You already know who your competitors are. What you don't have yet is a clear answer for why they win the attention you want, or what your team should actually do about it next week. That's what a competitor analysis step by step is supposed to give you: not a spreadsheet full of rival URLs, but a short, evidence backed decision about what to publish, fix, or change.
This guide walks through how to do a competitor analysis for content and SEO strategy, from setting your comparison boundary to assigning an owner and a review date. It assumes you've already picked the rivals worth studying. If you haven't done that part yet, that's a separate exercise. This one is about turning a known competitor set into an action.
Set your comparison boundary and record your own baseline
Before you compare anything, decide what you're comparing. Pick the market or country, the language, the business line, the site sections, and the date window you'll use for every competitor in the review. This matters more than it sounds like it should. If you compare your product pages against a rival's entire content heavy domain, you'll draw the wrong conclusion, because you're not looking at the same kind of thing on both sides.
Keep the set small on a first pass. Around three to five relevant competitors is a manageable size for a first SEO competitive analysis, and you can always widen it later for a specific question. This isn't a rule for figuring out who your real competitors are, just a working sample size for one review cycle.
Once the boundary is set, go get your own numbers. In Google Search Console's Performance report, choose your date range, pick Search results as the type, and pull clicks, impressions, click through rate, and average position, broken out by query and page. The default view shows the past three months, which is a fine starting point, but don't assume it fits your business if you're seasonal or just launched something. Note anything unusual about the period, a big campaign, a redesign, a product launch, so you don't mistake a one off spike for a trend later.
If you have conversion or pipeline data alongside your traffic numbers, keep it in the same worksheet, but label it clearly as your own first party data. That distinction matters once you start looking at competitor estimates, which come from a different kind of measurement entirely.
Common mistake: mixing countries, time windows, whole domains, and individual blog sections in the same comparison. You end up with numbers that look precise but aren't actually comparable.
You're done with this step when one worksheet header states your scope: dates, geography, site sections, the competitors included, your data sources, and your own baseline numbers.
Compare rankings on the searches that matter to your buyers
With the boundary set, pull a sample of queries that matter to your actual buyers, not every keyword a tool can find. For each one, record which page ranks for each competitor, roughly where it ranks, what the searcher seems to want, and what kind of page is winning: an article, a product page, a comparison, something else.
Tools built for this help you get there faster. Ahrefs' organic competitors report can show which rankings overlap between sites and which are unique to a competitor, with domain, subdomain, path, and exact URL modes so you can match the scope you set in step one. Semrush's keyword gap tool does something similar: run a domain comparison, switch to the missing view, and filter by position or intent to surface queries where a rival ranks and you don't.
Treat these as filters for finding promising candidates, not proof that every result deserves an article. For each one that looks worth a second look, check the actual search results. Does the query look informational, evaluative, or closer to a purchase decision? Where a competitor ranks and you don't, ask honestly whether your product and audience even fit that query. Where you both rank, compare which page actually answers the task better. Search volume and estimated traffic are useful context here, directional signals about how much attention a topic gets, not a promised number of visits for a page you haven't written yet.
Pro tip: stop at a strategic footprint comparison. A full keyword universe export is a different exercise, a content gap analysis, and trying to do both at once usually means you do neither one well.
You're done when your worksheet has a focused, buyer relevant set of queries, the winning URL for each, the intent you observed, and a one line reason each comparison actually matters.
Inspect the pages that are actually doing the work
Numbers tell you which pages rank. They don't tell you why. Pick a manageable sample of the competitor pages that came up as strong performers in the last step, plus a few of your own equivalent pages, and actually read them.
Look at who the page is written for, its format, whether it looks recently updated, what original examples or evidence it includes, whether an expert voice comes through, how clearly it explains the thing it's explaining, and what it asks the reader to do next. Stick to what you can observe on the page rather than assigning it a quality score out of thin air.
