Attribution Lookback
Last updated
Last updated
A lookback window defines the timeframe in which a customer’s interactions are considered for attribution.
Setting an inappropriate lookback window—either too short or too long—can skew your data. A short window might miss crucial early touchpoints, while a long window could over-credit interactions that have less relevance to the final conversion.
Example: Suppose you set a 7-day lookback window, but your typical sales cycle spans several weeks. Key touchpoints like initial blog posts or webinars, which occurred outside that 7-day period, wouldn’t receive credit in the attribution model. This can lead you to undervalue important content that contributes earlier in the customer journey.
Analyze your sales cycle: Determine an appropriate lookback window that reflects your customer journey. Example: If your sales cycle is 30 days, a 30- or 45-day lookback window might be more suitable.
Test and refine: Experiment with different lookback windows and review the results. Adjust based on whether you need to capture early touchpoints or focus on more recent interactions.
Use time-decay attribution: Assign more weight to recent interactions while still crediting earlier touchpoints. This approach balances the influence of content throughout the customer journey.
💡 Pro Tip → If your customer journey spans months and involves multiple touchpoints, consider extending your lookback window to 60 or 90 days.
HockeyStack provides tools to optimize your lookback window based on how conversions occur over time.
To use the Attribution Lookback feature:
Navigate to your report settings.
Under Advanced Settings, locate the Attribution Lookback options.
None: Disables the feature.
Time-Based: Specify days or months relative to the goal dates in the columns. Each column is calculated independently, meaning each column has its own lookback window based on the date of the specific goal.
Example: Use this option to only credit touchpoints between 12 to 6 months before opportunity creation. This helps analyze top-of-the-funnel interactions effectively.
