The old tradition and their old ways of thinking. But trends keeps on changing with their sorting. As Google stopped supporting last click attribution. This means ads will have to opt for other attribution models available in Google.
This blog will talk about some general commentary on attribution as well as an overview of the different models available in Google AdWords.
“An attribution model is a rule or set of rules, that determines how credit for sales and conversions is assigned to touchpoints in conversion paths.”
Currently, the default in Google AdWords is last-click attribution. Last-click attribution gives the click just before the conversion of the attribution credit.Every business has different marketing objectives so tailoring attribution models to those specific goals just make sense.
Generic factors on Attribution
Changing attribution models doesn’t change actual account performance, just your perception of it based on changes to what “counts” as a full or partial conversion. A new attribution model won’t sludge the account.
Thus, data-driven attribution model, the account default. It will be leading to the advertiser to opt into other attribution models. This is similar to how Google automatically opts you into ads that rotate for clicks, users searching in, around, about, etc.
There ‘s always a second way of thinking how to opt for attribution in Google AdWords.
Google Analytics gives different weight and credit to different channels like paid search, paid social, email marketing and direct channels.
It’s interesting that Google’s new attribution technology may now be seamlessly baked into Google Analytics (GA). We have to pick out a model, rather than have a separate tool.
The Power of Five attribution models
Some different attribution models that you can choose from in AdWords.
Position-based model- Acquiring 40% credit is assigned to each the first and last interaction, and the remaining 20% credit is distributed evenly to the middle interactions.As of now we never heard of anyone using this model, but if they did, they’d be looking to move some credit away from the last click, but not give too much credit to repetitive searching in the middle research phase, in cases where consumers really dithered but would never have done so, perhaps, without the power of that very first interaction. This model is actually very clever.
Linear model – Every touch point that contributed to the conversion gets the same score. The first click gets the same amount of credit as the last click. Google seems to apply for very partial credit in some cases, as little as 0.1 of a conversion. Does this mean there were potentially ten interactions by that user? Or does Google apply some weighting, even with the linear model? Google’s documentation on this isn’t extensive. This model is useful for any company that wants to sprinkle as much credit as possible around to any keyword that had a role in the user’s consideration process towards a conversion, so they can reduce the number of uninformative “zeroes” in the conversion stats. This can be especially important when we’re dealing with highly relevant, but low volume, long-tail phrases.
Time decay model – The touchpoints closest in time to the sale or conversion get most of the credit. The keywords consumers interacted with within a few hours of conversion would get the largest weighting. This model is the most similar to last-click and would be considered the most “conservative” change to an existing account. This is an excellent option if you want the same type of attribution that you’re getting currently with a last-click attribution account.
First-click model- An effort of 100% of the credit is given to the first touch point. This is generally used for when companies are looking for growth and focuses on new user/customer acquisition. An example of this would be a company whose goal was to introduce their offering to new prospects so they can remarket to them or place them on an email list and sell them from there.
Data-driven attribution model- Data-driven attribution is a black box out of all of the attribution models. It analyzes various data points to determine what the specific weighting should be when a conversion occurs. It redistributes credit in favor of converting ads and associated keywords, and groups, and campaigns.
Note: Try to measure up to these type of attribution. If in case you need more help in understanding this topic then please feel free to resolve your queries thru comments.