Ask what the page makes easy for the reader. Does it answer the main question fast? Does it walk through a process instead of just describing one? Does it back up a claim with something concrete? A page's ranking and estimated traffic tell you it has visibility in whatever dataset you're using. They don't tell you that its copy is what caused that, or that it's actually converting anyone.
This is one of the few spots in the process where your own content map earns a mention. DeepSmith's Content Map places your site and your competitors' sites on one shared topic taxonomy, so you can see comparative page coverage and funnel distribution across the set before you decide which pages are worth a closer read. It's useful for narrowing the sample here, not a replacement for actually opening the pages.
Common mistake: copying a competitor's outline, or treating a high page count as proof of quality. Neither one tells you what a reader actually gets out of the page.
You're done when every sampled URL has a short observation attached and a plausible implication for your own content, not a rewrite plan.
Map how each rival moves a buyer from question to decision
Read a competitor's educational, evaluation, and product facing pages as one connected path rather than as isolated articles. Write down who they say the page is for, the outcome they promise, what they claim makes them different, what proof they offer, which objections they address, and where they send the reader next.
Be careful to separate what a competitor explicitly states from what you're inferring about their strategy. Compare that language and evidence against your own positioning and, just as important, against what your product can honestly deliver. Only classify a page by buyer stage, awareness, consideration, or decision, when its actual purpose supports that label.
A useful output here looks like a specific contrast: this rival demonstrates the workflow with a worked example, while your equivalent page currently just states the benefit without showing it happen. That's an illustrative comparison you can act on, not a verdict on the competitor as a company. Follow it with a real next step, like commissioning an original example your team can actually stand behind.
Common mistake: mistaking a different tone of voice for a meaningful product difference, or repeating a competitor's claim as if it were your own without checking whether your product can support it.
You're done when you (or a reviewer) can trace how each competitor moves a buyer from a question to a decision, and you've identified at least one credible way to differentiate that your product can actually back up.
Read traffic and distribution signals without overclaiming
You can't see a competitor's real analytics, but estimated traffic and channel mix tools still tell you something worth knowing, as long as you keep the uncertainty attached to the number. Look at trends in estimated traffic and the apparent split across organic search, direct, referral, social, and paid, where your tool provides them. Organic search here means unpaid traffic from search engines, and referral means traffic arriving through links on other sites, while direct traffic covers typed addresses and bookmarks, categories worth knowing since they get mixed together casually in conversation.
What you're really looking for is a pattern worth investigating, not a precise figure. A competitor with an unusually strong referral presence might be worth a look at their partnerships or press coverage. That doesn't mean you know their exact referral visit count or the return on a specific campaign, and it isn't one either.
Also note which pages and messages a competitor is actively pushing on channels you can see directly, and whether their search visibility looks backed by other promotion or standing on its own. Keep what you can actually observe, a post going out, a partnership announcement, separate from what a tool is modeling. If a tool can't produce credible data for a smaller competitor, mark that cell unknown rather than filling it with a guess that looks more confident than it is.
Common mistake: reporting an estimated visits number as a competitor's actual traffic, or treating a high share of direct traffic as proof of brand loyalty when it might just mean people are typing in a URL they bookmarked once.
You're done when your worksheet names the apparent acquisition pattern for each competitor, where that read came from, how confident you are in it, and whether it changes anything about your own distribution plan.
Check who shows up and gets cited in AI answers
Search results aren't the only place your buyers are asking questions anymore, and this step of your competitive analysis process needs its own method rather than a guess based on one search. Build a consistent set of real buyer questions, including category and comparison questions that don't name any brand. For each one, ask it across the engines you care about and record the exact wording, the engine, the date, which brands got mentioned, which sources got linked, what page (if any) got cited for a competitor, and what the answer actually said.
Run this more than once. A single answer on a single day tells you what happened that day, not a stable pattern. A competitor being named without their site being linked is a mention, not a citation, and the two behave differently over time. Sometimes the page that gets cited for a topic your competitor owns isn't even their own site.
