I've been working in DevOps for around 14 years, and I've also been teaching DevOps and Cloud subjects at universities for the last 4–5 years.
One thing I've noticed recently: getting your first DevOps job seems much harder than it was even 2–3 years ago.
A few years back, students and juniors would send me their CVs and I'd usually have something useful to suggest. Many of them would at least get interview opportunities.
Today, the market isn't great, and on top of that, everyone has access to AI-generated CVs, cover letters, LinkedIn profiles, and so on. When hundreds of applications all look polished, it becomes harder for recruiters to figure out who actually knows their stuff.
So here are a few things I keep telling my students.
1. Don't Choose DevOps Because You Think You Can Avoid Coding
I've heard this quite a few times:
"I'm not interested in coding, so I'm thinking of DevOps."
That's the wrong reason to get into DevOps.
You'll write scripts, automation, YAML, pipelines, Infrastructure as Code, debugging tools, and sometimes proper application code. More importantly, you need to enjoy investigating systems and figuring out why something isn't working.
If you hate both coding and troubleshooting systems, DevOps probably isn't the escape route you're looking for.
2. DevOps Comes With Responsibility
Remember Spider-Man?
"With great power comes great responsibility."
That applies surprisingly well to DevOps.
The infrastructure you're managing might be running someone's entire business. Sometimes you're having dinner with your family and an alert appears on your phone. Sometimes something breaks at an odd hour. Sometimes everyone else is enjoying a holiday while you're wondering why a production node suddenly decided it no longer wants to participate in society.
Not every DevOps job has terrible on-call expectations, of course. But ownership and accountability are part of the profession. Know what you're signing up for.
3. Build a GitHub History, Not Just a GitHub Account
When I started 13–14 years ago, having an impressive GitHub profile wasn't nearly as important for many infrastructure roles.
Today, if you're a student with no professional experience, your GitHub can become evidence that you actually build things.
Don't obsess over making every project impressive. First build the habit of creating things regularly.
Write scripts
Build Docker images
Automate something with Python
Create Terraform modules
Write an Ansible role
Deploy Kubernetes applications
Build CI/CD pipelines
And commit your work.
I actually suggest turning this into a game.
When I was in school, some friends and I wanted to improve our English. We made a game where we had to speak English all the time — if someone caught you speaking another language, you lost points. We did this for years, and it genuinely helped.
Do something similar with GitHub.
Find a friend. Every day you commit something meaningful, you get a point. First person to 100 wins. Then reset and start again.
You'll miss days. You'll get bored. That's fine.
But after a year, compare your GitHub profile with the one you had before.
4. Stop Sending the Same Application to 500 Companies
AI has made mass-applying ridiculously easy — which also means everyone can do it.
I'd rather spend proper time on 10 jobs I genuinely want than blindly apply to hundreds with the exact same CV and a generic cover letter.
Read the job description. Understand what they're actually asking for. Then explain why your experience and projects match those specific requirements.
Quality over volume, every time.
5. Don't Just Ask AI to Write the Entire Cover Letter
Use AI to proofread it, improve it, or organize your thoughts if you want.
But put yourself into it.
Mention something specific from the job description. Explain a relevant project. Explain why you're interested in this particular role.
It's okay if your writing isn't perfect. I'd personally rather read something that sounds like a real person than another perfectly formatted:
"I am thrilled to apply for the exciting opportunity to leverage my passion for innovative cloud-native solutions..."
You know the type.
6. Add Something Human to Your Application
One thing worth experimenting with: a short Loom or video introduction.
Not 10 minutes. 60–120 seconds.
Something like: "I know you're probably reviewing a lot of applications, so instead of making you read another long introduction, here's a 90-second video about who I am, what I've built, and why I'm interested."
Whether recruiters watch it is another question. But at least you've given them another way to evaluate you — something that's genuinely hard to fake with AI.
7. Make It Clear You're Willing to Prove Your Skills
When appropriate, mention that you're happy to go through a technical evaluation or practical exercise if that's part of their process.
For someone without much professional experience, being able to show what you can do can be far more useful than another paragraph claiming you're "passionate about DevOps."
None of these are magic tricks.
They won't guarantee an interview, and they definitely won't guarantee a job.
But the way people apply for jobs has changed dramatically with AI. If everyone's CV looks good, simply having a good-looking CV isn't much of a differentiator anymore.
Build things. Show your work. Develop consistency. Personalize your applications. Give people evidence that there's an actual engineer behind the PDF.
Curious what others think — especially people involved in DevOps hiring.
Has the way you screen junior DevOps candidates changed over the last 2–3 years?
I saw a video a while back about old Disney animation and how they fooled viewers in the last couple of years. Same character movements, reused frame by frame, across completely different characters and different movies. Different faces, different worlds, same underlying motion. It worked for years. Nobody noticed, or nobody cared, because the backgrounds and story kept changing enough to sell it. Here is thevideoI am talking about. This was deliberate for many reasons as you read more about it, you will know. Some reasons, I understood were, nostalgia, saving time or maybe laziness too.
