Introductory offers: a key lever for growth

I tried to create some hype last week on LinkedIn by teasing this article. It’s my most recent successful case study (I’m running it right now) and the first time I’m getting solid results with introductory offers. My silence since then is because I’ve been locked in, writing the best piece possible for this blog!

With that explained, here are the questions we’ll answer today:

• Are introductory offers really worth it?
• Do they negatively affect LTV?
• How can you measure or estimate the best offer to attract new subscribers to your app?

These questions have popped into every subscription app marketer’s mind. Today I’ll give you a detailed guide on how to tackle them and how to navigate your tests.

When should you think about introducing introductory offers?

This is the most basic question, because introductory offers are not helpful for every single business. To give you a detailed answer, here’s some background on the recent case I’m talking about.

I’ve been working with this client for a few months, trying to run app campaigns in a profitable way according to the LTV that RevenueCat was showing us. We tested all the main channels to see what CAC/CPA (customer-acquisition cost / cost per acquisition) we could get, and the problem was always the same: the conversion rate of our campaigns produced a CAC we couldn’t afford, given our LTV.

In other words, we had two simple options: either improve the retention rate of the subscribers — or increase prices — so our LTV could grow, or improve the conversion rate of our campaigns to decrease the CPA.

What we had already tried

We obviously tried to improve all three elements before giving up, and believe me, we tried absolutely everything you can imagine. Here’s a short summary of what we tested:

• 200+ different creative angles to see if one converted better
• Different countries to target
• Different pricing in each subscription tier
• Different subscription tiers (weekly, monthly, yearly, quarterly, bi-weekly)
• Different free-trial durations
• Different optimization goals in our campaigns (installs, mid-funnel events, down-funnel events)
• Different channels (Google Ads, TikTok Ads, Meta Ads, Apple Ads, influencer marketing, ASO)
• 10+ different paywalls
• Different onboardings

And… none of the above worked out. We worked on all these changes during the last six months and couldn’t reach breakeven in any reasonable period of time, keeping retention in mind.

Why we decided to test offers

Then I thought about how our conversion rate to purchase improved when we tested removing the free trial (a strategy I already explained here with another client) and decided to go further with this approach. I really thought the app was better — or at least equal to — the baseline in the niche (I’ve worked with similar apps before).

In this case, we thought that by introducing the introductory offer, we could improve the conversion rate from install to purchase and bring our CPA to a level that LTV in 60–90 days could support.

We made this decision simply because it was the only thing we hadn’t tested. Decreasing subscription prices will 100% harm your LTV, and we were already struggling with the LTV/CPA balance, so the decision was obviously a bit risky.

However, there was a good reason: we were launching a major update that included a key feature trending like crazy on social media, something no competitor had at that time. We also redesigned the whole interface to make it more streamlined. With this context, we wanted to go bold; we wanted to be as aggressive as possible because we knew we had a unique opportunity to attract users from competitors just by offering something they couldn’t.

So the answer to the first question doesn’t have a simple answer. It depends on a whole context of decisions and variables that determine whether you should test decreasing your prices significantly.

The experiment we launched

Last but not least, here’s the exact subscription plan for this experiment. We moved from having one weekly subscription with a three-day free trial and one yearly subscription without a trial to three subscription tiers (weekly, monthly, yearly) with a reduction of:

• 85% in the weekly subscription
• 75% in the monthly subscription
• No reduction in the yearly → we simply increased the initial price by 66% so the “offer” price stayed at our original price. This was a small psychological trick: we put up a paywall where all original prices were greyed out and the new prices highlighted, creating the illusion of a good opportunity on the yearly subscription.

Apart from these pricing changes, we also decided to show the prices per day.

The logic behind this pricing structure follows a reason. From all the failed tests with subscription tiers, we realized weekly subscribers tended to have a higher LTV because retention and payment frequency were higher. So we decided to be more aggressive on this tier; making the pricing more attractive was key.

What we learned from earlier failures

On the other hand, we used that trick with the yearly strategy because we saw users who chose yearly didn’t care much about paying $40 or $60 (a test confirmed CVR stayed the same for both). We didn’t want to lose LTV just for the sake of an offer. Thanks to this learning, we made decisions with the offers later.

The key takeaway: when you test anything in your app, you are not wasting money. All the learnings we gathered during the last six months with these “fails” helped us build a well-founded strategy for this test. Next time you wonder whether to test something to improve performance, just go ahead. Best case you improve; worst case you learn something about your users.

What are the main effects when you apply introductory offers?

I already hinted at the first effect, but since I like showing real data, it’s better if I explain what we saw when we implemented the introductory offers.

Let’s start with the conversion to paying from new users:

The most obvious effect you should see after implementing an introductory offer is the conversion to paying subscribers. In our case, as I mentioned, we had already tried removing free trials and going directly to subscriptions (that’s why you see a peak in November), but that conversion wasn’t high enough to cover the CPA at that time.

However, after introducing the offers on 5 June, we saw an increase of 433% in CVR when looking at the highest peak compared to the average 3% we usually had with free trials. Seeing this was the first signal that we were heading in the right direction. With such a high CVR, our CPA started to decrease to levels we’d never seen.