This is where DeepSmith's AI Visibility module does the actual measurement work, since it's built for exactly this kind of tracking. You define the prompts you want to monitor, or use Discover Prompts to generate a starting set, and it captures answers on a schedule and reports mention rate, citation rate, and share of voice, including which competitor pages are winning citations and on which engines. Coverage depends on your plan: Pro tracks ChatGPT, Grow adds Perplexity, Scale adds Gemini, and Enterprise or Custom covers all ten tracked engines, so don't assume a plan you haven't checked includes an engine you care about.
Google has said that AI Overviews and AI Mode can use different techniques and can show different responses and links, and that the underlying SEO best practices still apply, with no special extra requirement just to show up there. Don't take that to mean a particular content format guarantees a citation.
Common mistake: calling a brand mention a citation, treating one run of answers as a permanent ranking, or claiming a cited page drove a specific number of conversions.
You're done when you have a dated, engine specific log of answers tied to real buyer questions, with the cited URLs attached.
Turn your comparisons into a ranked decision
Now pull everything together into candidates for action. For each one, write a single row: the buyer problem behind it, the evidence you observed, the original response you're proposing, how relevant it is to the business, how much effort it takes, who owns it, and how you'll know it worked. Valid actions include improving an existing page's explanation, building a genuinely useful comparison or demonstration, shifting an editorial angle, strengthening the evidence on a page, or distributing something that already works through a different channel. Publishing a copy of a competitor's page isn't on that list.
If your team needs a tie breaker between several reasonable candidates, use a simple editorial scoring rubric: rate business relevance, strength of evidence, how differentiated it is, and how feasible it is to execute, each from zero to two. Review the highest scoring rows first. That scale is a management tool for sorting your own backlog, not a validated SEO model or a forecast of anything, and a lead should be able to override it when something in customer research or product reality says otherwise.
This is the second spot where DeepSmith fits naturally, since Opportunity Agents read your AI visibility and Content Map data and return ideas with the specific data point that justifies each one attached. From there, an idea can move straight into Content Studio's New Ideas, Planned Content, and Produced Content workflow. Think of that as a way to carry your evidence into the production decision, not proof that an idea will rank or get cited once it's written.
Common mistake: prioritizing whatever has the biggest keyword estimate attached, instead of weighing buyer fit, whether you can actually produce distinctive evidence, and whether your team has the capacity to execute well.
You're done when the team has approved a short, ordered backlog, explicitly set aside the low fit findings instead of quietly dropping them, and can explain the evidence behind the top choice.
Assign the action and set a review point
A decision that nobody owns doesn't happen. For each approved action, name an owner, the next concrete deliverable, a date, and the one measure that will tell you whether it worked.
Go back to your own Search Console data to track changes in relevant impressions, clicks, and queries. Watch estimated competitor rankings as a separate, directional series rather than folding it into the same number. Review your tracked AI answers for changes in mentions and citations by engine and by prompt. Build in an editorial check too, confirming new work stays accurate and on brand as it goes out, not just that it shipped on time.
A light monthly check on what's changed, paired with a deeper strategy review every quarter or so, is a reasonable rhythm for most teams, though you should adjust it to your own publishing pace rather than treating it as a fixed rule.
Common mistake: declaring victory the moment something gets published, or crediting every later change in traffic or AI answers to that one page without any real evidence connecting the two.
You're done when the decision sheet says what will ship, who owns it, what evidence would change your mind, and when you'll come back to look again.
What to do next
You don't need to run every step at full depth the first time through. That's the point of treating this as a competitor analysis step by step: you can stop after any completed step with something usable, rather than waiting for a perfect finish. Pick the highest confidence action from your list, give it an owner, and put the review date on the calendar before you start another full audit. A competitor analysis that produces one well evidenced decision beats one that produces a spreadsheet nobody opens again.
If you're already tracking how your brand shows up in AI answers and want that evidence flowing straight into what you write next, start a free trial of DeepSmith and see how citation tracking and content planning work from the same data.