I didn't think much of it until I saw the same thing happening with AI.
I was working on generating some images recently. I kept feeding new context, new prompts, expecting newer results. The first couple of images were just fine. Then I started noticing a pattern of same poses, same layouts, same compositions repeating underneath a different backdrop. Not identical, but close enough that it felt like the AI found something that worked and decided to stop being creative.
I told myself, okay, maybe that's fine for images. Then I tested it on something that mattered more.
I use AI sometimes to teach certain concepts to my kid. I asked AI to write a short story. It was about living and nonliving things. It came out lovely. She enjoyed it, I enjoyed it. All good, right?
The next day, I asked for a story about vertebrates and invertebrates. Same structure. Same pattern with just a bit offind and replacemay be 😀. Practically the same story with a few words swapped out. If I had outsourced that to a person and gotten this back, I wouldn't have accepted it. And here's the thing, my kid is five. Even she would note this eventually. Kids get bored of the same story wearing a different costume faster than we think.
Now this is not about things impacting my personal work. The part that actually worries me isdocumentation.
I use AI heavily for documentation and writing. And I've caught the pattern there too. Same structure repeating article after article, same phrasing patterns, same rhythm, regardless of what the topic actually needs. It's probably efficient. But it's also lazy. And I worry people will lean onto this instead of catching it, hand AI a rough context, ask for a full end to end document, and publish whatever comes back without questioning why every article somehow reads the same.
Here is what I found being the technical reason to be. This comes down to how models pick outputs. They default to the highest-probability path unless you push them elsewhere, so once a structure works well in one response, it becomes the easy option for the next, especially in the same session where earlier context keeps shaping what comes after. Lower temperature and conservative sampling, which most tools default to for reliability, make this worse on purpose. It's not laziness the way we mean it. it's optimization doing exactly its job: minimize risk, maximize consistency.Creativity was never the objective. Which is exactly why the paragraph-by-paragraph, keep-pushing-back approach works, it forces the model off the safe path it would otherwise default to.
When I see this happening, here's what works for me instead. Don't ask for the whole thing to be written or documented at once. Go paragraph by paragraph. Feed fresh context deliberately every time. Question, Argue and provide newer/better context every time. And at any time something feels repeated, say so. Tell it directly. That nudge matters more than people think, it's not just fixing one document, it's teaching the model what you don't want. Some would comment and ask to create a new session for improvement here. I agree with it, but how I and sometimes even you may work, sometimes we need the session memory to exist for various reasons, so we use the same sessions for a long time.
Disney may have eventually moved past reusing the same animation cycles. Or even if they re-used some, they had some valid reason like ‘nostalgia’. They could have even stopped doing this because audiences quietly noticed this and started expecting more, and also maybe that the reused motion stopped being good enough. I think we're also at the exact same point with AI generated content, whether that's documentation, images, or bedtime stories. It'll get away with repeating itself for a while. Until the audience notices. And audiences always notice eventually, even five year old ones will.
So if you're using AI for content, don't just accept the first draft's texture as final. Push it. Ask it to genuinely think differently, not just swap a few nouns. Creativity isn't AI's job to protect. It's ours.
Yes, I know how that sounds. Documentation about documentation about documentation. Somewhere an intern is crying. Actually that's the phrase that hit me mid-conversation yesterday.
In DevOps we talk about monitoring the monitoring system. You don't just set up alerts and walk away. You watch the watcher too, because if your monitoring breaks silently, you find out the hard way. We handle it mostly by using a 3rd party monitoring Saas tool or a completely different monitoring tool that is on a different infra to monitor our monitoring. Many a times, we also setup multiple monitoring tools to monitor each other.
Turns out documentation has the exact same blind spot, that I realized yesterday.
Here's the problem. Documentation itself isn't static, but the system around it should be. A proper guideline of how the docs should look consistent across.
Think about what happens with two or three writers on the same knowledge base.
One person is deeply technical, every article turns into a dense reference doc.
Another writes for humans first, simple language, easy to follow, but a senior engineer might find it too light.
A third writes in a fun, engaging tone that people love reading but that doesn't actually answer the question they came for.
None of these people are wrong. But put them in the same wiki with no shared rules, and the documentation stops looking like one system. It may end up looking like three different people arguing.
That's what documenting the documentation fixes. Not the content. The structure around the content. Heading formats. Whether an index is required. How many bullets before it's too many. Screenshot rules, including the boring but important one, don't reveal internal or customer details in a screenshot(Nothing says 'security incident' like a screenshot where someone forgot to close the tab with the customer PI sitting right there). Whether emojis are fine or banned. Which kinds of diagrams are expected. Small decisions, but decided once, so nobody has to reinvent them article by article. Is humor ok. How much humor is ok? Procedure to build the context, etc.
Two things break without this:
When someone leaves the team, whoever replaces them has nothing to stand on. No rulebook, no shared style, just vibes and old examples to guess from.
Even with the rules written down, you can't assume people will keep following them. Rules you hand someone on day one quietly erode by month six. Rules handed over on day one have a shelf life shorter than office milk.
