Emilia Korczynska

Userpilot

VP of Marketing

Doing all it takes to get more Product People over the code barrier with our code-free Product Adoption, Marketing & Experimentation Platform:  
Designing, developing and implementing a marketing strategy that ensures higher visibility 
Leading our stellar content team 
Implementing and driving a paid ads strategy to diversify customer acquisition. 
Driving funnel growth
Implementing an email marketing strategy 
Identifing and testing new acquisition channels
Overseeing, training, and coaching team members

Head Of Marketing

Managing content operations for a fast-growing B2B SaaS company - Product Experimentation and Marketing Tool for Product Teams: 
- Content planning, SOP development, SEO keyword research and topic ideation 
- Content editing and creation (blogs, website copy, social media content, ebooks, landing pages) 
- Community building (esp. Facebook - growing Product Growth & Retention Group, and LinkedIn) 
- Partnership management (guest posting, co-promotion)
- Writing weekly newsletters and developing email marketing workflows
- Organizing Product Drive - an annual online conference for Product People  (first edition: 2200+ participants)
- Data Analysis and Reporting (Google Analytics)

Marketing Manager

Wisemont Language Services

Boutique language agency helping SMEs sell more through great content on international markets

- Content marketing: translating, localisation and copywriting of SEO blog content, articles, website copy, newsletters etc. 
- International Social Media management 

Responsibilities:
- Developing and marketing new service packages and new in-formation products 
- Supervising development of content marketing strategies and SEO content for our clients that bring results – e.g.  ranking on 1st page in Google for competitive keywords, growing Twitter following to 3500 in 4 months

Owner

Postfity.com

- Helping more startups and small businesses hear about the most affordable social media scheduler designed specially for SMBs through: 
- creating a documented content marketing strategy, planning paid and organic marketing activities (content resharing, social media listening using Brand24, email marketing), setting and monitoring KPIs, measuring results using Google Analytics and Social Media Insights 
- hiring and managing marketing professionals: Social Media Managers, Copywriters, Facebook Ads Specialists, Google Ads Experts, Graphic Designers
- setting up and optimising email marketing drip campaigns
- doing Competition and Keyword Research, optimising blog content for search engines, creating new blog content optimised for low-difficulty keywords
- A/B testing Facebook ad campaigns
-  managing the marketing budget

Marketing Strategist

Find in China

Find in China is an online directory of local services, venues and events on a map in English designed for the expat population in China. Together with other local websites for expats we are creating the largest database of information and socialising opportunities for foreigners in China, as well as  a nationwide discount card. [Acquired October 2018] 
Responsibilities:
- Designing a web-app - map-based directory of venues, ser-vices and events in China
- Front-end project management – liaising with the front-end developer to produce the desired results in terms of design, functionality and user-experience, and with the back-end pro-ject manager 
- Business development and general management -  searching for joint ventures and collaborations to grow our database of point of sales and events quickly (we launched with over 1000 POSs across China), social media marketing

Co-Founder and CEO

Birmingham City University

Leading an in-country presessional course for students of Nanjing University of Science and Technology 
Teaching Academic Study Skills

ICP Visiting Lecturer

Swansea University

Teaching (Polish) Translation and Interpreting MA students:
- Module: Advanced Translation) 
- Module: Public Service Interpreting: Local Government Option
Supervision: Translator's Tools 
Material, test and examinations development

Part-time Translation and Interpreting Tutor

Runner's Delight

Runner's Delight was an innovative brand of functional porridge designed especially for runners. 
Responsibilities: 
- Designing and launching a sports nutrition product for runners
- Assisting in branding, social media and influencer marketing

Co-Founder and product manager

PayPal

EMEA Collections Specialist

Stream Universe TV

- managing relations with contributors: 
- contacting influential bloggers attracting large numbers of followers with commissions for film / TV series reviews and content writing;
- providing writers with updates on new releases, writing opportunities, etc.
- keeping  submission deadlines

Account Manager

The Cambridge Student

- writing a weekly series of satirical columns

Columnist

Passionate about SaaS product growth, and both the pre-sign up and post-sign up marketing. Talk to me about improving your acquisition, activation and retention strategy. 


2 years ago I thought LinkedIn ads were a complete waste of money. Any “attempts” we made didn’t bring any results…now our ABM program is just over 1 year old, runs 100% on LinkedIn, and has brought in multiple 7 figures in pipeline & very soon 7 figures in revenue…

What changed? 

