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Choosing the Right Attribution Model

CaliberMind Which attribution model should I use?

Which Attribution Model Should I use?

When it comes to attribution models, people often ask consultants which model is the best for their business. However, different attribution models answer different questions, so it's important to understand which question needs answering to choose the right model. For example, you may want to know which campaign performs best at a particular stage, or how much pipeline and bookings marketing contributes compared to other departments.

Instead of promoting a single model, we will provide examples of when a specific model makes sense and when it doesn't. But first, it's important to understand attribution as a philosophy.

What Is Attribution?

The original intent of attribution was to understand which marketing tactics generate the most pipeline and bookings for a business, but businesses also use attribution to determine how much "credit" a department should receive for generating pipeline and bookings compared to others.

Proper CRM usage by the sales team, integrated marketing automation tools, and secure UTM tracking methods are essential for effective attribution.

When do you use Single-Touch Attribution vs. Multi-Touch Attribution?

Many people use single-touch attribution models such as "Opportunity Source," "Primary Campaign," or "Lead Source" without even realizing it. These models assign 100% of the opportunity value to whichever tactic or department interacted with the prospect at the point of measurement.

Multi-touch attribution (MTA) is a valuable tool for businesses to understand the contribution of each department to their pipeline. However, it was originally intended to demonstrate the overall value of marketers to the organization. The complexity of B2B buyer journeys, involving multiple decision-makers and changing priorities, necessitated the creation of MTA.

Instead of focusing on a single point in time, MTA enables marketers to demonstrate the effectiveness of campaigns in keeping an opportunity engaged throughout the decision-making process. While all MTA models can determine the contribution of each team to the pipeline, some are better suited for answering specific questions.

The difference illustrated:

the difference between single and multi touch attribution

Single-Touch Models

First-Touch Model

A first-touch model assigns 100% of an opportunity's value to the prospect's first brand interaction, regardless of when it occurred. A first-touch model answers the question, "What are the best tactics for getting someone to engage with our brand first?" With this model, you are looking for the first signal that a company is researching possible solutions for a problem.

example of first touch attribution
Last-Touch Model

A last-touch model assigns 100% of any opportunity's value to the interaction immediately before opportunity creation. A last-touch model answers the question, "What is the best channel to get our prospect to engage with sales?"

example of last touch attribution

Multi-Touch Models

Even-Weighted Model

The even-weighted or linear attribution model assigns equal value to each touchpoint in the buyer journey. This model looks back 365 days before opportunity creation and divides the attribution dollars until the opportunity closes. It removes the assumption that any single touchpoint or person is more significant than others and credits the most popular tactics.

example of linear model
W-Shaped Model

The W-shaped model assigns more weight to the first touch, last touch, and touches associated with the primary contact. This model is best suited for determining which department or tactics are most effective in engaging an account and hooking the primary contact. It is useful for organizations that recognize most touches after opportunity creation will be sales-driven but does not help marketers understand which materials and campaigns buyers engage with while working with sales.

example of w-shaped attribution
U-Shaped Model

The U-shaped model assigns more credit to the first and last touch before opportunity creation. All other touchpoints before opportunity creation receive some credit, but anything that takes place after opportunity creation is ignored. This model is ideal for determining which department or tactics are most effective in engaging an account and getting them to engage with sales.

example of u-shaped attribution
Chain-Based Model

The chain-based or Markov effect model is a machine-learning model that answers the question, “What is the blueprint for a successful buyer journey?” This model looks at touchpoints up to 365 days before opportunity creation until opportunity close and isolates the most common sequence of steps before purchasing the product. The model is trained against the success criteria of a closed-won opportunity, and points are deducted when a prospect deviates from this path. It removes bias towards any single point in time or person and is suitable for organizations with many closed-won deals.

example of markov attribution
It is essential to note that machine-learning models require many closed-won deals to accurately learn the best sequence of interactions. If an organization has less than 100 closed-won opportunities, it is best to use a different multi-touch model.

What About Custom Models?

Are there different variations of models? Absolutely. But there are a few reasons we see internally architected models fail.

  1. They’re expensive. A good attribution model requires connections to your marketing tools and your CRM. And it should even incorporate web signals. Each of these things is time-consuming to set up. The most expensive part of managing your model is change management. Marketers change technology a lot, and each time something changes, you have to map the data and update your model.
  2. They’re easy to over-engineer. All models have excellent intentions behind them, but none are perfect. We can’t track things like word of mouth, and digital tracking has a lot of gaps. We must acknowledge that any model is an estimate and be cautious of over-valuing specific personas and touchpoints. Weighing channels more heavily when they’re expensive may not reflect what works to attract people to your brand. Human bias is real.
  3. They forget WHY. Many models are over-engineered because the marketing department has difficulty selling the model to the rest of the organization. People who try to compensate by introducing complex weights or calculations usually have lost sight of their goal. If your goal is to prove that marketing “works,” ignoring sales and channel touches is often the culprit.

Finally - Ask some hard questions

If you hate attribution, it’s time to ask yourself some hard questions. What are you trying to do with the model? Which questions are you trying to answer? And do I need help or a different tool to get attribution right? Hopefully, this article gives you some ideas on where to start looking.

For video walk-throughs of attribution use cases that answer questions like the following:

  • Which campaign is best at name acquisition or creating the first engagement?
  • Which campaign is best at driving people to engage with sales or at pre-opportunity creation engagement?
  • Which campaign is best at generating meetings or the last touch before opportunity creation?
  • Which campaign is best at propelling in-flight deals forward?

Check out our Attribution Overview 2.0 article. There are videos that show how we answer each of these questions.

How did we do?

Chain-Based Attribution Model

The A-Shaped Model