So the system needs an audit layer too. Not a one-time handoff. A recurring check.
This is where it gets easier than it used to be. You write your rules once, in something like aCLAUDE.md, plain and specific. Then you let AI do the boring, repetitive part; scan every new article, and periodically scan the old ones too, and flag anything drifting from the standard. Wrong heading structure, missing index, a screenshot that shouldn't be there, tone that's drifted too casual or too dense. The AI doesn't decide what's true. It checks what's consistent. Humans still review before anything changes.
Monitoring the monitoring system caught on in DevOps because someone finally said the obvious thing out loud, watch your watchers. Documentation needs its own version of that sentence.
Document the documentation. Then audit it like you mean it.
Because the loudest idea in the room is not always the best experiment.
In the last post, I wrote about the main experiment types: A/A, A/B, Split URL, and multivariate testing. I also introduced the formula that quietly changed how I think about experimentation:
Success = Chance × Frequency
Once that clicked, I assumed the next step would be simple.
Pick an idea > Run a test > Measure it > Repeat.
Then I actually tried to do it. Because once people around you start thinking in CRO terms, ideas come from everywhere.
The designer wants to test the hero image. The founder wants to rewrite the pricing page because “it does not feel premium enough.” Someone from sales heard that a competitor changed their CTA and now wants to copy it immediately. Someone read a blog at midnight and suddenly wants to add a chatbot, a quiz, a floating banner, and possibly a dancing mascot.
Before you know it, your experiment backlog looks like a Jira board after a chaotic sprint planning session.
Lots of tickets.
Very little structure.
Everyone convinced their idea is the one that will change the company’s destiny.
This is where prioritization comes in. Not because prioritization makes you smarter, but because it protects the experimentation program from becoming purely opinion-driven. And in CRO, opinions can multiply faster than credit card debts.
The Hidden Cost of Running the Wrong Test
At first, “let’s just test this” sounds harmless. CRO is about experimentation, right? Just run everything. But every test has a cost.
Even when the testing tool makes setup easy, a test still consumes traffic, time, design effort, analysis attention, and usually a couple of weeks of your roadmap. If your site has enough traffic to run two meaningful tests this month, and your backlog has twenty ideas, running the wrong two means you delayed the better ones. That is not a small cost.
This felt immediately familiar from Devops work. You do not deploy every feature request just because someone suggested it in a meeting with confidence and a nice slide deck. You triage. You look at impact, risk, effort, dependencies, and what the system is actually telling you before you start making changes.
CRO needs the same discipline. An idea is not valuable because it sounds smart in a meeting. It becomes valuable when it is connected to evidence and scored against other ideas in the backlog.
The HiPPO Problem
Before getting into frameworks, there is a force you need to understand. It quietly kills many CRO programs. It is called the HiPPO: the Highest Paid Person’s Opinion. Every team has one. In fact there is a rude saying about opinions. “Opinions are like…” (well… Google it)
The senior leader who says, “I think the button should be green,” and suddenly the next sprint is about button colors. The founder who saw a competitor’s landing page and wants to rebuild yours to match it by Thursday. The VP who has “a feeling” about the checkout flow. The stakeholder who says, “Can we just test it?” in the same tone people use before creating six months of technical debt.
To be fair, these inputs are not useless. People close to the business often have genuine intuition worth exploring. Sales teams hear objections. Support teams hear confusion. Founders understand positioning. Product teams know what users struggle with. The problem is not the idea. The problem is when the idea skips the queue without being scored.
In DevOps, we do not prioritize incident response based on who is shouting loudest in Slack. At least, we should not. We prioritize based on system impact and what the metrics are actually saying.
CRO needs the same structure. A prioritization framework is your polite defense against the HiPPO.
You are not saying: “No, your idea is bad.”
You are saying: “Great, let’s score it the same way we score everything else. If it ranks high, it goes first.”
That small shift changes the conversation. The framework depersonalizes the argument. The number does the heavy lifting. Very convenient, because numbers do not get awkward in meetings.
Start With the Funnel, Not the Idea
Before scoring individual test ideas, figure out where to look first. Not every page deserves equal attention. A page with very little traffic will take forever to produce statistically meaningful results. A page with high traffic but no meaningful business impact may still not be worth optimizing ahead of a page that sits directly in the conversion path.
Press enter or click to view image in full size
So before asking: “What should we change?” ask, “Where are we losing people?”, and then. Map your funnel.
Landing page > Form view > Form start > Form submit > Qualified lead
Then look at where the biggest drops are happening. If 70% of users are abandoning at checkout, testing a homepage headline is probably not the first move.
It might be interesting.
It might even win.
But it is probably not where the biggest leak is.
Analogy in DevOps: If the database is causing 90% of your latency, shaving 20ms off a frontend asset is not the priority. Sure, the frontend improvement may look nice in a performance report. But the database is still sitting there like an overloaded elephant in the server room.
Find the bottleneck first. Then fix it. In CRO, high-traffic pages close to revenue, signup, checkout, or lead generation are your hot paths. Start there.
