audience & revenue growth tips for B2B startups and creators

My mission is to build predictable pipelines for B2B startups. I share what I learn along the way. 

Read more if you are ready to build your pipeline 👇

💡 I am super relevant if you are:

→ Indie Hacker
→ Freelancer
→ Early Startup Founder
→ B2B Sales Person
→ Creator (any profession)

✍️ I write about:

→ B2B sales
→ Go to market strategies
→ Signal-based prospecting

Little more about me:

Cryptocurrency Investor. SaaS Enthusiast. Agriculture Business Owner. 

Lived in 3 countries.

I'm a T-shaped growth person who has experience in sales, content marketing, and growth.

Drop me a LinkedIn message if you need any help.

We will keep it simple.

Twitter: @sehaokudan

https://calendly.com/sehaokudan/30min

Seha Okudan

Helping B2B founders turn LinkedIn into deals | GTM Strategist

5 worst discovery questions:

5 difficult scenarios for salespeople:
(Even for the top ones.)

5 difficult scenarios for salespeople:

Stop trying to fake urgency in B2B. 
(it doesn't work)

𝗬𝗼𝘂 𝗰𝗮𝗻𝗻𝗼𝘁 𝗰𝗿𝗲𝗮𝘁𝗲 𝘂𝗿𝗴𝗲𝗻𝗰𝘆 𝘄𝗶𝘁𝗵𝗼𝘂𝘁:

→ Knowing pain
→ Understanding the impact of pain

𝗙𝗼𝗿 𝟮 𝗿𝗲𝗮𝘀𝗼𝗻𝘀:

1️⃣ Not every problem is worth solving

Trying to create an urgency won't work if they don't have something that is worth solving.

Nobody will buy a strong painkiller for a mild headache, even if it’s the last day of a 20% discount.

If the impact of the pain is close to zero, no urgency will stick.

2️⃣ A problem can have different solutions

Even if the pain is real, buyers still have options.

𝗧𝗵𝗲𝘆 𝗰𝗮𝗻:

•⁠ ⁠Build in-house
•⁠ ⁠Pick a cheaper workaround
•⁠ ⁠Wait until the budget frees up

Both a raincoat and an umbrella protect you from rain.

If you don’t show why your path is better, faster, or safer;

Urgency dies.

𝗧𝗟;𝗗𝗥

𝗧𝗼 𝗰𝗿𝗲𝗮𝘁𝗲 𝗿𝗲𝗮𝗹 𝘂𝗿𝗴𝗲𝗻𝗰𝘆:

-⁠ ⁠Find the pain that matters most
- ⁠Show the true cost of doing nothing
- ⁠Prove why solving it now beats later
- ⁠Position your solution as the safest path

Deals move when urgency is real. 

Not when it’s forced.

How do you create urgency without sounding pushy?

5 Unexpected Things in Sales
(hope they happen to you today)

5 Unexpected things in sales:

Contact data is out of control.
(for real)

Don't get me wrong.

I don't mind people pitching me.

I do it every day, too.

BUT

Every single week, some people:

- Add me to lists I never joined.
- Drop me into random waitlists.
- Email me again after I unsubscribe.
- Promise what no one can deliver.
- Sends emails written with poor prompts.
- Message me on WhatsApp out of nowhere.

I block them. They switch mailboxes.
I say, “I’m not a fit,” they keep texting.
I say it on the phone, they keep selling.

“I want to tell you about a promo…”
 “There’s a new offer…”

I don't want to click on report Spam.
I don't want to be rude.
I get it, we all try to make a living.

But this is not outreach.
This is not marketing.

This is SPAM.

Are some people ruining the industry?

Bad contact data can kill your business.
(real examples)

Your data provider is the most important part of your tech stack.
It is non-negotiable.

If your data is bad, you lose.
Simple as that.

𝗦𝘁𝗮𝗿𝘁𝘂𝗽 𝟭:
Picked the best provider, tested ICP coverage.
Checked accuracy and verified test emails.
Only had HubSpot + the data provider (not ideal).

𝗥𝗲𝘀𝘂𝗹𝘁𝘀:
Hit targets.
Scaled the team.
Added new tools later.

𝗦𝘁𝗮𝗿𝘁𝘂𝗽 𝟮:
Paid for expensive tool stacks for sequencing, etc.
Then, they selected the most cost effective data provider.

𝗥𝗲𝘀𝘂𝗹𝘁𝘀:
High bounce rates.
Burned mailboxes.
Low call connect rates.
The team missed targets.
The company started downsizing the sales team.

𝗠𝘆 𝘁𝗶𝗽𝘀:
1.  Build a sample list. Verify it.
2. Check coverage across your ICP + regions.
3. Choose tools that use waterfall enrichment.

