Book a demo

Book a meeting with one of our marketing consultants and see how we can help you improve your ad spend and increase your LTV.

Oops! Something went wrong while submitting the form.
arrow to left

The State of PLG marketing

Maya Caspi
Head of Product Marketing

 Make your marketing strategy more profitable in 2023 by learning from the mistakes of others - our research shows an average budget loss of 21% on ineffective ads.

A beginning of a new year is always a great time to ask ourselves whether the things we do are still working for us and what we can do to improve.

So to help marketers make the most of the new year and their shiny new marketing budgets, we set out to discover how well different ads perform over time and how much of the budget is wasted on ineffective ads.

Over the past month, we conducted an extensive analysis of more than 50,000 ads of high-spending PLG brands on their main performance channels - Google and Facebook.

 And the results?  Eye-opening, to say the least: It turns out 34% of ad groups or creatives had a 20% lower return on investment than the target LTV.

Did you know that you may be losing a significant portion of your budget on ineffective ads? 

As we all know, in 2023, profitability is the new growth. With the state of the market dictating this shift towards profitability, most savvy marketers are already focusing on LTV as a critical factor in driving long-term success for their company. 

But are our marketing efforts really supporting our company's long-term profitability strategy?  

As we mentioned, to find this out, we first did a deep dive into over 50,000 Google and FB ads from high-spending B2B (PLG) brands.  

The goal of our analysis was twofold:

  1. Evaluate the efficiency of these ads (how much revenue they yield, did they drive high CR, bring high or low-value users, did they meet payback expectations?)

      2.    Understand how much of the average marketing budget is wasted on ineffective ads

Initially, our analysis across all channels and customers showed that both 7-day and 180-day performance generally met targets, on average. However, when drilling down to the ad group or creative/keyword level, our analysis revealed this to be misleading, as a significant portion of the budget was being wasted on ineffective ads: 

  • 34% of ad groups or creatives had a 20% lower return on investment than the target LTV
  • This lost revenue amounts to 21% of the marketing budget on average

Our analysis also revealed that underperforming ads tend to attract "bad" users over time and are consistent about that, incurring more "bad" spend as time goes on.


Let's put this in real-life context:

Let's say you're running a Google campaign. You are looking to achieve a payback period of 18 months and have determined that your average lifetime value (LTV) over 18 month period is $300, and over 6 months is $100 per user (to support evaluation purposes)

Based on this, you set your target customer acquisition cost (CAC) for Google at $300.

Looking back at the campaigns you ran 6 months ago, you see that your LTV was indeed $100 on average, and your CAC was $300 on average, resulting in a return on ad spend (ROAS) of 33% after 6 months. However, you notice that one campaign (Campaign A) had a CAC of $300 but an eventual 6-month LTV of only $60. This lower LTV is consistent across different weekly cohorts and seems to be caused by low conversion rates among the users attracted by the campaign's creative.

Had you been able to identify this issue sooner, you could have reallocated the budget from Campaign A to another campaign (Campaign B) with a more consistent LTV of $120. However, by the time you realized the issue, both Campaign A and B had already run their course, and it was too late to make changes.

On average, you lose 21% of your budget to campaigns like Campaign A, which have low LTV but are difficult to identify until it's too late to make adjustments.
By using a good LTV prediction model, you can react more quickly and avoid these losses.

The takeaway: Accurate as possible predictions can save you a lot of time and money

It can take 6-9 months to fully understand an ad's LTV, especially when it comes to freemium models, where the funnel to conversion can take months. Most companies, especially these days, can't really afford to wait that long. This means it is imperative to identify underperforming ads as early as possible in order to stop the bleeding. 

OK, so what should marketers do to optimize for high-LTV users and drive long-term success for your company?  

Drumroll, please… 🥁🥁🥁 It's 2023 resolutions time: 

Here are a few recommendations based on our research findings:

1. Use predictive models /data to understand future performance earlier. In our analysis, we used actual data, but you should use predictive data (because you can't afford to wait 6 months for results) to understand future performance early on

One way to do this is by building an in-house Intent/LTV model or working with a prediction partner.

2. Once underperforming ads have been identified, there are several steps that marketers can take to address the issue:

  • Turn off inefficient campaigns or the bottom-performing percentage of inefficient campaigns
  • Group together creatives by predicted ROAS to create new ad groups and lower the bid according to the expected ROAS
  • Re-allocate the budget to double down on ads or keywords that generate high LTV

3. Lastly, consider incorporating LTV at different maturities (30, 45, and 60 days) into your measurement process. This will give you a more holistic view of your ad groups' performance over time and allow you to make more informed decisions about your acquisition efforts.

Let's sign off for now with this - shifting your campaigns' focus to LTV is not just a strategy, It's the new normal. The best thing you can do this year is to make sure the efforts you put in, will eventually be worth your while. A great strategy, by the way, in marketing and life in general. 

stay updated

join our newsletter

Request Sent. We'll be in touch!
Something went wrong. Please try again.

Continue Reading


Stop Retrospecting. Start Futurespecting.

Request Sent. We'll be in touch!
Something went wrong. Please try again.