Small disclaimer:Read at your own risk. This may make you hungry
The Two Frameworks: ICE and PIE
Once you know which pages to focus on, you need a way to score the ideas sitting in your backlog. Two common prioritization frameworks in CRO are ICE and PIE. Both are simple, both use overlapping dimensions and both are easier to remember if you stop thinking about frameworks and start thinking about food. Which, honestly, improves most business concepts. ;)
ICE: Ordering Food at Midnight
ICE stands for:
Impact
Confidence
Ease
Think of ICE as ordering food on delivery app at 11 pm. You are hungry. You are tired. You are already in bed. This is not the time for adventure. You are not trying to discover a hidden culinary gem. You are trying to avoid sleeping angry. You see a biryani place.
Before you tap “Order,” three thoughts happen automatically.
First:
“How good is this actually going to be?”
If it is bad biryani, why bother?
That is Impact.
Second:
“Can I trust this place right now?”
Will it arrive hot, on time, and with raita? Or will it arrive cold, late, and missing the one thing that emotionally holds the whole meal together? You check the rating, reviews, delivery time, and whether someone recently complained that they received only rice and sadness.
That is Confidence.
Third:
“How easy is this?”
Is the restaurant open? Is delivery available? Will it reach in 25 minutes, or will the app say “arriving soon” until you lose hope? Will it ask you to verify your phone number again even though it has known you for five years?
That is Ease.
ICE is practical and slightly paranoid. It wants evidence before committing.
In CRO terms:
Impact asks: If this test wins, how much could it move the business? Confidence asks: How sure are we that this is a real problem worth testing? Ease asks: How difficult will this be to build, QA, launch, and analyze?
Score each dimension from 1 to 5. Then average the three. The highest-scoring ideas usually go first. ICE works especially well when you already have research data: heatmaps, recordings, analytics, surveys, support tickets, or past experiment learnings. Low confidence should lower the score, no matter how exciting the idea sounds.
Because “I saw this on a competitor site” is not research. It is browsing with ambition.
PIE: Choosing What to Order When You Have Options
PIE stands for:
Potential
Importance
Ease
Now stay in the same food delivery app. You are still hungry. It is still 11 pm. You still should have eaten earlier like a responsible adult, but here we are. This time, you are not evaluating one biryani place. You are deciding what category of food deserves your attention first.
Biryani? Pizza? Rolls? South Indian? Chinese? That one “healthy bowl” option you add to cart and then remove after seeing the price? PIE helps you decide where the biggest opportunity is.
First:
“How good could this get?”
Could this be the kind of meal that fixes your mood, your evening, and possibly your belief in humanity?
That is Potential.
Second:
“How important is this meal?”
Is this a casual snack, or have you skipped dinner and now your stomach is sending production-level alerts?
That is Importance.
Third:
“How easy is it to get?”
Is it nearby and deliverable in 25 minutes, or is the restaurant far away, closing soon, and likely to cancel after making you wait?
That is Ease.
PIE is more optimistic than ICE.
ICE asks, “Can I trust this specific choice?”
PIE asks, “Where is the biggest opportunity?”
In CRO terms:
Potential asks: How much improvement could this page or funnel step realistically have? Importance asks: How valuable is this page or funnel step to the business? Ease asks: How simple or difficult is the test to implement?
PIE is useful when you are comparing broader areas of opportunity.
For example, if you are deciding whether to focus on the homepage, pricing page, checkout, signup form, or onboarding flow, PIE can help you decide where the biggest opportunity may be.
So, in food delivery terms:
ICE helps you decide whether to trust one restaurant. PIE helps you decide which food category is worth exploring first.
ICE vs PIE: The One-Line Difference
Press enter or click to view image in full size
Here is the easiest way I remember them:
ICE helps you decide whether to trust one restaurant. PIE helps you decide which food category is worth exploring first.
ICE is about confidence. Can I trust this specific option to deliver? PIE is about opportunity. Which area has the biggest upside if I focus there? Same food app. Different thinking.
With ICE, you are asking: “Should I order from this biryani place?” With PIE, you are asking: “Should I even be looking at biryani first, or is pizza, rolls, South Indian, or Chinese the better opportunity tonight?”
Both end with Ease, because nobody wants unnecessary complications at 11 pm. Especially not when they are hungry and the app is already saying, “Restaurant is closing soon,” like it is adding pressure to your life.
Which Framework Should You Use?
Use ICE when your team already has research data and wants to prioritize based on evidence. ICE is useful when you are evaluating a specific hypothesis. For example:
“Should we move the CTA above the fold on the pricing page?”
You already have heatmap data. You know users are not scrolling far enough. You want to know whether this specific idea deserves to be tested next. That is ICE.
Use PIE when you are earlier in the process and still deciding where the biggest opportunity is.
For example:
“Should we focus on the homepage, pricing page, checkout flow, signup form, or onboarding?”
That is PIE.
