LinkedIn Ads A/B Testing in 2026: What Changed

Luke Costley-White

Adclear and DOJO AI partnership graphic: 'Close the loop on agentic marketing. Compliance at the speed of creation.'
疑心暗鬼
Doubt Creates Phantoms

In June 2026, LinkedIn overhauled measurement inside Campaign Manager's Testing tab. Conversion Lift Testing now works at the campaign level, not just account-level. Brand Lift Testing got a clearer reporting interface. iOS measurement expanded. And LinkedIn switched its lift-test statistics from a Frequentist to a Bayesian model, aiming for more conclusive results.

Most advertisers running LinkedIn ads a/b testing haven't noticed any of this happened. Here's exactly what changed, what didn't, and what to do about it.

What Actually Changed in LinkedIn's Testing Tab in 2026 (and What Didn't)

Start with what's not new, because a lot of coverage of this topic gets it wrong. The Testing tab itself, along with native A/B testing, launched in April 2023 (LinkedIn Marketing Blog, "Unlock Your Campaign's Potential with LinkedIn's New A/B Testing," Jun 27, 2023). Before that, advertisers tested manually: duplicating campaigns to compare variants, or using a "rotate ads evenly" setting inside one campaign and eyeballing the split.

Brand Lift Testing has also been around since before this year. Neither the tab nor the two testing types housed inside it are new in 2026.

What is new is what's running inside that same tab as of June 2026: campaign-level Conversion Lift Testing, a redesigned Brand Lift reporting layer, expanded iOS measurement coverage, and a switch in the underlying statistics from Frequentist to Bayesian. None of these changes required advertisers to opt into anything or click a new toggle. They shipped inside tools most accounts were already using, which is exactly why they've gone largely unnoticed.

Being precise about this distinction matters if you're the person explaining "what's new" to a client or a CMO. Telling them the Testing tab is new when it's three years old undermines your credibility on everything else in this piece.

Campaign-Level Conversion Lift Testing

What It Replaced

Conversion Lift tests previously ran only at the ad-account level (LinkedIn Help Center, "Conversion Lift Testing," linkedin.com/help/lms/answer/a7413311). If you ran multiple campaigns in the same account, your lift result was a blended read across all of them. You couldn't isolate whether campaign A or campaign B was the one actually driving incremental conversions.

Advertisers can now run Conversion Lift Testing scoped to a single campaign.

Why Campaign-Level Isolation Matters

For an agency running several concurrent campaigns per client, or an advertiser running multiple initiatives at once, account-level lift testing was close to useless for decision-making. A blended lift number tells you the account did something. It doesn't tell you which campaign to fund next quarter or which one to cut.

Campaign-level testing fixes that specific problem: you can now attribute incremental lift to the campaign that produced it, rather than guessing which of your concurrent efforts is actually working.

The Budget Reality

LinkedIn's own stated minimums for Conversion Lift Testing are a cumulative $80,000 USD budget and a 30-90 day test duration (LinkedIn Help Center, "Conversion Lift Testing requirements," linkedin.com/help/lms/answer/a7409462).

Be direct about what that means: for many mid-market accounts, an $80,000-per-campaign minimum puts this out of reach at the campaign level. A challenger brand running a $15,000/month LinkedIn budget split across three campaigns isn't going to clear that bar on any single one of them. If you want to run this, the realistic options are testing at the account level first, or concentrating enough spend into your highest-priority campaign to actually qualify.

One Named Result, Not a Market Average

LinkedIn published a single case in its own June 18, 2026 blog post: a company using campaign-level Conversion Lift Testing found exposed audiences were 26% more likely to convert than the holdout group (LinkedIn Marketing Blog, George Tabet, Product Marketing @ LinkedIn, "How to Prove Incremental Marketing Impact with LinkedIn's New Tools," Jun 18, 2026). That's one advertiser, in one test, published by LinkedIn as a showcase example. Treat it as evidence the mechanism works, not as a benchmark for what your account should expect.

Brand Lift Testing's Reporting Refresh

The June 2026 update also redesigned how Brand Lift results get reported: clearer summaries and confidence-based lift statuses, built to be readable by someone who isn't a statistician.

