One common way to recover revenue is to offer discounts through automated email campaigns. In general, I’m not a big fan of discounts. But after seeing companies make an extra 15–20%, I’m willing to reconsider.

From seeing successful setups, there are 3 principles we need to follow:

• Timing

• Urgency

• Bargaining model

Let’s look at each of them.

Timing

Finding the right timing is extremely important.

Offering a discount too soon reduces the value of the product. It tells prospects that our product is not worth the original price and that the real price is much lower.

On the other hand, sending the first email too late leads to lower conversions. After all, our product was not important enough in their life to convert straight away. As time goes, their priorities shift and our product loses relevance.

While the best timing requires experimentation in each case, a good starting point is 24 hours after they drop from the funnel. At that moment, they still feel the struggle that brought them to our product, and the offer does not affect perceived value as much.

Urgency

Adding urgency to your offer is powerful – people do not like missing a good deal. To do this at scale for every new lead, you need the right automation:

Step 1: Add a time limit to your offer – e.g., valid for 2 days.
Step 2: Use a platform that provides email countdown timers. When someone opens the email, they see the clock ticking.
Step 3: Generate individual promo codes in your payment provider and apply the same time limit to them.

Step 3 is not required to launch the campaigns, but it helps in two ways:

• Promo codes won’t get leaked.

• People who miss the offer can clearly see that your deadline was real.

Bargaining model

Sending a single email with a discount gives only partial results. To make the system work, we need a sequence of offers, delivered with delays.

In his book Never Split the Difference, Chris Voss describes a bargaining technique called the Ackerman Model. Offering a sequence of discounts is a form of price bargaining, so we can use the same structure.

The idea is simple:
Choose the maximum discount you are willing to offer. Then calculate increments based on four steps: 65%, 85%, 95%, and 100% of the maximum.

Let’s say our maximum discount is 50%. The sequence might look like this:

• Email 1 (after 24 hours): Offer 32% (50% × 65%)

• Email 2 (after a few days): Offer 42% (~85% × 50%)

• Email 3 (after a few more days): Offer 47% (~95% × 50%)

• Email 4 (final offer): Offer 50% (it’s important to highlight the offer is final)

Following this model achieves a few things:

• We get more revenue from people who convert early, instead of giving everyone a 50% discount.

• The decreasing size of the increments convince prospects they are getting the best deal possible. By the final email, they feel they received the maximum offer.

This extra 15% will not double your business overnight. But it may be exactly what you need to hit your ROI goals and scale ad spend to the next level.

These cats found their way to get extra 15%.

As described in the previous issue, Defining business model, to relaunch the product within my constraints I’ll have to sell high-tier subscriptions only – annual or lifetime. This achieves two things – it helps me get ad spend back quickly and keeps the experimentation cycle short.

Not everyone is ready to commit to an expensive subscription though. I need people who are motivated enough. I need a high-intent audience.

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Marketing funnel

According to Jobs-to-be-Done, people go through several stages in their purchasing journey:

• Stage 1, Unawareness. They are unaware that they need a new product. That’s the lowest-intent audience.

• Trigger 1. Some events in their life prompt them to start looking for an upgrade and move them to the next stage.

• Stage 2, Passive Exploration. At this stage people start noticing relevant ads, doing some initial research, checking out websites.

• Trigger 2. Another event in their life pushes them to start actively looking for a solution.

• Stage 3, Active Search. These people are usually ready to make a purchase right away if they find a suitable solution. That’s the highest-intent audience.

We can also think of these stages as a funnel:

Defining broad audience

Now, let’s see how I can apply the same funnel to my product.

As a first step, I need to broadly define my target audience overall. If I want my message to resonate, people at stage 1 cannot be everyone (at least, if you’re not Coca-Cola).

My app is most useful to people in leadership positions – managers, team leads, and so on. People who own their time and need a structured approach to daily prioritization. This is a broad enough market with very distinguishable characteristics – chaotic days, a lot of meetings, changing priorities.

So, that’s my stage 1 audience – people in leadership roles.

Narrowing down to high intent

Usually, leaders are a difficult audience to sell to. They have tried many tools, built strong habits, and are very reluctant to change them. To narrow it down to a high-intent audience, I need to catch them at a moment when they are most ready for change.

Based on my personal experience and some user interviews, there are several common triggers:

• Stepping into a leadership role. People who have just become managers or team leads, especially for the first time, are most ready to change their behaviour. The nature of their work changes completely. New requirements and stress crush their existing productivity habits. They are ready to try a new tool and are even actively looking for it.