At Convert.com, the Compass feature includes both PIE and ICE inside the hypothesis builder. When you create a new hypothesis, you can choose a framework, score each dimension on a scale of 1 to 5, and use that score to sort your backlog.
That helps because the score becomes visible to everyone. It is much harder to push your pet idea to the top when the number is sitting right there on the screen, judging you quietly. A visible score is like the delivery rating on a food app. You can still order from the suspicious restaurant. But now everyone can see that it has 2.3 stars and seven reviews mentioning “never again.”
Turn Ideas Into Hypotheses Before You Score Them
Press enter or click to view image in full size
One thing both frameworks require is that your ideas are written properly before scoring.
“Change the button color” is not a hypothesis.
“Move the CTA above the fold” is not a hypothesis.
“Make the page look more premium” is definitely not a hypothesis.
That is a mood.
A proper hypothesis looks like this:
If I change X, then Y will happen, because Z.
The “because” is the most important part. Without it, you are not testing an assumption. You are just making a change and hoping the graph goes up, which is basically the CRO version of deploying to production and whispering, “Please work.”
Or, to stay with the food delivery analogy, it is like ordering from a restaurant with no ratings because the photo looked nice. Could it be amazing? Maybe. Could it be regret in a plastic container? Also maybe.
For example:
If we move the main CTA above the fold on the pricing page, more visitors will start the demo request flow, because heatmaps show most users are not scrolling far enough to see the current CTA.
Now you have something worth scoring.
You know:
The page
The change
The expected outcome
The evidence behind it
Even if the test loses, you learn something specific. A backlog full of vague ideas becomes a dumping ground. A backlog full of hypotheses becomes a learning system.
Or in food terms:
“Order something nice” is not useful.
“Order from a highly rated nearby restaurant because delivery time is low and reviews mention fresh food” is much better.
Specificity saves you from bad decisions. And occasionally, bad paneer.
The One Thing ICE and PIE Get Wrong
ICE and PIE are useful, but they share a blind spot. They often treat every idea as if it exists in a vacuum. But it does not.
Every idea exists inside the history of your previous tests. If you have run five tests on your pricing page and four of them lost, your confidence score for the next pricing-page idea should reflect that history. Something on that page may be resisting your assumptions.
Maybe the traffic quality is different from what you expected.
Maybe the real problem is upstream in the funnel.
Maybe people already understand the pricing page, and the actual friction is in signup.
Maybe your pricing page is innocent and has been framed by bad hypotheses.
The same applies to food delivery. If you have ordered from the same restaurant four times and three times they forgot the raita, you should not treat the fifth order like a fresh mystery.
You have history. Use it. Past experiment results should feed into how you score new ideas. They should not sit inside a spreadsheet named something like: final_results_v3_really_final_updated_NEW.xlsx
This is why documentation matters as much as the framework itself.
After every experiment, write down:
The hypothesis
The result
The confidence level
The primary metric
The key learning
What it suggests about the next test
Even a losing test is useful if it tells you where not to look. Without this history, your backlog resets to zero every quarter. With it, your scoring gets more accurate over time because you are building a picture of what your specific audience actually responds to. A CRO backlog without documentation is like a food app that forgets every bad order you ever had. You keep making the same mistake. And somehow, the raita is still missing.
What to Do When Everything Scores the Same
Sometimes three ideas all score 4.2 and suddenly you are back in the same argument. This is normal. Frameworks help, but they do not eliminate judgment. A few tiebreakers can help. Pick the page with the most traffic. More visitors usually means faster results and less time waiting for statistical significance.
In food delivery terms, this is like choosing the restaurant with enough recent orders and reviews. If only two people have ordered from it since 2021, the rating may not tell you much.
Pick the test that is fastest to build. A high-scoring idea that takes three weeks of development work may be worth doing after a slightly lower-scoring idea you can ship in a day. Remember the frequency side of the success formula.
This is the difference between ordering something that arrives in 25 minutes and choosing a dish that says “preparation time: 95 minutes.”
Technically, it may be great. Emotionally, you may not survive. Pick the test that teaches you the most regardless of outcome. Learning value is underrated. A test that answers a fundamental question about your audience is worth more than a test that optimizes something already working reasonably well.
In other words, do not only ask:
“What might win?”
Also ask:
“What will we learn even if this loses?”
That question saves a lot of time. And occasionally, a lot of ego.
Define Success Before You Launch, Not After
One mistake that shows up in beginner backlogs is choosing the primary metric after the test has already started. That is dangerous. If you look at enough metrics, something will always appear to have improved.
Clicks went up.
Scroll depth changed.
Form starts increased.
Time on page looked different.
Someone in Nebraska spent 14 minutes reading the footer.
But what was the experiment actually supposed to improve? Every test needs a primary metric before launch.
Demo requests.
Purchases.
Trial signups.
Form completions.
Revenue per visitor.
Pick one. Commit to it. Let that metric make the call. This is like deciding what “good food” means before ordering. Are you optimizing for taste? Delivery speed? Price? Portion size? Not waking up with regret? If you decide after the food arrives, you can justify anything.
