Deterministic vs Probabilistic: Why You Need Both Models in Marketing

Katie Holmes
26th March 2024

The debate around deterministic vs. probabilistic modelling is ongoing, but their combined use offers a more effective approach.

The marketing world is facing a big shake-up. 

With tighter budgets and a growing need to prove return on investment, marketers are scrambling to show the real impact of their campaigns.

The way we track and attribute conversions is being fundamentally rewritten due to privacy regulations like GDPR and CCPA, and the deprecation of third-party cookies.

This blog post explores the emerging world of probabilistic attribution, contrasted with the existing world of deterministic attribution, and how marketers can navigate this changing landscape.

💡 Pro Tip

Traditional attribution methods miss the whole picture. Ruler uses deterministic and probabilistic tracking to analyse both clicks and ad impressions to give you a more complete view. It considers all the touchpoints, not just the final click, to accurately credit conversions and revenue across your marketing channels. 

Skip to the part about Ruler or book a demo to see how Ruler provides a more complete view of your marketing efforts.

What is deterministic and probabilistic attribution?

Before we dive into the specifics, let’s lay the groundwork and briefly explain what probabilistic and deterministic attribution means in marketing measurement.

Deterministic attribution relies on cookies and user identifiers (IDFA, GAID) to track users across different touchpoints and attribute conversions and revenue back to marketing.

You can think of deterministic attribution as a series of checkpoints. 

Deterministic attribution definitively says which checkpoint – a social media ad, a website visit, or an email – directly led to a conversion or revenue. 

Traditionally, this method reigned supreme due to its simplicity and the perceived certainty of a cause-and-effect relationship between touchpoints and conversions.

However, with the decline of third-party cookies and stricter privacy regulations like Apple’s ITP, deterministically identifying user journeys has become increasingly difficult.

This leads us nicely to probabilistic attribution. 

Related: What is probabilistic attribution and its impact on marketing?

Unlike deterministic attribution, probabilistic attribution uses statistical models and machine learning to estimate the likelihood of each touchpoint influencing a conversion or revenue. 

It analyses user behaviour and compares it to existing data to determine the possible role each interaction played in the customer journey.

The differences between deterministic and probabilistic attribution modes

To clarify, deterministic and probabilistic attribution both have a place in marketing measurement. 

Instead of viewing them as bad or wrong, let’s focus on the situations where deterministic and probabilistic attribution might be more or less effective.

Deterministic attribution

The number one advantage of deterministic attribution is that it offers precise customer journey tracking.

Related: How to view full customer journeys with Ruler

It tells you exactly who interacted with your marketing campaigns and what actions they took. 

Imagine Sara clicks a Google ad for a new pair of trainers. She browses your website for a couple of minutes, and eventually purchases. Deterministic attribution precisely connects these dots.

With this level of granularity, you can gain deeper insights into your customer segments and target ICP.

You can look back at past campaigns and identify high-value customers.

Deterministic attribution then shows how you acquired them, revealing which campaigns attracted them and where they converted.

This analysis lets you replicate success by focusing on similar marketing strategies, an advantage not possible with probabilistic attribution.

That said, deterministic attribution is getting trickier to set up. 

Currently, we find ourselves in a transitional phase, but once we bid farewell to third-party cookies entirely, deterministic tracking will exclusively rely on first-party data.

Without a first-party data strategy, your ability to track user journeys and stay compliant will be constantly hampered.

Related: How to collect first-party data in a cookieless world

Tech giants like Google and Meta do track first-party data, but the caveat is that they own it. 

They control what they share with you, how it’s presented, and what they may (or may not) rent back to you.

Going a step further, these tools ultimately serve their own purposes. 

Google does a good job at getting you to spend effectively on Google Ads data and aims to increase your budget on their platform.

Facebook operates similarly, encouraging you to optimise your Facebook ad spend and ultimately invest more.

Neither platform provides data that would fully incentivise users to spend money elsewhere. 

This highlights the importance of capturing first-party data from your customers and visitors. 

💡 Pro Tip

Ruler leverages first-party cookies to track visitors to your website. Its code triggers on every visit, matching this data to user profiles for a clear picture of your audience and their journey. When a visitor converts, Ruler connects their conversion to the specific path they took to get there.

This lets you bypass the limitations of data-walled gardens and the phasing out of third-party cookies, giving you control over your marketing attribution.

Book a demo to see how Ruler tracks the customer journey

Probabilistic attribution 

While first-party data offers a promising future for campaign measurement, it’s not a silver bullet. 

