Introduction – Attribution Modeling

In the last two posts, I tried to explain the basic concept of Attribution Modeling and how to use Google Analytics to create custom Attribution Models. The previous two stages are relatively quite easy to execute. This last part is the point things get a bit trickier. If you are not yet clear on the concept of Attribution Modeling, make sure to go back and read the two previous parts of this series. To proceed reading without doing that will honestly be a waste of your time. So, make sure you understand the basic concepts before you read this final part of the series.

To read the previous parts of this series click here:

• Part 1 – an Overview of Attribution Modeling
• Part 2 – Attribution Modeling using Google Analytics

Let’s dive in. The whole purpose of understanding Attribution Modeling is so that as online retailers and marketers you can learn how to calculate and measure ROI (Return on Investment) precisely. It’s now time to dive in deeper, in order to truly understand the real impact that different channels have on your revenues.

For the sake of simplicity, I have classified all the commercial websites in the world in the following three main categories:

1. E-Commerce
2. Lead Generators
3. SaaS

Since each one of these websites has its own unique characteristics, it is mandatory to deal with each differently. As a general rule of thumb, attribution-wise the e-commerce sites are the simplest to understand whereas SaaS and Lead Generators are more complex in nature and therefore require a different approach.

In the following lines, I am going to talk about new user acquisition. Let’s leave user retention and lead nurturing for some other time.

E-Commerce

In e-commerce websites, all activities take place online and most of these activities are completed within the 90-day conversion window from the time of the first click to the first purchase. Google Analytics, therefore, provides a solid solution for Attribution Modeling if implemented the right way. However, you have to deal with a cross-device tracking issue (users who enter a website from different devices therefore not tracked properly). This can be easily resolved by making users register. This way each time they log in from a new device you can perform the cross-device tracking by placing a cookie on their device.

Lead Generators

Lead Generator sites, on the other hand, struggle with different issues. Since the actual sales do not occur online,  it becomes even harder to attribute real value to each lead; therefore, a rough guess is applied for overall averages. There is a separate process to address this issue. For that, I recommend reading this post on Offline Conversion Tracking in Google AdWords.

SaaS

SaaS sites are the most difficult to crack when it comes to Attribution Modeling. This is because the acquisition funnels in SaaS sites are the longest ones and it takes a lot of time to progress through the funnel until a final sale is closed. On top of that, the buyers usually use different devices which increase the level of complexity.

In order to fully understand the complexity of SaaS systems, let’s use this fictitious example.

Think of a cyber security company that offers a SaaS product that provides cyber security defense to your site. The product offered is cloud based and is distributed by a SaaS product called ACS. As a customer, you enjoy a 60-day free trial period with some features. Once 60 days are over, you need to upgrade to a paid account or you do not get to enjoy these services anymore. Let’s have a look at the user’s journey.

As you probably noticed, it’s hard to calculate how much credit each channel deserves and how to track the 120-day buying cycle when Google Analytics allows only a 90 day Lookback window. Under such circumstances, we have to find a connection between the marketing and sales (CRM).

In order to sort things out, let’s divide the buyer’s journey into two segments:

1. Non-Registered Users
2. Registered Users

As soon as a user registers for the first time, the magic begins.

If we can somehow track all the touchpoints up until he/she registers, and save the path to the CRM, we might be able to create a longer, more accurate time-bound tracking.

It is only when the user upgrades their account to a paid account, that the attribution can take place.

The question, however, remains – how much should we attribute for each channel. This question becomes more complex on occasions where the user upgrades their account to a monthly retainer making the customer life time value indefinite and accumulative.

There are two approaches to solve this issue:

1. Using Customer LTV
   Since by now you know how many customers to attribute to each channel, you can calculate your current average LTV, and deduce an average value to attribute

2. Accumulate Revenue and Take a Longer Lookback
   Data accumulates over time, take a long look back in order to get a clearer picture of what’s working better. Looking a year back, you can see how much revenue was generated within the first quarter. Compare that data to the rest of the year to analyze whether or not over time your estimations are in line with reality.

I recommend combining the two approaches. Customer LTV is good for the ongoing optimization. A longer Lookback will provide you with an understanding whether the data remains the same.

