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In many organizations, the SEO roadmap is a list of tasks dressed up as a strategy. The to-do list is based on guesswork, with ideas sourced from a tweet, a LinkedIn post, an agency audit, or past experience implementing a best practice. Someone decides the site needs more internal links, or the title tags need rewriting, or a new content cluster would improve SEO. There is no “do this work because X will happen.”

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What happens after the work is implemented is even worse. Sometimes they monitor Google Search Console for a few weeks and either declare victory or blame the algorithm for updating and missing the change. But, from my experience as a consultant over the last seven years, most companies never check back on progress. They just do the work and move on.

More than a lack of follow-up, there was no logical basis for the task before it was suggested. Nobody stopped to ask the most important question before any of it started: what, specifically, do I expect to happen, and why?

Add a hypothesis to your roadmap

Whenever I see one of these roadmaps (Check out my PRD template to improve) built as a to-do list, I immediately add a column for “hypothesis.” A hypothesis is not complicated and need not be statistically sound. It is just a sentence that says “if I do X, I expect Y to happen.” You can add a third part explaining why you expect it, but that isn’t strictly necessary.

The reason this matters is not philosophical, nor is it busy work. Without a stated hypothesis before you start, you cannot determine if the work is even worth doing. More than that, without a hypothesis, you close the door to learning afterward because you don’t know what you are supposed to learn. A company I worked with wanted to create an expensive process to update their XML sitemap daily because it seemed like a best practice for a site of their size.

They were already shifting their prioritization efforts toward THRICE, so naturally, they needed a hypothesis to support this recommendation. The hypothesis they formulated was that it would improve crawl depth by a certain percentage. Without that hypothesis forcing the question of what they were actually trying to prove, they would have built the daily updater, shipped it, and kept maintaining a tool with no measurable impact indefinitely, out of fear of unknown downsides.

Since this was an expensive engineering effort, I talked them down from building a daily updater and convinced them to build a single, comprehensive, up-to-date sitemap instead, because this would prove or disprove the hypothesis with far less investment.

After some internal pushback from team members who were attached to their technology solution, they agreed to the test. It was obviously an easier way to prove the hypothesis, and if they were right, they’d have greater justification to build the daily updater later.

Most results will be inconclusive

The result was inconclusive, with no meaningful change to crawl depth in the following month. Crawling did improve six months later, but it was so far removed from the work that connecting the change was impossible. Having seen that the test was inconclusive, the team moved on to work that drove meaningful revenue.

This is why a hypothesis is necessary. It helps prioritize work and builds a knowledge base of what actually works on your specific site, rather than doing something just because it’s a best practice. It forces you to have a reason. Not “I think this will help” but a logical explanation for why the change should produce the result. If you can’t write that sentence, you don’t understand your own strategy well enough to execute it.

The SEO and AEO industry is swimming in false confidence. Practitioners point to ranking bumps and traffic increases that were coming regardless of what they did. This is also why so much SEO advice misses the mark when applied to sites other than the one it was originally observed on.

For example, many social media case studies (I am deliberately not linking to them) show that adding an FAQ schema increased LLM traffic by some huge percent. Another company reads it, adds the FAQ schema to their site, and nothing happens. Both teams walk away with the wrong conclusion. The first team thinks the FAQ schema is a reliable driver of traffic, while the second thinks it doesn’t work.

Neither team understands what they actually learned because neither team started with a hypothesis about why the FAQ schema would help their specific site answer their specific queries better than the current format.

As a bonus, successful efforts in which you proved or disproved a hypothesis are the strongest resume builders for in-house employees or case studies for agencies. You have a solid example of work that you can recommend that doesn’t come from something you read; it is your own experience.

Here’s how a proper hypothesis sounds

For an AEO project: “Product pages need an FAQ section on the page, and is therefore underperforming relative to competitors. If I add an FAQ section, I expect Google traffic to increase within 45 days.” That hypothesis is SMART. It has a timeframe with a specific mechanism. It identifies a specific set of pages rather than the entire site. You can run that test, wait out the timeframe, and actually learn something from whatever happens. This is a real test I have done many times over the last two years, and the results have been mixed, but it is still worth trying.

The best example of a clean hypothesis test I have run was adjusting title tags to attract more clicks. The test set URLs saw a 100% increase in CTR while the control stayed flat. Could there have been noise in the data? Maybe, but it was solid enough that we rolled it out across the site and saw a similar bump everywhere.