☠️ We used to spend $1500-2000 per month on “experiments”, and measured results by…demos! 🤦‍♀️ that we expected to drive directly, and almost instantly, from a few random ‘we’re so great, book a demo’ ads (with not enough budget to even get enough clicks to the website, which we didn’t even measure).

🍀 Now we know a) Linkedin is just a content distribution channel, yes, it’s pay to play (but you pay to have a pretty darn good targeting) - but ultimately, you’re building awareness through good content.
You need to resonate with the pain, show different benefits of your product, social proof, etc. enough times 

☠️ We didn’t have target account lists. We just used broad targeting by job titles. 

🍀 Now we know 50% of the success is building the right target account list, and spent a lot of time tweaking the SAM criteria, and use Clay, Builtwith, Apify, Wappalizer etc. to build our lists based on intent signals. We use almost exclusively matched audiences. 

☠️ Our goal was to “generate leads” - “MQLs” - we didn’t look at leading engagement metrics at all. We didn’t look at the correlation between which company saw our ads in which campaign - we only looked  

🍀 Now we look at “campaign influence” - the deals (and pipeline / $ spent) from accounts that saw/engaged with our ads enough time BEFORE the deal was opened in ZenABM (my husband's startup, so do sign up for free 😜 https://lnkd.in/dp-Ym6Wj) We have account stages and monitor progression through them on weekly basis (and velocity). We know that it takes 100s of impressions, not a single "last touch" click, to get someone to book a demo 😉

☠️ We looked at LinkedIn as an isolated channel. We didn’t do any email follow up, any re-targeting campaigns, we didn’t target the same lists on different channels. 

🍀 Now we run email (with Smartlead) & Linkedin DMs (with HeyReach.io) follow up campaigns (planning to send 32k+ emails per month), personalised with hundreds of different intent signals (including intent we’ve gotten from LinkedIn ads, but not only). We’re splitting the audience between cold and warm. We’re enriching retargeting. We’re experimenting with reddit ads, and planning to look into other channels too.

☠️ I don’t even wanna know what we did in terms of campaign settings (objectives, goals, audience expansion…all the booby traps LinkedIn has in store for inexperienced marketers)

VP of Marketing @Userpilot. Author @ Product Rantz.

Every marketer will tell you the same thing “We have enough data. We don’t have enough insights.” Especially at the end of the year, when we’re all going into “reporting mode” - we’re drowning in data and desperately trying to make sense of it, to plan our 2026 budgets. 

AI should change that. 

This is why I’m so proud that Michal Jackowski is launching Zena - LinkedIn Ads AI Analyst today on Product Hunt - please check out the launch here: https://zenabm.com/ph

Zena answers all your questions about your LinkedIn Ads performance, your ABM campaigns, your target companies’ engagements with your ads *AND* the impact of your campaigns on revenue and pipeline. 

TL;DR - my husband has been working very hard on making it happen for almost a year, and I’ve been the harshest critic ;D I rarely like anything he does that doesn’t involve pasta - and I wouldn’t be sharing this launch with you if I didn’t think this is a game changer for reporting on ABM Campaign performance through LinkedIn ads. I’ve been feeling all the pain of doing this manually myself and this product is made of my tears (proverbially, of course :D) 

How does it work? Zena combines data from 30+ LinkedIn ads API endpoints with data from your CRM, and processed data from ZenABM (that already combines all the above data sources into one single source of truth, and calculates account engagement scores, ABM stages etc.) 

Here are the types of questions you can ask Zena about your LinkedIn ad and ABM performance data: 

 🟢 Ask Zena about your top performing ads, and ad formats - which had the highest number of clicks & CTR. 
🟢  Ask Zena to get you a list of your most engaged companies in different timeframes by clicks, impressions etc. AND by inventory type (image, TLA, chat).
🟢 Ask Zena which of your ad campaigns are driving pipeline, and which are only wasting budget. 
🟢  Ask Zena about the impact of your ABM campaigns on your funnel and TAM - how companies you’re targetting moved stages month over month, how much you’ve spent on them etc.
🟢  Ask Zena which of your ABM campaigns (and LinkedIn Campaigns) had the highest impact on revenue e.g. this vs. last month. 
🟢  Ask Zena about your ICP’s engagements - which job titles engaged most with your campaigns - month by month.

Getting all these questions answered just by looking at LinkedIn Campaign’s Manager or my Hubspot dashboards would take me ages - now I can get them answered in seconds, with beautiful reports generated by Zena.  