If your data provider is wrong, nothing you do will work.
You cannot afford bad data.

How do you choose your contact data provider?

12 mistakes that cost you deals:

Global Data Office - Head of EY.ai Simulation Labs

Spearhead strategic development and market positioning of Tax AI - driving its adoption and success through innovation, education, and community.

Global Tax AI Labs - Strategy Lead, Simulations & Synthetic Data

CDP provides a platform where professionals can develop technical skills, ideate on ways to improve internal and client work processes, and build re-usable tools for anyone in the firm.

From cultivating an innovative mindset within the community to leading product development and market reach, the vision is to share the tools to automate repetitive work and have professionals focus on more purposeful, needle moving work.

Americas Innovation - Crowdsourcing Development Platform (CDP) Product Manager

Research and build innovative, robust, and flexible financial analysis and modeling solutions to provide insight into client decisions for strategic planning, investment valuation, financial forecasting, and M&A transactions.

Decision, Modeling & Analytics Senior Manager

The Real Estate Tax technical liaison with the Tax Technology and Transformation team on the development of the "next-generation" technology for the partnership/fund tax compliance process.

Led the way for the automation and redesign of numerous tax workpapers and processes for partnerships and corporations for multiple engagement teams.

Selected as the Senior Manager Sponsor for EY's counseling family -- providing emotional and work support, facilitating coaching and career development discussions, and organizing events for team bonding.

Real Estate Tax Senior Manager

Oversaw tax compliance and advisory engagements for both domestic and international real estate firms. 

Experience with federal, state and international tax issues, including year-end K-1 reporting, quarterly estimates/withholding, REIT compliance, REIT due diligence, and transactional planning in complex partnership structures.

Real Estate Tax Manager

Deloitte

Advised Fortune 500 companies worldwide regarding tax opportunities on inbound and outbound operations. 

Designed, collaborated, and reviewed various tailored tax models using large data sets to quantify and measure the impact of tax planning strategies on clients' tax profile. Modeling support ranged from complex tax attribute projects to M&A and global restructuring transactions.

International Tax - Quantitative Consulting Services Manager

Managed the development of the Integrated International Tax Reform Model to help companies quantify the potential impact of various tax reform provisions and its interplay, including transition tax, global intangible low-taxed income, foreign derived intangible income, base erosion and anti-abuse tax, and interest expense limitation. 

Developed a Global Funds Model focused on global real estate and infrastructure funds to help assess fund structuring alternatives, tax reform planning considerations (e.g., impact of 163(j), 100% expensing), and other tax structuring considerations (e.g., internal leverage, withholding leakage, exit considerations, etc.)

International Tax - Quantitative Consulting Services Senior

Manage and led staff on year end compliance process, which involves review of tax basis balance sheets and capital accounts, partner allocations in accordance with 704(b) regulations, review of partnership agreements for allocations, and Schedule K-1 reporting.

Selected by Directors and Partners to participate in various committees: (1) Quality and Risk Management Committee, (2) Campus Recruiting, (3) Google Guide and (4) Market Tax Technology Team.

Real Estate Senior Associate

Prepared and reviewed federal, international, state and local, partnership and corporate tax returns for clients in the Real Estate industry. She took the initiative to streamline repetitive, manual processes via automation using technologies such as Visual Basic and JavaScript.

Real Estate Tax Associate

Ernst & Young

National Tax Technology Group

Real Estate Tax Intern

Goldman Sachs

Federal Tax Intern

Empire State Development

Accounts Payable Intern

Macy's

Human Resource Staffing Intern

I Help Corporate Professionals Gain Career Clarity & Fulfillment | Founder @Amplify Your Home | Global AI Tax Copilot Strategist @EY

Don't be fooled by my appearance. Quiet, Asian, tiny girl.

In the last decade, I've...

Successfully navigated the corporate giants: Goldman Sachs, PwC, Deloitte and EY.

Built communities, tax and financial models, and automations. (Excel wizard here)

Empowered employees to own their damn careers,

Challenge their norms to do something special.

That's when Problems become Opportunities,

Strategies into Actions.

You are not Alone.

Ready to redefine your career? Let's make it happen.

Mary Fung

Everyone’s racing to adopt AI.
No one’s stopping to ask what it’s supposed to mean.

The irony?
AI won’t save broken systems. It’ll just expose them faster.

The question isn’t “How fast can we adopt AI?”
It’s “What problem is worth automating in the first place?”

Own Your Damn Career | Founder @Amplify Your Home | Head of Synthetic Data & Simulations @EY

Every company has them - the quiet ones.
They don’t ask for credit.
They just make things work.

You don’t notice them until something breaks.
Until their 3 a.m. commits stop showing up.
Until the system they held together starts to fall apart.