On the negative side, removing canonicals from the test set caused impressions to drop immediately. Both outcomes were obvious enough that the science barely mattered. That kind of clarity only happens when you define what you are measuring before you touch anything. Scientific testing in SEO is extremely difficult because many variables are unknown, so most results will be anecdotal

The reality is that, in most cases, SEO tests will be inconclusive, so doing something purely for SEO is hard to justify if it requires considerable engineering effort. In those cases, I would prioritize UX or other KPIs over work that will be unproven either way. If it performs better for users, then do it, even if there’s no SEO upside.

Hypothesis writing leads to compounding learning

Writing a hypothesis is quick and delivers outsized value because it builds a knowledge repository. This compounding is where you get the real value because every confirmed hypothesis teaches you something about how your site, in your specific category, with your distinct competitive set, behaves in search. That knowledge doesn’t come from a blog post or a conference talk.

A narrow, falsifiable hypothesis produces something useful regardless of outcome. If it’s confirmed, you’ve identified a repeatable pattern on your site. If it’s rejected, you’ve ruled something out, and ruling things out is underrated. When it’s inconclusive you have learned something too.

Your biggest surprise will be how often you sit down to write the hypothesis and find you can't. That inability is more diagnostic than any post-project review. It means you don't actually have a reason for the work, and the right call is not to proceed on instinct.

When a team commits to not recommending a change without a hypothesis, the conversation shifts from tasks to value. That is not a small thing when the SEO team's contribution is already being questioned as it is in this AI-obesessed world. Don’t neglect the hypothesis.

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What happens when you combine best-in-class SEO with conversion optimization?

BigRentz’s traffic increased by 186% (85k), added 1950 conversions in 12 months.

Self Financial’s traffic increased by 50k/month, and added 685 new customers.

Lastly, Secure Data added 1968 phone calls.

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Operationalizing SEO as a product means collaborating with engineering through product language and processes. PRDs are my framework of choice for that, but PRDs are not where most teams fail.

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SEO teams often fail in internal prioritization because, without a disciplined system for deciding what gets asked of engineering and in what order, SEO just becomes a to-do list. Early in my SEO career, I burned months of goodwill on useless tasks because it was a “best practice”, and then wondered why engineering never even opened my tickets when I had an urgent request.

Before making any requests of an engineering team, make sure you are using your political capital effectively; otherwise, the teams you request from will not implement your fixes next time.

Think of any SEO effort like withdrawing from a finite budget. You can ask for the biggest effort, but if it doesn’t pay off, you have spent all of your allowance with nothing to show for it. When you prioritize efficiently, you give yourself the space to make big requests that might otherwise fail because you have banked other successful efforts. Goodwill is not finite.

Even if a prioritization process still supports making big requests, at least you have clarified from the outset what the expectations will be. Next week, I will go deeper into how to develop this thinking, but this post is about the framework I use to make all of this systematic: THRICE.

Introducing THRICE for SEO

THRICE stands for Time, Headcount, Reach, Impact, Confidence, and Effort. The ICE method (pioneered by Sean Ellis) is in my book, and it's still useful, but adding the other dimensions of Time, Headcount, and Reach includes additional metrics that matter enormously for SEO.

THRICE allows you to score and prioritize any initiative openly and on a standardized format. When you do this transparently, you can “show your work” of why something matters in the big picture and then even score diplomacy points by letting go of things that don’t score high enough.

When you walk into a meeting and say, “I would really like to do this particular project, but it didn’t score as well as YOUR project in our THRICE framework,” you are banking political capital and showing that you are a team player.

I score each metric in this model on a 1-10 scale, but you can use 1-100, 1 -1,000,000, low, medium, high, or whatever works for you. (Note: If you choose to use a textual ranking system, make sure it is linked to a quantitative parallel so you can score automatically.)

Here are the definitions from an SEO lens of each aspect of THRICE and how to utilize this framework.

Time

On many occasions in my career, I have advocated for SEO efforts only to discover that it took a long time to launch. If you are going to get raises and bonuses (or retained as an agency) on an SEO effort, the last thing you want is to put all your eggs into that basket and then just wait months to even have something to report. By using time as a prioritization lever, you can resource initiatives that deliver faster ROI and bank political capital to use on efforts that will take longer to show results.