P.S. You can try Zena for free - you get 10 chats with Zena in ZenABM’s free trial - perfect for your end of the year reporting.

Someone (from the tech industry) asked me where I worked and what Userpilot was this weekend. And for the first time (hold my coffee), without thinking much, I responded: it’s a “Lovable for Product Adoption”. I know it’s a bold statement…and a complement for Lovable for becoming a household name for the category of no-code software in such a short time. And I know - sadly, no matter how much I bend myself over backwards here - it will never be as popular - because product adoption, unlike acquisition (which I have a hunch Lovable is mostly used for) is still “Cinderealla’s ugly cousin”. 

But here’s the irony - as the entry barriers to shipping software are getting lower and lower with Lovables of the world (and the competition is getting more and more cutthroat) ease of use and Time to Value are the strongest factors in determining whether someone will continue using your product, or start shopping around again at the slightest hint of friction. Nobody’s reading help docs anymore. You either provide instant answers in-app, or the users leave.

The irony why perhaps product adoption software hasn’t been that popular is also because the legacy solutions in the industry were notoriously difficult to use (iykyk).

The popularity of Lovable lies in a) how easy it is to use b) how fast it allows non-technical users to ship market-acceptable assets they could never dream of producing without a designer and a developer. “Acceptable” is better than “perfect” - because the difference between acceptable and perfect, in my experience, is exactly the same as “done” vs. “Not done”. 

👉 Yes, you could hard-code your onboarding experiences. But you know you won’t.
👉 Yes, you can use the legacy product adoption platforms…but that’s the modality of the word ‘can’. If you can’t ship what you need in < a few hours without developers’ / designers’ help - you won’t.
👉 You could buy 5 different solutions for lifecycle email, in-app communications, and product analytics - but you know the data will be falling through the cracks and you don’t use it consistently between the different tools. Your use communication will be broken. 

So give Userpilot a try for 30 days - we’ll support you with our new “Product Growth Jumpstart 2026” program (Chat to our product Growth Expert: https://lnkd.in/duKmW2hY to participate) to help you make most out of your free trial, and demonstrate to you how much more product-led growth (not only in terms of new acquisition, but also expansion) your team can produce if it’s empowered to engage users and build in-app product onboarding & adoption experiences truly on their own, with data available on their fingertips.

Open article: 15 Minute Meeting - Kelli Truelove by calendly.com

Idk if it’s perception bias but I see everyone dunking on attribution here. But I believe it’s because we’ve perverted attribution. It’s almost turned into “performative action”. Every week we’re spending hours and hours poring over our channel performance, trying to figure out how we can squeeze more last-touch conversions out of a channel by tweaking things to hit quota within budget, while at the same time believing it’s largely meaningless - because 99% of the time it’s not the single touchpoint that made someone book a demo or buy. As Ashley Faus wrote recently - you don’t know if it was the first touch, last touch, or the 57th touch that made you marry your husband ( no pun intended 🤣 ). It all starts to boil down to CRM credit. It becomes theatre.

Hot take: Attribution shouldn’t be about determining if you should spend more $ on Google Ads or LinkedIn ads based on last touch conversions or initial traffic source.

But there’s one super-important question that we should answer - and if we were playing with our own money, not some sort of marketing-budget-monopoly-money-: we’d want to answer it ASAP: 

Which channels don’t make any sense to spend your $$$ on? 

E.g. They may be there, as a touchpoint - but that touchpoint just simply doesn't matter, and you're throwing $10k per month on it.

If you turned off Google Ads/ LinkedIn Ads/ Reddit/ Stopped posting on LinkedIn/ Stopped running webinars/ events today - would your pipeline stay the same? 

Might it be that a lot of your last-touch conversions from your branded cold search ads are just taking a lazy “shortcut” to your homepage, and would have converted anyway from organic? 

I’m not good enough at statistics (the class was at 8 am on Friday…) but it sounds like we should be doing multiple regression analysis on cohorts of SQLs, taking out different channels and seeing what happens, like with a food allergy test. 

But we wouldn’t dare. And we probably couldn't figure it out anyway, because dark social yadda yadda, and because...most people themselves don't even know what made them buy. 

Like - we’ve been running certain campaigns since before I even joined Userpilot (which was before covid…yes, I’m that ancient). We never dared to stop. 

What would happen if we did? 

But then we started running LinkedIn ads only ~14 months ago, and see an increase in conversions from the target lists vs “control group.” 

