Knowing the customer’s journey to the product is very important for marketers. In order to do that, multiple channels need to tracked and documented. Thanks to technology, we have Google Analytics and similar tools to do the tracking for us.
However, there are two factors that require special attention:
- The entire path followed by customer leading to conversion
- And, the impact of each touchpoint.
The purpose is to understand: What’s working for your business? And how can you leverage information to improve marketing strategy? To get the answers, start with attribution modeling.
What is Attribution Modeling?
Every effort made by marketer is to generate leads and improve ROI. And attribution modeling
Attribution modeling is a system to track how a customer interacted with your brand before making a purchase. And then help you decide where you should put your efforts to further increase revenue.
Businesses have to set aside a budget for advertising. With attribution modeling, marketers can check the user’s behaviour throughout the various touchpoints and put most credits (money) where required.
What are the Different Touch Points?
Customers often don’t buy the first thing they see on the internet. They do research and compare the products before making the final purchase.
During the research, customers come in contact with various acquisition channels available for the product. There are four ways a customer gets in touch with your product.
- Direct Channel: When customer directly enters the URL and visits your webpage.
- Organic Channel: Your product or website appeared on the search results and customer clicked to visit website.
- Paid Channel: Pay-per-click campaigns and social media campaigns are examples where customer clicks on the link of your website.
- Referral Channel: Referral links are non-direct and unpaid links to your website.
When customer goes through these channels to finally purchase your product, we get multi-channel attribution. Google Analytics helps to track these channels. However, you can try other web analytics tools too.
Types of Attribution Models
These are some of the common attribution models:
Last Interaction Attribution Model
Last interaction attribution model is a simple model where the complete credit for a sale is given to the last channel in the model. Google Analytics uses last click model as the default settings.
This model is simple and easy to use. Also, it highlights the conversion channel, based on these data campaigns primarily focused on conversion are created. But, at the same time, it ignores all other interactions of the conversion funnel.
First Interaction Attribution Model
First interaction attribution modeling gives the 100% credit to the first touch point that eventually leads to the conversion. In US, 37% organizations use first interaction model for their business.
It’s easy to use and implement too. First interaction model highlights channel that creates most awareness which is ideal for campaign primarily focused on awareness. But it limits the chance for the marketer to review and optimize other channels.
Linear Attribution Model
From the first interaction to last interaction, each touch point will get equal credits for the conversion. Linear model acts as a benchmark to compare other models.
The advantage of this model is that marketers equally focuses on every channel for better awareness and conversion. But it can be too much as a marketer is now required to create campaigns for every acquisition channel.
Time Decay Attribution Model
The touchpoint closer to conversion get the most credits and the channel next to the conversion channel will get slightly less credit than the conversion channel and so on. Similarly, as we go down the channels, the first interaction channel will get the least credit.
Time decay gives credit to each channel involved by focusing on the conversion channel. But, it still creates a situation where the first interaction might not get enough credits.
Position Based Attribution Model
The first and last interactions get the 40% credits each while the rest of the interactions divides the remaining 20% credits.
This model works efficiently for conversion based and awareness based programs by focusing on the first and last interactions. However, all the channels in between are ignored and some of them may actually have a big impact on your business.
Issues with Attribution Modelling
Attribution modeling doesn’t come without challenges. A report by AdExchanger stated, “The customer journey is never completely trackable.” This brings us to some major problems with attribution modeling.
What about offline stores?
Consider a case where customers saw a paid advertisement for products. Then customer did some google searches and checked the website for that product. But when it comes to making the purchase, the customer went to the offline store and made the purchase. Just because no digital conversion happened, attribution modeling will not be applied. However, the paid campaign and search results were major factors that lead to the ultimate sale.
How can we count external factors?
We can’t. Suppose one of your friends has suggested,
Is attribution modeling data accurate?
It’s not. Tracking the user’s behaviour depends on cookies. But if customer uses tools that disable tracking the user’s performance, the collected data will be inaccurate.
Is there any perfect attribution model that always works perfectly?
Nobody actually understands which model works perfectly. There is no best model, each of them has its own pros and cons.
In order to reach out to their audience and potential buyer, the marketers make many efforts. Some of those efforts result in conversion while others don’t. But a marketer should know about these efforts and their results. And attribution modeling offers that.
Attribution modeling is a system to assign credits for the various channels that lead the customer to purchase your product. However, the models are not always perfect and results are not always accurate.
Then why use attribution modeling? Because marketers need something to start with, attribution modeling is not perfect but offers useful insights into user behaviour.