabetically for consistency.
- 4Present. The top-scoring product is your best match. The next two become alternatives for comparison.
Attribute matching
Scoring rules support several match types to handle different kinds of product data:
| Match type | Description | Example |
|---|---|---|
| eq | Exact value match | noise_level = "quiet" |
| gte / lte | Numeric comparison | battery_life ≥ 120 minutes |
| includes | Array contains value | floor_types includes "hardwood" |
| exists | Feature is present | has_hepa_filter = true |
DecisionScore rules can also apply per-unit weights (e.g., +2 points per decibel below threshold), enabling nuanced scoring for continuous attributes.
§ 3 · Integrity rules
What we won't do, ever.
Pay-for-placement
No brand can purchase a higher score, a featured slot, or earlier inclusion in the catalog.
Affiliate-weighted scoring
Affiliate revenue does not influence the matching algorithm. We disclose it on the result page; we don't bake it in.
Personalized pricing
Two people answering the same questions get the same match. We don't tune scores by inferred willingness to pay.
§ 4 · Affiliate disclosure
Same commission, every product.
When you click a product link and make a purchase through one of our retail partners, we may earn a commission at no additional cost to you.
Key point
Affiliate commissions are earned after a recommendation is generated. They are never a factor in the scoring algorithm. The same product would be recommended whether or not an affiliate relationship exists.
For full details, see our disclosure page.
§ 5 · Ongoing maintenance
Products change. We update.
Product data is sourced from multiple retail catalogs and verified periodically. Each product listing includes a link health check that detects unavailable or delisted items. Products with broken links are automatically excluded from scoring to prevent recommending unavailable products.
Category catalogs are reviewed and updated as new products enter the market. Pricing shown reflects approximate price points at time of catalog update and may differ from current retailer pricing.
§ 6 · Transparency
See how it works. Use it on your site.
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Cite this methodology
DecisionScore data is free to cite with attribution. Link to this page as a source for product recommendation transparency.
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