Many brands use Google Analytics (we do too!) and rely on Last Click Attribution to categorize and report on where traffic and orders are driven from.
Google Analytics has a default channel grouping that cover the most commonly used traffic sources such as organic and paid search, affiliates and social media. It does a pretty good job of categorizing traffic even when there are no UTMs in the link. However, there are a few pitfalls with GA that can make analysis a challenge:
- By default, GA groups all social media traffic together; many brands are both spending ad dollars on social campaigns as well as doing organic posts and don't have an easy way to measure their investment
- Traffic is mis-attributed. We often see Paid Social attributed to channel (Other).
- Changes to GA's Channel Grouping to correct the above issues only apply to orders starting the day the change is implemented. Historical data will remain mis-categorized.
- GA doesn't know anything about your promotions or discount codes. It can't redistribute traffic from one channel to another based on something other than UTMs.
To address the issues above, Daasity built attribution rules into the Daasity platform that brands can control, setting rules to categorize orders the way that makes the most sense for your business.
Daasity ingests Google Analytics data, including GA's channel attribution for First Click and Last Click channels and UTM codes.
Each night, Daasity's attribution method runs on new orders in this sequence:
- Channel Mapping rules are applied. The end result of this re-categorization is labeled Modified Last Click Channel.
- Discount Code Mapping rules are then applied to the Modified Last Click Channels. The end result of this re-categorization is labeled Daasity Attribution Channel
- For Professional level customers, you may have integrated a survey solution that includes a post-checkout survey for the purpose of attribution. The survey results are then applied to any orders that the survey was completed for; this re-categorization would be seen in the Daasity Attribution Channel.