CR value on Crazypatterns: How we measure product quality

Overview

• Simple explanation
• How we calculate our CR value
• Benefits and signals
• Formula & algorithm
• What you can do specifically
• Our promise

We know how much heart goes into every single pattern. For more than a decade, we’ve been working on fair visibility. In June 2022, we took the next big step: the adjusted conversion rate (ACR) as an improved method. Since then, we’ve kept refining it—today it’s in a robust, proven form. Here we explain in detail how it works and what you can do to increase the success of your digital products. You can also find lots of helpful information in our Guide to selling on Crazypatterns.

Note on wording: For better readability, we use terms like patterns, products, digital content, or content interchangeably in this article. We always mean all those wonderful creative works—whether they’re patterns, videos, or whole courses.

The CR value “explained simply”

What is the CR value?

Think of the CR value as an important success indicator. It measures how well your product detail page turns user interest into a real transaction (purchase or download)—not just how often it was clicked.

Three things matter most

• Transactions: purchases of your paid content or downloads of your free content.
• Verified views: from “real” users—less noise, more meaningful. “Noise” means views without genuine interest, e.g. from search engine crawlers, social media previews, or automated security scans.
• Category & maturity: Different topics (e.g. knitting, crochet) have different potential; new products get a fair start.

What can you do?

• Present your content clearly: title, description, suitable category.
• Show strong product photos and provide an error-free pattern.
• Reply quickly to questions and keep your offer up to date.
• Price and content should match.

The CR value rewards real quality and genuine interest—and helps make sure great digital content is visible, whether free or paid.

✔ This is the end of the short, simple explanation. Next up: Calculation, Signals, Formula.

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How we calculate our CR value

The CR value reacts directly to the quality of your product presentation: an unclear description, unattractive images, or poor reviews can noticeably lower the value—and with it, your visibility. The formula helps us spot and promote real quality based on data, because it reflects actual user interest very accurately.

Benefits and signals

1. Transactions (purchases & downloads): This is the strongest signal, because it shows that a product convinces users so much that they make a real decision. In other words, it favors content that’s not only clicked, but actually bought or downloaded.
2. Views (all vs. verified): We count all views to measure overall reach. But we put special weight on verified views from “real” users. These are much more meaningful, because they separate genuine interest from possible random clicks—and that makes things fairer.
3. Category calibration: Every craft category has a different demand potential. So that products from smaller niches get just as fair a chance as bestsellers in big categories, values are adjusted internally.
4. Smoothing¹⁾ to protect new products: Brand-new patterns naturally have only a few views at first. So their CR value doesn’t swing unfairly due to statistical chance, it’s smoothed at the beginning. That gives every new piece of content a fair start.

We combine these signals with weights. Verified interactions have more influence; raw data from all views is added as supporting context. When the data situation is uncertain, conservative smoothing kicks in.

Formula

The following representation is accurate, but written in a way that details relevant for manipulation remain symbolic.

Note on naming in the formula: Internally we use the precise technical name “ACR” (Adjusted Conversion Rate), because the value is adjusted by several factors. Externally, we communicate this adjusted value as the familiar “CR value”.

$\begin{array}{l}\text{Base metrics:}\\ R_i=100·\frac{t_i}{v_i}\\ {R_i}^{\star }=100·\frac{t_i}{{v_i}^{\star }}\\ \\ \text{Calibrations:}\\ \kappa ,m\left(c_i\right),s\left(i\right)\\ \\ \text{Candidates:}\\ M_i=\frac{w_v·\kappa ·{R_i}^{\star }+w_r·R_i}{w_v+w_r}\\ A_0=m\left(c_i\right)·M_i\\ F_i=m\left(c_i\right)·\frac{{R_i}^{\star }}{s\left(i\right)}\\ \\ \text{Adjusted Conversion Rate (ACR):}\\ {\mathrm{ACR}}_i=\left\{\begin{array}{l}m\left(c_i\right)·R_i\text{(if}{R}_i\ge L_1\text{)}\\ A_0\text{(if}{R}_i<L_1\text{∧}{A}_0\ge L_2\text{)}\\ \max (A_0,F_i)\text{(if}{R}_i<L_1\text{∧}{A}_0<L_2\text{)}\end{array}\right\}\end{array}$

Legend:

• t_i – Transactions: purchases (paid) or downloads (free).
• v_i – all views of the product; v_i^⋆ – verified views.
• R_i – raw CR in %; R_i^⋆ – verified CR in %.
• κ – baseline bridge (internal, from a reference cohort), brings R_i^⋆ onto the same baseline as R_i.
• m(c_i) – category calibration for c_i.
• s(i) – smoothing function depending on the age/maturity of the content.
• w_v, w_r – weights of the robust mix (verified vs. raw).
• L₁, L₂ – thresholds for switching and fallback checking.
• M_i – robust mix (calibrated to a common baseline); A₀ – category-calibrated candidate; F_i – verified-based minimum floor (smoothing).

Here is the same ACR formula as above in a text version
Base metrics:
R_i = 100·t_i / v_i
R_i* = 100·t_i / v_i*
Calibrations: κ, m(c_i), s(i)
Candidates:
M_i = (w_v·κ·R_i* + w_r·R_i)/(w_v + w_r)
A_0 = m(c_i)·M_i
F_i = m(c_i)·R_i*/s(i)
Adjusted Conversion Rate (ACR):
ACR_i = {
m(c_i)·R_i (if R_i ≥ L_1)
A_0 (if R_i < L_1 ∧ A_0 ≥ L_2)
max(A_0, F_i) (if R_i < L_1 ∧ A_0 < L_2)
}

How to read this: The calculation runs in stages. First, robust intermediate values are formed from raw CR and verified CR. Then category calibration always applies; smoothing is added when the data is still thin. With sufficiently reliable data, the category-calibrated raw value can be used directly. When the data situation is weaker, the mixed candidate A_0 is used, or the cautious minimum floor max(A_0, F_i) kicks in. This keeps the CR value fair, stable, and more robust against random fluctuations.

What you can do specifically

• Increase relevance: Use clear titles & descriptions (e.g. “Beginner crochet pattern: Cute elephant amigurumi”), precise keywords, and the right category for your product.
• Convince at first glance: Show high-quality product photos of your finished craft item, provide structured info (e.g. about yarn, needle/hook size, care instructions), and offer variants if applicable.
• Reduce hurdles: Set reasonable prices for paid patterns, and provide complete information for free patterns—both help users make real decisions. Also read our carefully prepared tips on pricing patterns.
• Ensure a great customer experience: Reply quickly to questions, provide a carefully created, error-free PDF file, and publish updates if a mistake slipped in.

For more information on how to present your products in the best possible way, we recommend our Guide to becoming a bestseller designer.

Our promise

• Honest transparency: We explain which signals matter and why—without putting integrity at risk.
• Ongoing calculation: The CR value for each product is recalculated continuously to reflect changes quickly. The formula described here remains stable to ensure comparability.

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1) Smoothing, or the smoothing function, means: we balance out small random fluctuations so that new content isn’t over- or undervalued because of only a few views.

As of: October 2025.