“The biryani was cold, but the container was sturdy.”
That is not success. That is coping. You can still watch secondary metrics for learning, but the decision should rest on the primary one. And alongside your primary metric, define your guardrail metrics. These are the things you are not directly trying to improve, but cannot afford to break. For example:
If you are optimizing demo signups, lead quality should not drop.
If you are optimizing checkout completion, average order value should not fall.
If you are optimizing form completion, spam submissions should not explode.
If you are optimizing clicks, downstream conversions should not suffer.
This is the CRO version of SLOs. You define what success looks like and what failure looks like before you are emotionally invested in the outcome. A test that increases clicks while damaging qualified leads is not a win. It is noise wearing a party hat.
Or in food delivery terms: A restaurant that delivers in 12 minutes but sends the wrong order did not “win on speed.” It failed dinner.
A Practical First Backlog
If you are starting from zero, here is the workflow I would follow.
Map your funnel and identify the biggest drop-off point.
Use heatmaps, session recordings, analytics, surveys, and any sales or support feedback you can access.
Write down every test idea without judging too early.
Convert each idea into a proper hypothesis using the “If X, then Y, because Z” format.
Score each one using ICE or PIE.
Pick one high-scoring, reasonable-effort test.
Define the primary metric and guardrail metrics before launch.
Run the test.
Document the result and the learning.
Then re-score the backlog based on what you now know.
That last step is what most teams skip. And it is what separates a backlog that compounds from one that stagnates. A CRO backlog should not be a parking lot where ideas go to quietly disappear. It should be more like a living system.
Or, to stay on brand, a well-maintained CI/CD pipeline for learning. Every test should make the next decision smarter. Just like every food order teaches you something.
Restaurant is reliable.
Delivery is slow on weekends.
The “extra spicy” option is not a personality test you need to pass.
Never trust a place where every menu item has the same stock photo.
Learning compounds when you write it down.
The Real Takeaway
Prioritization is not about finding the cleverest idea. It is not about the biggest redesign. It is not about the most senior opinion in the room. It is about running the tests that have the best combination of meaningful impact, strong evidence, reasonable effort, and clear learning potential. Done consistently, this turns CRO from a collection of random experiments into a learning system. You find the biggest leaks, collect evidence, write hypotheses, score ideas, run the most valuable tests first, document what happened and then repeat.
The DevOps parallel is hard to miss. Good infrastructure teams do not just ship more changes. They improve how they decide what to change, how they measure it, and how they learn from it.
Good CRO teams do exactly the same thing. And the food delivery parallel is also hard to miss. The goal is not to order everything on the menu. The goal is to make better choices each time, based on what you know, what matters, what is realistic, and what you learned last time.
That is prioritization.
In the next post, I want to go deeper into reading results without fooling yourself: statistical significance, confirmation bias, and why the most dangerous moment in CRO is when a test is almost winning.
I work with Convert.com and write about the intersection of DevOps and experimentation. If you are building your first CRO backlog or trying to move your team away from opinion-driven testing, drop a comment or reach out. Happy to compare notes.
If you read the first post in this series, you now have a working definition of CRO, a sense of why conversion rate matters, and hopefully a free account on some combination of Clarity, GA4, and Convert.com(sshhh… I work for them).
Maybe you even opened a heatmap, stared at the colors for a while, and thought: “Okay. Now what?”
This post is that now what.
Once you understand the basics of CRO, the next challenge is figuring out what kind of experiment to run. A/A test? A/B test? Split URL test? Multivariate test? They all sound similar at first, but they solve different problems at different stages.
And if you are coming from a technical background, the temptation is to overthink the setup before shipping anything. I know, because that is exactly what I started doing.
So let’s break down the main experiment types, when to use each one, and how to think about them without getting stuck. I will also share a formula from a CRO course I have been working through that quietly changed how I think about the whole thing.
The Four Experiment Types, Explained Without Jargon
1. A/A Testing
Before you trust your first experiment result, run an A/A test.
In an A/A test, both groups see the exact same page. No headline change, no button change, no layout change. Nothing at all. The purpose is not to improve conversions. The purpose is to check whether your measurement setup is actually trustworthy.
If your tool is configured correctly, both groups should show roughly similar conversion rates over time. If they do not, something is off with how traffic is being split, how visitors are being tracked, or how conversions are being recorded.
The DevOps parallel is straightforward. You would not set up Prometheus alerts on a misconfigured scrape target and then make infrastructure decisions based on that data. An A/A test is the same idea: confirm the instrument is working before you start using it to make calls.
A lot of teams skip this because it feels boring. There is no variation, no redesign, no winner to announce. But if your measurement layer is broken, every A/B test after this becomes questionable. You may spend weeks debating a result that was never reliable in the first place.
On Convert.com, setting up an A/A test takes about three minutes. Do it before anything else, and leave it running for at least a week.
Press enter or click to view image in full size
2. A/B Testing
Once your A/A test passes, you are ready to actually experiment.