To show that with data, here’s our CPA based on SKAN during June versus the cost we had back in November when we didn’t have the offer:

• CPA (SKAN) November 2024 (paywall: 3 tiers, monthly and yearly, no trial): $105
• CPA (SKAN) June 2025 (paywall: weekly, monthly, yearly, no trial, introductory offers): $13
• Difference: -86%

Huge, right? A big drop in cost per paying subscriber, and the main reason we generated many more subscribers using the same budget as in November. Check the next screenshots to see that:

We were seeing a huge change, a completely different story, and it was also affecting our MRR, a metric we’d never pushed above $50k with previous experiments:

That line goes quite up in a very short period of time, doesn’t it? We were so happy to see these trends going up in all directions and, I’m not going to lie, I felt like we’d discovered a gold mine. But there was a factor which was clearly affecting all these graphs.

The extremely low CPA was caused not only by the introductory offers but also because we were the only app in the niche offering a unique feature and promoting it with a huge trend on social media, which gave us crazy conversion rates on all the TikTok videos (yes, all this magic happened on TikTok Ads).

So we knew this was a bubble that could burst if the TikTok trend faded or competitors launched the same feature at a better price. That’s why we move now to the third section.

Monitoring and adjusting your introductory offers

With all the context given, we have a couple of questions left. Our CPA was extremely low due to a seasonal factor (a trend on social media) and a differential factor (unique feature).

The next question: was the super-low cost per new subscriber enough to cover the drop in LTV? Because, yes, if you start your first subscription with an 85% reduction in your most popular tier, you’ll decrease LTV.

This is what happened. Comparing LTV-30 in November (no trials) vs. June: we dropped from $55 to $10 (-81%). The decline matches the average price reduction (about 80% between weekly and monthly).

The declining trend in April and May was due to another experiment (where we aggressively boosted installs to spark organic traffic). The key is what happened in June. The cohort is still immature, but it’s clear our LTV will drop at least 70% versus the $38 average we had October–March.

Our average CPA this month was $13, so we were obviously not covering CPA, but we were close.

The next step was to measure the proportion of our subscriptions:

In order, and by colors, you have:

• Weekly offer (green)
• Monthly offer (blue)
• Yearly offer (orange)
• Special yearly offer (red) → shown when users closed the onboarding or the paywall a few times

We started with a huge number of weekly subscriptions and a much lower number of monthly and yearly subs, and we were expecting this because, looking at the paywall, the weekly offer was much more attractive due to the higher reduction in price.

At this point you have two options:

1. Wait and see how these weekly and monthly subscriptions keep paying after the introductory offer ends. In this case, you should also expect much lower retention than usual when using an introductory offer, because there are many users who just want to try your app and pay the minimum. For example, in our case, our weekly retention was above 50% in the first renewal back when we had the free trial with the weekly subscription, but in June only 40% paid the full price for the first time. This number keeps declining with the next renewals as well.
2. Change the distribution of your subscriptions so you can increase the LTV in a short time frame to support that LTV.

And this was the direction we took. We didn’t want to wait 90 or 180 days to see if we were covering the investment; we needed to ensure a positive projected ROI in 60 days or less, because we knew the trend could end quickly. So we started to play with the pricing again, and this is what happened with our LTV in seven days:

On 26 June, we decided to increase both the weekly and monthly introductory prices to make the yearly more attractive (remember that the yearly didn’t have any real discount), so users would see a paywall where, despite the reductions on weekly and monthly tiers, the price per day of the yearly was still lower.

That quickly changed the distribution of our subscriptions, as well as the trend of our conversion rate shown in the previous screenshot, but also marked here again:

That drop in the conversion rate was not because the campaigns started to convert worse. It happened because we decided to change the price of the offers, and that directly affected the distribution and the conversion rate.

This decision also affected the CPA, of course, but the outcome was positive:

• Conversion rate was still quite high (average 12%).
• CPA increased to $17.
• Our LTV-D7 is now going up steadily, even with immature cohorts, and that’s the biggest difference. During the last seven days (I am writing this on 17 July), our average CPA is $17.35 and our LTV-D7 is $14.27, so we are covering 82% of the cost in just seven days. We are sure we will eventually cover the investment in 60 days or less.

Our main goal now is to closely monitor that our CPA stays under control so we can keep this profitability over time while we start to scale our paid campaigns to a level that we could never achieve.

Conclusion

Now that we’ve seen the data, let’s revisit those questions posed in the introduction:

• Are introductory offers really worth it? Yes, if the context is right.
• Do they negatively affect LTV? Yes, but that doesn’t mean you can’t grow.
• How can you measure or estimate the best offer to attract new subscribers to your app? Depending on the goals of your business, you will have to make some decisions while you run introductory offers. Measuring the balance between CAC and LTV is key and RevenueCat gives you the tools to do this job efficiently.

I hope you enjoyed this article. I put all of my effort into giving you the whole context of all the decisions we made because in my opinion, it is the most essential part of this whole case study. 

Everything we tested, all the learnings we gathered through the journey, were in this case a key component in creating a successful growth framework at the end. This is what you can achieve if you keep persisting and iterating. This is what defines a successful story.

Don’t stop fighting with your managers/CEOs/CMOs — whoever you have to deal with — to test new things. You will never know what works and what doesn’t until you test.

I will finish this article by paraphrasing Confucius:

“He who tests a new hypothesis is a fool for a minute;
he who does not test is a fool for life”