📂 Advertising scales when you can crack payback period

​

​

​

Today's newsletter is proudly supported by Conversion Factory 🎉

I started Conversion Factory to help startups go from a nice-to-have to a must-have. We're the only product marketing agency accessible on a subscription. That's right — no quotes, no scopes, no billable hours, no contracts. Pay us a flat fee (we have 100% transparent pricing on our site) and we'll fly through your backlog. Follow the link to check out our example work and learn more about how we can help.

Check out Conversion Factory →

​

​

📖 The following is an excerpt from my work-in-progress book, Founding Marketing. It's a (very) rough draft of thoughts, notes, and research... so feel free to reply with your feedback on what I should expand more on and what needs to be clarified. Enjoy!

Performance ad channels are called “performance” for a reason: You have to monitor and adjust based on how they are performing according to your budget and conversion rate. These channels are:

• Google
• Facebook
• Instagram
• Twitter
• LinkedIn
• Quora
• Reddit

Ads themselves are made up of two things:

• Copy
• Creative

Copy is a fancy word for any text involved: Headlines, different sections of text, the call to action.

Creative is a fancy word for any multimedia involved: Images, videos, GIFs, illustrations.

Now, I’m going to do an overview of each channel but it’s not going to be very practical, per se, because the UI changes all the time, I couldn’t tell you exactly what’s going to work for you anyways, and the whole reason why ads work is because you tweak them and optimize them over time.

If a channel does start working for you, you’ll either:

1. Hire someone to manage and scale it
2. Outsource it to an agency or consultant
3. Keep doing what you’re doing and manage it yourself.

And to do any one of those three options, you just need to know the principles here.

Most ad channels share a similar campaign structure:

Campaign → Ad set → Ad

Campaign

Create one campaign per product or per product category you’re advertising.

• For example, you’d have one campaign for your free plan, one campaign for your middle tier plan, and one campaign for your enterprise plan.

Ad set

If, for example, you’re selling email marketing software, you may have two valuable audience segments: email marketers and founders. For each, you may pitch distinct or overlapping value props, such as “easy to use“, “affordable,” or “smart.”

You then create one ad set per combination of value prop and audience segment. This keeps your targeting and messaging controlled so the set’s ads can find the best wording and imagery for pitching the value prop to the audience segment.

• For example, your ad sets would look something like “Marketer - Affordable”, “Founder - Affordable”, “Marketer - Automations”, “Founder - Automations”

Ad

Within an ad set, create one ad per combination of unique copy and imagery.

• For example, ads could look something like “Marketer - Affordable - Illustration”, “Marketer - Affordable - Gif”, “Marketer - Affordable - Gif”

Setup conversion pixel

Working with an ad channel always begins by setting up the channel’s conversion pixel. This is the JavaScript code snippet that reports conversions occurring on your site back to the channel.

This is how the channel knows which ads are ultimately performing best — since cost-per-click is just an intermediary metric that isn’t necessarily correlated with conversion.

You can embed this directly or use a service like Google Tag Manager or Segment to do it for you. Which I would recommend using, personally.

Determine budget

Set your budgets for each set high enough for your ads to individually reach at least 1,000 impressions, but more likely in the 2-5k range comfortably. You need a significant sample size like this for your cost-per-click (CPC) to stabilize and get to statistical significance.

As you see consistent CPC or CPA numbers that are financially viable according to your CAC and payback period, incrementally increase your budget as high as you’re comfortable with.

• CAC is the amount it cost you to acquire each customer. So if you spent $500 on a campaign and acquired 10 customers from it, your CAC would be $50.
• Payback period is the time it takes to recoup your CAC from the revenue new customers generate. So if your plans start at $10/month, it would take 5 months to get “paid back” for acquiring those customers and now make a profit from them.

Create initial ad sets and their ads

Monitor

Check the channel’s dashboard once daily to see which ad set audiences, ad set value props, and ad copy/imagery combinations perform best.

Turn off the ads with much lower click-through-rates (CTRs) and/or cost-per-customer-acquisitions (CPAs) relative to the others in its ad set.

If some ads perform much worse than their siblings, you can turn those off too instead of wasting money on them.

Optimize

Begin tweaking your highest-performing ads with changes to their copy and creative. Similarly, tweak your ad sets’ targeting settings.

