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Understanding CAC and Attribution Model while using multiple channels

1. How do we go about a particular attribution model?


Consider this scenario:

(Digest it properly as we are going to use this to explain the models)


1. A user clicks on your Instagram ad. Explore your product inventory but does not add any product to his/her cart.


2. After almost a week, the same user sees your ad on facebook and again clicks on the ad. He then adds a product to his cart. However, he didn’t buy the product.


3. Finally, the user searches for your product on Google Search console and clicks on a text ad and buys the product (Woohoo!)


Now, how would you credit the conversion to Instagram, Facebook and/or Google?

That's where attribution to marketing channels/activities comes in to picture. Got the idea? Now let’s dive into top 6 attribution models.



1: First click model

In this model, the first user interaction takes 100% credit for the conversion (purchase on a website, sign up or whatever).

So in the above example, Instagram will take 100% credit.


Pros:

If you want to focus more on the site visits and sessions, this model may be a fit for you. This is suited if you want to focus on the number of inflows coming to your website. Lowering the cost per click and improving the CTR would be the only optimisations here.



Cons:

As I said, this model focuses on the “number“ of people coming to your e-commerce store and not on the quality of the audience. So this model does not take into account the conversions / add to carts / sign up forms this -audience got you.


Pro tip: Not much levers to make conversion optimisation here. Not recommended for web/app with over 2000 sessions/day. Only good for early traction days where you are just getting your first 1,000 customers/users.


2: Last click model


In this model, the last user interaction takes 100% credit for the conversion.

So in the above example, Google Search ad will take 100% credit.


Pros: This model solely focuses on conversions and hence is widely used in the industry. Best suited for e-commerce store with a large number of sessions (more than 2000/day). Use this if you have a large number of cart abandoners or product viewers and want to optimise for only conversion events.


Cons: This model will tell you the half-baked story. You are no more giving credit to the channels which are driving you session/ new users/brand awareness.

Once your web/app has gained traction, you might not worry about brand awareness but you should always (always!) give due attention to sessions and new user inflows.


I would not recommend using only this model. Combine the first and last click model to analyse both- new sessions and conversion funnel.


Pro tip: Use Google analytics attribution tool to play with first and last click attribution for every conversion you received on your web store. Apps-flyer for app products.


3: Linear model


In this model, every interaction takes equal credit for the conversion.

So in the above example, Instagram, Facebook & Google search ad will take 33.33% credit each.



Pros: This model assigns equal credit to each brand touch point. This helps you analyse the user journey rather than just a single activity.


Cons: As this model gives equal credit to a click on an email newsletter and to those actually converting ones, this model fails to give due credit to the channels giving outcomes. This makes the optimisation for outcome-based ones a tedious task.


Pro Tip: When you have a good user base and are getting traffic through a lot of media, this model might become a little bit tricky. Hold On, You have Time decay model to the rescue.


4: Time Decay model


In this model, recent interaction takes greater credit than the older one for the conversion.


So in the above example, the credit split will be Instagram (~14.25%), Facebook(~28.5%) & Google search ad (~57%).



Pros: This takes the good part of linear attribution in terms of multi-touch points and gives due credit to the recent touch point which drove the conversion. Marketers can use this model to optimise for brand touch point that drives revenue as well as the ones which increase the likelihood of a conversion in the near future.



Cons: Consider an example — first user touch point is a free product sample sign up. In this scenario, the model will give a very little credit to the first interaction just because the user interaction came in early. This gives lower credit to the user interaction which might give a conversion in next stage.


Pro Tip: This model is the good to go strategy if you have a good amount of data and lots of conversion numbers. Use this when you have a strong understanding about what works and what doesn’t work for your brand.


5: Position Based

I

n this model, first and last interaction takes 80% credit & 20% is divided equally among remaining brand touch points for the conversion.


So in the above example, the credit split will be Instagram (40%), Facebook(20%) & Google search ad (40%).



Pros: This helps you to give due importance to the last(40%) and first(40%) touch-points while also giving the in-between touch-points at least some share. This means you can assign significant credit to the channel that introduced your brand to the customer as well as the campaign that eventually drove them to convert.



Cons: This could easily result in two very low-value touches being given too much credit. Think about a scenario: does it make sense for a first touch Instagram story (organic)is going to get the same amount of credit as the paid search ad that resulted in a purchase?


Pro tip:

Don’t even think about using this in this cross channel (Search/Social/Referral/Email/Direct)

& cross device (mobile web/desktop web/ mobile app) marketing world.


6: Data Driven


In this model, a weighted average of all the interactions with the brand is taken into account & then the most converting path gets the highest credit.



Pros: It allows to weigh each type of user interaction and not just the interaction position in the user journey. It may be the best fit for a marketplace model such as Amazon where you may want to understand user behaviour rather than just clicks and vague engagement data.



Cons: Minimum requisite to use this model? 15,000 clicks and 600 conversions in the last 30 days. For a small to medium eCommerce store, this may not be a case. I would not recommend going into the complex model when you are just starting your store.


Read More about data-driven attribution modelling Here.



Bottomline:

You know which attribution model works better for your only when you experiment enough.

Additionally, it may change over the course of time as your eCommerce business gains traction.


Typically at the start you will have a first click based model while at a matured stage, you would want to go for data driven attribution model.

It all boils down to how you think about each of the user journey marketing channel and give due credit to each one of them. Understanding user engagement level is also another important aspect of making a conclusion just on the basis of data.


Remember : The right kind of data + Gut Feeling = Marketing magic


2. Which type of model works?

Above answer sums it up.


3. How to calculate CAC with multiple touch points?


This is interesting. Here's what my rule of thumb.



1. Focus on contributor report for what channels drive you traffic & what channels drive you conversion.

2. Understand how much do you spend to reach people vs how much you you spend on end conversion.

3. Look at overall spends done in your reach channel vs conversion channel. Always look for assisted conversion as a metric.


No silver bullet to this but you would look at total money spent vs total conversions. A proxy to it is go dark on one or more channel and see what happens to overall cost and overall conversions. That was you know what is %incremental conversions that are being brought by the individual channels.


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