Most GTM teams are not struggling because they lack LinkedIn signals. They are struggling because they are using the same ones the same way as everyone else else.

Hiring triggers. Job changes. New funding. Post engagement. Event attendee lists. None of these are bad. But once a signal becomes widely available, the message pattern attached to it usually becomes widely recognizable too.

That is the trap.

A lot of what gets called “signal-based GTM” today is really just workflow automation wrapped around stale logic. The signal fires. The playbook says “send message B.” The rep references something technically true. The buyer sees the pattern immediately. And the message dies on arrival.

That does not mean LinkedIn stopped working. It means the dumb use of LinkedIn signals stopped working.

After our podcast with Kellen Casebeer from The Deal Lab, that was the biggest thing that stood out to me: the edge is no longer in merely having access to the signal. The edge is in how you interpret it, stack it, and turn it into a motion that feels timely instead of templated.

That matters because LinkedIn is still one of the few channels where identity, context, and outbound can work together at the same time. The profile matters. The public graph matters. The category conversation matters. And unlike email, the channel gives you a visible way to create familiarity before you ask for anything.

That is also why I think a lot of teams misread LinkedIn altogether. They treat it like an intent feed. The better teams use it like a relationship placement channel.

That sounds softer than it is. It is actually a very tactical way to think about outbound. Because once you stop trying to force conversion in the first touch, a lot of the right plays become obvious.

In this post, I want to break down:

• why most LinkedIn signal selling fails

• the signal hierarchy that actually matters

• 6 practical LinkedIn plays worth stealing

• the landmines that quietly kill response rates

• where Syft fits in if you want to operationalize this without turning it into spam

Why Most LinkedIn Signal Selling Fails

There are two broad ways GTM teams misuse LinkedIn signals.

The first is treating a commodity signal like proprietary insight.

A hiring signal is the easiest example. Five years ago, reaching out right when a company was hiring might have felt sharp. Today, everyone has some version of that trigger. So if your logic is just “I saw you are hiring SDRs, therefore you must care about pipeline,” you are not delivering insight. You are arriving late to a crowded pattern.

The second is using context that belongs to someone else as a reason to pitch.

This is the class of motion where someone likes another person’s post, comments on a thread you were not part of, or interacts with a category conversation in public, and a random vendor jumps into their inbox as if that were a buying signal.

Usually it is not. Usually it is just creepy.

The buyer has no affinity to you. No relationship with you. No reason to think you should be in that exchange. Even if the underlying signal is real, the interpretation is bad.

That is the key distinction. Signals do not create pipeline on their own. Interpretation does.

The Signal Hierarchy on LinkedIn

Not all signals are created equal. And most teams overreact to the weakest ones. Here is the hierarchy I would use.

1. Commodity timing signals

These are posts around:

• hiring

• funding

• job changes

• public announcements

These are useful for timing. They are usually weak for messaging by themselves. They tell you that something may be changing. They do not tell you how to frame the outreach.

2. First-party engagement signals

These are much stronger:

• someone engaging with your content

• someone engaging with your ads

• someone viewing your profile

• someone messaging you

• repeated interaction with your team’s presence on LinkedIn

These matter because there is actual affinity. There is at least some legitimate reason for you to follow up.

That still does not mean every like is intent. A one-off like is usually just a weak hint. But repeated engagement, especially from the right account, starts to mean something.

3. Contextual signals

This is where the best operators live.

These are signals that only make sense when combined with surrounding context:

• the person is active in a specific category conversation

• the account is showing multiple weak signals at once

• the sender profile is relevant to the buyer

• there are mutuals inside the account

• there is event proximity

• the problem is being discussed publicly, even if your product is not

This is the layer where LinkedIn goes from being a notification stream to being a working surface for GTM judgment.

The teams that do well here do not ask, “what trigger fired?” They ask, “what is the most plausible explanation for what is happening here, and what is the least annoying way to enter that situation?”

Play 1: Use Blank Connects to Build the Right Audience

This is the most underrated LinkedIn play because it feels too simple.

A lot of teams treat connection requests like mini cold emails. They try to compress targeting, personalization, credibility, value prop, and CTA into one step. That is usually a mistake.

A connection request is not a pitch. It is not even a conversation yet. It is a lightweight acknowledgment that two people may benefit from being in the same professional orbit.

That is why blank connection requests work better than many people think.