When you do this, duplicate your ad sets and ads and keep the old ones running. You want to see how your tweaks simultaneously compare to their originals. Build the habit of archiving all your work so you can look back at your costly-to-acquire advertising data when setting up new channels in the future.

When tweaking, you’re ultimately looking to lower your CPA numbers and to increase total conversion volume.

Depending on your total audience size, as your ads continue running for weeks and beyond, audiences will tire of seeing your ads and will increasingly turn a blind eye to them. Your CTRs will steadily drop. You can then turn them off for a while and resume, which usually gives enough room for the ads to become fresh again. Or you can introduce new ads and essentially start the process over again.

Run ongoing experiments

As you’re optimizing the campaigns that already work, you must also test brand new targeting, value prop, copy, and imagery combinations. Your work never ends.

Setting aside 10% of your budget for weekly or bi-weekly experiments is a good way to budget for it while still having enough to play with to get actual results.

Google Ads

​19 Reasons Why Single Keyword Ad Groups (SKAGs) Always Win​

• No multimedia (all text)
• Based on behavior — people searching for specific things and expecting to find a result that matches their search
• Competitive and potentially expensive

Google users enter keywords to search for pages that satisfy their educational and technological queries. Essentially, you target users by what they’re currently searching for.

There is no more direct way to target an ideal customer for your email marketing company than to show ads to people searching for “email marketing technology.”

It’s why AdWords performs so well: People are already expressing interest in your product, and it’s up to you not to drop the ball pitching yourself to them. It’s also why it’s so widely used, competitive, and potentially expensive.

This is especially useful if there are keywords you want to be associated with, but that you don’t have the clout to show up for. Buying ads on search engines allows you to show up for those keywords now instead of having to wait until you’ve developed the SEO and credibility.

First, you need to identify keywords that are relevant to your business and that prospective customers are likely to use when searching for your products.

Thorough keyword research can also help you identify negative keywords – search terms that you should exclude from your campaigns.

Negative keywords aren’t terms with negative connotations, but rather irrelevant terms that are highly unlikely to result in conversions. Maybe your business or product name is the same or similar to a name of a place, food, or animal. Or maybe there are keywords that are irrelevant to what your product does but are still in the same category.

For example, if you sell email marketing templates, you might want to exclude the keyword “email marketing software”, as users searching for email marketing software are likely looking for something different and may end up costing you a lot of money in clicks without ever converting into leads or customers.

This concept is known as search intent, or the likelihood that a prospect will complete a purchase or other desired action after searching for a given term. Some keywords are considered to have high commercial intent, or a strong indication that the searcher wants to buy something.

Consider targeting competitors, descriptive keywords close to your product, and conferences your target audience likely go to.

For more, take a look at Google’s official guide to Google Ads (which will be linked below):

Facebook Ads

• Large ad that looks like regular posts
• Profile-based targeting on demographics, interests, and other gathered information
• Huge audience
• Less competitive

The trick with Facebook ads is to match the right ad with the right audience in the right way. Facebook has so many different options that this is actually a lot harder than it sounds.

Targeting new audiences from scratch will require knowing a lot about your potential customers and constantly optimizing. A Lookalike audience can accelerate that process by finding more people like the ones you show them. And retargeting can show an ad to someone you got to the website through another channel.

For more, see Julian Shapiro’s Facebook Ads guide:

​Advanced Guide: Facebook + Instagram Ads​

The rest of this list isn’t as dependable for SaaS products so I’ll spend less time on them.

​Our Facebook Ads Playbook for B2B SaaS​

Instagram Ads

• Profile-based
• Younger audience
• Mainly mobile

The reason why Instagram is not an optimal channel for SaaS is because the vast majority of its usage is on mobile, so the conversions are much lower. It’s really optimized for mobile games and e-commerce. But software is a bit harder.

Twitter Ads

• Relevant audience interests.
• Engaging ads
• Large tech community
• Desktop and mobile friendly

Typically low conversions and high costs, which is not an ideal combination.

You can give it a shot, but typically it’s more expensive and has lower conversions.

​Our Twitter Ads Playbook​

LinkedIn Ads

Not only does LinkedIn have an enormous, exclusive inventory of job and company data you can target, it’s also the most appropriate channel for pitching business services: Consider how, unlike Facebook, you visit LinkedIn for business-related consumption. B2B’s find their most engaged audiences here — ones that convert.

