r/LeadGenSEA

Hi everybody,

I’m just starting lead generation.

I’m offering free leads generation service in exchange for a review.

The offer is for limited time and limited availability.

Thx

There are so many lead gen tools now, and so many AI options too. So curious, how people are actually handling their lead generation?

For us, the workflow is usually:

• build the first list (The Grid)
• clean and validate contacts (LinkedIn)
• push to outreach (Instantly)
• route replies back to CRM (Hubspot)

Curious what others are using. Are you running all-in-one setup no, or still stitching together different roles?

Hi

I recently built a free tool that extracts businesses from Google Maps along with validated email addresses. Right now, I'm looking for people who can try it out and share feedback - mainly whether the data quality is actually useful for lead generation compared to other tools.

Current Features:

Fetch businesses based on rating (e.g., less than or more than 3 stars)

Fetch reviews from within specific years

Find businesses with a low review count

Find Businesses without a website

Extract negative reviews from businesses

I'd love to know if this gives you valuable results or if something feels missing.

Genuine question for people doing b2b prospecting right now. Are you still usinga stack of different tools or trying to consolidate into one platform?

We've been using separate toold for data, enrichment, validation, outreach, and CRM updates. It works, but it also gets messy fast. Duplicate records, mismatched fields, manual cleanup, andtoo much switching between tabs.

At the same time, all-in-one tools sound convenient, but I'm not sure if they're actually better when you care about data quality, especially across markets SG/PH/ID.

Curious how others are handling it. Do you prefer a stack, or one tool that does most of the workflow?

I've noticed a lot of B2B teams still treat content like it's only for awareness. Post something helpful, get attention, maybe drive traffic. But in our experience with b2b saas campaigns across sg/ph/id, the content that actually helped pipeline was usually much deeper in the funnel.

Case studies, comparison posts, roi breakdowns, objection-handling posts, implementation notes. Boring stuff, but it moved deals. Awareness content got people in. Middle and late-stage content helped them decide.

So how are your approaching this? Are you actually building content for the full funnel or mostly just posting for visibility?

We tested AI-personalized outreachfor a B2B SaaS campaign targeting HR and ops leaders in SG, PH, and ID. It helped with speed, no question. We could research accounts faster and draft first-touch email in minutes instead of hours.

But the first version didn't perform as well as expected. Open rates ere fine around 45-50%, but replies were weak. A lot of the emails sounded personalized, but still felt fake. Things like, "congrats on your recent growth." or "I noticed your company is transforming the industry"

Technically relevant, but very obviously AI. What worked better was using AI for research, then rewriting the final message like a normal person. Here's a simple exaple:

"Saw you're hiring more ops people in SG, are you also trying to reduce manual admin work?"

That kind of message got better replies because it sounded specific without trying too hard. After we made the tone more huma, reply rates improved from 3% to 6-7%. My take: AI is useful for research and structure, but not for judgment. Especially in SEA, people can tell when a message is too polished or too generic.

So do you have the samne experience? Are AI-personalied emails still working for you, or are buyers starting to tune them out?

Been doing more prospecting across PH, ID, and SG lately, and curious how others are handling this. Cause I'm honestly confused.

Don't get me wrong finding companies is easy enough. But finding the actual decision maker is where it really gets messy.

Had a lot of bad expereinces so far. Like titles don't always translate cleanly across markets. Sometimes the "Head" isn't the final decision-maker. Then, sometimes founders are still involved, especially in PH. Sometimes the best contact is the person closest to the problem, not the most senior person. So it's a case-to-case basis.

Curious to know how you are handling this chaos? Cause it's becoming tedious and time-consuming for us

We ran into this pretty hard while building prospect lists across SG, PH, and MY.

At first, the data looked fine. Thousands of companies, job titles, emails, filters, all the usual stuff.

But once we actually used it, the cracks showed up.

In one list of around 2,000 prospects, roughly 25–30% of contacts were either outdated, too generic, or not the right role anymore. Bounce rate was manageable, but reply quality was bad because the targeting was off.

The frustrating part is that bad data makes every channel look worse.

Outbound looks like it’s failing. Ads look low quality. Sales follow-up feels messy. But sometimes the real issue is upstream: the list was never clean enough to begin with.