They are low friction. They do not force context where none exists. And they let you build relevant audience density over time instead of trying to manufacture urgency on day one.

The tactical version of this is straightforward:

• pick a narrow ICP

• send connection requests consistently

• do not ask for anything in the request

• treat acceptance as access, not conversion

This is especially powerful if you sell into the same audience repeatedly. Over time, your network becomes an asset. Not because all of those people are ready to buy now, but because familiarity compounds. You are no longer starting from zero every time.

The mistake is expecting cold connects to behave like hot buyers. The point is not conversion. The point is placement.

Play 2: Follow Engagement, But Only When There Is Real Affinity

One of the better LinkedIn plays is still the simplest one: if someone engages with your content, connect with them and continue the interaction privately.

That works because the public engagement already did the hardest part. It created familiarity. But this play gets butchered constantly because teams overread weak engagement.

A single like is rarely enough. A one-off comment is often not enough either. The move becomes much stronger when you see repeated engagement, account fit, and some plausible reason the topic matters to them.

This is where I think a lot of signal tooling either helps or hurts.

If your system just says, “person liked post, trigger outreach,” you are going to generate a lot of false positives.

If your system helps you stack the signal against the account, the topic, prior engagement, and broader GTM context, now you have something useful.

A practical version of this play looks like:

• identify repeated engagers, not one-off engagers

• filter for ICP fit before acting

• use the DM to continue the thread, not restart it from scratch

• keep the ask small

If they engaged with a post about rep ramp, do not pivot immediately into a generic product pitch. Stay with the topic. Show that you understood what they were actually interacting with. Then earn the right to take the conversation one step further.

Play 3: Enter Through the Conversation, Not the Trigger

This was probably the sharpest tactic from the conversation with Kellen.

Most signal-based outreach still starts from the trigger.

Company is hiring.
Person changed jobs.
Team raised money.

And then the outreach simply mirrors the trigger back to the buyer.

That used to work better when fewer people were doing it. Now it often just sounds like everyone else.

The better move is to enter through the category conversation already happening around the trigger.

If a company is hiring sales reps, the interesting question is not “can I mention hiring?”

It is “what problem sits underneath that hiring motion?”

Maybe it is rep ramp.
Maybe it is manager capacity.
Maybe it is enablement debt.
Maybe it is territory design.
Maybe it is outbound consistency.

That is the move. Use the trigger to identify potential relevance. Use the broader conversation to frame the outreach.

This is much harder than plugging a field into a sequence. But it is also much harder to ignore. Here is the operating rule:

Commodity signals are for targeting. Category conversations are for messaging.

That shift alone will clean up a lot of bad LinkedIn outbound.

Play 4: Connect One Layer Below the Obvious Buyer

Most teams are too transactional in how they build their LinkedIn graph.

They go straight to the highest title they can find, pitch too early, and then wonder why the account feels cold.

There is a better compounding play: connect one layer below.

If you sell to VPs, build density with managers and directors.
If you sell to CISOs, build density with the people who will become CISOs.
If you sell to founders, build relationships with their operators.

There are two reasons this works.

First, today’s manager is often tomorrow’s executive. If you sell into the same market long enough, those relationships appreciate.

Second, network density inside the account creates trust. When a more senior person clicks your profile and sees that you already know people on their team, the interaction feels less random. Mutuals act as lightweight trust bridges even before anyone says a word.

This is one of the cleanest examples of LinkedIn working as a long-duration channel rather than a one-shot prospecting surface.

The tactical move here is not complicated:

• stop measuring every new connection by immediate pipeline potential

• build network density in the layer beneath the formal buyer

• revisit those people over time as they grow in seniority

• let mutual connection depth do some of the work for you

This is a slower play. It is also one of the least fragile ones.

Play 5: Match the Motion to the Sender Profile

One thing Kellen’s playbook gets very right is that not every LinkedIn sender is interpreted the same way.

A founder message is read differently than an SDR message.
An operator message is read differently than an AE message.
An executive message is read differently than a junior rep message.

That means the play itself should change depending on who is sending.

If the sender is a founder

Lean into curiosity, point of view, and long-term relevance. Founders often get more latitude because buyers assume there may be partnership value or future optionality in the relationship.

If the sender is an operator

Use peer language. Operators respond well to other operators when the message feels grounded in the actual work.

If the sender is an AE

Credibility matters. Competence matters. The recipient is asking whether you understand the space enough to be worth replying to.