Worth restating why Brand Lift and Conversion Lift aren't competing options: they measure different funnel stages. Brand Lift Testing covers awareness and consideration; it tells you whether people remember or feel differently about your brand after seeing your ads. Conversion Lift Testing covers the down-funnel question of whether your ads actually caused conversions. Run Brand Lift when you're evaluating an awareness campaign. Run Conversion Lift when you're evaluating a demand gen or pipeline campaign. Using one to answer the other's question will give you a technically valid result to the wrong question, the same mismatch we've seen trip up LinkedIn attribution more broadly.

The Quiet Stats Shift: Frequentist to Bayesian

The change with the widest practical impact is the one LinkedIn has been least loud about. Its Help Center states plainly: "As of June 2026, we're changing our measurement framework from Frequentist to Bayesian methodology" (LinkedIn Help Center, "Conversion Lift test results," linkedin.com/help/lms/answer/a7755286).

In plain terms: Frequentist statistics ask "how likely is this data, assuming there's no real effect?" and often answers with a flat inconclusive when the sample or effect size isn't large enough to clear a fixed threshold. Bayesian methods instead ask "given this data, how likely is it that a real effect exists?" and produce a probability. That framing generally produces fewer flat inconclusive results and gives you a graded read on how confident you should be, instead of a binary pass/fail.

That's a meaningful change if you've been frustrated by lift tests coming back inconclusive. It's also a more useful number to put in front of a non-technical stakeholder or a board: "there's an 85% probability this campaign drove incremental conversions" reads more clearly than a p-value most executives were never trained to interpret anyway.

None of this means every test now resolves cleanly. LinkedIn's own A/B testing product page states it "does not guarantee conclusive results" and flags that "there may be instances where audience overlap occurs" between test groups (business.linkedin.com/advertise/ads/testing). That caveat predates the Bayesian shift and still applies. Read it as a real constraint on what the tool can tell you, not as boilerplate legal language.

iOS Measurement: Closing the Tracking Gap

The June 2026 update also extended a privacy-preserving matching approach, previously available only inside Brand Lift Testing, to Conversion Lift Testing. The stated purpose is closing measurement gaps created by iOS tracking restrictions, where user-level signal has been degraded since Apple's App Tracking Transparency changes. If your account skews toward iOS traffic and your lift tests have historically underreported, this is the change worth checking against your own numbers before assuming the platform's read is still accurate.

What Practitioners Are Actually Running Into

The June 2026 changes are about lift and incrementality measurement. They don't touch the mechanics of LinkedIn's native creative A/B testing tool, which is where a separate set of practitioner complaints keeps showing up on r/LinkedinAds.

One thread from around August 2025 captured a common complaint directly: a practitioner said they do manual testing only, because "LinkedIn's AB test feature creates duplicate campaigns which creates a disorganized account structure that just becomes an archived mess" (Reddit, r/LinkedinAds, ~Aug 2025).

A separate thread from around November 2025 raised a structural limitation: LinkedIn's A/B testing tool "is not really flexible because you can only test a single variable (creative, audience segment, etc.)" (Reddit, r/LinkedinAds, ~Nov 2025).

Worth flagging clearly, because this one isn't confirmed against LinkedIn's own documentation, only observed and discussed by practitioners: a separate r/LinkedinAds thread from around November 2025 noted that LinkedIn's ad-rotation control appears to have shifted its default behavior from "Rotate ads evenly" toward "Optimize for performance." The thread concluded this makes genuine A/B testing via ad rotation "basically broken," and recommended running one ad per ad set, or separate campaigns entirely, to get a clean test signal. Treat this as a practitioner-reported observation to verify in your own account, not as an established platform fact.

Practical guidance on when to use which approach:

  • Use LinkedIn's native A/B testing tool when you want a clean, single-variable test and campaign clutter isn't a concern (small accounts, occasional tests).

  • Use manual multi-ad testing within one campaign when you need more flexibility or want to avoid duplicate-campaign sprawl. It's more work to track the comparison yourself, but it keeps your account structure clean.

A Practitioner's Testing and Measurement Checklist for LinkedIn Ads in 2026

  1. Match the test type to the funnel stage. Brand Lift for awareness and consideration campaigns, Conversion Lift for down-funnel campaigns. Using the wrong one gives you a valid answer to the wrong question.

  2. Budget for Conversion Lift Testing realistically. The $80,000/30-90 day minimum is real. If a single campaign can't clear it, test at the account level first rather than forcing a per-campaign test you can't afford.

  3. Expect some tests to stay inconclusive. LinkedIn's own "does not guarantee conclusive results" caveat is a real data and audience-overlap constraint, not boilerplate.

