Avoid errors and improve data quality in Optimizely
Misfired Experiment Events
Challenge: If you don't accurately track when experiments are activated or when conversions occur within them, it can lead to results that are either inconclusive or misleading.
Impact: Inaccurate tracking of experiment events undermines the validity of your A/B tests and other optimization efforts. You might draw incorrect conclusions about which variations are performing better, leading to misguided decisions that could negatively impact your key metrics.
Incorrect Audience Segmentation
Challenge: If there are errors in your targeting logic or you're missing key user attributes, experiments might end up running on the wrong segments of your audience.
Impact: When experiments are shown to the wrong users, the results you gather won't be representative of the intended target group. This can lead to flawed conclusions about the effectiveness of your variations and potentially cause you to implement changes that don't resonate with your core audience.
Lack of Variant Attribution
Challenge: If data about which experiment variant a user saw isn't captured correctly, your results will be skewed or lost entirely.
Impact: Without proper variant attribution, you won't be able to accurately compare the performance of different experiment versions. This makes it impossible to determine which variant is actually driving better results, rendering your experiments meaningless and wasting valuable time and resources.
No QA for Experiment Launches
Challenge: If experiment launches go live without quality assurance (QA), you risk having broken tracking or flawed audience targeting logic.
Impact: Launching experiments without proper QA can lead to inaccurate data collection, making it impossible to reliably measure the impact of your changes. It can also result in the experiment being shown to the wrong users, skewing your results and potentially leading to incorrect conclusions about what works best.
Delayed or Missing Events
Challenge: If the events that signal conversions are delayed in reporting or not tracked at all, the success of your experiments will be underreported.
Impact: This inaccurate reporting can lead you to underestimate the positive impact of successful experiment variations. You might prematurely end effective tests or fail to recognize winning strategies.
Variant Visibility
Verifies that all A/B test variations are correctly tracked.
Accurate Goal Tracking
Confirms metrics align with your experimentation goals.
Cross-Platform Synchronization
Checks data consistency between Optimizely and tools like GA, Mixpanel, or Amplitude.
Real-Time Error Detection
Alerts you immediately if any experiment or variation tracking breaks.
Can Trackingplan check if Optimizely experiments are firing correctly?
Yes, Trackingplan provides comprehensive validation of Optimizely experiment tracking by ensuring that experiment activations, variant assignments, and goal tracking are firing correctly. This level of monitoring guarantees that your A/B tests and multivariate experiments run exactly as planned, helping you avoid data discrepancies and unreliable test results.
Will it help ensure segmentation is accurate?
Absolutely. Trackingplan verifies that all user attributes used for audience segmentation and targeting within Optimizely are complete, consistent, and properly applied. Accurate segmentation data ensures your personalized campaigns reach the right users, improving targeting precision and boosting the overall effectiveness of your optimization efforts.
Can it detect when variant data isn’t being collected?
Yes, Trackingplan continuously monitors variant attribution data in your Optimizely experiments. It alerts you immediately if variant data is missing or inconsistent, preserving the integrity of your A/B testing results. This helps you quickly identify tracking issues that could otherwise lead to misleading insights and poor decision-making.
Still have questions?
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Because life’s too short for tedious data work
Our results in numbers
Achieve more by getting rid of manual processes and validations
From weeks to hours
Reduction of measurement error resolution time
90%
Hours saved per month per FTE
Reduction in data errors in reports
Improvement in campaign performance
Efficiency increase in marketing automation
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