If the sender is an SDR

Keep it light. Keep it short. Keep it respectful. The goal is not persuasion. The goal is to open a door.

This is one of the biggest mistakes I see in LinkedIn outbound. Teams centralize message logic too aggressively and forget that the profile itself is doing a huge amount of work before the message is ever read.

Your copy does not land in a vacuum. It lands on top of identity.

Play 6: Use Events as Momentum, Not Just Meetings

LinkedIn and events are much more complementary than most teams realize.

The reason events work is not mystical. It is momentum. The buyer already decided this category matters enough to spend time on. They already chose to be in the room.
They are already in a mode where conversation is expected.

That means LinkedIn outreach around an event has a much better chance of feeling near and familiar. There are two especially good versions of this play.

The pre-event connect

Get the attendee list. Connect ahead of time. Keep the note simple. “Saw you’ll be there, would love to say hi.”

This works because the context is already real. You are not inventing relevance.

The post-event miss

After the event, connect with the people you did not meet. “Sorry we missed each other, would have loved to connect.”

This is a great play because it borrows the event’s momentum without requiring a full meeting at the event itself.

The broader lesson is that LinkedIn often works best when it sits next to another source of context. Events are one of the cleanest examples of that.

The Brand Layer Most Teams Miss

There is another mistake buried under all of this.

A lot of companies still use LinkedIn like a glorified PR wire.

The company page posts announcements. The founder posts polished updates. The comments go unanswered. The employees repost things they did not write. Everybody claims to be “doing LinkedIn,” but nobody is actually participating in the category.

That creates the worst possible setup for outbound. You have activity without familiarity. Content without conversation. Presence without permission.

The better version is a flywheel:

• the company creates useful context

• the founder contributes real point of view

• employees become credible nodes in the category

• public familiarity makes private outreach easier

This matters because the best LinkedIn outbound rarely starts in the inbox. It starts in the feed, the comments, the profile, and the surrounding category conversation.

Landmines That Quietly Kill Response Rates

A few mistakes show up again and again.

Pitching in the connect request

Too early. Too heavy. Usually unnecessary.

Treating one weak signal as intent

A like is not a meeting request.

Using third-party engagement as your excuse to pounce

This is how a lot of “signal-based” outreach turns creepy fast.

Forcing context where none exists

If there is no real connection, keep it simple. Do not pretend there is one.

Overexplaining

Long messages often signal insecurity more than relevance.

Confusing silence with failure

Silence is normal. It is data, not drama.

Talking to yourself in follow-ups

A couple of thoughtful touches can work. A stack of unanswered messages makes you look careless.

Trying to convert before the person becomes responsive

This is the big one. Responsiveness creates the conversion window. Not the other way around.

How We’d Operationalize This at Syft

The temptation with LinkedIn data is to collect more of it.

I think that is the wrong goal.

The goal is to get better at separating weak signals from strong ones, isolated signals from stacked ones, and first-party context from random public exhaust.

That is the lens we increasingly care about at Syft.

Not “more triggers.”
Better judgment.

In practice, that means a few things:

• prioritize first-party engagement over third-party voyeurism

• do not fire on one-off signals unless there is other context

• stack engagement with account fit and surrounding category context

• route different plays based on who is sending

• use LinkedIn as part of a broader outbound system, not as a standalone channel

That last point matters.

One of the best parts of Kellen’s playbook is that LinkedIn is not treated as some sacred standalone motion. It sits alongside email, calls, events, and broader outbound design. Sometimes the DM is the opener. Sometimes it is the recognition layer that makes email work better. Sometimes it is simply the fastest way to get into the right people’s orbit.

That is a much healthier way to think about the channel.

Closing Thought

LinkedIn signals did not stop mattering but they just got cheaper. And once a signal gets cheap, the edge shifts somewhere else.

Usually it shifts into:

• interpretation

• timing

• sender identity

• surrounding context

• and the discipline to keep the interaction light until the buyer gives you permission to do more

That is the game now. Not who has the most signals but who can operationalize the best motions with the right context.

If you want the full conversation behind these ideas, watch the full episode with Kellen here.

That question came up repeatedly at a lunch event I attended this week with CMOs and marketing leaders. And I get why they’re asking. Traffic is down. AI overviews are eating clicks. ChatGPT scrapes 1,200 pages for every single human page view on your site (Anthropic’s Claude does 6,000). Your beautiful top-of-funnel content strategy is increasingly performing for an audience of bots.