  4. Decide upfront on your A/B testing approach. LinkedIn's native tool for single-variable, low-clutter tests; manual multi-ad testing within one campaign when you need flexibility and don't mind managing the comparison yourself.

  5. Reconcile every lift result against your own revenue data before reporting it upward. A platform-reported lift number in isolation doesn't tell you whether it converted to real pipeline.

How to Stay Ahead of Measurement Changes Like This

Platform measurement methodology changes like the Frequentist-to-Bayesian shift roll out quietly, inside tools you already use, with no announcement most advertisers ever see. By the time a team notices, they may have spent months reading test results against a framework that no longer applies. Continuous monitoring of what's actually changed under the hood of the platforms you run, checked against your own revenue outcomes rather than the platform's self-reported numbers, is exactly the kind of standing problem an AI marketing intelligence system like DOJO is built to sit on top of.

FAQ

What is Conversion Lift Testing on LinkedIn, and how is it different from A/B testing? Conversion Lift Testing measures incremental impact, whether your ads caused conversions that wouldn't have happened otherwise, by comparing an exposed group to a holdout group. A/B testing compares two ad variants against each other directly. They answer different questions: A/B testing tells you which creative or audience performed better; Conversion Lift Testing tells you whether your advertising caused real incremental results at all.

How much budget do I need to run a LinkedIn Conversion Lift test? LinkedIn's own stated minimum is a cumulative $80,000 USD budget over a 30-90 day test duration. That puts a per-campaign Conversion Lift test out of reach for many mid-market budgets; running it at the account level, or saving it for your highest-spend campaign, is often more realistic.

Why did my LinkedIn Brand Lift or Conversion Lift test come back inconclusive? LinkedIn's own A/B testing product page states plainly that it does not guarantee conclusive results and flags that audience overlap between test groups can occur. The June 2026 shift to Bayesian methodology should reduce flat inconclusive outcomes going forward, but it doesn't eliminate the underlying data and audience-overlap constraints entirely.

Sources Cited

LinkedIn Ads A/B Testing in 2026: What Changed

Luke Costley-White

Adclear and DOJO AI partnership graphic: 'Close the loop on agentic marketing. Compliance at the speed of creation.'
疑心暗鬼
Doubt Creates Phantoms

In June 2026, LinkedIn overhauled measurement inside Campaign Manager's Testing tab. Conversion Lift Testing now works at the campaign level, not just account-level. Brand Lift Testing got a clearer reporting interface. iOS measurement expanded. And LinkedIn switched its lift-test statistics from a Frequentist to a Bayesian model, aiming for more conclusive results.

Most advertisers running LinkedIn ads a/b testing haven't noticed any of this happened. Here's exactly what changed, what didn't, and what to do about it.

What Actually Changed in LinkedIn's Testing Tab in 2026 (and What Didn't)

Start with what's not new, because a lot of coverage of this topic gets it wrong. The Testing tab itself, along with native A/B testing, launched in April 2023 (LinkedIn Marketing Blog, "Unlock Your Campaign's Potential with LinkedIn's New A/B Testing," Jun 27, 2023). Before that, advertisers tested manually: duplicating campaigns to compare variants, or using a "rotate ads evenly" setting inside one campaign and eyeballing the split.

Brand Lift Testing has also been around since before this year. Neither the tab nor the two testing types housed inside it are new in 2026.

What is new is what's running inside that same tab as of June 2026: campaign-level Conversion Lift Testing, a redesigned Brand Lift reporting layer, expanded iOS measurement coverage, and a switch in the underlying statistics from Frequentist to Bayesian. None of these changes required advertisers to opt into anything or click a new toggle. They shipped inside tools most accounts were already using, which is exactly why they've gone largely unnoticed.

Being precise about this distinction matters if you're the person explaining "what's new" to a client or a CMO. Telling them the Testing tab is new when it's three years old undermines your credibility on everything else in this piece.

Campaign-Level Conversion Lift Testing

What It Replaced

Conversion Lift tests previously ran only at the ad-account level (LinkedIn Help Center, "Conversion Lift Testing," linkedin.com/help/lms/answer/a7413311). If you ran multiple campaigns in the same account, your lift result was a blended read across all of them. You couldn't isolate whether campaign A or campaign B was the one actually driving incremental conversions.

Advertisers can now run Conversion Lift Testing scoped to a single campaign.