For this, you’ll probably pay 3-4x times more than Facebook or Google.

LinkedIn offers multiple well-differentiated ad units so you can experiment until you find the perfect ad unit match for your business:

1. LinkedIn’s right-hand-side Text Ads are garbage: clicks on these are expensive and the conversion is terrible. These ads only allow a few words of text, so people clicking them have little context before they land on your page. Skip these.
2. LinkedIn’s Sponsored Content ads are great. They appear in users’ feeds just like a Facebook Newsfeed or Twitter ad. They also similarly consist of a big image complemented with surrounding text. I’ve seen great performance out of them. Linkedin finally introduced native video content, which will help create a better experience for users as well as give advertisers more options.
3. LinkedIn also offers Lead Gen Forms, for which users willingly opt into giving you their demographic and contact data so you can follow up directly with a sales demo. Lead Gen Forms are fantastic at reducing the friction toward getting someone’s email address. (Consider how this normally entails getting them to your site, read it, scroll down to your CTA, then submit their email.)
4. LinkedIn also offers direct message ads.These ads are harder to nail: offering audiences a good reason to spend time chatting with you is not foolproof. Nowadays, InMail is the digital equivalent to the spam you get in your mailbox, only worse. InMail only works on chumps, aka don’t bother.

​How to run killer LinkedIn Ads for B2B​

Quora Ads

• Ads are fairly small and also look like ads, which hurts.

1. Quora has a large and engaged audience.

With over 200 million unique monthly visitors worldwide, Quora has a considerably large user-base. In fact, 40 million of those users are in the U.S., which makes up about 10 percent of the population.

2. People experiencing the issues your business solves are reading content on Quora.

Quora is a great platform for reaching top-of-the-funnel customers who are just starting to identify and define their challenges. Users will often ask questions or look for content on Quora that relates to issues they are experiencing which your SaaS product works to solve.

3. The cost-per-click for Quora ads is lower than that of Google AdWords.

While Google AdWords has a lot of competition for the relevant keywords your company might target, Quora does not yet have that same level of competition that drives up cost-per-click.

4. Quora ads display ad content in front of people actively searching for information.

With Quora ads, you are bidding on topics within the platform, which means that your ads are displayed in front of people who are actively looking for information that pertains to your software. With AdWords, you may be serving ad content to people who are just searching around. However, Quora users are interested in increasing their knowledge of a subject and are therefore more likely to be interested in your ad content.

Reddit Ads

• Still very new and has yet to be proven.
• Hard to target the right audience. You have to find the topically correct subreddit, the subreddit has to have enough pageviews to meet the minimum spend, and the subreddit has to be small enough to be relevant.
• Reddit users are tech savvy and often very critical of marketing tactics.

Where do you send people from an ad?

With any ad, you have to send them somewhere.

Do you just send them to the homepage? The blog? Anywhere?

It depends.

But in general, your best bet is going to be to a special version of the homepage. This is because the homepage (should) be built to convert and make the best of your adspend. Other good options include landing pages for gated content and resources like downloadables, webinars, guides, courses, and other resources where you’re gaining contact information in exchange.

The only place you’d likely send someone to a blog post or ungated piece of content is with facebook ads, either to attract an audience you can retarget, or give the content an initial boost of traffic to jumpstart it’s ranking on search engine results.

Let’s talk about sponsorship ad channels.

• Podcast sponsorship
• Newsletter sponsorship
• Site sponsorship

The trick is sponsoring long enough to see the results. You can’t just sponsor one newsletter or one podcast.

Always do a minimum of 4, and if you can, negotiate a discount for doing more upfront, like 8-12.

Newsletter sponsorship:

Sponsoring a newsletter is actually quite simple: ignore all inbound requests and only sponsor newsletters that have highly engaged communities which you share an audience with.

Expect to pay $25-$100 CPM, or cost per 1,000 subscribers. Be open to hearing what they think makes for a great ad, but don’t let them do it all on their own.

Generate a list of niche newsletters your audience likely subscribes to, find their sponsorship page for details or email them and ask what the details are, and they’ll be happy to hear about how you want to give them money.

