The “Landing Page → Paid Ads” approach

Conclusion first:

This method is not effective if a) your objective is to select the best idea out of a list AND b) if the list contains ideas with different target audiences. This method is effective for understanding demand of an idea relative to other ideas for the same target audience or relative to the baseline demand for that target audience.

If you’re starting from zero and want to test ideas with this approach, you’ll be out ~$3,000 if you do everything right. For that $3k you’ll understand if a landing page converts better than an industry average. Going down this path feels like a case of paralysis by analysis – if you’re lucky you’re left with a landing page and an offering articulation that you can reuse, but the metrics you generate are probably not going to be helpful. So what do you do?

Leverage free or low-cost marketing channels: Instead of relying solely on paid advertising, leverage free or low-cost marketing channels such as social media, content marketing, or email marketing to drive traffic.
Talk to people: Talking directly to your target customers can provide valuable insights into their needs, pain points, and preferences. Use this exposure to validate your assumptions, refine your value proposition and messaging, and to develop a email list of early leads should you ever move forward.
Use existing data: Utilize industry reports and other trends using online tools to understand where demand lies.
Analyze competition: Understand what is already selling and if customer are already willing to pay for similar value propositions. Refine your own value proposition and messaging accordingly. Identify areas where your idea or execution can have a competitive advantage or potential weaknesses that you can address (see: Testing B2B ideas)

I am reminded why the Content Creator archetype is so powerful – free data generation, validation, and distribution – that said, really only one of the above feels like a tenable path forward for someone starting at zero.

Let’s assume you develop conviction in an idea (by any means) what then?

Build an MVP or P
GTM
Conclusion: You might end up running paid ads anyway.
Second conclusion: The value of the Landing Page → Paid Ads = Landing page done, ads made, forcing function to articulate your value proposition.

Mathematical logic for the conclusion:

Some quick back of the envelope math to determine how much you’ll need to spend in order to gather enough data to extrapolate conversion metrics to get to ROI:

Average CPC¹ = ~$0.90 – $2.50
Assumed conversion rate²: 2% – 3%
Desired sample of conversion = 100
CPC / Conversion rate = cost per conversion
100 * cost per conversion = cost per 100 conversions
- Conservative: 100 * ($2.5 / 2%) = $12,500
- Aggressive: 100 * ($0.9 / 3%) = $3,000

This means that if you have 5 ideas and want to model the ROI for each, you’ll need to invest $15,000 at a minimum to get strong signal but could be looking at as much as $62,500.

The actual article:

This article includes pre-requisite information as well as tradeoffs for testing product or service ideas using the “Landing Page → Paid Ads” method. This method is proposed by many Twitter SaaS gurus I’ve seen online, so naturally I wanted to understand if it could work for me. This article is brought to you in part by chatGPT, who I’m convinced will ruin a few thousand performance marketer’s days in the coming years. Let’s get into the validation method in question:

Create a landing page with prototype or demo: Creating a destination to promote which clearly articulates the product / services value proposition. (Tools: Unbounce, Instapage, or Leadpages, Bubble, Softr, WordPress, etc)
Run paid ads: Create targeted ads on platforms like Google AdWords, Facebook Ads, or LinkedIn Ads, directing users to your landing page.
Analyze metrics: Measure click-through rates (CTR), cost per click (CPC), conversion rates, bounce rates, and retention rates to get a sense of whether there is demand for your product.

The idea is simple but I had a few questions to begin. How should different landing pages be compared to one another? What is a meaningful sample size? How do I disaggregate the attractiveness of the landing page and the clarity of the value proposition from the actual promise of the underlying product? How do I solve for variance in user behavior across target audiences and industries when comparing these metrics? Here is what I have learned:

The approach of comparing metrics only works within like-for-like experiments, and you can’t simply run these experiments with disparate ideas and disparate methodologies and choose the one with the best metrics. There are a few reasons why the metrics will not give you a simple answer:

Traffic source: It’s essential to ensure that the traffic source for each landing page is the same. For example, if you’re running paid ads on Google for each landing page, you’ll want to ensure that the targeting, bidding, and ad copy are all the same for each campaign.
Timing: The timing of the campaigns can impact the metrics produced by the landing pages. For example, if one campaign is run during a holiday period, it may produce higher metrics than a campaign run during a slower period.
Sample size: It’s important to ensure that the sample size for each landing page is large enough to produce statistically significant results. A small sample size can skew the results and make it challenging to make an accurate comparison.
Target audience: The target audience for each product may differ, and this can impact the metrics produced by each landing page. Ensure that the target audience is the same for each landing page and that the messaging and design are tailored to their needs.

Really the biggest issue that you can’t solve for through good experimental design is the last one. If the target audiences for each product are inherently different, your metrics will not be comparable because the behavior of users will be different (ie, target group X clicks on everything but buys nothing while group Y clicks only with high intent).

In this case, you need to compare the results of the experiment to a baseline for the target audience or for similarly positioned products. By focusing on the relative performance of each landing page within its own target audience, rather than comparing the absolute metrics across products, you can get a sense of if the offering is compelling relative to the rest of the offerings in that market.

