Tracking: The WebMechanix Way

The right data, collected properly, drives the right strategy. We deeply understand how GTM, GA4, and other ad-tech (Facebook/Meta, Google Ads) work.  We understand and embrace that data collection (and implementation) is a detail oriented job. Cutting corners and taking shortcuts isn’t in our vocabulary. We create both open source and proprietary software that can help alleviate or address common needs/challenges.

Data Collection Should be Abstracted

No direct pixel/tag implementation – data collection should be implemented via a TMS such as Google Tag Manager & its corresponding dataLayer to provide easy debugging, versioning, and data reuse.

Naming & Format Matters

Data should be easy to understand without needing a dictionary to translate internal acronyms/jargon. You should be able to look at an event name and understand what it’s tracking without hours of institutional knowledge.

Clean & consistent data = Happy Data. Not all organizations can afford to deploy/maintain elaborate ETL pipelines. Events and parameters should follow a consistent casing, such as snake_case or PascalCase – don’t mix & match.

You don’t need to track everything

More data = more noise. Yes – we can track click events on your navigation bar or scroll % thresholds… but do we need to? How will that data help us make informed decisions?

Start at your conversion event(s) and work your way backwards (up funnel). Put the focus on what truly matters for measuring a campaign’s success.

Quantitative + Qualitative Data Paints a Better Picture

The use of both Quantitative + Qualitative data is a must for activities like Conversion Rate Optimization (CRO).

Quantitative data is great for measuring aggregate metrics that are abstracted from a website or UX. (e.g. how many leads from X source did we generate over Y timeframe).

Qualitative data (such as heat maps and user session recordings) provide context that no quantitative tool can – but (can) fall short on accurate metrics.

Strive for Accuracy not Precision

Regional digital privacy regulations and increased privacy settings in browsers make “precision” an impossibility. Marketing data should be accurate (we’re always working with some sample of a true number) . What is more important is that the sample of the data can be explained and that the sample is predictable and consistent.

Avoid False Positives

Meaning, there should be no way to trigger a conversion event without actually performing the action that should trigger it (e.g. a successful form submission). Biggest offender? URL based conversions. Throw those in the trash – the year 2004 called and wants its best practices back. URLs change over time. Events are abstract and can be reused over the course of a business’s time.

Know your IoI’s and IoQ’s

Indicators of Intent (IoI) – actions users take that show or correlate with an intent to become a customer. (E.g. Watching a video).

Indicators of Quality (IoQ) – traits of an action that show or correlate with a person that meets a business’s ICP. (E.g. A demo request for a business with > 10M annual revenue in the Financial Industry)

Close the Loop

Businesses need to be able to define their own meaning of attribution (no tool will do this for you).

Converting session campaign data needs to be stored with lead records inside a CRM. That includes UTMs, 1st party cookie identifiers, and advertising click identifiers if available.

Connecting closed sales to advertising channels helps organizations (and advertising platform machine learning) to understand what is working and what isn’t.

Offline data sources like CRM deal activity are a must for platform integration in 2023 (especially for Paid Media) – if you aren’t closing the loop you’re falling behind.

Reach Out Today to Straighten Out Your Data Collection