Are you in control of your data, or is it controlling you?
Businesses are drowning in data, chasing machine learning trends without extracting real value. As a trusted advisor to 200+ enterprise clients, I’ve seen this struggle firsthand. The hype around AI-driven analytics—especially in Google Analytics (GA) – often leads to black-box solutions that can raise more questions than answers.
But here’s the good news: you don’t have to surrender to GA’s pre-packaged models. Rule-based systems in BigQuery and Looker Studio offer transparency, customization, and scalability—putting the power back in your hands.
Let’s talk about the future of rule-based analytics – where control, precision, and flexibility come first.
I’ve seen this challenge come up time and time again in my work. Let’s break it down. GA provides some rule-based options inside the UI, such as:
Google follows industry norms, in some cases it even creates the norms – but that doesn’t mean your organization will neatly fit into the predefined GA4 playbook. This is where a custom approach is most appropriate.
| Feature | GA UI (Out-of-the-Box) | BigQuery + Looker Studio (Rule-Based Approach) |
| Attribution | Google’s machine learning-based data-driven attribution (DDA) | Custom rule-based attribution (e.g., first-touch, last-touch, position-based, linear) |
| Segmentation | Limited to GA’s audience builder | Fully custom segmentation using SQL logic |
| Data Retention | Max 50 months for event-level data | Unlimited historical data in BigQuery |
| Custom Reporting | Predefined report types in Explorations | Fully customized dashboards in Looker Studio (& beyond) |
If you want to truly own your analytics, relying on GA’s UI alone won’t cut it. I’ve worked with businesses that needed more than just canned reports—they needed analytics tailored to their strategy. Let me show you what we’ve been doing for our clients – real-world rule-based solutions that go beyond GA’s built-in capabilities.
Problem: GA’s attribution models force businesses to either accept Google’s machine learning-driven DDA model or rely on outdated last-click models. However, companies often need a custom attribution model that aligns with their marketing strategy.
Solution: With BigQuery, we can build a rule-based attribution model that assigns credit based on defined business logic. Here’s a practical SQL example:
SELECT
user_id,
traffic_source,
SUM(
CASE
WHEN application_touchpoint > 0 THEN 0.50 -- 50% credit for application page visits
WHEN brochure_touchpoint > 0 THEN 0.30 -- 30% credit for brochure downloads
WHEN contact_touchpoint > 0 THEN 0.20 -- 20% credit for contact page visits
ELSE 0.10 -- Low weight for general visits
END
) AS attributed_score
FROM multi_touch_data
GROUP BY user_id, traffic_source;An approach like this lets you set custom weights which get aggregated into a final attributed score. Such a score can provide a value that more closely reflects a user’s multi-touch journey than what a standard last click attribution model will provide.
Why This Matters: We have helped businesses transition from GA’s built-in attribution to custom models, and the results are always eye-opening. It’s not just about control—it’s about making data work for you, not the other way around. We no longer live in a world where you need to limit yourself by analytics tool capabilities, as BQ offers nearly unlimited customization at your fingertips.
One of the most common questions I get is about the cost of using BigQuery and Looker Studio for rule-based analytics. Yes, these solutions come with a price tag, but here’s the reality—if your organization understands ROI, the investment justifies itself.
Take this real-world example: A retail client of ours was spending $50,000 per month on digital advertising. After implementing a rule-based attribution model in BigQuery, we discovered that nearly 40% of their ad spend was going toward low-intent traffic that never converted. By reallocating that budget to higher-performing channels based on data-backed insights, they saw:
Going beyond GA’s UI alone, BigQuery and Looker Studio allow you to extract deeper insights, optimize marketing spend, and reduce inefficiencies. Many of our clients have seen higher conversion rates, smarter budget allocation, and operational time savings that far outweigh the costs.
For organizations that rely on accurate data to drive growth, the ROI is undeniable – it’s not about spending more on analytics, it’s about making analytics work harder for you.
The message is clear: machine learning isn’t always the answer. In fact, it can often beg more questions than it answers. GA’s UI may work for basic use cases, but serious organizations need advanced, rule-based solutions in BigQuery and Looker Studio to gain full control over their data.
At Cardinal Path, we’ve helped enterprise clients replace GA’s limitations with custom rule-based analytics, providing them with:
If your business is tired of guessing how GA’s machine learning models assign credit or if you want a custom, rule-based analytics framework that puts you in control—we’re here to help.
Looking for analytics that truly fit your business needs? Let’s explore how rule-based solutions can give you the control and transparency that GA’s built-in models just don’t offer.
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