Podcast sponsorship:

Podcasting is a different story. Podcasts are HUGE right now. 67 million Americans listen to podcasts monthly and 42 million Americans listen to podcasts weekly. Podcasts are highly personal and can reach a huge audience.

The best podcast sponsorships give audiences a special offer and don’t sound too salesy.

When it comes to sponsoring existing podcasts, you’re looking at a CPM of anywhere from $25–100. Pre- and post-roll, in other words in the very beginning or very end, will be less expensive. Mid-roll will be the most expensive, but also likely the most effective.

Pros:

• Highly targeted, highly engaged audience.
• Should give you baseline expectations for conversion rates on successful shows.

Cons:

• Often pricey. Could be more cost-effective to launch your own professionally produced podcast. A premium show with 500,000 regular downloads can cost you $50,000 just for a few minutes of airtime.
• Could have a waitlist. Successful podcasts are going to have a waitlist of other sponsors knocking down the doors to get in.

You’ll give yourself the best shot by creating unique landing pages for subscribers of the newsletter or podcast that you sponsor so you can send them to a specialized page and address them personally.

You can also sponsor niche sites that could be personal projects, news sites, aggregators, and other sites like that.

Here’s an example of my friend Harry Dry’s site Marketing Examples that’s sponsored by EmailOctopus and Trainual, which are both front and center.

Out of Home

OOH (Out of Home) advertising is a lost art these days. With the last decade being dominated by digital marketing, it’s easy to forget that there are still analog channels. I’m especially a fan of OOH advertising in big metropolitan areas. That’s why NYC makes for one of the best places to throw up an ad.

The ads get to serve dual purpose: offline and online.

You’d think that during the covid lockdowns that OOH (out of home) advertising would evaporate.

Maybe the demand dipped a bit, but it remained just as effective (maybe even more effective than before).

Brex is a key case study for this channel.

At a high level, here’s what goes into a OOH strategy:

1. Key considerations
   • Market Intelligence
   • Frequency vs. Reach
   • Static vs. Digital
   • Anchors & Amplifications
2. Design best practices
   • Creative tips
   • Design basics (fonts/logo size, colors)
   • Social media considerations
3. Measurement & attribution
   • Industry tools
   • Value of surveys

More practically, and to sum up key points from Trung’s thread, here are some highlights.

OOH ads are relatively cheap, which allows you to get more bang for your buck.

So all things considered, OOH ads are actually cost-effective way of reaching people.

Get super-targeted for maximum effect.

Brex chose to go all-in with a launch campaign in San Francisco, which allowed them to saturate the area.

OOH can serve a duel purpose with social media.

“In many ways, the point of OOH is to get on social media so that the creative can be shared. OOH without social media is a waste of money. Anchors are often created to get onto social media. It helps with the campaign’s amplification.” — Kasper Koczab, Brex’s head of OOH media

With clever copy and piggybacking off a major trend (Brexit), this billboard went viral on Twitter getting shared thousands of times and leading to millions of free impressions.

Here’s the original article.

OOH is more alive and attractive than ever!

The campaign was simple. Three million in Canadian dollars was placed inside the advertising casing at a bus stop in Vancouver, Canada. The poster case was covered at the edges with a 3M product, Scotchshield, a see-through film that, when applied, makes glass stronger.

The premise of the campaign was simple. Members of the public were challenged to break the glass, and if they did, they would walk away with the money.

It was an ingenious marketing scheme. Who could resist having a go at trying to break the glass and walking away with $3 million? I know I’d give it a try.

Then there was the benefit for 3M. The repeated attempts would highlight the strength of Scotchshield and the benefit of using it. After all, if members of the public couldn’t break a glass casing at a bus stop, then it must be a good product.

It was set up for one day and when no one succeeded, it was quietly taken down. The enduring legacy of the stunt serves to highlight how effective it was.

The campaign is estimated to have generated $1M in advertising value.

Payback Period

You may have heard that a “good” LTV:CAC ratio for a SaaS startup is 3:1 or more.

Well, I’m here to tell you something contrary.

The LTV:CAC Ratio is an utterly useless metric.

In fact, it’s destructive.

We should wipe it from our vocabulary completely.

The main issue is with the definition of lifetime value.

Traditionally, lifetime value was defined as the average total revenue collected from a customer over time.