• Personal events. A real story from one of the user interviews: “I was working hard at my job. Alongside that, I was finishing my MBA. Everything was under control, but in the middle of it I learned that I was pregnant and realized my simple todo list could no longer cope with that.”

• Increased workload. Another variation could be starting a personal project, getting promoted to the next level, or switching careers.

Let’s sum it up

• Audience overall – people in leadership roles

• High-intent audience:

  • New leaders or managers

  • Experienced leaders who go through life-changing personal events (such as becoming a parent)

  • Leaders who experience an increase in workload – recent promotion, starting a business, and so on

Now, as I’ve identified the high-intent audience, I can start working on ads.

But before I move there, I need to define a couple of attributes that would make my product stand out and resonate with this target audience. These attributes will become the core of my marketing message and offer. To sell high-tier subscriptions, I need to make my product one of a kind, even if it’s a boring todo app. More on this in the next article.

These cats look at you as their target audience.

Four years ago, I tried to launch my own product and failed – mostly because I didn’t know much about growth and marketing. A couple of years later, almost by accident, I started working on growth from the engineering side and found my passion there.

Now I want to test how much I’ve learned. I’m going to take the same product and grow it to $1M ARR. The most boring product in the most crowded market – a todo app in the productivity space – feels like the best way to test my knowledge.

Let’s see if I can make it.

First, let’s start with the business model.

Four years ago it sounded like some magic phrase and I was lost when asked about it. “I’m going to make a good product, people will come and pay” – that was pretty much my understanding of this concept. I didn’t know much back then.

In a nutshell, a business in a classic sense is a money-printing machine. You put in one dollar, and it prints you back two. The business model captures the repeatable process behind the scenes – what exactly happens when we put in that dollar and how it makes another one.

With my todo app, I could just say it’s going to be a subscription-based business, SaaS. But to make the business model useful, I need more details than that.

So, let’s work it out from our constraints:

• It’s a side project for me and my co-founder, so we don’t have a lot of time.

• We don’t have any funding apart from relatively small amounts we’re willing to spend personally.

• We don’t have any influence in the productivity space and can’t attract users through it.

These constraints leave us only a couple of options:

• Work with influencers via affiliate programs by offering them a cut from each sale.

• Or run ads.

Affiliate programs might be a great option. But they take time to kick off and are tricky to run successfully – from fraud prevention to finding the right affiliates. Neither my co-founder nor I have any experience doing it.

But we do know how to run ads.

However, to run ads successfully within our constraints, we need to play it smart.

As we don’t have any funding, we need to make our ad spend back as quickly as possible – ideally within a few weeks. This means we’re going to sell high-tier subscriptions only – either annual or lifetime. Also, to move fast when experimenting with ads, we need to keep our sales cycle short – a very short trial or no trial period at all.

Let’s sum it up:

• It’s a subscription-based model.

• We’re going to run ads to attract users.

• We should get our ad spend back in a matter of weeks, plus some margin.

• To achieve that, we’ll need to sell only expensive tiers – annual or lifetime.

• To enable quick experimentation, we also have to keep the sales cycle tight – we’ll start with a 3-day trial.

To make this work, we need the right audience.

Not just any potential customers, but high-intent customers – who are motivated enough to pay for an annual subscription with a very short trial period. In the next issue, I’ll show how we’re going to find them.

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This cat is curious about the results as well.

In practice, it was based on 3 key metrics – to double the business, we needed to double our revenue, profit, and monthly active users. Having this triangle of metrics was essential to protect the scheme from gaming. We couldn’t double revenue by bringing low-quality users. We couldn’t spend all our profit on marketing and ignore margins. And we couldn’t simply squeeze more money from existing customers – the user base wouldn’t grow. We needed to grow the business sustainably.

That was the first time I saw the concept of guardrail metrics put into practice at such a scale.

In an ideal world, we could just give people a single goal and they would focus on achieving it.

Reality is more nuanced.

According to Goodhart’s Law – “when a measure becomes a target, it ceases to be a good measure.” This means that over-optimization of any individual metric makes things worse as a whole.

This is especially true at the department level. With multiple department silos, people tend to optimize for their local maximum. Product teams focus on retention without paying enough attention to monetization. The marketing team focuses on bringing new users and initial conversions, without taking into account the long-term performance of these users. Top-of-the-funnel teams focus on initial conversions. As a result, the whole system doesn’t work well.

This is where guardrail metrics become important.

Whenever we set a target metric for a department, we need to think of a few common ways this metric can be gamed. Not necessarily with bad intentions – just over-optimized.

Let’s look at a few examples in growth.