A growing number of consumers are actively rejecting cookies and employing ad blockers, while platforms such as Safari automatically delete cookies after 7 days of user inactivity on a website.

Additionally, Apple’s App Tracking Transparency (ATT) restricts companies from sharing user data with third parties. 

These advancements have notably added complexity to tracking user behaviour and gauging campaign efficacy, prompting marketers to delve into the possibilities of probabilistic attribution.

Unlike deterministic models that focus on individual interactions, probabilistic models zoom out to assess broader trends across diverse customer segments.

Imagine a thousand visitors land on your website, but you lack precise data on which channels they originated from. 

Probabilistic models might estimate that half came from SEO, a quarter from paid search, and the rest from Facebook.

It can’t pinpoint individuals, but it assigns probabilities to different channels based on their likely influence on conversions and revenue.

There are many instances where marketers have no choice but to resort to probabilistic attribution.

For example, when airing a commercial during a major event like the Academy Awards. 

It’s challenging to track who viewed the ad and subsequently made a purchase. 

However, probabilistic attribution can estimate the overall impact of the commercial on sales, even in the absence of user identifiers.

In essence, probabilistic attribution enables marketers to bridge the gaps when unique identifiers are unavailable.

Combining deterministic and probabilistic data for richer insights

The question of whether deterministic or probabilistic attribution is best for understanding customers and marketing effectiveness is a hot topic.

But the truth is, the most powerful strategy combines them both.

By blending these two data types, you gain a more complete picture of what is and isn’t working.

Probabilistic data fills in the gaps when specific user identifiers are missing. 

It helps you reach similar audiences or predict behaviour based on general trends.

On the other hand, deterministic attribution improves probabilistic models.

By comparing model predictions to confirmed user actions, you can ensure their probabilistic insights are accurate and useful.

For example, Ruler uses both deterministic and probabilistic attribution methods.

Its deterministic attribution uses first-party cookies and user identifiers to track the click path journey by pinpointing the exact sequence of channels, ads, campaigns, and pages that lead to a desired outcome, such as a purchase.

By mapping these click-paths, you can gain insights into which channels attract your best customers and how they navigate your website before converting.

When deterministic identifiers are missing, Ruler’s probabilistic attribution, powered by machine learning, steps in. It uses advanced techniques like Marketing Mix Modeling (MMM) to estimate channel contributions.

In the absence of MMM data, Ruler’s probabilistic attribution model turns to key indicators such as click-through rates, impressions and other user-level signals to generate impartial and data-driven reports. 

This unbiased assessment enables marketers to gain a holistic understanding of the true impact of their marketing endeavours, empowering them to make informed decisions and optimise future campaigns for maximum impact.

What are the benefits of blending deterministic and probabilistic attribution?

By leveraging both deterministic and probabilistic attribution, you unlock a treasure trove of insights for your marketing measurement strategy. Here’s how this powerful duo empowers you:

1. Run accurate lift tests for display and social media campaigns. Probabilistic attribution can be a useful tool after a successful conversion lift test to understand how credit for conversions might be distributed across different touchpoints in the customer journey, including your ads.

2. Redistribute conversion credit from the BOFU to the top. As probabilistic attribution recognises the influence of upper-funnel channels, it facilitates the redistribution of conversions from bottom-of-the-funnel channels such as direct and organic search, giving credit back to upper-funnel channels that likely played a role in driving conversions.

3. Optimise marketing spend with data-driven insights. By understanding the true ROI or ROAS of each channel, you can prioritise those delivering the highest returns. Probabilistic attribution helps you identify hidden gems – channels that might not directly contribute to conversions but play a crucial role in nurturing leads. Deterministic attribution, on the other hand, helps you pinpoint the channels driving the final conversions.

4. Showcase campaign value to stakeholders and clients.  By presenting a combined picture, you can effectively communicate the true impact of your marketing campaigns on revenue generation and make a strong case for continued investment or budget increases.

Final thoughts on the deterministic and probabilistic debate

Marketing measurement is undoubtedly getting harder. 

Deterministic and probabilistic attribution both have value, but using them together unlocks a goldmine of insights.

So, ditch the “either-or” mentality and embrace the power of deterministic and probabilistic attribution for a clearer view of your marketing funnel and a roadmap to success.

Remember, Ruler makes it easy to leverage both deterministic and probabilistic attribution. Don’t just take our word for it. Book a demo today and see it for yourself.