As acquisition costs rose and marketers could no longer justify spending money to acquire a customer based on their first purchase, they invented a new metric to show profitability over time: The LTV:CAC Ratio.

Maybe a customer wasn’t profitable to acquire based on their first purchase, but over their lifetime and through multiple purchases, their value outweighed the cost.

It’s fine for ecommerce and other non-subscription business models. But it never should have made its way into the world of SaaS.

In SaaS, we calculate lifetime value differently to try to account for the nature of subscription revenue:

Average monthly revenue per customer / average monthly customer churn

This seems acceptable until you realize a few things:

1. It assumes every customer eventually churns (not true)
2. It assumes customers churn at around the same time on average
3. It doesn’t account for multiple plans with huge variances in prices
4. It doesn’t account for a free plan or free trial‍

Let’s dig into the nuances of each and then I’ll tell you why Payback Period is a perfect alternative to LTV:CAC.

LTV:CAC assumes every customer eventually churns.

The assumption that every customer eventually churns is literally not true.

Colin shows Customer.io’s cohort retention over several years. The bottom orange layer shows steady revenue all the way back from their 2013 cohort.

In fact, they have exceptional expansion revenue with most cohorts GROWING in revenue over time.

A more accurate representation of LTV:CAC would be if you calculated the cumulative LTV of each annual cohort and compared that against the CAC for each year.

But what you’d still find is that LTV:CAC keeps getting better and better for each cohort since customers stick around for years and years.

And I struggle to find how that’s useful information.

It’s simply not a true or useful concept to make decisions on.

LTV:CAC assumes customers churn at around the same time on average.

Beware of averages.

The danger in relying on averages is that the range of historic outcomes may be very wide. Too wide to be able to accurately represent a sample. **

It’s also quite likely the outcome will be nowhere near the historic average.

Since the traditional calculation of LTV is based on ARPU / User Churn, we need to take a closer look at churn and why it’s used.

​ConvertKit’s User Churn report shows that with a monthly churn rate of 3.4%, customers will churn out after 2 years, 5 months, & 1 week on average.

So what the original equation of LTV = ARPU / User Churn is saying is ARPU x 27 (27 months = 2 years, 5 months, 1 week-ish).

But if you conduct a cohort analysis, you’ll see a much different story.

25% of customers will be gone within 3 months and 50% gone within 12 months.

After looking at thousands of tables like this in my time as Head of Growth at Baremetrics, I can tell you that more churn occurs in the first 3 months of the customer lifecycle than in any other period.

It makes sense too. The first 3 months will weed out the majority of bad fit customers, price-sensitive customers, customers who never truly got onboarded, what are effectively paid trials, and customers who ended up switching to a competitor.

So the reality is that only a fraction of your original customers will still be around by the time your supposed “average time to churn” comes around.

You’ll be making decisions based on a small subset of sticky customers, which, in and of itself isn’t a bad thing, but it’s contrary to how the data is portrayed.

Going back to the warning about averages… the problem is that the range of data gets wider and wider as time goes on, thus skewing the average further and further away from a true representation of what you’re looking for.

On a customer-by-customer LTV basis, we had an absolutely enormous range from just a few hundred dollars collected from new customers to tens of thousands of dollars from long-time customers.

LTV:CAC doesn’t account for multiple plans with huge variances in prices.

This is the real kicker.

The reason why LTV:CAC works just fine for one-time sale business models is that price points are consistently close to each other. Even if you have hundreds of SKUs, it’s unlikely that they’re going to vary in price too drastically.

An ecommerce brand might sell A at $49, B at $69, and C at $79, for an average order value of ~$66.

You don’t have to be a statistician to understand that $66 is pretty representative of the true price points.

SaaS businesses regularly have multiple plans with huge variances in prices.

A SaaS business might sell A at $9/mo, B at $99/mo, and C at $999/mo, for an ARPU of $369.

You also don’t have to be a statistician to understand that $369 is not representative of the true price points.

Why does this matter?

Let’s say your CAC is $1,000 and you use $369 as your ARPU, divide by user churn of 3.4%, and get a LTV of $10,853.

10,853:1,000 = 10.8:1 = your LTV:CAC is amazing!

Think again. It’s only amazing for your highest-paying customers.