You may need to use some attribution software/tools in order to reach the most precise conclusion. Here is a list of some popular attribution tools you can use:

• HubSpot
• MixPanel
• KissMetrics
• Bizible
• Google Attribution 360

If you are a SaaS company and you practice Inbound Marketing, I personally suggest you use HubSpot as it offers a complete marketing cloud. Do not mix PPC with Inbound marketing, PPC is actually a part of Inbound marketing and is not a totally different ball game as some assume. You can get more insight into this topic in my post about Inbound Marketing v.s PPC.

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Attribution Models for Mobile Apps

When it comes to mobile apps, most publishers and advertisers rely on the Last Click Attribution Model. There are a number of mobile-app attribution software programs that can easily track In-app conversions and installs and easily attribute them to the agency/channel that has created the last click. An example of these programs is:

• Appsflyer
• Tune

Pitfalls to avoid

Using attribution modeling without the proper knowledge and experience can cause even more damage then not using them at all. Here are a few pitfalls to avoid:

• Not Testing/Testing Without a Coherent Theses
  It goes without saying that like every digital activity, you should constantly test these attribution models. However, what to test and how to test sometimes remains answered. Keep in mind that different attribution models work differently for different businesses. At the end of the day, it all boils down to your understanding of these models. You should know which models work best for you and by changing these models you can analyze any increase or decrease in your profitability and by what percentage.

• Multi-Testing
  In order to avoid inaccurate assumptions and an unclear understanding of the tests results , it is recommended to avoid multi-testing different theses. It’s best to conduct each test as uniquely as possible and to measure  the results before proceeding with another test.
• Data Leaking
  If you are not able to handle Google Analytics correctly, you may experience breaking sessions. With incomplete data, you can easily lose track of your progress. Frequent session breakage can result in Google Analytics creating a new session for a user who is already on your website. This can lead to  traffic source displayed as “not set”. As a result, you may not be able to attribute the revenue correctly.
• You should also be familiar with the factors that can cause these sessions to break:
  • incorrect implementation
  • sessions are taking too long
  • cross domain tracking

• Trusting the System
  You should never blindly trust the system, you need to consistently test the system and make sure that the data is accurate. Large websites with alot of data tend to loose information on the way. Try to avoid discrepancies as much as possible.

• Not Drilling Deep Enough
  Make sure to use raw data and try to round up averages from different segments. You may think it will be easier, faster and better, but it is not the case. Sometimes a 5% difference can shift a campaign from a profiting one to a losing one. So, never use estimates or average data figures, dig deep to use accurate data.

• Do not Miss the Big Picture
  You need to keep in mind that Attribution Models are models and by no means facts. They produce results based on the set of rules that you define. These rules are not the same for every other business, so you should test every model to see whether it generates more business or not. Never take your eyes off the big picture, i.e. making profits.

Conclusion – Attribution Modeling

Regardless of the type of business you own or you are advertising for, using the most appropriate Attribution Model can result in an increase in your marketing campaigns and ultimately your business profitability. If you miss out on estimating the valuable contribution each marketing channel provides to your business, you can find yourself facing failure. There is a good saying that truly reflects business growth “if you’re not moving forward, you’re moving backward”.

In the end, it all boils down to keeping yourself ahead of the game. Although your main focus should be on increasing ROI, you also need to keep on testing what to do to find new audiences and channels to add to your media mix.

If you found this useful, and you’re curious to learn more and find out how to improve your lead generation, we invite you to download our 30 greatest lead generation tips tricks and ideas eBook.

Introduction

On my last post, I shed a light on the role, importance and kinds of attribution models by giving examples and elaborating on the logic behind each model. In the second post in this series, I am going to share how to apply and compare different attribution models by using Google Analytics.
Before you head on to read this post, make sure you read my previous post in the series: Overview of Attribution Modeling

Attribution Modeling using Google Analytics in 4 Steps

1. You can start by clicking on the sidebar of the Conversion Tab and choosing Attribution > Model Comparison

3. Now we need to start putting values in the following fields:
   • Conversion – select the type of conversions you want to include in the report
   • Type – select the type of conversion (AdWords/All)
   • Lookback Window – select the timeframe when the first clicks occurred, you can select between 1-90 days.
   • Model A – Model A is the baseline for the comparison. the model you select on the Model B part will be compared to model A
   • Model B – If you want to select a different model, you need to select it from the drop-down
4.  Primary Dimension – you now need to choose the dimension breakdown (source/medium, etc)

Once you are done putting in the values, a new report will be generated by Google Analytics comparing one model to the other and calculating new CPAs.