Performance marketing

The most common goal of a marketing team is to scale the spend. Usually, we want to invest in growth as much as possible if it’s profitable. To ensure profitability, we want to keep our cost-per-action under control (usually, the cost of acquiring a paying customer). That becomes our first guardrail metric.

Another metric is often overlooked – audience fit. In performance marketing, it’s quite easy to come up with an ad that doesn’t bring the right audience for our product. Especially if people convert before they see the actual product – through landing pages and onboarding quizzes. As a result, people go through our funnel, make an initial purchase or activate a trial, and then discover that the ad promised them something completely different. They cancel their subscription and never come back. I’ve seen cases where the 24-hour cancellation rate differs between creatives by a factor of three.

What do we do? We add it to the list of guardrail metrics for our marketing.

As a result:

• Main metric – scale the ad spend

• Guardrail metric #1 – cost-per-action under $XX

• Guardrail metric #2 – 24-hour cancellation rate lower than Y%

SEO

In SEO, our goal is to bring as much organic traffic as possible. But, traffic that converts. Bringing relevant traffic works on several levels. First, we make sales. Second, it increases the time users spend on our website, which sends the right signal to search engines and improves our rankings.

However, individual pages might not receive enough traffic to measure conversions. That’s where clustering comes into play.

There are several ways we can cluster SEO visits. A couple of examples:

• Website sections – the main page, various landing pages, the blog overall, or separate blog categories

• Keywords – we determine the most popular pages per keyword and then check conversions from these pages

As a result, our SEO metrics might look like this:

• Main metric – number of visits from Google and Bing

• Guardrail metric #1 – conversion rate from organic traffic

• Guardrail metric #2 – conversion rate for each cluster

Counterintuitively, setting the right guardrail metrics helps teams focus on the main metric.

In any game, we need rules to play. Chaos paralyzes and creates even more chaos. Rules give us a system we can rely on.

The same happened in our case. With 3 metrics balancing each other, we had to find ways to grow all 3 of them sustainably. It also gave the entire management team one common goal and language to work with.

So, did we double the company? We managed to achieve only the first 25% milestone, and then the company was sold and the scheme was abolished.

Set goals, but keep an eye on how they’re implemented.

They were right. After making all sorts of strange decisions and pivots in our “go-to-market strategy,” in the end, we couldn’t grow it further.

One thing has stuck with me ever since. While preparing for the interview, you often see their motto – “Make something people want.” Back then, it sounded like just a nice phrase.

Nowadays, specializing in product growth, I realize the importance of every single word in that motto. And I often see how people confuse it for something else — just like I did back then.

Misunderstanding #1. Make something people need

This is the most common way people misread that motto.

The idea is simple – if you meet someone’s need, you will make money and grow your business. It’s the basis of all those “painkiller over vitamin” theories. Reality is not that simple. Otherwise, all casinos and games would go broke.

Needs are passive. I might have a need, but without the desire to buy something, I might spend another two years just living with that need. To help people make a decision, we must work with their emotions, not just convince them logically.

Misunderstanding #2. Make something people like

People in this group believe that if you make something people really like, they will pay for it – because it’s beautiful, useful, or cool. Unlike the previous approach, this one captures emotion. But it introduces another risk – we start over-emphasizing the product itself. We want to make it so good that people cannot say no.

In reality, our products are not that important in people’s lives – they are just a way to achieve something. I don’t want to like my mattress; I just want to sleep well. I don’t care about the mission of the company that makes my toothpaste; I just want to avoid cavities.

We generally don’t care much about the products we buy. We want the story to be about us, not about them. After all, we are the ones who pay.

As people working in growth, to make sales, we need to think deeply about our customers – their emotions, struggles, and desires. The product is only a tool; it should simply do what it promises.

Words matter. Changing a single word changes how we approach business entirely – what we put at the center of our attention.

Not to mention, many people change more than one word: make something my mom likes, make something to impress my friends, make something I like, and so on.

Make something people want.

To attract a cat, you need to know what she wants.

Finally, someone wrote a modern guide on digital marketing. As someone who works with ads at scale, I already knew many of the concepts, but a few things were new to me.

One of his pieces of advice stood out – don’t buy branded search terms on Google. Mainly because those people will convert anyway, as they’re already looking for your brand specifically. This goes against the common zero-sum game – that if we don’t buy those keywords, competitors will outbid us and steal our conversions.

Intrigued, I decided to check our own numbers.

Apparently, once you reach a certain market share, there’s an even stronger reason to stop buying branded keywords. More and more of your existing users start using Google just to reach your product. Instead of typing your domain name directly, they just type the company name into Google Search – and click the first link, which is often an ad.

That’s where the problems begin.