Why Campaign-Level Isolation Matters

For an agency running several concurrent campaigns per client, or an advertiser running multiple initiatives at once, account-level lift testing was close to useless for decision-making. A blended lift number tells you the account did something. It doesn't tell you which campaign to fund next quarter or which one to cut.

Campaign-level testing fixes that specific problem: you can now attribute incremental lift to the campaign that produced it, rather than guessing which of your concurrent efforts is actually working.

The Budget Reality

LinkedIn's own stated minimums for Conversion Lift Testing are a cumulative $80,000 USD budget and a 30-90 day test duration (LinkedIn Help Center, "Conversion Lift Testing requirements," linkedin.com/help/lms/answer/a7409462).

Be direct about what that means: for many mid-market accounts, an $80,000-per-campaign minimum puts this out of reach at the campaign level. A challenger brand running a $15,000/month LinkedIn budget split across three campaigns isn't going to clear that bar on any single one of them. If you want to run this, the realistic options are testing at the account level first, or concentrating enough spend into your highest-priority campaign to actually qualify.

One Named Result, Not a Market Average

LinkedIn published a single case in its own June 18, 2026 blog post: a company using campaign-level Conversion Lift Testing found exposed audiences were 26% more likely to convert than the holdout group (LinkedIn Marketing Blog, George Tabet, Product Marketing @ LinkedIn, "How to Prove Incremental Marketing Impact with LinkedIn's New Tools," Jun 18, 2026). That's one advertiser, in one test, published by LinkedIn as a showcase example. Treat it as evidence the mechanism works, not as a benchmark for what your account should expect.

Brand Lift Testing's Reporting Refresh

The June 2026 update also redesigned how Brand Lift results get reported: clearer summaries and confidence-based lift statuses, built to be readable by someone who isn't a statistician.

Worth restating why Brand Lift and Conversion Lift aren't competing options: they measure different funnel stages. Brand Lift Testing covers awareness and consideration; it tells you whether people remember or feel differently about your brand after seeing your ads. Conversion Lift Testing covers the down-funnel question of whether your ads actually caused conversions. Run Brand Lift when you're evaluating an awareness campaign. Run Conversion Lift when you're evaluating a demand gen or pipeline campaign. Using one to answer the other's question will give you a technically valid result to the wrong question, the same mismatch we've seen trip up LinkedIn attribution more broadly.

The Quiet Stats Shift: Frequentist to Bayesian

The change with the widest practical impact is the one LinkedIn has been least loud about. Its Help Center states plainly: "As of June 2026, we're changing our measurement framework from Frequentist to Bayesian methodology" (LinkedIn Help Center, "Conversion Lift test results," linkedin.com/help/lms/answer/a7755286).

In plain terms: Frequentist statistics ask "how likely is this data, assuming there's no real effect?" and often answers with a flat inconclusive when the sample or effect size isn't large enough to clear a fixed threshold. Bayesian methods instead ask "given this data, how likely is it that a real effect exists?" and produce a probability. That framing generally produces fewer flat inconclusive results and gives you a graded read on how confident you should be, instead of a binary pass/fail.

That's a meaningful change if you've been frustrated by lift tests coming back inconclusive. It's also a more useful number to put in front of a non-technical stakeholder or a board: "there's an 85% probability this campaign drove incremental conversions" reads more clearly than a p-value most executives were never trained to interpret anyway.

None of this means every test now resolves cleanly. LinkedIn's own A/B testing product page states it "does not guarantee conclusive results" and flags that "there may be instances where audience overlap occurs" between test groups (business.linkedin.com/advertise/ads/testing). That caveat predates the Bayesian shift and still applies. Read it as a real constraint on what the tool can tell you, not as boilerplate legal language.

iOS Measurement: Closing the Tracking Gap

The June 2026 update also extended a privacy-preserving matching approach, previously available only inside Brand Lift Testing, to Conversion Lift Testing. The stated purpose is closing measurement gaps created by iOS tracking restrictions, where user-level signal has been degraded since Apple's App Tracking Transparency changes. If your account skews toward iOS traffic and your lift tests have historically underreported, this is the change worth checking against your own numbers before assuming the platform's read is still accurate.

What Practitioners Are Actually Running Into

The June 2026 changes are about lift and incrementality measurement. They don't touch the mechanics of LinkedIn's native creative A/B testing tool, which is where a separate set of practitioner complaints keeps showing up on r/LinkedinAds.