We built performance reviews to seem fair.
But they reward visibility over value.
The loud over the loyal.

And when the quiet ones finally leave, it’s not anger - it’s realization.
They see the system for what it is:
One that measures what’s seen, not what matters.

If you’re a leader, your job isn’t to spot the performers who shout.
It’s to notice the ones who never need to.

The next advantage won’t come from the smartest AI.

It’ll come from the people who can translate intelligence into systems that actually move money, people, and data.

The ones who know how the business really runs -
how approvals flow, how reconciliations break, how decisions actually get made.

AI isn’t missing potential.
It’s missing context.

I keep banging the drum on this one.
Business folks are just as - if not more - important than data scientists in AI.

Because it doesn’t matter how good your model is
if no one in the room actually knows what “good” looks like.

Most orgs don’t have an AI problem.
They have a domain judgment problem.

The people who actually know their craft - the ones who can look at a model’s output and say “that’s wrong” without a dashboard -
are the ones who’ll make AI useful.

You don’t need more data scientists.
You need more people who know what they’re doing.

AI is taking accounting jobs.
But not his.

When layoffs started, he didn’t polish his resume -
he opened ChatGPT.

Now he’s “vibe coding” — building apps, automations, and tools that replace the work he used to do manually.
No computer science degree. Just curiosity.

He didn’t wait for AI to take his job.
He learned to take AI’s job for himself.

AI isn’t taking people’s jobs. It’s deleting the entry point.

A Harvard study found that when companies start using AI, they don’t usually fire people -
they just stop hiring beginners.

So the seniors stay.
But no one new comes in.

That might sound smart in the short term,
but it’s a slow disaster - because those “junior” roles were how people learned the craft.

If no one learns the basics,
who becomes the next expert?

AI didn’t remove opportunity.
It removed training disguised as busywork.

Now companies have to build real ways for people to learn...
or they’ll run out of people who actually know what they’re doing.

This is the shift no one wants to admit:

Automation isn’t killing careers.
It’s killing bureaucracy.

The people who’ll thrive aren’t the ones defending old roles.

They are the ones rebuilding new ones around intelligence, not repetition.

The question isn’t “Will AI replace us?”

It’s “Are we doing work that deserves to survive?”

Every company has both:

People who follow processes.
And people who question, rework, rebuild.

Before AI, that difference was subtle.
Now it's blindingly obvious.

Because AI gives leverage.
But only to those who know how to use it.

So the gap grows.

The followers wait for instructions.
The builders get 10x output, quietly and consistently.

This isn't about who's smarter.
It's about who's playing offense with new tools.

Ask better questions.
Builder sharper scaffolding.
And use AI to do your thinking, not replace it.


https://lnkd.in/eb-ycK-e

Woohoo, I made it in the hype video

Life goal accomplished!

It's almost time for #MSIgnite!  Will you be joining us on the show floor or in the #EYFrontierZone where you can dive into proven AI strategies, learn more about the #TaxDepartmentoftheFuture, and hear from #EY and #Microsoft leaders?  Learn more here https://lnkd.in/eRKECYDq 
#EYMicrosoft #AgenticEnterprise #AgenticFrontier #Tax
Colleen Sebra Christopher Aiken Daren Campbell Justin Shafer Ivan Roussev Suzi Russell-Gilford Coskun Cavusoglu Jon Cisler Phillip Mazzie Kristen Matthews, MBA Rory Moore Rebecca Paterson Jessica Maher Dhivya K

Accenture spent $2.3B - not to grow, but to shed.
To move faster. To rebuild around what’s next.

That’s what bold looks like.

Meanwhile, half of the enterprise world is still in pilot purgatory -
writing decks about “AI strategy” while the future ships without them.

You can’t plan your way into disruption.
You act your way into it.

The risk isn’t doing too much too soon.
It’s thinking you still have time.

If your job is just tasks, you’re not an employee.
You’re a cog.

That’s what Jensen meant when he said:
“Your job has to be more than the task.”

Because the task is the part AI eats first.
The checklist.
The template.
The handoff.
The thing anyone could do with enough time and instructions.

If that’s your identity at work, you won’t survive this wave -
not because you're not smart,
but because the system never asked you to be anything more.

AI exposes that.

It shows you exactly where you’ve been replaceable all along.

The future belongs to the people who stop being the task and start being the reason the task exists.

People pleasing looks helpful.

But it kills clarity in AI work.

❌ “Sure, we can try that too.”
❌ “Let’s just keep both versions.”
❌ “Whatever works for you.”

You end up with no ownership, no standards and a bloated mess of tools.

Saying “no” is part of building.
So is being disliked sometimes.

Good AI work is useful, not agreeable.



https://lnkd.in/eQcfDTDH

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The Battle

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