This was especially obvious with SMEs and more local industries. Global tools were helpful, but they missed a lot of local context like actual company size, active decision-makers, or industry fit.

After tightening the data and cutting the list down, volume dropped by almost 40%, but reply quality improved. We booked fewer random calls and had more conversations with people who actually fit the ICP.

My take: in SEA, more data isn’t the advantage. Usable, local, validated data is.

So how are you are handling this. What data sources or workflows are actually working for SEA prospecting right now?

Been in lead gen for 10 years no, and it's wild how different things are compared to before. A few years ago, decent data and decent outreach could still get you meetings. Now, buyers ignore anything that feels even slightly generic.

For us, broad cold outbound has been the biggest drop-off. Still orks when ICP is tight, but the old "build a big list and personalize lightly" approach feels pretty dead. So what stopped working for you this year?

AI is becoming impossible to ignore in lead gen. It helps with efficiency.

We've been testing it across targeting, scoring, list cleanup, and outreach drafts. Some parts genuinely help. Lead scoring and account research are faster. Outreach is easier to personalize at scale. Follow-ups are more consistent.

But I'm als seeing the downside. A lot of teams are using AI to send more emails, not better ones. Same generic pitch, just dressed up witha company name and a few personalized lines. From what I've seen, AI only improves lead gen when the inputs are clean like ICP, reliable data, good segmentation, strong offer, huma review stillmatters too. Without these, it just scales bad targeting.

So my take onAI. It's useful as the engine, but it shouldn't be the driver. It can speed up research and exection, but humans still need to decide who is worth reaching and why.

So I just want to know, how are you using AI right now inyour lead gen stack? And what are the results?

We tested predictive lead scoring for a B2B SaaS company selling into SG, PH, and ID.

The goal was simple: help sales figure out which leads to chase first.

At first, the scores looked smart, but sales didn’t fully trust them. Some “high intent” leads had clicked 3–4 emails or visited the pricing page, but were the wrong company size or not actual decision-makers.

After we cleaned the data and added sales feedback, it started working better.

Rough numbers:

• lead volume dropped by around 25%
• sales follow-up became faster
• booked meetings improved by around 15–20%
• fewer calls were wasted on bad-fit accounts

The biggest lesson was that scoring only works if the data used is good.

For SEA especially, weak company data can make the model look smarter than it is. Curious if others here are using predictive scoring. Did it actually improve conversion, or just make your CRM look more impressive?

I'm moving into a 24-day multi-channel sequence and would love to hear from anyone running similar high-precision models.

The Volume: 1,000 cold leads per month (receiving up to 4 emails each in the sequence).
The Strategy: Multithreading 2 decision-makers at each of the 500 companies to trigger internal conversation/social proof.
Personalisation: Every email is mapped to specific company triggers (acquisitions, budget shifts, new sites) and individual pain points.
The Stack: Using MillionVerifier/Prospeo/Scrubby/Mailtester for data hygiene, Instantly for email, and HeyReach for LinkedIn automation.
The Ask: A low-friction discovery call focusing on framework alignment rather than a hard sell.

The Question: Beyond the 80 warm leads (intent-based), what should I be seeing as a standard booking rate for the cold 1,000? I’m calculating based on a 1.5% conversion—is anyone seeing higher with a similar "surround" strategy and tight ICP? Is 1.5% more realistic for booked calls or reply rate?

One pattern that's become pretty clear lately: relying on a single channel for lead gen is getting less effective.

Cold email alone struggles without context. Ads along bring traffic but inconsistent quality. Content alone builds trust, but too slowly if you need pipeline now.

What's working better is when these channels are intentionally connected. But should still be in a corrdinated way.

- content builds initial trust and familiarity
- ads retarget and reinforce the message

- email converts that familiarity into conversations

- LinkedIn adds a human layer

We've seen cases where email reply rates improved without changing the copy, just because prospects had seen a post or ad before. So the shift is really about reducing coldness across touchpoints. Especially in SEA, where trust and familiarity play a bigger role in B2B decisions, this becomes even more important.

So it's really how you channels work together. So what are best channels working for you right now?