Spend $1,000 to acquire a $9/mo customer = horrible 👎

Spend $1,000 to acquire a $99/mo customer = decent 🤞

Spend $1,000 to acquire a $999/mo customer = amazing 🔥

LTV:CAC doesn’t account for a free plan, free trial, or a lengthy sales cycle.

Again, with one-time sale business models, the revenue is collected immediately at the time of the sale.

Not so in SaaS.

A free trial can delay collecting revenue by 7-30 days.

A free plan can delay collecting revenue by 30-180 days.

A lengthy sales cycle can delay collecting revenue by 180-365 days.

So even if your CAC is reasonable for the amount that you charge customers on various plans, you still have to factor in the time it takes to start collecting that revenue.

Delayed revenue presents a huge problem for LTV:CAC because you have to be able to float the acquisition costs before recouping with the revenue new customers generate.

Taking the earlier example from above… a $999/mo customer might take 6 months to close from the time you spent $1,000 to get them in your pipeline.

If you’re acquiring 10 of those customers a month, you’ll be spending $60,000 over 6 months before those customers even start to generate revenue for you and recoup the cost.

Do you have $60k to float customer acquisition costs? LTV:CAC won’t help you figure that out.

Why Payback Period is better than LTV:CAC.

The LTV:CAC Ratio is supposed to be a measurement of how long it takes a new customer to become profitable after recouping acquisition costs.

However, hopefully by now you’re convinced that there are too many flaws with LTV for the LTV:CAC Ratio to be even remotely helpful in figuring that out.

There’s a much simpler, much more reliable way of measuring how long it takes a new customer to become profitable after recouping acquisition costs.

It’s called the Payback Period.

It has similar roots to LTV in that it involves ARPU, except it skips all the roundabout calculations and gets straight to the heart of the issue.

Payback Period = CAC / ARPU

Isn’t that so much more straightforward?

To use the same numbers as before, if CAC is $1,000 and ARPU is $369, we get a Payback Period of ~2.7 months.

Looking at the Payback Period per plan paints a much clearer picture of how long it takes customers on each plan to become profitable.

$1,000 / $9/mo = 111 months (yikes!)

$1,000 / $99/mo = 10 months (manageable!)

$1,000 / $999/mo = 1 months (printing money!)

Ideally, you want your Payback Period to be between 3-12 months. Anything shorter and you don’t even have to blink twice about CAC. Anything longer and you’ll need a lot of cash and a lot of patience.

I love how ProfitWell helps visualize it on a graph in their article on Payback Period.

This makes it incredibly easy to model in Excel or Summit.

We can even layer in assumptions about a free plan, free trial, or lengthy sales cycle.

Let’s assume that, on average, we figure out that a free trial delays revenue by 1 month, a free plan delays revenue by 3 months, and a lengthy sales cycle delays revenue by 6 months.

$1,000 / $9/mo = 111 + 3 = 114 months (more yikes!)

$1,000 / $99/mo = 10 + 1 = 11 months (still manageable!)

$1,000 / $999/mo = 1 + 6 = 7 months (pretty good!)

If you want to model this even further, you can account for churn by “discounting” ARPU.

Churn eats into the true payback period since a portion of the customers you acquire will end up canceling and you can’t collect the revenue needed to recoup the cost of acquiring them.

Let’s call it Discounted Payback Period:

CAC / (ARPU x annual retention rate)

To use the same numbers as before, if CAC is $1,000, ARPU is $369, and annual retention is 85%, we’d calculate:

1,000 / (369 x .85) = 1,000 / ~314 = ~3.2 months

We’d tack on an additional .5 months compared to the ~2.7 months before.

This is far more helpful than the 10.8:1 LTV:CAC example we originally started with.

Now that you know how long it will take for a new customer to become profitable, you also know (1) if it’s even profitable in the first place and (2) how much cash you need to have in order to float the acquisition costs.

In both LTV:CAC and Payback Period, CAC stays the same. While it’d be ideal to be able to segment CAC based on customers on different pricing plans, that requires near-perfect attribution, which we all know is far from possible.

And while ARPU is used in both metric calculations, Payback Period more accurately accounts for expansion revenue since the final calculation isn’t heavily manipulated by User Churn.

To recap…

• Aim for a Payback Period of 3-12 months
• Account for delayed revenue with free plans, free trials, and lengthy sales cycles
• Account for churn with Discounted Payback Period

—Corey

​