An A/B test compares two versions of something. Version A is the original, called the Control. Version B is the changed version, called the Variant. You split traffic between the two and measure which one performs better on your chosen metric.
The element you change could be a headline, a CTA button, a hero image, a form layout, a pricing message, anything on the user journey. The constraint is this: change one meaningful thing at a time.
If you change the headline, button color, pricing layout, and image all in the same test, and the variant wins, you have no idea what actually caused the lift. You got a result but no learning.
The DevOps analogy that clicked for me: a clean commit. If one commit touches ten unrelated files and something breaks, debugging is painful. If one commit changes one logical thing, the cause is much easier to trace.
A good A/B test starts with a hypothesis in this format: “If I change X, then Y will happen, because Z.”
For example: “If I change the CTA text from ‘Buy Now’ to ‘Get Your Copy Today,’ then more visitors will click through to checkout, because the new copy feels less transactional and creates a stronger sense of ownership.”
The “because” is the part most people skip, and it is the most important part. Without it, you are not testing a hypothesis. You are just nudging things and hoping the graph goes up.
Press enter or click to view image in full size
The confusion between A/A testing and A/B testing
I was always confused as to why A/A and A/B testing are needed. If you are also in the same state, here is a crude example that may simplify it.
A/A Testing: The Paranoid Caterer
You are catering a wedding. You make one giant pot of biryani. You serve it to Table 1 and Table 2 from the same pot, same quantity, same everything.
You have not changed anything. Both tables got identical food. So why is Table 2 eating so little?
You investigate. Turns out the waiter assigned to Table 2 was busy on his phone and forgot to refill the serving bowls.
The biryani was not the problem. The delivery system was broken.
That is an A/A test. Before you start testing new recipes, you confirm that the serving system itself is not lying to you.
A/B Testing: The Sensible Caterer
Same wedding. You make two versions of biryani. Version A has the usual spice level. Version B has a little more pepper because your cousin who went to Bangalore said people there like it spicy now.
You serve Version A to the left side of the hall and Version B to the right side. You count which side finishes faster, asks for seconds more often, and complains less to the mother of the bride.
One change. One comparison. One winner.
You do not also change the raita, the naan, the serving bowl size, and the background music at the same time. Because if the right side loved it, you will never know if it was the pepper or the fact that the DJ on that side was finally playing something decent.
So:
A/B test: “Which version is better?” A/A test: “Can I trust the test setup?”
3. Split URL Testing
A standard A/B test usually changes something on the same page. The URL stays the same and the testing tool modifies what users see. Split URL testing is different: users are sent to entirely different URLs.
Control: /pricing. Variant: /pricing-v2. Traffic is split between them.
Use this when the change is large enough that modifying the existing page gets messy. Major redesigns, completely different landing page structures, new checkout flows, testing a long-form page against a shorter one. If it is a minor copy tweak, A/B is faster and simpler. If you are rebuilding the page from scratch, Split URL is cleaner.
The infrastructure analogy is blue-green deployment: two versions running simultaneously, traffic routed between them at the experimentation layer instead of the load balancer. Same principle, different domain.
One thing to watch carefully: tracking consistency. Both pages need to fire the same analytics events and conversion tracking correctly. A common mistake is having GA4 or pixel tracking set up on the original page but missing or misconfigured on the variant. When that happens, the variant looks like it is losing when the actual problem is your observability. As DevOps people know well, bad telemetry makes healthy systems look broken.
Press enter or click to view image in full size
4. Multivariate Testing
A/B testing changes one element at a time. Multivariate testing (MVT) changes multiple elements and tests every combination of them simultaneously.
Say you want to test two headlines and two hero images. MVT creates four combinations: Headline 1 + Image A, Headline 1 + Image B, Headline 2 + Image A, Headline 2 + Image B. Traffic is split across all four.
The benefit is finding the best combination without running four separate sequential tests. The downside is traffic dilution: more combinations means each version gets a smaller slice of traffic, which means it takes longer to reach a reliable result.
This is why MVT is better suited for high-traffic pages. On a low-traffic site, running multivariate tests is like spinning up a four-node Kubernetes cluster to host a static HTML page. Technically possible. Practically painful.
If you are just getting started, stick to A/B tests until you have enough traffic and enough process confidence to justify MVT.
Press enter or click to view image in full size
Confused between Split URL and Multivariate?
Here’s the crude example:
Split URL Testing: The Ambitious Caterer
This caterer is not making a variation of the existing menu. He has built two entirely separate stalls on opposite ends of the lawn.
Stall A is the traditional setup: biryani, raita, gulab jamun, done.
Stall B is the “fusion experience”: biryani bowls, deconstructed raita in shot glasses, and gulab jamun on a stick with a QR code to leave a Google review.
Half the guests are directed to Stall A and half to Stall B. You measure which stall has a longer queue, higher plate counts, and fewer people quietly throwing food in the dustbin.
This is a Split URL test. You are not tweaking one element on an existing page. You have built two completely different pages and you are routing real traffic to both to see which one works.
The infrastructure cousin of this is blue-green deployment. Same idea, different catering budget.
