Cadence #7 - SaaS Lead Gen Attribution Models

Hello! Welcome to another edition of Cadence, in which I'll be covering a conversation I've had multiple times over the past few years, with various SaaS companies (and stakeholders).

Earlier this week Google announced that they were removing a number of attribution models from the impending Google Analytics 4.

Yay... ??

Starting in June 2023, Google will remove the ability to select first click, linear, time decay, and position-based attribution models for conversion actions in Google Ads that do not already use one of these models.

This will also undoubtedly reignite another conversation I've had with a number of SaaS companies, and that's around attribution models for all marketing channels in determining ROI and CPA.

The majority of companies use a default attribution model of many analytics packages, Last Click.

Last click attribution (LCA) is a model for tracking the success of lead generation campaigns for SaaS companies. It's based on the idea that the last touchpoint a customer has with a company before converting is the most important for marketing attribution.

At its core, LCA assigns credit (and subsequent conversions) to the final source that leads to a customer clicking on an ad or link and entering their details. For example, if someone sees a Facebook ad but only ends up converting after seeing your website, then the new customer is attributed to website traffic.

One of the advantages of using LCA is its simplicity.

Since it focuses on one single source of acquiring customers, it's easier to track which campaigns and channels are most effective in lead gen. This can help you develop future campaigns with more accuracy and confidence since you’ll have data-backed information on which sources contribute most to conversions.

Why Last Click Attribution Is Inadequate for SaaS Companies

When measuring the effectiveness of your lead generation efforts as a SaaS company, it can be easy to rely on a last-click attribution model. After all, this model gives credit to the last touchpoint in the user journey before they converted—making it seem like an easy way to measure marketing ROI.

But here’s the catch: Last Click attribution fails to account for the multiple touchpoints that occur in most user journeys. All too often, a SaaS customer will reach out via social media, get targeted ads on LinkedIn, conduct research via search engines or read a blog post before eventually converting.

That’s why last Click attribution falls short for SaaS companies—it only considers one piece of data and fails to give credit where credit is due. It’s important to recognize each step in a user journey, regardless of which channel or platform it was sourced from.

Last Click ignores all of the other touchpoints that a user has before they convert, which is detrimental to your business if you're trying to make sure that you're getting the most out of your marketing efforts.

So what's the solution? Improving your lead attribution model with cross-channel measurement. This will help you gain visibility into all of the channels involved in a user’s journey before they convert, so you can make sure that they're getting everything they need from each channel to move down the funnel. Here are some examples of how cross-channel measurement can be used to improve lead attribution models:

• Measure how well different channels work together—find out which channels work best in tandem and tweak accordingly.
• Monitor every touchpoint throughout the user journey—understand where users drop off and figure out why, so you are able to optimize for better performance.
• Identify what content resonates with users—better understand which content works best across each channel, so you know where to put more focus in the future.

By taking advantage of cross-channel measurement tools, companies can gain visibility into the full customer journey and make sure that their marketing efforts are helping users move down the funnel.

Understanding The ROI of SEO

The majority of SEO campaigns and projects boil down to measuring:

• Traffic (organic sessions, users)
• Keyword rankings
• Organic CVR

But in reality, the metrics the business cares about are:

• Revenue tied to organic search
• MQLs tied to organic search
• SQLs tied to organic search

The first three are merely "functional" metrics of achieving the last three.

With Saas, and most verticals, user search activities can fall into three categories, these being:

1. Branded search
2. Branded compound search
3. Non-branded searches

And we achieve all three through different content types, elements of product marketing, and messaging. Measuring the ROI of these activities requires an understanding of how the buyer cycle actually works.

Typically in B2B SaaS the buyer journey goes along the lines of:

This journey will obviously differ, but as you can see, multiple teams will perform research, and interact with different websites at different stages - creating event-less and goal-less sessions, exchanging internal emails, going direct... Before eventually procurement is made usually by an internal team or via a stakeholder with a credit card.

How the hell do you decipher that to say for definite - YES, channel X is the attributed one. You can't. Even in paid search and pointing users to landing pages that are only accessible by the advert - you can't dismiss all prior interactions that specific user and others in the company have had before completing a form.

So what's the solution?

The solution is better reporting, and understanding the modern role of SEO in lead generation for SaaS companies.

It's about working with product teams, and wider stakeholders to create content and user journeys that match your ICP (Ideal Customer Persona), and content that resonates at different stages of the funnel in your SOM and SAM.

It's also about creating a "value experience forecast", in which a user can easily and adequately forecast their experience of the product and how it will help their needs. This reduces friction during the onboarding process, as well as helps reduce churn down the line leading to more stable MRR and ARR.