Avoid errors and improve data quality in AB Tasty

Discrepancies Between AB Tasty and Analytics Tools

Challenge: AB Tasty and tools like Google Analytics often report differing metrics due to variations in data collection methods, filters, and attribution models. For instance, AB Tasty may count unique conversions differently than Google Analytics, leading to confusion.

Impact: These inconsistencies can erode trust in data, making it challenging to draw accurate conclusions from experiments and potentially leading to misguided decisions.

Outdated or Misconfigured AB Tasty Tags

Challenge: If the AB Tasty tag isn't updated after campaign changes, or if it's misconfigured, variations may not display correctly, and data collection can be compromised.

Impact: This can result in incomplete or inaccurate experiment data, undermining the validity of test results and wasting resources.

Challenges with Single Page Applications (SPAs)

Challenge: AB Tasty can struggle with SPAs, where content changes dynamically without full page reloads. This can lead to issues like flickering or variations not applying correctly.

Impact: Inaccurate data collection and poor user experiences can occur, affecting the reliability of experiments and potentially leading to incorrect conclusions.

Inconsistent Scroll Tracking Data

Challenge: Implementing multiple scroll tracking widgets within a single campaign can lead to inconsistent data reporting.

Impact: Misinterpreted user engagement metrics can result, leading to flawed insights and ineffective optimization strategies.

Performance and Latency Issues

Challenge: Despite improvements, AB Tasty's script can still impact page load times, especially if not optimized correctly.

Impact: Slower page loads can degrade user experience, increase bounce rates, and negatively affect conversion rates.

‍