So yeah if you’re measuring your website by traffic volume, things look bleak.

But the website isn’t dying. Its job description just changed. And the new role is a promotion.

The classic funnel drove traffic down. The inverted funnel captures intent at the center and amplifies it outward.

The Funnel Flipped

For two decades, we ran the same play. Drive traffic to the top of a wide funnel with SEO, social, paid ads and then nurture visitors down through consideration to conversion. The website was the starting point.

That era is over.

Today, buyers do their research before they ever touch your site. They ask ChatGPT. They read a thread on LinkedIn. They hear about you in a Slack community or a podcast. By the time they land on your website, they’re not browsing; they’re deciding.

This is the inverted funnel. Your website used to be the wide mouth at the top. Now it’s the narrow point where intent concentrates. The visitors who make it there carry real signal which is why LLM referral traffic converts 3-4x higher than traditional organic search.

Read that again: the traffic that AI sends you closes better than the traffic Google used to send you.

Your site didn’t lose its purpose. The purpose upgraded from awareness to conversion.

The “Waist” Is Where the Magic Happens

The inverted funnel has a waist - the narrowest point where human visitors arrive with intent. This is where you need to instrument everything.

Most marketing stacks are still optimized for the old funnel. They measure page views, sessions, and bounce rates - vanity metrics from the traffic era. In the inverted funnel, what matters is:

• Who showed up (not how many)

• Where they came from (LLM referral? Branded search? A partner’s link?)

• What they did (pricing page? Security docs? Integration pages?)

• How many people from the same account are visiting (one stakeholder or three?)

These are first-party signals that only you have. No competitor can buy them from a third-party vendor. No intent data provider can replicate them. They’re yours.

The Bottom Half: Amplification

Below the waist, it opens back up: wider than the old funnel ever was. This is the amplification zone.

You take the signals from that narrow intent point and fan them out to every channel that touches your buyer: social posts, partner co-marketing, email sequences, community engagement, even the context you feed back to LLMs through your content strategy.

Think of it as a feedback loop. A prospect visits your site from a ChatGPT referral. You capture the signal. You see they’re researching a specific use case. Now you amplify: your team shares relevant content on LinkedIn, your partner mentions you in a webinar, your SDR sends a personalized note that references the exact problem the prospect was researching.

The amplification loop works because it’s informed by real intent, not guesses. And many of the channels you’re amplifying into - social, community, partner content - are the same places LLMs pull from when forming their recommendations. So the loop compounds.

What This Means for Your Team

If you’re a Growth Marketer reading this, here’s the practical upshot:

Stop mourning traffic declines. The volume game is over. A smaller number of high-intent visitors who actually convert is worth more than a firehose of tire-kickers who bounce.

Instrument the waist. If you can’t tell me who visited your site today, where they came from, and what content they engaged with at the account and person level you’re flying blind in the most important part of the funnel.

Invest in the amplification layer. Most marketing teams are still organized around the top of the old funnel. The inverted funnel demands a different muscle: taking bottom-funnel signal and distributing it across channels. This is where in-person events, community, and partnerships become critical; but they need to be fed by signal, not run on vibes.

Reclaim your content strategy for LLMs. Your website now feeds two audiences: humans and AI. The content you publish shapes how LLMs talk about you, which shapes the referral traffic you receive, which shapes the intent you capture. It’s a flywheel, and it starts with content you control on your own domain.

Websites Aren’t Obsolete. They’re the Tip of the Spear.

The question isn’t whether you need a website. The question is whether you’ve upgraded your website’s job to match how buyers actually behave now.

In the old world, your site was a lobby - welcoming, broad, trying to appeal to everyone. In the new world, it’s a signal station - capturing concentrated intent from the few visitors who matter most, then broadcasting what you learn to the channels where your next buyers are forming their opinions.

The funnel flipped. Your website got promoted. The only question is whether your GTM stack noticed.

This is what we think about every day at Syft Data — helping GTM teams capture and act on the first-party signals that matter most. If you’re rethinking how your website fits into your go-to-market, I’d love to hear what you’re seeing. Reply to this email or find me on LinkedIn.

The Shifting Power Dynamic in Data Enrichment

Recent spending data from Ramp reveals fascinating trends in the data enrichment market. While ZoomInfo maintains its leadership position with 42% market share, LinkedIn Sales Navigator has been steadily gaining ground, now commanding 18% of wallet share - a significant increase from just 4% in Q3 2023.