First, we end up paying for users who already have an active account.
Second, instead of being taken straight to the app, these users have to go through the entire onboarding flow before they can reach the sign-in screen. Not ideal.
And third, this traffic contaminates our funnel data – conversion rates drop, and the numbers become less reliable.

And it’s not just a small share of traffic – we’re talking about a significant share.

Now, there are a couple of ways to fix this:

1. Set up exclusions.
   Google Ads allows you to sync a list of your existing users so ads won’t be shown to them. In this case, the exclusion list should include all registered users, not just paying ones, since their probability to convert is much lower than that of new users. Besides, for existing users, we already have better channels like email or in-app messages.

2. Stop paying for branded keywords.
   Ideally, this should be tested through an experiment. If we stop paying, will we see an increase in organic conversions through SEO? Even if not all the traffic shifts to organic, winning back the rest with paid branded ads may still be too expensive. For example, if we lose only 20% of conversions, it means we’re paying 5x more to get those 20%.

Growth checklist:

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In subscription-based products, especially in B2C where the average purchase is small, it often takes several billing cycles just to recover ad spend, let alone make a profit. That means we need users who stay and keep using the product — not just people who convert once and leave. This is where optimizing only for CPA becomes risky.

It’s easy to imagine an ad that looks great, grabs attention, and gets people to try our offer. They might even complete a 20-minute quiz or ignore the downsides of our landing page just to give it a shot. But when they finally use the product, they realize it’s not what the ad promised and churn immediately. Leaving aside the reputational damage — refunds, disputes, and bad reviews — the numbers alone tell the story: users from such ads cancel quickly, retention drops, recurring payments fall, and we struggle to earn back the spend.

There’s a simple way to think about it: an ad is a window — we should be able to see the product through it. If we see something else, the window is dirty.

When we optimize only for CPA, we fall into a trap — promoting what sells, not what we actually deliver. And there’s a bigger risk: by focusing too much on CPA, we miss ads that might have a slightly higher cost but bring in customers with higher lifetime value (LTV). These are the users who truly match the product. If we tracked the right metrics, we’d identify such creatives earlier, test them more, and improve performance overall.

So what should we measure along with CPA? Retention is the first thing that comes to mind, but it’s tricky. People don’t always use the product daily, and that’s often fine. Plus, we need a faster signal to separate good and bad creatives.

A simple and powerful metric is the subscription cancellation rate — especially cancellations in the first 24 hours. Cancellations tend to grow at the same rate after that, and the creatives with the lowest early cancellations usually stay on top. It makes sense — users either find what they were promised or they don’t. They’re either our audience or they’re not.

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Sometimes, it’s even worse — three departments get involved. Engineers add tracking, data teams try to make sense of the data and send it into marketing tools, and then, somehow, we end up with completely different numbers across platforms. One conversion count in Amplitude or Mixpanel, and a completely different one in the ad platforms.

It’s fascinating, considering how much companies today rely on performance marketing — and how complex it’s become with all the tracking restrictions. You have MMPs, Conversion APIs, SDKs, and pixels, yet most companies still operate in silos.

After having a completely different experience at Speechify and seeing what’s possible, I want to make a case for growth engineering. You need engineers to build great products — and you also need engineers to bring in and monetize users. These engineers should operate at the same level of seniority as product engineers. It’s a full-time job, sometimes even for several people.

Why not just rely on data people? Because only engineers who work with the product or onboarding know what data they send and how it’s structured. If you add another layer — like a data analyst — ownership gets lost, and everything slows down. Analysts find issues in analytics, reach out to engineers, explain and validate problems, wait for prioritization, then for release. Rinsing and repeating. It’s inefficient — especially in performance marketing, where speed matters.

For this role, you need someone who can own the entire process end to end — implement the events in the app, set up data pipelines, and validate results in marketing platforms. Most likely, they’ll need to repeat the process several times before the data becomes fully reliable. They need to be comfortable reading confusing ad platform documentation, finding workarounds for half-broken integrations, and debugging dashboards that never seem to work as expected.

Once you have someone with an engineering mindset inside marketing — someone who treats it as their main responsibility instead of a side task — things start to change. Data becomes reliable. Conversions start to match. New integration methods are discovered. Automation improves. Over time, you begin to outcompete others simply because things work.

And if that person needs support from product or backend teams, it happens faster — engineer-to-engineer communication is just more efficient.

And that’s only the start. With proper engineering in marketing, you can build better experimentation tools, support localization, and unlock many other growth opportunities.

The hardest part is finding an engineer who genuinely cares about the company’s revenue — and wants to help it grow.

This is how different departments operate.