It’s easier to see because they are defined. But with inbound? 

Is it all trial and error? Is there no way out? 

I’m genuinely curious how you folks make peace with attribution and what tools you use to inform your decisions how much $$$ to put in each channel and when to stop 😅

Most people know whether they are "just dating" someone or "in a relationship". Except for my cousin. She doesn't know. Meanwhile - most Product Managers I talk to are like my cousin. They don't know if the features they shipped have been "activated" or "adopted" - and what % of their users have done the latter. And how to measure it. 

If your KPI is retention - this is something you need to measure (and improve). 

Cover photo credit to Vrinda Tyagi, whose Medium post (link in the article) inspired my Product Rant today!

Feature activation is not the same as engagement, and not the same as adoption. Most people know whether they are "just dating" someone or "in a relat

Open article: 🤔 📈 Are they in a relationship or not?  by Emilia Korczynska • 3 min read

🤔 📈 Are they in a relationship or not?

After George tagged me in his post about which of his 2025 “marketing projections” panned out and which didn’t - I had a strong feeling of deja vu. At the start of 2025 - we were all betting on “original, thought-leadership content” as an antidote to “AI slop” and organic traffic decline. I’ve announced our blog is dead - long live the PITT - Product Innovation Think Tank. 

Well, turns out the PITT lived just for a short 6 months. And that “thought leadership” is extremely hard to scale. I’ve tried so many times, in so many ways:

 A) 2021 - hiring “subject matter experts” in-house - something that even OpenAI (how ironic) is trying to do now (check out Kyle Poyar’s recent post about it - in the comments). It was a disaster; they all quit within 3 months - turns out thought leaders don’t want to mass-produce thought leadership to OKRs. I know of maybe one company that has done it successfully (Ahrefs) - so I wonder how that pans out for OpenAI. We say 'a star is born', after all, not made. 

B) 2023-24 - partnerships & sponsorships - like herding cats and playing one-armed bandit, was just burning cash while creating a disproportionate amount of nuisance for our demand gen team to manage;

C) 2025 - Content syndication in an “owned” online magazine + boosting LinkedIn posts based on the original content with Thought Leader ads. You can’t just find more Akashs Guptas, Kyle Poyars, or Maja Vojes to keep playing that game over time. Plus - getting the influencers to agree to participating in a program like that is art. So after a strong start, the quality just kept going downhill, and we fizzled out after a few months. 

No, onto what has worked, so far, ish:

ABM - moving from a purely inbound motion to building an account-based marketing motion has worked for us - driving a significant % of the pipeline every month. BUT - because of all of the above, we’re really only just capturing demand. We didn’t manage to create this exceptional program full of amazing content that would really “generate demand” (some people are going to tell me I believe in Santa in the comments - fair enough, I hear your point. As we all know, nothing exists in marketing anyway ;)) 

“SEO is dead” - well, that was my biggest “positive disappointment” - turns out both SEO and link building still exist, and have respawned as a whole new channel - AI referrals (and no, we haven’t changed much about how we do SEO). 

How has your 2025 been? Different than you expected? 😉

If (as I wrote last week) - most PMs don't know how the "regular" features are performing and whether they have been adopted by their users - imagine what it's like with AI features...

Hot take: digital products ship AI features faster than the users are adopting them: 

We’re great at signing contracts, we’re terrible at changing habits.
Adopting AI doesn't mean using AI. 

This is probably the most uncomfortable truth:

AI is not failing technologically - it’s failing behaviorally.

If you look at enterprise adoption, you see the same curve we see in product teams adopting new analytics tools:

💠 Excitement (“this will change everything”)
💠 Procurement
💠 Pilot
💠 Two power users 
💠 Everybody else goes back to Google Docs
💠 Leadership says adoption is low
🫠 Tool gets replaced next year

Why? 👇

If (as I wrote last week) - most PMs don't know how the "regular" features are performing and whether they have been adopted by their users - imagine 

Open article: AI is not failing technologically — it’s failing behaviorally. by Emilia Korczynska • 2 min read

AI is not failing technologically — it’s failing behaviorally.

Rovio Entertainment Corporation

Senior Data Analyst, Ads

Aiven

Senior Data Scientist

As the sole data hire in Aiven's product-led growth function, I specialize in user journey analysis. This includes research on how users are acquired, what makes a successful product trial, and what kinds of behavioral patterns emerge at various stages.

