Inside Jonathan Berthold’s Secret Playbook for Beating Flat Traffic with Zero Budget

Ever found yourself staring at a flatlining traffic chart, wallet barely holding dust, wondering what on earth to test first? And how in the world do you convince the higher-ups to greenlight an experiment when the outcome is as uncertain as the next Google core update? That’s the exact puzzle I tossed over to Jonathan Berthold, VP of Revenue at Ziff Davis, on The Search Engine Journal podcast. With decades of marketing cunning under his belt, Jonathan sheds light on zero-budget strategies, how to keep tests too small to wreck your site, and why marketers often miss the goldmine—what happens after that click. Spoiler: It’s not just about traffic anymore, folks. Dive into this treasure trove of insights before your next move, because sometimes the smartest test is the one you don’t see coming. LEARN MORE.

Your traffic is flat, and you don’t have a budget to throw at the problem. What should you test first? And how do you get your boss to approve an experiment when you can’t promise it will work?

I put those questions to Jonathan Berthold, VP of Revenue at Ziff Davis, on The Search Engine Journal podcast. Jonathan is a longtime marketing strategist who works in both SEO and paid media.

We covered where to find bottom-of-funnel opportunities and how to keep a test small enough that it can’t take down a site. We also discussed how long to wait before judging a result, along with a gap he sees in both SEO and paid media: Marketers focus on generating traffic and overlook what happens after a user lands on the site.

[00:45] What To Test When Traffic Is Flat And Budget Is Tight
[02:44] Getting Leadership Buy-In For Experiments
[06:35] Testing Small Before Making Sitewide Changes
[07:37] How Long To Run An SEO Experiment
[09:00] How To Evaluate Paid Media Tests
[10:51] Landing Page And Funnel Tests Marketers Overlook
[13:04] Finding Link Opportunities In AI Citation Data
[14:29] Why Search Intent Matters More Than Volume

Start With A Site Analysis, Then Target Bottom-Of-Funnel Content

When traffic is flat and there’s no budget, Jonathan starts with a full site analysis to find out why traffic stalled. He has inherited sites in that condition before, ranging from B2C SaaS companies to informational lead-generation sites. What he finds in the analysis determines the fix. Sometimes that means improving existing pages, and sometimes it means building new pages to fill a topic gap.

Either way, his experiment would target commercial intent. Top-of-funnel traffic is being “siphoned by LLMs, AI Overviews, AI Mode,” he said, and that traffic “is going away and/or doesn’t really convert or add to the bottom line.” So he would test one of two approaches: optimizing existing pages for commercial and transactional keywords, or creating new content to fill topic gaps in the market.

Without a budget, Jonathan relies on AI to do the research. He uses it to learn what type of content ranks for bottom-of-funnel terms and where the opportunities are to scale traffic in areas competitors aren’t covering. He also uses it to figure out what added perspective would make the new pages “unique and inviting to search engines like Google.”

I asked what test most marketers should be running but aren’t. Jonathan said there is “a massive market for bottom-of-funnel traffic that a lot of SEOs sleep on.”

It’s easier to build a strategy around keywords with more than a thousand monthly searches, but many of those keywords are informational. Giants like Reddit, LinkedIn and Wikipedia already rank for them, and LLMs, AI Overviews and AI Mode have absorbed much of that traffic. He called bottom-of-funnel targeting “100% the easiest low lift opportunity” available.

Get Buy-In By Defining What You Will Learn

Jonathan sees approval as a communication problem. He never tells a leader that the team will test something and hope for a result. Every experiment is an opportunity to learn. He starts with a hypothesis, and the pitch explains what the test will reveal. It might show whether a tactic or process will help in the future, or whether a messaging or creative idea holds up.

He recommends framing the ask around the long-term payoff by explaining what the team will learn and how that knowledge will make it better at its craft. When the pitch is positioned as “this experiment will allow us to learn X, Y, and Z so that we can earn this much into the future,” he said, it is “easier understood by a leadership team that largely works within P&L.”

Start Small So One Test Can’t Tank The Site

I raised a concern about the quick fixes circulating now that newcomers are using to enter SEO through AEO and GEO. Those tactics work until the algorithm catches up. I’d rather keep experiments contained than put an entire website at risk.

