A/B testing platforms bucket every visitor the moment a page loads, whether they interact with your test or not. If you're testing a mini-cart experience that only 15% of visitors ever open, the other 85% are noise in your results. We've spent years writing custom JavaScript to work around this. Optimizely's Conditional Activation replaces all of that with a dropdown.

When our clients ask how they compare to competitors, we don't just browse the website. We inspect what they're measuring, how they're measuring it, and what that reveals about their priorities. Here's a sneak peek how we do it.

A mobile checkout test drove $6.2M in incremental bookings. It also killed 41,000 loyalty enrollments a year. Most optimization teams celebrate the first number and never measure the second.

Cart abandonment rate hides three completely different behaviors. The analysts who split them apart are the ones who find something actionable.

There are two dominant philosophies for structuring data teams: centralized and distributed. Both are wrong. Centralized teams have the expertise but lack business context. Distributed teams have proximity but lack leadership, governance, and consistent data. The answer is a hybrid, centralized expertise with embedded liaisons in the business units you serve. Here's why, and how to rank the three models.

A demo of the Optimizely Opal connector for Adobe Analytics. Query any dimension, metric, or segment using plain English. No report builder, no drag-and-drop, no workspace setup. Just ask.

McKinsey's latest AI research is everywhere on LinkedIn: "57% of jobs automated! $2.9 trillion opportunity!" But the viral posts are cherry-picking stats and stripping context. We read the actual 60-page report. Here's what decision-makers need to know instead.

New York became the first state to require businesses to disclose when algorithms use personal data to set prices. The law took effect November 10, 2025, survived a constitutional challenge, and is being actively enforced. California is taking a different approach through antitrust law. Multiple other states have pending legislation. This is following the exact same trajectory as GDPR → state privacy laws, creating a compliance patchwork with no federal preemption in sight.

If you've been on LinkedIn lately, you've probably seen your fair share of posts about how "AI will replace your analysts!"

"ChatGPT just made my whole data team obsolete!"

We got curious. Not about whether AI can theoretically do analysis, we know it can be a very useful tool, but whether it can deliver an actual work product. The kind of thing a marketing director might hand to their exec team. So we ran a simple test.

We seen it used on several Subreddits, it made rounds on Facebook, and of course it has been shared ad nauseam by LinkedIn thought-leaders. But do the charts, as one poster on LinkedIn put it, make it obvious that we are not in a bubble?

The charts seems legit, although they have been so overshared at this point the pixel quality has clearly degraded. The numbers seem authoritative. The argument feels data-driven.

But is it obvious?

Let's break down exactly what's going on here, and more importantly, how you can spot similar red flags in any analysis you encounter.

The claim.

“If this were a bubble, we'd see massive cash burn and unsustainable growth. But the numbers show real adoption, real revenue, and real productivity. What matters is net profit growth, something that frankly wasn't there at all in the dot com bubble. This is what separates AI from past 'bubbles'."

The White House shared a viral electoral map that's been debunked for a decade. Here's why geographic county maps systematically misrepresent election results and why they'll never die.

Optimizely Opal's Custom Tools feature lets you extend the Opal AI agent's capabilities by connecting it to your own services and APIs. In this guide, we'll walk through creating a simple custom tool from scratch, deploying it to the cloud, and integrating it with Opal.

By the end of this tutorial, you'll have a working custom tool that responds to natural language queries in Opal chat. We'll keep it straightforward, no complex integrations, just the essentials you need to get started.

I need to tell you something that might change how you see yourself: you already have the capacity to be an analytical thinker. You don't need a statistics degree. You don't need to know how to query a database. You don't need to become an analyst.

For Wambui, choosing 33 Sticks wasn't just about fulfilling an internship requirement, it was about finding the right environment for meaningful growth. With her experience, she sought a boutique agency with specialized offerings and a reputation for excellence.

"At this stage in my career, joining a boutique agency felt essential, somewhere with a tailored service offering and a reputation for excellence. I wanted more than just 'checking the box' for my master's internship, I wanted an enriching, really focused experience that truly aligned with my goals."

Perhaps most importantly, she was drawn to our people-first philosophy. "Success to me, is about building with integrity and people at the center. That people-first approach made me confident this is the right place to grow and receive invaluable mentorship."

Disinformation is not confined to politics or public health, it lives wherever data can be bent to serve an agenda. Analysts hold a unique responsibility as translators of data into action.

When we strip away context or selectively frame numbers, we risk becoming part of the same machinery that erodes public trust.

At 33 Sticks, we believe ethics cannot be optional. Our manifesto commits us to objectivity, transparency, and context because without objectivity, the true value of data is lost.

For the past fifteen years, i’ve worked with data practitioners across industries, retail, healthcare, tech, finance, entertainment. Different businesses, same story. Teams feel frustrated and underappreciated. They’re buried in requests for reports and dashboards, treated as service providers, and excluded from strategic conversations.

The complaints are familiar:

• “We don’t have a seat at the table.”

• “The business doesn’t respect us.”

• “Leadership ignores our insights.”

Yet beneath the frustration lies a paradox. The very behaviors and beliefs that define how most data teams work are the same ones sabotaging their influence. We want respect but avoid accountability. We want to be strategic, but we refuse to step into the role.

Earlier this month, Cracker Barrel unveiled a new brand identity. The familiar imagery of a man leaning on a barrel disappeared. Within hours, social media lit up and headlines followed:

• “Board member under fire for DEI background after restaurant ditches traditional logo”

• “Conservative activist slams Cracker Barrel; company left reeling after logo redesign”

• “Marketing fiasco shows investors are making ‘woke’ a massive risk factor”

The narrative was clear, the redesign wasn’t about business strategy, it was about politics.

At 33 Sticks, we believe the most fascinating insights often emerge when we apply analytical rigor to unexpected datasets. Recently, we discovered a treasure trove of human behavior patterns hiding in plain sight: LinkedIn professional responses to a policy analysis scenario.

Until we bridge the gap between data and felt experience, our dashboards will remain beautiful, sophisticated, and ultimately ineffective displays of information that change nothing.

The digital analytics industry stands at an inflection point. As generative AI and automation sweep through organizations, promising to handle everything from data collection to basic analysis, we face a fundamental question: What becomes of the digital analyst?

The answer isn't found in fighting the tide of automation or doubling down on technical skills. Instead, it requires us to acknowledge an uncomfortable truth that digital analytics, as we've known it, is already obsolete. Not because the work doesn't matter, but because the name itself has become too small for what we must now become.

What emerges from this disruption isn't the death of a profession but its radical evolution from mechanical data workers to what we call "organizational anthropologists," practitioners of Human-Centered Analytics who help businesses see the humans behind the data.