One thread from around August 2025 captured a common complaint directly: a practitioner said they do manual testing only, because "LinkedIn's AB test feature creates duplicate campaigns which creates a disorganized account structure that just becomes an archived mess" (Reddit, r/LinkedinAds, ~Aug 2025).

A separate thread from around November 2025 raised a structural limitation: LinkedIn's A/B testing tool "is not really flexible because you can only test a single variable (creative, audience segment, etc.)" (Reddit, r/LinkedinAds, ~Nov 2025).

Worth flagging clearly, because this one isn't confirmed against LinkedIn's own documentation, only observed and discussed by practitioners: a separate r/LinkedinAds thread from around November 2025 noted that LinkedIn's ad-rotation control appears to have shifted its default behavior from "Rotate ads evenly" toward "Optimize for performance." The thread concluded this makes genuine A/B testing via ad rotation "basically broken," and recommended running one ad per ad set, or separate campaigns entirely, to get a clean test signal. Treat this as a practitioner-reported observation to verify in your own account, not as an established platform fact.

Practical guidance on when to use which approach:

  • Use LinkedIn's native A/B testing tool when you want a clean, single-variable test and campaign clutter isn't a concern (small accounts, occasional tests).

  • Use manual multi-ad testing within one campaign when you need more flexibility or want to avoid duplicate-campaign sprawl. It's more work to track the comparison yourself, but it keeps your account structure clean.

A Practitioner's Testing and Measurement Checklist for LinkedIn Ads in 2026

  1. Match the test type to the funnel stage. Brand Lift for awareness and consideration campaigns, Conversion Lift for down-funnel campaigns. Using the wrong one gives you a valid answer to the wrong question.

  2. Budget for Conversion Lift Testing realistically. The $80,000/30-90 day minimum is real. If a single campaign can't clear it, test at the account level first rather than forcing a per-campaign test you can't afford.

  3. Expect some tests to stay inconclusive. LinkedIn's own "does not guarantee conclusive results" caveat is a real data and audience-overlap constraint, not boilerplate.

  4. Decide upfront on your A/B testing approach. LinkedIn's native tool for single-variable, low-clutter tests; manual multi-ad testing within one campaign when you need flexibility and don't mind managing the comparison yourself.

  5. Reconcile every lift result against your own revenue data before reporting it upward. A platform-reported lift number in isolation doesn't tell you whether it converted to real pipeline.

How to Stay Ahead of Measurement Changes Like This

Platform measurement methodology changes like the Frequentist-to-Bayesian shift roll out quietly, inside tools you already use, with no announcement most advertisers ever see. By the time a team notices, they may have spent months reading test results against a framework that no longer applies. Continuous monitoring of what's actually changed under the hood of the platforms you run, checked against your own revenue outcomes rather than the platform's self-reported numbers, is exactly the kind of standing problem an AI marketing intelligence system like DOJO is built to sit on top of.

FAQ

What is Conversion Lift Testing on LinkedIn, and how is it different from A/B testing? Conversion Lift Testing measures incremental impact, whether your ads caused conversions that wouldn't have happened otherwise, by comparing an exposed group to a holdout group. A/B testing compares two ad variants against each other directly. They answer different questions: A/B testing tells you which creative or audience performed better; Conversion Lift Testing tells you whether your advertising caused real incremental results at all.

How much budget do I need to run a LinkedIn Conversion Lift test? LinkedIn's own stated minimum is a cumulative $80,000 USD budget over a 30-90 day test duration. That puts a per-campaign Conversion Lift test out of reach for many mid-market budgets; running it at the account level, or saving it for your highest-spend campaign, is often more realistic.

Why did my LinkedIn Brand Lift or Conversion Lift test come back inconclusive? LinkedIn's own A/B testing product page states plainly that it does not guarantee conclusive results and flags that audience overlap between test groups can occur. The June 2026 shift to Bayesian methodology should reduce flat inconclusive outcomes going forward, but it doesn't eliminate the underlying data and audience-overlap constraints entirely.

Sources Cited

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FAQ

Frequently asked questions

Frequently asked questions

What is DOJO AI?