Introduction

We now live in a digital age where users visit several sites by means of various devices while going through their buying journey. Your website is not different, users will visit your website a number of times before completing a transaction or providing their credentials and turning into a lead. You have to track their shopping behavior, know which marketing channels and marketing campaigns have the most impact on their behavior and what products and services they are most interested in. By keeping an eye on these aspects, you can make timely adjustments in your marketing mix. This is the only way you are going to survive the intense online competition.

Studies show that online users visit your site several times before they make their final purchase decision. As an online business owner, you have to know about the marketing channels that brought the buyers to your site and made them close the deal.

You will be surprised to know that your online shoppers pass through various supporting channels before the final encounter. In this post, I am going to share some examples of an online buyer’s journey and show you what kind of effect different attribution models can have on your sales and ROI.

Defining Attribution Modeling

For an online marketer, the term Attribution Modeling can be a complicated concept to understand, but it is worth understanding. Let us try to understand this term in its simplest form. The definition of Attribution Modeling as per Google is:

“It is a set of rules that determine how credit for sales and conversions is assigned to touchpoints in conversion paths.”

The fact is that most online marketers are already taking actions based on different attribution models without even knowing it. Various advertising platforms such as Google, Facebook, and other ad networks, as well as Google Analytics, provide conversion measuring functions. The data from these platforms can be used to attribute sales and conversions to different marketing channels.

The problem is that most marketers do not know that they are making important optimization decisions without understanding the big picture. Let us have a look at an example to further understand this concept.

A college named BIG (Business Is Great) runs an advertising campaign in order to enroll new students to next year’s MBA course. An accounting grad named Andrew is interested in proceeding with his academic studies and he starts to search the net for information. Andrew takes the following steps before converting to BIG’s landing page for MBA studies. Keep in mind we are focusing on the steps that took place on BIG’s site only:

This example clearly shows that the user had 4 touchpoints with BIG’s site before the actual conversion. If you look at Google Analytics, it will show Google Display (Remarketing Ad) as the source since this was the traffic source when Andrew actually converted.

What about the contribution of Facebook Ads, Google Organic, and Google search? Don’t they get any credit for this conversion? Well, sure they do and it is actually the result of attribution modeling.

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Some Common Attribution Models

Before looking at the different Attribution Models and choosing one of them, online business owners or marketers must first understand and identify their marketing objectives. Here is a quick overview of the most common attribution models:

• First Click

As per this model, all the conversions/conversion value is attributed to the source of the first click. In the example cited above this refers to Google Organic Search. Although it is not very common, it is basically used to measure the impact of a brand-awareness campaign.

• Last Click

As per this model, all the conversions/conversion value is attributed to the source of the last click. In our example, this would refer to Facebook Remarketing Ad. This is the default attribution model of Google Analytics and is most commonly used while working with performance agencies. You will see this model at work in the mobile app industry.

• Linear

In this model, an even amount of conversion value is attributed to each touchpoint. This seems like a more logical approach, but this model is not applied in every business. The linear model provides a better and more relevant insight than the first/last click model and it is quite easy to implement and measure.

• Time Decay

This model assigns the most credit to the touchpoints which fall near to the time in which the actual conversion took place. This model is a bit sophisticated and therefore harder to implement and it is not relevant to every business. Businesses which run time-bound campaigns or sales cycles that involve a short consideration phase find this model more relevant since they want to know the value of clicks that were closer to the campaign.

• Position Based

This model is a combination of the last click, first click, and the linear models. In case you want to find out the value of touchpoints that help you make sales by attracting your customers, and the final touchpoints that result in the actual conversion, you can simply use the position based model. In this model, the last and the first touchpoints are attributed a certain percentage of the conversion value whereas the remaining touchpoints are attributed linearly the rest.

Let us have a look and see how we can apply the Position Based model to our example. Joe eventually ended up enrolling in the MBA program and he will pay about $100,000.

This table clearly shows how much of the $100,000 has been attributed to each touchpoint in different models.

Conclusion

Attribution modeling can assist you in understanding how each marketing channel assists your overall business goals. Think of the entire marketing operation as a wrist watch. The watch needs all the gears to operate correctly, removing one the functioning gears could result a in complete failure to your watch. So watch out!