Determine the benchmark: Use industry research such as wordstream to determine the average CPC and Cost per Conversion by industry.
Compare your metrics to the baseline: If you’re well ahead of the benchmark metric, this is signal that your offering is more compelling or more in demand that those being offered by the industry (or at least whatever it was when the benchmark was calculated).

Takeaway #1: You can’t just run the landing page → paid ads playbook to select the best idea if those ideas are in different industries. You’ll also need to determine the industry and audience baseline to understand the relative performance of each offering before you can rank sort them.

Let’s say you have great benchmark data. Let’s do some quick back of the envelope math to determine how much you’ll need to spend in order to gather enough data to extrapolate conversion metrics (based on averages from FB, Google, Twitter according to chatGPT):

Average CPC$^1$ = ~$0.90 – $2.50
Assumed conversion rate$^2$: 2% – 3%
Desired sample of conversion = 100 (assumed, considered best practice)
CPC / Conversion rate = cost per conversion
100 * cost per conversion = cost per 100 conversions
- Conservative: 100 * ($2.5 / 2%) = $12,500
- Aggressive: 100 * ($0.9 / 3%) = $3,000

This means that if you have 5 ideas and want to model the ROI for each, you’ll need to invest $15,000 at a minimum to get strong signal but could be looking at as much as $62,500. This is IF they are within the same target audience, which is not the case for me (also if you have 5 ideas within the same target audience, you probably know enough about that market to take a call without this approach).

At the end of the day you’re still out ~$3,000 to understand if your landing page converts better than an industry average. So what do you do if you can’t afford that?

Leverage free or low-cost marketing channels: Instead of relying solely on paid advertising, consider leveraging free or low-cost marketing channels such as social media, content marketing, or email marketing to drive traffic to your landing pages. While these channels may not provide the same level of traffic as paid advertising, they can still help you gauge the level of interest in your ideas and refine your messaging and positioning.
Talk to people: Talking directly to your target customers can provide valuable insights into their needs, pain points, and preferences. You can conduct customer interviews to validate your assumptions about your target audience and refine your value proposition and messaging accordingly.
Use existing data: If you have existing data from past marketing campaigns or customer surveys, you can use this data to inform your assumptions and refine your ideas. Look for patterns or trends in the data that can help you identify areas of opportunity or potential challenges. Identify trends using online tools to understand where demand lies.
Analyze your competition: Analyzing your competition can provide valuable insights into the market and its customers. By understanding how your competitors are positioning their products and messaging to customers, you can refine your own value proposition and messaging accordingly. You may also be able to identify areas where your idea has a competitive advantage or potential weaknesses that you can address.

Definitions

Click-through rate (CTR): This metric measures the percentage of users who clicked on your ad and landed on your landing page. A high CTR indicates that your ad is relevant and compelling to users, and that they are interested in your product.

Conversion rate: This metric measures the percentage of users who completed the desired action on your landing page, such as filling out a form or making a purchase. A high conversion rate indicates that your landing page is effective at converting visitors into customers.

Bounce rate: This metric measures the percentage of users who leave your landing page without taking any action. A high bounce rate indicates that your landing page may not be engaging or relevant to users.

Cost per click (CPC): This metric measures how much you are paying for each click on your ad. A high CPC can indicate that there is a lot of competition for your target audience or that your ad is not well-targeted.

Return on investment (ROI): This metric measures the profitability of your ad campaign. It takes into account the cost of running the ad and the revenue generated from conversions. A high ROI indicates that your ad campaign is generating a significant return on investment.

Note: For the context of testing ideas, I’m assuming you don’t have an actual product. This means there will be no “Return” for your ROI. You’ll need to estimate the LTV of a customer and multiply that by the number of conversions in order to estimate the ROI of the campaign.

Metrics assumptions (chatGPT)

1. CTR & CPC:

Facebook:

Average CTR: 0.90%
Average CPC: $1.72

Google Ads:

Average CTR: 3.17% (search) and 0.46% (display)
Average CPC: $2.69 (search) and $0.63 (display)

Twitter:

Average CTR: 1.5%
Average CPC: $0.38-$0.63

Instagram:

Average CTR: 0.52%
Average CPC: $1.28-$2.72

2. Conversion rates:

E-commerce:

Average conversion rate: 2.86%
Top 25% of e-commerce sites: 5.31%
Bottom 25% of e-commerce sites: 0.10%

Software as a Service (SaaS):

Average conversion rate: 4.77%
Top 25% of SaaS sites: 11.45%
Bottom 25% of SaaS sites: 0.5%

Business and Finance:

Average conversion rate: 3.26%
Top 25% of business and finance sites: 5.31%
Bottom 25% of business and finance sites: 1.25%

Health and Wellness:

Average conversion rate: 2.82%
Top 25% of health and wellness sites: 5.32%
Bottom 25% of health and wellness sites: 0.48%

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