DOJO is an intelligent marketing system that watches every channel continuously, builds a living knowledge graph of your brand's marketing reality, and deploys specialised agents that execute work autonomously before you've had to ask. Not a tool. Not a platform. A system. Every signal your brand produces flows in, every action feeds back, and the system compounds its understanding over time. Most marketing software gives you data. DOJO gives you a system of record, context, and execution: one place where everything is captured, connected, and acted on. Instead of switching between Google Ads, Meta, LinkedIn, GA4, and social dashboards, you get one intelligent system that shows you what's working across all channels - and tells you exactly what to do about it. Specialized AI agents analyze your campaigns 24/7, identify opportunities competitors miss, and help you move faster than companies 10x your size.

Who is DOJO built for?

DOJO is built for marketing teams that want to spend their time on decisions that require human judgment, not on tasks that don't. If your team is stretched across too many channels, too many tools, and too many reports, DOJO replaces the operational burden with a system that runs continuously and arrives with work already done. It's used by in-house marketing teams, agencies managing multiple client accounts, and founders who want the output of a full marketing department without the overhead of one.

Is DOJO suitable for marketing agencies?

Yes. Agencies are one of DOJO's core use cases. The system connects across multiple client accounts, automates reporting and content production, and runs campaign monitoring continuously — so account managers spend time on client relationships and strategy, not on manual tasks that don't require their judgment. DOJO builds a separate knowledge graph for each client, so every recommendation and every piece of content is grounded in that client's actual brand history, not generic best practice.

How does DOJO work with existing tools?

DOJO connects to your existing channels through proprietary connectors and a live web crawler. Google Ads, Meta, LinkedIn, your website, brand mentions, competitor movements — everything flows in automatically, with no manual pulls required. You don't have to replace your stack to use DOJO. The system reads your existing data, connects it, and builds context on top of it. Over time, that context becomes the foundation for every recommendation and every action DOJO takes on your behalf.DOJO builds a separate knowledge graph for each client, so every recommendation and every piece of content is grounded in that client's actual brand history, not generic best practice.

What ROI can I expect?

DOJO customers typically see measurable cost reductions and efficiency gains within the first 90 days, with outcomes compounding as the system builds context over time. Here's what customers have reported: 79% drop in cost per acquisition(Morningstar) 3x conversion volumein the same 23-day window (Morningstar) 40% drop in acquisition costs(Broadvoice) 15x faster marketing reporting(Ozone API) 3x more efficient Google Adsquarter over quarter (Ecologi) 290% increase in content output(Broadvoice) 20 hours saved per month, returned to strategy (Morningstar) The compounding effect matters here. The longer DOJO runs, the more context it builds, and the more precisely it acts. Early results are strong; they get better.

How does DOJO compare to HubSpot, Jasper, or other AI marketing tools?

Most AI marketing tools fall into one of two categories: workflow automation (HubSpot, Marketo, ActiveCampaign) that executes campaigns you set up, or content generation (Jasper, Copy.ai) that produces copy on demand. Both share the same limitation: they start from scratch every session. No memory of your brand history, your previous campaigns, or what your competitors have been doing. DOJO maintains a continuously updated knowledge graph of your entire marketing reality and runs specialised agents that read it daily, surface what needs attention, and execute work before you've asked. The longer DOJO runs, the more precisely it acts — because it compounds what it learns about your specific brand, market, and competitors. If you're evaluating options: Email and workflow automation: HubSpot, Klaviyo, Marketo AI content writing: Jasper, Copy.ai A system that watches every channel, builds brand context, and executes proactively: DOJO

Does AI marketing software actually improve over time, or does it reset every session?

Most AI marketing software resets every session. It has no memory of your brand, your campaigns, or what worked before. Every interaction starts from a blank slate. DOJO works differently. Every signal it captures, every workflow it runs, every recommendation it makes is fed back into the DOJO Graph. The system learns what works for your specific brand, in your specific market, against your specific competitors. It builds institutional knowledge that no other system carries. A team that's been using DOJO for six months has a system that understands their brand history, their campaign patterns, and their market in detail. That depth of context changes what the agents can do. The advantage grows every day the system runs, and it never stops running.

How does DOJO handle data security and privacy?

DOJO is built on enterprise-grade infrastructure with security and data privacy at its core. Your brand data, campaign history, and knowledge graph are kept entirely separate from other customers' data. For detailed information on data handling, storage, and compliance, see our Privacy Policy and Data Processing Agreement, or speak to our team directly when you book a demo.

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WIRED Magazine Top 100 Startups

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G2.com 4.9 out of 5 stars

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G2.com 4.9 out of 5 stars