Yesterday, after finishing “Demand-Side Sales 101”, it was time to decide what to read next. I didn’t go far this time – I checked the reading list shared by Bob Moesta at the end of his book. As usual, I opened Amazon and started reading the reviews. But this time, I noticed something I had never realized before: while the books I was checking were overwhelmingly rated with five stars, I didn’t read the five-star reviews at all – completely subconsciously.

Let’s see why. Whenever we’re deciding on a new product, we look for clues that help us make a decision. Product descriptions already highlight all the benefits, so at that point, I’m not looking for more positives. I want to know what I’d be paying for – not just in money, but in tradeoffs. What will I lose, give up, or compromise on by choosing this product? I need to see both pros and cons. If I can’t find the cons, I struggle to decide – it feels like something is missing, and I’ll keep looking.

Most landing pages show only five-star reviews, and that misses an important point.

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After reading Demand-Side Sales 101 by Bob Moesta, I finally found a way to resolve the dilemma around performance vs traditional marketing. It’s not one or the other – they serve completely different purposes and target potential customers at different stages. Performance marketing is great for converting people and driving sales, while traditional marketing solves an entirely different problem.

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That’s not the whole picture, though. One important dimension is missing — the timeline. People switch products not because of their age range or some other traits, but because of very specific events in their lives. These events make them overcome their anxiety about trying a new thing — their habits. So, it’s not just who’s buying our product, but also when they’re doing so. There is always a story behind every purchase. Knowing these events helps us target our marketing efforts far more precisely. We’re not just spending advertising dollars on typical buyers, but also on moments when they’re more likely to buy.

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Peers form our social circle. And that’s the circle where we’re constantly looking for ways to elevate our status. We want to be a little bit better, a little bit wealthier, healthier, and so on. The status game becomes more important than people consuming our product – they’re rarely our circle anyways. At least we don’t compete with them.

I’m not trying to complain though. I’m not the person to change human nature.

But if we want to unlock word-of-mouth growth, we need to build products that people brag about to impress their peers.

This cat focuses on other cats too.

Logically, I understand it – these products compete in the same market, they target the same audience. Intuitively, it felt wrong, though.

First of all, in this specific niche there is still no clear winner. Because winners don’t put comparisons with others on their home pages. But, more importantly, by putting such tables on your website, you turn your product into a commodity. You expect people to compare your product with another one feature by feature. Like features were ever important.

Don’t get me wrong. It’s important to clearly communicate how your product is different from others. In 1 or 2 key attributes. Something that makes you the best. Not for everyone, but for people for whom these attributes are very important. But if you start creating line by line comparisons, you already lost it.

People reading such tables don’t understand the main value, the key differentiator. And if they’re not after your main value, they’re simply not your audience. Even if you manage to convince them to buy, they’ll churn or flood your customer support with requests that have nothing to do with your product.

On the other hand, people who came for the main value you’re offering, that key attribute would make them overlook many other shortcomings.

Don’t believe it? Try imagining Apple putting a comparison table with Android on their iPhone landing page.

This cat wants your product to feel special.

Triple checking all the settings doesn’t help, everything seems in order. But, there is one hidden feature of Google Ads – if you optimize for conversions on a fresh account, Google doesn’t have any history yet. And it either shows your ad through Search Partners. Or, if you turned it off, it doesn’t show it at all. It’s just stuck in learning – interesting though, what it could possibly learn from zero impressions.

So, there are two options:

• You either have to turn Search Partners back on

• Or you launch a campaign maximizing clicks and wait for some data to spin up data for your conversion campaign

Thank you, Google

This cat doesn’t get it either

Feels like a fast, reliable and human-based support is bound to become a competitive advantage once again.

At least, these cats hope it will.

While reading it, I kept thinking, “I should’ve read this years ago.” It’s practical, well-focused, and it reshapes how we launch (or relaunch) any product.

It’s a book about positioning, yet it pulls together Jobs-to-be-Done interviews, marketing tactics, product creation, and validation into one clear, concise framework.

If I had to read a single book before launching anything new, this would be it.

[this Georgian knows something about positioning]

[this Georgian cat isn’t afraid anymore]

1. Use an AI tool to brainstorm 10-15 reasons someone might not buy. Ideally, you already know these off the top of your head – but no one’s perfect.

2. Rewrite each concern as a clear question with a concise answer.

3. Publish them as FAQs on your landing page or paywall. Randomize the order on every load and keep answers collapsed by default.

4. Track which questions get opened the most (that’s why the randomization is important).

5. Use these insights to improve the funnel: clarify copy, tweak the flow, or add targeted promos if needed.

[this Turkish cat is tired of guessing why people aren’t buying your product – help him]