This growth isn't accidental. LinkedIn's strategy combines three powerful elements:

• Ownership of the world's richest professional network data

• Strategic expansion into intent data offerings

• Aggressive stance against data scraping, directly impacting competitors who rely on LinkedIn as a data source

LLMs: The Dark Horse in Data Enrichment

But there's an even more intriguing disruption happening: LLMs are becoming remarkably effective at synthesizing public data for enrichment purposes. Our recent experience with incorporating LLMs in Syft’s waterfall produced some eye-opening results.

When we tested company revenue enrichment across providers, we found:

• Clearbit: 27% success rate

• Perplexity (LLM): 16% success rate

• Apollo: 12% success rate

What makes this particularly impressive is that Perplexity achieved this 16% rate even when consulted last in our enrichment waterfall! For our customers (B2B companies) using revenue data to define their Ideal Customer Profile (ICP), this represents a significant improvement in lead qualification accuracy.

The Nuanced Reality of LLM Integration

However, this doesn't mean companies should immediately abandon traditional data providers. Here's what we've learned:

1. Private Data Maintains Value: Providers with proprietary datasets (like ZoomInfo, maintaining 40%+ market share) continue to offer unique value

2. Emerging Partnerships: The recent Perplexity-Crunchbase partnership signals a trend toward integration rather than pure disruption

3. Quality Control Challenges: LLM hallucinations remain a concern, making robust evaluation datasets essential

The Future: A Hybrid Approach

Based on our experience and market data, we're seeing a new enrichment stack emerge:

1. LLMs as the first layer for public data synthesis

2. Traditional providers for private and verified data

3. Hybrid solutions that combine both approaches

The Ramp spending report shows that the market isn't winner-take-all - different providers serve different needs. Even as LinkedIn Sales Navigator grows and new players like LLMs emerge, companies like ZoomInfo maintain strong positions through their proprietary data advantages.

What This Means for B2B Companies

For companies relying on B2B data, the implications are significant:

• More accurate data enrichment through diversified sources

• Potential cost optimization by leveraging LLMs for public data

• Need for sophisticated evaluation and validation mechanisms

Looking Ahead

The writing is on the wall: if your data lives on LinkedIn or anywhere public, traditional enrichment providers will face increasing pressure. At Syft, we're already seeing the impact of this shift in our enrichment success rates, and we're investing heavily in building robust evaluation datasets to maximize the potential of both LLMs and traditional data sources.

The future of data enrichment won't belong to any single approach, but rather to platforms that can effectively combine the power of LLMs with traditional data sources while maintaining high accuracy standards. The market data and our own testing confirm this hybrid future is already taking shape.

Unfortunately, without this context, growth and sales teams spend hours trying to research these signups. It becomes extremely difficult to qualify them as product-qualified leads (PQLs) for their sales-assist motions.

Knowing your users

So how do most companies solve this problem? We have seen three approaches:

1️⃣ Incentivize signups with work email
Capsule prioritizes early product access for work email signups. HeyGen offered extra credits with a work email signup. Weights & Biases makes it clear that certain features such as teams and invitations require a work email. Get creative based on your offering!

2️⃣ Build a profile during onboarding

You can ask the users about their role and company during onboarding. has a great framework you can use for this.

3️⃣ Enrich email signups

With a stack of data providers and public data, you can enrich a significant fraction of signups. We see upto 90% success with work emails and close to 25% with personal emails at Syft with this strategy.

Putting it all together

We recommend layering these three approaches to get better identification rates:

1.  Nudge for work email at sign up. Be Creative!

2.  Enrich emails during signup with an enrichment waterfall.

3. Augment non-enriched profiles with onboarding questions.

This will ensure that you get good coverage on the firmographic information about a user once they sign up.

We recently launched a benchmarking tool to compare the accuracy of website visitor de-anonymization services. In this post, we will share some preliminary benchmark results from the data we collected from folks who used the tool.

tldr: Accuracy rates for company identification remain low across services. Clearbit has higher identification and accuracy rates compared to others1 but is limited to company-level identification.

Methodology

We took a sample of of data from global visitors who used the tool. The FAQ on the whoami page explains how the tool works. But as a quick recap, the tool identifies a visitor with multiple de-anonymization services. An identification is considered accurate if the company identified by a service matches the work email that the visitor submits. This is not always perfect - some folks work at multiple companies - but is close enough.

