From the very start, last click attribution is acknowledged by digital marketers. This factor is easy, easily noticeable; it is quite close to the factors that are in winning situations. These things make PPC managers to have a good impression. However, this is the main problem. Last click metrics are overly assigned factors for acknowledgment.
So now we know that the last click is the wrong attribution metric. So what are the right kind if metrics? First, click attribution, position based, time decay and data-driven acknowledgments have some drawbacks as well.
What are the right attributes?
So what are the right kind of attributes? That relays on objectives and motives and also on the story that you want to convey. You will get acknowledgment only if your attribution model is 100%. You can choose the factors and channels according to your set importance.
We are still on the same level that which of attribution model is right and which is best for the use? Is all of them are right, or none of them are? Is it a big misunderstanding?
Pros and Cons of different attribution models
Following are some of the attribution models, their working and their pros and cons.
- First Touch
First, AdWords touch point is given 100% credibility.
Helps to grab customers. Make the most of TOFU credit distribution.
It is extremely inefficient.
- Unshaped position based
First and last touch point gives 40% of credibility. The 20% that is left over is distributed evenly.
Some of the credit is given to early points. Rest is given to initial efforts.
Middle touch points cannot be given the value that they deserve. Mainly this happens in a long cycle of purchase.
All the touch points are given equal credit.
None of the touch points is left behind. All are given equal importance for consideration.
Key touch points are often undervalued. Minor touch points sometimes get more value.
- Time Decay
Much credit is assigned specifically to the last touch. Earlier points are assigned to values that are diminished.
There is high efficiency. Along with this efficiency, credit is given to TOFU as well.
Touch values of branding and remarketing are given more values.
- Last Decay
It makes the most of the efficiency.
Brand terms are increased beyond the limit. Along with brand terms remarketing is also enhance manifolds. It allows recycling of customers.
Efficiency increases from top to bottom starting from the first model. Below is the complete chart of the models mentioned above.
Which attribution model to choose?
It depends highly on what your priority is? If you are motivated toward being more efficient ten, you should go with last click attribution model. Various people have a different opinion about this topic. Many of people disfavor the last click, and they work with time decay attribution model.
Contrary to the scenario mentioned above, if your priority is to grow and acquire customers, then the top models are better. There will be a change in performances of both last click and first click.
According to Aaron Levy, Manager of Client Strategy at Elite SEM, the best-suited attribution is not yet offered by Google in AdWords or Analytics. He recommended using time decay in reverse iteration. However, this model in not provided so position-based or u-shaped can be chosen from present models.
Should one go for Google’s data-driven attribution modeling?
It is quite worthy on paper grounds. Google utilize the behavior data of countless users and general math-y smartness to generate a variable model. This model is expected to enhance conversion volume.
However, there are some problems with this model. Some of them are listed below.
- It is single channel view
- Despite the fact that it will analyze every aspect of AdWords data for the decision, but it will use AdWords to make a choice only.
- You will not be able to get the information about the traffic that came your way from non-search channels like email
- There will be a slight effect on new customers targeting
The problems do not mean that we should go entirely against this model. It will be an important model. Though its authenticity would depend on right kind of data set.
So how can we decide that which of the attribution model is best? It depends totally on our priorities and industry where we have to apply them.