How Intelligent Attribution Modeling Can Benefit Marketers
One of the biggest struggles for marketers today is proving ROI based on their efforts. Attribution modeling may be your solution to demonstrate marketing’s impact on the bottom line.
Need direction to get started? AI Academy for Marketers offers a deep-dive certification course to help marketers make attribution modeling a reality.
If you’re not familiar, AI Academy is an online educational platform that helps you understand, pilot, and scale AI. We created this platform to help marketers gain access to affordable, AI-focused education that advances learning and aids in AI implementation.
Intelligent Attribution Modeling for Marketers is one of the Academy’s certification courses for members. In the course, Katie Robbert (@katierobbert) and Christopher S. Penn (@cspenn) of Trust Insights dive into what attribution modeling is, the different types of attribution models, and best practices for setting up your own analysis.
Keep reading for an overview of the Attribution Modeling Lifecycle from Trust Insights to help you kickstart a flawless attribution model.
What to Expect from the Course
What is attribution analysis and why does every marketing executive demand it?
Attribution modeling is all about who or what gets credit for a business impact you care about. This helps marketers understand what is and what’s not working in their marketing strategy.
In this intermediate/expert-level, 3-hour certification, you will learn:
- The 5 methods of attribution analysis
- The 6C Data Quality Framework
- How to structure your attribution analysis correctly
How Does Attribution Modeling Benefit Marketers?
Fair warning: Before taking this course, it’s recommended that students have a high-level understanding of attribution and how it works.
Attribution modeling helps marketers assign credit to touch points in their customer journeys. For example, when you make a sale, which tactic claims the sale? Is it Facebook ads or email newsletters? Attribution modeling removes the confusion to create a clear understanding of where credit is due for a successful conversion.
The second part of the definition is about cause and effect. Being able to prove cause and effect is a vital part of attribution modeling as it helps prove causality in our annual marketing programs. Ask yourself, “Did you cause an effect that caused a business impact?”
So, how does it work?
The Stages of the Attribution Modeling Lifecycle
The Attribution Modeling Lifecycle helps us to conceptualize all of the stages of implementing attribution modeling into our marketing programs. Katie and Chris break down the lifecycle into four stages: planning, data, development, and deployment.
In this stage, we uncover the business requirements and goals of the project.
A helpful way to begin with requirements is to formulate the phrase, “As a (person), I (want to), (so that).” “Person” is your audience, “want to” is your intent, and “so that” is your outcome. An example of this is: “As a CMO, I want to understand which channels are performing, so that I know where to allocate budget and deploy resources.”
Another crucial part of the planning stage is to understand your goals and values. Your goals are critical to the success of attribution modeling implementation. Tips for setting realistic goals include attaching a value to your goal and using software to measure them effectively.
The data stage consists of data requirements, data collection, attribution data Q&A, and data preparation.
The quality of your data affects the quality of the model. Factors you should focus on when considering the quality of your data include if it is clean, complete, comprehensive, chosen, credible, and calculable.
When dealing with data storage, you can track data through spreadsheets and visualization tools (i.e. Tableau). When you’re looking at databases for advanced analytics, make sure it’s secure and user friendly. A key part of data storage is to examine data and we do this through data inspection.
During the development phase, we focus on model selection and evaluation.
There are three types of models you can select when implementing intelligent attribution modeling. These three models include out of the box models, custom models, and AI-driven models.
Out of the box models include first touch attribution, last touch attribution, linear attribution, time decay attribution, and position based attribution. Out of the box models are the easiest to use, but the accuracy of the data is the lowest.
Custom models are useful when you want to build a model that is tailored just for your company. Custom models are not as easy to use, but the accuracy is better than out of the box.
AI-driven models use machine learning to try and understand when things are not always in a given sequence or something is changing rapidly. Just like custom models, AI driven models are not easy to use, but accuracy is the best option of the three.
When you evaluate these models and consider which is best for your company, key factors to focus on include time/resources, budget, and effort/skill sets.
In the last phase of the Attribution Modeling Lifecycle, we focus on model deployment and tuning.
The model we choose is only as good as what it tells us to do. This means that the reporting capabilities of our chosen model equates to how valuable attribution modeling can be in your business. Effective data visualization charts should be clear, clean, complete, concise, cited, and conclusive.
Ready to discover these and other important AI concepts? Sign up for the AI Academy today.