There is another caveat. A service like RB2B only works in the US so their metrics might be diluted given the data considers global visitors. We felt global data was more representative given every other service works globally.

🥁🥁 The Results

The chart below show the two major metrics we used to measure the coverage and accuracy of de-anonymization services.

Total Identified: This simply measures the percentage of visitors who were identified by a service.

Accurately Identified: This is the percentage of visitors whose company was accurately identified by a service across *all* visitors (identified or unidentified).

We can however get more precise. What really matter when it comes to accuracy is the false positive rate i.e. how many “identified” visitors are accurate. Here’s the money chart:

As you can see, Clearbit and Snitcher do quite well in terms coverage but Clearbit is clearly superior in terms of precision along with Dealfront. Apollo’s and People Data Lab’s offerings are quite new so it doesn’t surprise us that they still have ways to go.

We will have more data to share in a follow-up post with more cuts to identify how they all stack up in terms of overlap coverage across different geographies e.g. US vs non-US. Please sign up here to get it.

🔑 Key Takeaways

Given the mixed results here, you might be tempted to give up on de-anonymization. After all <50% coverage and precision doesn’t look good! But there is another way to look at this. Every % point in coverage gives you visibility into your traffic that otherwise you wouldn’t have!

There are also techniques you can use to improve the precision as well get more signal out of this noisy data. Here are four concrete ideas we recommend:

1️⃣ Use confidence scores. Vendors like Clearbit and PDL will offer confidence scores (High/Medium/Low). We strongly recommend using these to filter out Low confidence visits.

2️⃣ Use multiple visitors as a signal. A single visitor is typically a weak signal by itself but with inaccuracy issues, it becomes even weaker. Multiple visitors from the same company in a short timeframe provide a stronger evidence, especially in the Work-from-home era (where they don't share an office IP).

3️⃣ Combine multiple providers. Using multiple data providers can not only increase coverage, but also accuracy when they agree on an identification. When providers that use different technologies e.g. for company vs person-level, match each other, it tends to be quite accurate. With a solution like Syft, you automatically get access to a world-class de-anonymization/enrichment waterfall that takes care of this for you.

4️⃣ Combine with first Party signals. If you identify a company on your website after emailing a prospect there, then you have a strong 1st party signal (the prospect's company and location) and a strong geographic signal (location of visitor). You can combine them to build a stronger confidence.

If you have any questions, please let us know in the comments below.

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Website visitor de-anonymization is all the rage nowadays within the GTM community. It is a foundational piece that powers ABM campaigns, outbound motions, and website content personalization, among other things.

But every other day there is a debate in the GTM community about which vendor has the highest 'identification rates'?

Picking the Right Metrics

There is no "one-size-fits-all" answer to this question for two main reasons:

1. ID rates are highly dependent on the visitor profile. Which country is the visitor coming from? What is their persona? Are they likely to have an "online" presence? A Construction Tech SaaS targeting contractors in Canada might not identify traffic at the same rate as a B2B SaaS selling to US digital marketers.

2. ID rates vary based on precision. You can identify visitors at company-level, company and role level, and person-level with diminishing ID rates. Different services offer varying coverage across these granularities. Most offer only company-level identification unlike newer solutions such as Syft and RB2B.

Finally, the most important consideration is accuracy of identification. ID rates don't tell you whether the ID was correct!

Given these factors, our advice has been to evaluate multiple vendors, in parallel, and do two basic tests:

1. The Friends & Co-workers Test: Visit your website yourself and ask a few of your co-workers and friends to do the same. Check which of you are identified and whether the result matches your current company, role, linkedin.

2. The Signup Test: Check if the users that signed up were de-anonymized correctly prior to them signing up. This is the best way to check accuracy at scale.

However, performing these side-by-side tests is time consuming. They require you to talk to vendors, go through procurement, deploy these solutions, and then do evaluations. We speak to a lot of folks who are evaluating solutions including ours and this was one of their top frustrations.

Introducing WhoAmI

So, we decided to build a tool that makes it easy for you to do the "Friends & Co-workers" Test. Our tool - WhoAmI - helps you decide which vendors have the best coverage and accuracy objectively. With this tool we will identify you using multiple de-anonymization vendors and show you side-by-side results. If you are interested, we will send you a report with aggregated benchmark results from all the submissions as well.

Our goal is to educate the GTM community about the wonders and limitations of this technology. Please try it out and let us know what you think!