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Bold messaging, pattern interruption, going after competition …We all want to try and experiment with more daring marketing content and copy. But is it worth it? And what’s the best way to optimize it. Let’s do the math!

Examples of bold marketing

I wrote about the types of risk-taking strategies here(What kind of risk-taker are you: Maximax, Minimin, or Minimax? A decision-making framework) but can we actually measure the impact of “edgy” marketing and how to limit the risk.

Let’s consider 2 marketing strategies:


This is a question I’ve been asking myself for a while and we now have some results to share!

We’re looking at a common scenario: Visitors land a home page or a landing page with a Call to Action for registration. They can use the solution and then buy the product/service.

Should you optimize your page for Registrations (hoping that more registration will lead to more purchases) or optimize for the end goal: purchases? Does that make a difference?

We ran an intelligent experimentation with 2 variants on a home page messaging:

  • One that focuses on value and expertise
  • One…

I claimed it before, A/B testing is dead. Why?

  • It’s too long and most likely makes the conversion rate drop during the test
  • It takes more time and resources than it brings value
  • It’s inaccurate and biased

(Read: Don’t accept these results from a testing or conversion optimization campaign)

I was challenged quite a bit on that claim(fair enough) so I decided to start a series on why I believe.

The notion of “content-audience matrix”

Let’s look at real-life examples (Cauzal customer)

This customer has 20 variations of content for multiple elements on many pages (CTA, H1, Links, etc…). …


In the world of conversion optimization, experimentation is king. Test content, messaging against different audiences to understand what really engages, and converts your visitors.

We’ve discussed the strong limitations and flaws of A/B testing in this article and today, we’ll look at a few results that we usually accept when we should be pushing back and questioning!

TL;DR: Status quo in testing can be challenged in many ways.

  • Demand results in days and weeks, not months
  • Conversion loss while testing is not required! Intelligence can prevent that!
  • Experiments should give you more than 1 winner. …

Today, in our series on demystifying AI, ML, and applied algorithms, we’re looking at gains, regrets, and risks.

TL;DR:

You can wire your algorithm or your brain to make decisions based on:

  • Maximizing the payoff at all cost (risk taker) — Maximax
  • Maximizing the minimum payoff (risk aversion) — Maximin
  • Minimizing the potential regret (loss or missing out) — Minimax

We all want to avoid regret while maximizing success in what we do. Oh, and all that with controlled risk, of course. How do we model that?

Jeff Bezos made his Regret Minimization framework pretty famous:

Jeff Bezos’ regret minimization framework

Now…while this is inspirational…


Chances are: your A/B testing campaign will fail. Fail to drive conversions, fail to drive significant and actionable results.

This doesn’t mean experimentation shouldn't happen; quite the contrary, let's double down on testing but let’s do it smartly, with actual goals in mind that are not simple validation of our own assumptions and the understanding that it’s beyond human reach to analyze and optimize data exhaustively.

I believe the main reason A/B testing hasn’t lived up to its promise is that we’ve oversimplified it for years. We run and compare 2 (or even X) copies and see what *performs* the…


Advanced detection mechanisms have been used for decades in medicine and psychophysics; with the first assessment of human signals dating back to the 1950’s. Data sets grew bigger and naturally called for more intelligent processing, which Machine Learning (and to some extent AI) can fulfill.

This is what we’re solving at Cauzal.ai: How can we apply some of this methodology in Marketing, and specifically in content management?

1st thing first, let’s put our scientist hats on and understand the concept of ROC Curve: Receiver Operating Characteristic. It looks like that:

ROC Curve (Source: Wikipedia)

In this example, we are looking at 3 detection predictors…


Effectiveness in content management is a factor of matching 2 key elements:

  • Your content (copy/creative/CTA/…): what message are you conveying and how)
  • Your audience: who are you showing this message to and what is their intent

Timing/sequence is also a highly important factor but we’ll cover how we believe it is part of the audience building process.

Why content optimization on your website matters. In 7 numbers.

80–10–10: What your site visitors are searching for

  • 80% Informational queries, typically early in the buying process
  • 10% Transactional queries, with a product and service in mind
  • 10% Navigational queries, typically just using a search engine

Less than 1 second: Visual affinity is…


Today, we’re looking at (some of) the psychology behind online behavior and how it can/should inform our content strategy.
At Cauzal AI (cauzal.ai), our mission is to empower anyone to deliver the most relevant content to every visitor. It starts by understanding our visitors to make content that matters.

Below is a 7–10min read, summary of our findings when learning about content psychology.

1- How does the brain function?

The need to control

Humans crave control because it offers comfort.

When things feel out of our control, our minds and bodies respond, with stress and tension. Our visitors are humans too (well, most of them), and if they start to…

Gwendal Mahe

Building Cauzal.ai. We predict the content that will convert your audiences

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