How to understand CAC & Attribution with multiple channels?

Do not index

CTA Headline

CTA Description

CTA Button Link

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)

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

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.

Finally, the user searches for your product on Google Search console 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 into the picture.

Got the idea?

Now let’s dive into the 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-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 many 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, a 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 stores 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 conversion events.

Cons: This model will tell you the half-baked story. You are no longer giving credit to the channels that are driving your 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 the conversion funnel.

Pro tip: Use the Google Analytics attribution tool to play with first and last-click attribution for every conversion you receive 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 ads 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 those 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 the 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 that drove the conversion. Marketers can use this model to optimise for brand touch point that drives revenue as well as the ones that increase the likelihood of a conversion shortly.

Cons: Consider an example — the first user touch point is a free product sample sign-up. In this scenario, the model will give 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 the next stage.

Pro Tip: This model is a 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 of what works and what doesn’t work for your brand.

5: Position Based

In this model, the first and last interaction takes 80% credit & 20% is divided equally among the 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 us 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: The minimum requisite to use this model is 15,000 clicks and 600 conversions in the last 30 days. For a small to medium eCommerce store, this may not be the case. I would not recommend going into the complex model when you are just starting your store.

The Bottomline:

You know which attribution model works better for you only when you experiment enough. Additionally, it may change over 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 a data-driven attribution model.

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

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

2. Which type of model works?

The above answer sums it up.

3. How to calculate CAC with multiple touchpoints?

This is interesting and here's my rule of thumb.

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

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

3. Look at overall spending 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.

Understanding CAC and Attribution Model while using multiple channels