Your boss says to, “Fire up a new campaign.” Or a prospect walks in your door in a market you’ve never worked in.
How do you define success? How can you tell the difference between a ‘good’ campaign vs. a ‘bad’ one? A promising one vs. one that’s futile?
The shower answer: You can’t. At least not at first.
There’s too many unknowable variables. You can’t possibly predict the future. Unless… you try to.
Turns out, most ad campaigns share a few guiding principles that should apply in most cases. The ratios are similar. You just need to guesstimate what the final end result looks like based on a few pieces of information you do know something about.
Here’s how to use a simple sensitivity analysis to fail-proof your ad campaigns.
How to Price ‘Risk’ in Advertising
Business is risky. Investopedia calls ‘business risk’ the “possibility a company will have lower than anticipated profits or experience a loss rather than taking a profit”.
There are a number of external factors that influence risk. A new president gets elected. Interest rates could climb. Policy makers change their minds. We can’t do anything about these. So there’s not a whole lot we (inside a company) can do besides adapt.
Economists and investors use different methods to assess and price that risk. This is what they teach you at business school. A bunch of theoretical, quant-jock modeling that you might recall through another hazy, glassy-eyed, hungover morning. (Just me?)
Most of that stuff is forgotten. Neglected and fallen by the wayside. But sometimes, those techniques can be brushed off, simplified, and put to good use.
The reason? There may not be a lot we can do about external risks, but there’s still a ton of internal risks that affect what we do on a daily basis. Hell – advertising, in and of itself, is risky.
You’re ponying up money – in advance! – with the hopes of one day, hopefully, possibly, seeing some of it come back to you, days (or weeks) later. That same definition can also be used to describe playing cards in a casino. Or buying a scratcher at the local liquor store on a Friday night. (Gotta do something while enjoying that six-pack.)
It’s no wonder some people think advertising is a waste of money. (Besides all those giant conglomerates that got, and stayed, huge through advertising.)
Wouldn’t it be nice if there was some way to at least forecast or predict results before starting so you knew you were on the right path?
Well, have I got news for you!
A sensitivity analysis can help.
BusinessDictionary calls it an:
“Analysis in which key quantitative assumptions and computations (underlying a decision, estimate, or project) are changed systematically to assess their effect on the final outcome. Employed commonly in evaluation of the overall risk or in identification of critical factors, it attempts to predict alternative outcomes of the same course of action.”
In English, that means looking at how changes in a variable affect the outcome.
Case in point: how do changes in Cost Per Click or Conversion Rate affect your advertising ROI? I’m so glad you asked.
I created a simple ad model years ago to help primarily in budgeting for clients of my company, Codeless. (So at least there’s one benefit to my business school debt I’ll be paying back the rest of my life.) It’s overly simplistic. Purposefully so.
But it does help at least define some of the risks we marketers face when attempting to start a new ad campaign or any other traffic acquiring endeavour (in about five minutes or less).
Let’s start with the baseline to see how it works.
Step #1. Inputting Your Variables
What’s it gonna take to make one sale? That’s the first objective to find. That Golden Ratio that will drive and dictate what you can afford and how much you might be able to make.
Let’s start with a few assumptions first. The first is your historical conversion rate. (Don’t have one? Industry benchmarks work well too.
Average website sales tend to be around0.05%-3% range. But don’t quote me on that. Again – the point of this exercise is to make quick, fast estimates and then iterate as we go.)
In finance, they teach you to create Pessimistic vs. Expected vs. Optimistic scenarios. Basically, a ‘worst case scenario’ low-end that you should almost clear with 100% accuracy, a middling range that you think might work, and higher-end that would be great to hit. So let’s select 1%, 2%, and 3% conversion rates because… easy math.
Next up, what’s your effective Cost Per Click range? Again, it’s gonna take some historical estimate, best guesses, or benchmarking. Gee – I wish there was a simple source to reference for this information.
Once again, let’s pick worst case, expected, and best case ranges. For simplicity, let’s choose $0.25, $0.75, and $1.25).
Here’s what it should all look like now based on those ranges we’ve put together:
That tells you the number of visits needed to generate one sale (the number in blue is a formula generated).
Still with me so far? Good. Let’s see why all of this is important.
Step #2. Determine Your Breakeven Point
You’ve determined what it’s going to take to generate a single sale (based on the number of visits needed).
Now: What’s an average sale worth to you? $100 value makes our life simple for this example.
Second question: What’s your breakeven point on each sale? In other words, how much does it cost you to make (plus profit) one widget or hour of service? Let’s assume 50%, because… easy math.
So let’s start slapping it all together now to see what your expected Sales and Profit off of one sale looks like (based on the conversion rate and Cost Per Click range you selected earlier).
Let’s zoom into the 1% conversion rate scenario to see what’s going on in detail:
Here’s what your ROI on one sale looks like at a 1% conversion rate.
And it already tells you a few important things.
For example, you’re teetering precariously on the point of profitability at this CPC range with only a 1% conversion rate. In fact, an effective CPC north of $1 will almost certainly mean you’re losing money.
Here’s some basic formula skillz for that last equation: =if(Ad%>50%,”Yes”,”No”)
So… that means you’ve got two options:
- Lower your Cost Per Click by better audience targeting, coming out with better ads, etc.
- Or increase your conversion rate through A/B testing your landing pages, using marketing automation to follow up, etc.
Thanks, numero dos, for that convenient segue. Let’s back up our view to see how the ROI changes at a 2% and 3% conversion rate:
Now if spending ~50% of a sale on advertising is too rich for your blood, and you can’t do much about the market-driven Cost Per Click ranges, the God Light has shown itself at exactly the right time, illuminating the path to profitability through raising your conversion rate.
Hallelujah! It has risen, indeed!
Step 3. What’s Your ROI Look Like at Scale?
Ok. So at least you know that you’re not going to lose money on a sale. Negative margin ain’t no joke. But…
Does it scale? How much money can you really make? Can you build a business and still clear payroll at the end of the month?
Easy. Let’s multiply everything by ten sales to start with and see what happens.
That negative margin rears its ugly head on the far left, which should be obvious because we couldn’t even break-even on one sale. However, the picture becomes rosier the further right you go. And most importantly, it’s starting to give you an idea of what a ‘reasonable’ advertising budget looks like.
That illusive, highly subjective, moving target that befuddles bosses and makes clients squeamish. But here it is. In black and white. Which makes it tough to argue or push back when you walk them through it from the beginning.
Is it perfect? No. Full-proof and realistic? Yes and no. But it is something. It’s more than you started with for sure.
It’s an easy-to-adjust tool that you can use to determine which campaigns might pan out from those that need to be killed (because their CPC is too expensive).
Or to direct your day-to-day efforts not at optimizing CPC, but follow-up tactics like automation, SMS, or direct mail to increase conversion rates.
Today’s marketing landscape is vastly different than just a few years ago. Facebook itself has introduced more new advertising options that didn’t even exist 3+ years ago. Many times you’re doing things on a day-to-day basis that you’ve never done before. And there’s no map or guide to show you the way.
Which means there always some element of risk at play. You’re forced to make decisions without having all of the available information up-front.
You can’t control most forms of ‘risk’… But there are some you can. And you can get a handle on that risk by employing some basic finance methods like a sensitivity analysis.
The goal isn’t to slave for hours over Excel. But to quickly define what Cost Per Click ranges might work, what conversion rate to shoot for, what a ‘reasonable’ advertising budget looks like, and how much you might be able to make in return.
Compute. Learn. And adjust on-the-fly as real data starts coming in.
And after youre done with all of this… Come back and leave us a comment!