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Documentation Index

Fetch the complete documentation index at: https://docs.gocrux.io/llms.txt

Use this file to discover all available pages before exploring further.

The Customers pillar is about new customer acquisition. Anything related to marketing channels, campaigns, ads, keywords, or top-of-funnel website performance lives here. It is not about repeat purchases. If the question involves CRM, retention, or returning buyers, you need to be in the Frequency pillar instead.

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The core formula

total_users × new_customer_cr = new customersWhere new_customer_cr = SUM(new_orders) / SUM(total_users)
There are only two things that drive new customer acquisition: how many people came to the site, and what proportion of them became a first-time customer. Every diagnosis comes back to which of those two has moved. Always base this analysis on new_orders, not total_orders. Mixing in repeat orders contaminates the signal.

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The metric tree

Customers metric tree showing total_users and new_customer_cr branching to supporting metrics and dimensions

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Diagnosing total_users

Traffic is the volume side of the equation. It splits cleanly into paid and organic.
Channel typeExamplesWhat you control
PaidPaid Meta, Paid Search - Brand, Paid Search - Generic, Performance Max, Demand Gen, Influencer, Affiliates, Paid ShoppingSpend levels, targeting, creative
OrganicDirect, Organic Search, Organic Social, Email, ReferralIndirect, but heavily influenced by brand activity, link building and content
When traffic drops, the question is always: was it paid, organic, or both?

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Supporting metrics

Two metrics give you context beyond raw user counts.
  • new_user_% = SUM(new_users) / SUM(total_users). A drop here suggests you’ve over-indexed on lower-funnel activity, retargeting the same people instead of reaching new ones.
  • cost_per_user = SUM(spend) / SUM(total_users). If this rises, you’re paying more to bring fewer people to the site.

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Dimensions to slice by

Always work in this order. Going straight to landing pages before checking channel performance is a common trap that leads to false conclusions.
1

Channel

Is one channel responsible for most of the drop? If paid Meta is down 30% and everything else is steady, you’ve found your driver.
2

Campaign

Within the suspect channel, which campaigns moved? A drop on a single campaign is a creative or budget issue. A drop across multiple campaigns suggests a platform-level problem.
3

Landing page, Ad Set (Group) or Ad/Keyword

The Most granular level. Useful to further diagnose whether a specific element of the campaign drove the change.

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Diagnosing new_customer_cr

Conversion rate can be slightly more nuanced, your channel mix can shift conversion rate significantly as well as potential on-site issues. We use the same dimensional hierarchy as traffic here; Channels, campaigns, landing pages / ad sets / ads or keywords, in that order. Start by understanding whether its driven by a customer intent shift (channel mix) by looking at session -> engagement rate. Engagement is defined by GA as anybody who has stayed on site longer than 10 seconds, has a key event or has at least 2 pageviews. Secondly, review engagement -> checkout %. This essentially tells you whether you have an issue with your conversion funnel (something out of stock, slow loading pages, checkout or UX issue). If there’s no obvious driver across any of those dimensions, the issue is usually one of two things:
  • Site-wide: something has broken in checkout, on a key product page, or in payments. Test the buying flow end-to-end before going further with the data.
  • Category-level: demand for the product itself has shifted. Isolate brand search specifically and review. Compare against industry benchmarks or sister-category trends if you can.

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Worked example: a sudden traffic drop

You notice total_users is down 18% week-on-week. Contribution margin has slipped accordingly.
1

Confirm the pillar

Repeat orders are flat. AOV is flat. The drop is concentrated in new customer volume. This is a Customers pillar question.
2

Split paid vs organic

Paid traffic is down 28%. Organic is down 4% (within noise). This is a paid issue.
3

Slice by channel

Paid Meta is down 41%. Paid Search is steady. Performance Max is up. The channel is Meta.
4

Slice by campaign

Two prospecting campaigns are responsible for 80% of the Meta drop. Spend on those campaigns dropped 35% mid-week.
5

Recommend

Investigate why budget pacing slowed on those campaigns. Restore spend or reallocate to the working ASCs. Set a budget alert to catch this earlier next time.
The whole diagnosis takes one report and four filters. Without the framework, the same investigation often spreads into landing page tests, ad creative reviews and product page audits before getting to the actual cause.

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Worked example: conversion rate drop with stable traffic

Different scenario. Total users are flat, but new customer conversion rate has dropped from 1.8% to 1.2%.
1

Identify whether its a channel or site issue

Session to engagement % is down across all channels by a similar magnitude. Comparing channel new_cr% confirms this and points to something site-wide.
2

Review Website Funnel

Review the Website Conversion report to identify which funnel stage the drop is occuring; engaged session -> add to cart, add to cart -> start checkout, start checkout -> purchase. There is a noticeable drop in the start checkout -> purchase stage.
3

Investigate

Jump into the checkout and review end to end. You notice free shipping isn’t being triggered despite being over the order threshold. Something has broken the free shipping rule, investigate Shopify shipping rules.
When channel-level data shows no single culprit, the answer almost always lives outside the dashboards. Check the site itself.

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Where this data lives

The primary reports for acquisition diagnosis are channel_reporting and ecommerce_funnel. The channel_reporting report contains total_users, new_orders, new_customers, spend, cost_per_user and new_customer_cr, sliceable by channel, campaign_name and landing_page. While the ecommerce_funnel provides a conversion rate breakdown across the various buying steps on the website (visit -> engagement -> add to cart -> enter checkout -> purchase). For the dashboards that visualise this:

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Common mistakes

Total orders include repeat customers. If you mix them into your acquisition analysis, you’ll mistake retention strength for acquisition strength, and vice versa. Always filter to new_orders for this pillar.
Paid traffic responds within hours to spend changes. Organic traffic moves on weekly to monthly cycles. Comparing them on the same time scale produces misleading conclusions. Look at paid trends in days, organic trends in weeks.
A channel with 200 users showing a 50% CR change is rarely a meaningful signal. Weight your investigation toward channels that drive most of your volume. Investigate small channels last, not first.
If channel-level data shows no clear driver, the issue is often functional. Broken checkout, slow page load, removed payment method. Walk the funnel before going deeper into the data.