A page update can have a fast turnaround and score 10/10, while a new subdirectory will take longer and score 5/10. A brand-new website requiring design and marketing resources will be a 1/10.

Headcount

Many times, SEO teams get excited about initiatives and push for their launch, only to discover how expensive they are when they require external support or many internal employees to spend time on them. Including headcount, whether internal or external, as a lever provides an honest way to assess the cost side of the SEO equation before looking at the upside in benefits.

Simple SEO fixes will require minimal engineering hours and will always earn a score of 10/10, while anything requiring external agencies that bill for hours will receive a 1/10.

Reach

When it comes to SEO forecasting and quantifying opportunities, I have never been a fan of keyword research. Keyword research is notoriously inaccurate for various reasons, and I hardly see the point of building an entire forecast from a wrong genesis. Instead, I like to use the total addressable market (TAM) for SEO forecasting and forecast an expected reach rate. Keyword research tells you how many people searched a specific phrase last month. TAM (an upcoming newsletter will share more) tells you how many people have the problem described by that phrase.

For Reach in the THRICE model, I use a variation of TAM, but narrowed down to the people who will be impacted. If I am adjusting a homepage, I start by estimating how many potential customers there are for the product, then narrow that to how many people might interact with the updated homepage.

If I launch a new product or page for SEO, I start with TAM and then narrow that down to predicting how many people it might attract with improved search efforts. Remember, these are not precise predictions; they are just numerical scores. If it’s the whole world, it might be a 10/10; if it’s just a handful of people, I would score it a 1/10.

Impact

This is where you will score how much the change you are making will affect the total outcomes of what you are doing.

For example, if I were to create a new subdirectory for a new language on the site, I would estimate its impact on revenue. I would imagine quite a bit, so that would be a 10/10. Alternatively, if I add alt text to every image on the site, it may not affect revenue, so that would be a 1/10 score. Something in the middle would be a meta description designed to increase CTR, it may or may not change things, so it could be a 5/10.

Confidence

While many marketers like to put a strong sales effort behind getting buy-in for a new initiative, this is where you will be brutally honest about the likelihood of the impact happening.

Interestingly enough, this is where scores would flip on the big initiatives. I would have absolute confidence that adding image alt text will have a low impact, so this would be a 10/10, but I am only mildly confident that translating a site into a new language would have a major impact, so that would be a 5/10. Our previous example on the meta description might still be a 5/10 in this column too.

Effort

The last metric in this model is how much work something might take, and this is where you win all the diplomacy points. To make the model work, you score things that take a tremendous amount of effort lower, and easy tasks will score higher. This will mean that low-impact but easy work will be approved, but messy, complicated, high-return projects will not.

This is an additional filter on the Headcount metric, which is primarily intended to capture expensive projects, while this metric captures the size of the project.

When an engineer sees that you've honestly assessed their team's workload and let a promising idea go because it was too resource-intensive, they start to trust the process. They know you are a team player who wants the best for the company.

Returning to my earlier examples, it might be almost no effort to create a new page, so that would be a 10/10. Translating the entire site in a new language might be 1/10, and adding text to every image might be 5/10. Meta descriptions are 10/10 if you have a programmatic tool in the CMS, or 1/10 if they need to be done manually.

There is no perfect way to score things, so I try to be honest about how much work it might take relative to other things.

Once I score all initiatives, I add the scores in each column. The items that score the highest will be normalized between Time, Headcount, Reach, Impact, Confidence, and Effort.

To illustrate with two examples that show how this works

THRICE benefits in action

When you present your methodology, you can show how you considered and discarded each option, and why you chose the plan you did. Score the rows in partnership with the teams working on them. This gets them invested early and increases the likelihood they actually ship what you need. Most of my THRICE meetings include stakeholders from other teams, so their assessments are reflected in the prioritization.

The framework extends to SEO tests, acquisition ideas, and agency selection. Most SEO teams will read this and think they already prioritize well. They don’t. They prioritize loudly, where everything is mission-critical, and then nothing gets done. THRICE forces the argument into numbers before it becomes a meeting.

Paid subscribers to this newsletter can start with this template. If I can help you with prioritization, please reach out.

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When you walk into a meeting and say, “I would really like to do this particular project, but it didn’t score as well as YOUR project in our THRICE framework,” you are banking political capital and showing that you are a team player.

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