Multivariate Testing: The Caterer Who Has Lost the Plot
This person is testing:
2 types of biryani (chicken vs. mutton)
2 types of raita (boondi vs. cucumber)
2 types of bread (naan vs. rumali roti)
2 types of dessert (gulab jamun vs. rasgulla)
That is 16 combinations. You need 16 separate tables, each getting a different combination, and enough guests at each table to statistically prove which combination is the winner.
The wedding has 200 guests. You need at minimum 1,600 guests for the data to mean anything.
You have invited 200.
You wont get that big venue on any of the wedding seasons, but lets just say… you did.
This is multivariate testing on a low-traffic website. The math does not work. The experiment runs for six months. By the time you have a result, the wedding is over, the couple has moved cities, and nobody remembers what the original research question was.
Run multivariate tests only when you have enough traffic that dividing it sixteen ways still leaves each slice statistically meaningful. Otherwise, stick to A/B.
Heatmaps Are Research, Not Experiments
This was one of my early mental shifts, and it is worth being explicit about it.
A heatmap does not tell you what will win. It tells you what might be wrong.
Click heatmaps show where users are clicking (and where they are not). Scroll heatmaps show how far down the page users travel before leaving. Session recordings are anonymized playbacks of real user journeys, where you can watch someone hesitate over your pricing table, try to click something that is not a link, or abandon a form halfway through.
None of these tools give you a winner. They give you a hypothesis.
Here is how the workflow actually connects: you open a heatmap for your pricing page and notice that most users never scroll past the first fold. Your main CTA button is near the bottom. Now you have a possible problem and a possible hypothesis: “If I move the CTA above the fold, more users will see it and click it.” You run an A/B test to confirm or deny it.
The heatmap gave you the question. The A/B test gives you the answer.
That distinction matters because it stops you from treating research tools as decision-making tools. Heatmaps, recordings, analytics, and surveys help you decide what to test. Controlled experiments help you decide what to change.
The Formula That Changed How I Think About All of This
Success = Chance × Frequency
Success is the number of winning experiments you produce in a year. Chance is your win rate, the percentage of experiments that produce a meaningful positive result. Frequency is how many experiments you run per month.
Now run the numbers with me.
Team A wants every test to be excellent. They spend weeks on research, debate every design decision, and get thorough sign-off before anything ships. Their win rate is high, say 100%. But all that effort means they can only run one test per month. Twelve winners a year.
Team B is less precious about each individual test. Their research is decent but not exhaustive. Their win rate is only 25%. But they run eight tests a month. Twenty-four winners a year.
Worse hit rate. Double the output.
This is not an argument for carelessness. Bad experiments waste time, and random button-color testing is still random button-color testing. But it is a clear argument against perfectionism as a strategy. The team that ships eight decent experiments a month will outlearn and outperform the team that ships one perfect experiment a month, almost every time.
If this sounds familiar, it should. DevOps made the same argument against waterfall delivery years ago. Smaller releases, shipped more often, create faster feedback loops. You learn sooner, recover faster, and stop treating every release like a quarterly ceremony. CRO has the same rhythm: define the hypothesis, design a clean test, ship it, measure it, document the learning, and move on. The goal is not to win every test. The goal is to learn fast enough that your wins compound.
A Note on Statistical Significance
Every testing tool gives you some version of a confidence percentage as your test runs. This tells you how likely it is that the result you are seeing is real and not just random variation. Most practitioners aim for 95% confidence before calling a winner.
But one of the most common beginner mistakes is stopping a test early because the numbers look exciting on Day 2. Early data is noisy. Weekday and weekend behavior can differ. Email campaigns, seasonality, and traffic source changes can all skew early results. A variant that looks like it is winning after 200 visitors may look very different after 4,000.
Let tests run for at least one full week, preferably two, before drawing conclusions.
And here is the question I now ask before trusting any result: not just “Did the tool say 95%?” but “Do I trust how we got to 95%?” The number matters. So does sample size, test duration, traffic quality, and whether the tracking was set up cleanly to begin with.
What Should You Run First?
If this feels like a lot, start small. Here is the beginner loop I would follow:
Run an A/A test to confirm your measurement setup is reliable.
Pick one high-traffic page with a clear conversion goal.
Use heatmaps, session recordings, or analytics to find one point of friction.
Write a hypothesis using the “If I change X, then Y, because Z” format.
Run one clean A/B test.
Let it run long enough to collect meaningful data (at least one week).
Document what happened, win or lose.
Repeat.
That loop is what CRO actually is, more than any specific tool or technique. Observe, hypothesize, test, learn, repeat. The experiment types are just the mechanics. The real skill is building a habit of running the loop consistently, and gradually running it faster.
In the next post, I will get into prioritization: how to decide which test to run when you have more ideas than time, and how to build a backlog driven by data rather than whoever spoke loudest in the last meeting.
I work with Convert.com and write about the intersection of DevOps and experimentation. If you are a technical person finding your footing in CRO, drop a comment or reach out. Happy to compare notes.
Posts