• User journey & growth: Provided strategic recommendations aimed at enhancing user experience and acquisition. Insights based on customer clustering, time-to-value modeling, and statistical examination of the most significant behaviors and touchpoints related to conversion.

• Product trial logic: Rebuilt the business logic on trials and all related models and measurements through data auditing and in-depth historical research using probability analysis, statistical modeling, and unsupervised ML.

• Marketing data visibility: Maximized visibility into end-to-end user journeys by combining probabilistic and deterministic methods for anonymous event stitching. Increased Marketing attributability rate in user acquisitions (previously tied to cookie acceptance) from approximately 64% to 97%.

• Cost optimization: Saved $6000+ in monthly BigQuery costs through code optimization with zero feature loss. Identified pervasive dashboard cost/performance issues and outlined optimization strategies.

• Automation: Set up daily detection and Slack alerting of anomalous product usage behavior by customers. Created Python notebooks to automate monthly KPI analyses presented to the Marketing leadership team.

• Enablement & coaching: Modeled data to enable self-serve product analytics in PostHog. Conducted learning sessions for the central and marketing data teams on a range of technical topics such as EDA best practices, query parameterization, data pipeline optimization, A/B testing, and clustering.

Data Scientist, Product-Led Growth

Smartly.io

• Customer analytics: Identified the top predictors of half-year customer churn through feature engineering and statistical analysis. Created a multifaceted "customer health score" daily metric and dashboard.

• Analytics engineering: Built a scalable "feature adoption" data pipeline for transforming data on all current and future product features from end-to-end in a programmatic way.

• Data integration: Handled migration of all customer data during a change in CS tooling and engineered 150+ new metrics. Integrated account- and contact-level data across Snowflake and Salesforce.

Data Analyst - Customer Analytics

Aalto University

• Research: Conducted Academy of Finland- and NSF-funded research projects. Performed randomized experiments, collected data via web scraping, analyzed data with R, and wrote journal articles.
• Teaching: Facilitated a graduate course on quantitative research. Redesigned the course to a fully online format in 2020. Supervised 5 master’s thesis students and supported them to timely degree completion.
• Service: Presented at research seminars, delivered guest lectures, and peer-reviewed articles.

Research Scientist

Ames IT and Numeric Solutions

• Managed projects related to web analytics, data collection, and data mining. 
• Developed project milestones, coordinated resource allocation, and evaluated performance.

Project Manager

George Washington University

• Collected, cleaned, and analyzed productivity data from over 86,000 scientists for dissertation research. 
• Published articles and presented research at leading business journals and conferences.
• Delivered lectures at the undergraduate, doctoral, and executive levels.
• Chaired the virtual teams research symposium at the 2016 Academy of Management global conference.
• Presented at research seminars, reviewed conference papers, and performed miscellaneous service duties for the university and academy.

Ph.D. Candidate

The University of Auckland

• Used factor analysis to refine survey measures over several iterations and improve research validity.
• Conducted undergraduate discussion sessions and assisted with curriculum design.

Graduate Research And Teaching Assistant

My passion is using data to solve problems. I like building robust and scalable end-to-end solutions with great documentation. Trained in analytics with a background in quantitative research and university teaching, I have 10+ years of experience drawing insights from data and communicating results in digestible ways.

Analytical skills
• Applied use cases: Behavioral segmentation, A/B testing, anomaly detection, churn prediction, customer health scoring, time-to-value estimation, user journey analyses
• Statistical techniques: Generalized linear and additive models, multilevel modeling, factor analysis, structural equation modeling, experimental design, bootstrapping, Monte Carlo methods
• Machine learning techniques: Supervised learning, clustering, multi-armed bandit algorithms, Bayesian optimization

Technical tools
• Modeling: SQL, Git, dbt, Jinja
• Analysis: Python (sklearn, xgboost, pandas, seaborn, scipy), R (dplyr, ggplot2, lavaan)
• Visualization: Tableau, plotly Dash, Sisense/Periscope, Redash, Apache Superset
• Warehousing: BigQuery, Snowflake, PostgreSQL
• Tracking: Snowplow, Segment
• Integration: Fivetran, Hightouch, Census
• Notebooks: JupyterLab, Deepnote, Hex, Colaboratory

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Emilia Korczynska

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The Battle

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Performance Score.css-goub6s{font-size:var(--chakra-fontSizes-md);font-weight:500;color:var(--chakra-colors-gray-600);display:block;margin-top:var(--chakra-space-1);}Emilia Korczynska's LinkedIn Performance

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