Jonathan agrees. When a team runs an SEO test across a full website and the test goes in the wrong direction, “you tank the whole organic,” he said. His rule for both SEO and paid media is to start with a very small section, then iterate and expand slowly as the results come in.

Paid media taught him the same lesson. Tripling a budget in one day “ruins all the optimization,” he said, so budgets need to rise gradually each day while the system learns. In SEO, the equivalent mistake is rolling out a schema or content-writing experiment sitewide and tanking pages that already perform well.

Results also need to be replicated. A result should repeat “so that it’s not just a random result,” he said, until a pattern emerges that you can apply to the rest of your work.

Give SEO Time, And Set A Different Rule For Paid Media

When a test isn’t going the way you hoped, when should you stop? Years ago, the standard advice was to give an SEO test six to nine months, especially for a new site, but iterations move much faster now. I asked Jonathan how he decides whether to give a struggling experiment more time or end it.

For SEO, he gives an experiment about a month. He relies on Search Console and treats its data as directional. Google needs time to crawl the updated pages, process the changes and adjust rankings before impressions and clicks move, so “definitely within a week, you won’t see results.”

After 30 days, there is usually enough data to see whether impressions are moving, and he would wait 60 days at most. He said starting small helps here, too, because it keeps expectations in check and lets the rest of the team’s work continue while the experiment runs.

Paid media follows a different rule because the feedback is immediate. He either reviews tests every two weeks or uses his preferred method, which is a spend threshold of two to three times the target cost per acquisition. Once a test has spent that much, it has run its course, and the team can either iterate on it or kill it.

The Test Most Marketers Skip Happens After The Click

Jonathan sees the same gap in SEO and paid media. Marketers put nearly all their attention on generating traffic and overlook “what happens when a user actually hits your website.”

Any page that already draws a lot of organic traffic, or that a team spends heavily to send paid traffic to, is a good candidate for split testing and funnel optimization. Landing page and funnel experiments should always be “an on thing,” he said, because you can keep raising conversion rates at each stage of the funnel, “even by a few basis percentage points.”

Use Citation Data To Find Outreach Opportunities

Jonathan is also running his own experiment that connects bottom-of-funnel targeting with link building. He uses AI citation data “to unearth link building opportunities.” He sees it as a way to spot where LLMs are heading and to build brand presence by earning the citations competitors have.

Many companies stop short of that step. A lot of them track AI visibility and watch which sites cite their competitors, he said, “but not a lot are taking the next step, which is reaching out to these publications.”

The payoff compounds. Pages that cite competitors for bottom-of-funnel keywords likely rank for those keywords themselves and get cited often in LLMs. Earning a mention on those pages can send traffic to your site and get your brand cited alongside your competitors, which he called “a more holistic approach.”

Use Intent To Choose Keywords When Search Volume Is Low

Jonathan no longer puts much stock in search volume. Search habits change every day. If the context and topic are right, a long-tail keyword can still be worth targeting even when Google, Moz or another platform reports it at one, 10 or zero searches a month.

Volume is useful as a directional signal, especially in paid media, where it helps set expectations for the total addressable market. In organic search, fragmentation changes the math. People search in more ways than they used to, including inside LLMs, and LLMs run their own query fan-outs. That makes targeting “how your target ICP searches” more important than the number in a keyword tool.

His example comes from his agency days. A client in the composites industry supplied fiberglass conduits for commercial projects. Its total addressable market was very small, and its keywords had very low search volume. But if 10 people searched a month and two of them landed on the site, “very likely they’re in that market. That’s a half-a-million-dollar contract.”

That’s where conversion rate comes in. Someone searching for the best mortgage rates will likely end up on informational content and probably won’t convert on that site. A search like “I need a plumber to fix my toilet” may show little search volume, but the person typing it has an urgent need and high intent, so they are much more likely to convert. As LLMs become more widely used, he expects search volume to be “not fully representative” of the phrases people use or the many fan-out queries a single keyword can generate.

Test Where the Revenue Is

As AI answers absorb informational searches, Jonathan says focus on high-intent searches, even when the volume is low. He also recommends running tests on the pages where visitors decide whether to convert. A small test in those places can show a measurable business result within a month or two.

That result also makes the next experiment easier to get approved. A test tied to conversions produces a number that a leadership team can use to make sound decisions.

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