How to Lose Money on Paid Marketing

Paid marketing is a powerful tool, it’s one of the few ways to scale user growth but if not measured correctly it’s also one of the best ways to drain your bank account, fast. If you’re going to use paid marketing as a sustainable growth mechanism you have to know a lot more than if it’s driving new visits, you have to know if its profitable. It seems like something that is simple enough to calculate, but most marketers actually get it wrong. Let’s explore why.

How To Calculate Profitability

Most marketers have settled on a simple metric for measuring how profitable their channels are called return on ad spend (ROAS). ROAS is defined as the revenue a user generates divided by the advertising costs. It is usually calculated in two ways, across all channels together and for each channel individually. To understand the performance of our entire marketing budget for executive level reporting we would calculate it across all channels, but to make marketing decisions about what channels are working and which ones are not we would calculate it for each channel individually and compare the results. In equation form it can be stated like this:

ROAS = (Revenue)/(Marketing Spend)

Back to Algebra Class
This equation looks simple but is actually tricky because Revenue and Marketing Spend use different units. If you think back to your Algebra classes I’m sure you’ll remember having to solve problems that combined units like miles/minute and miles/hour into a single equation. In order to get the right answer, you first needed to change miles/minute to miles/hour. The ROAS equation is similar. One unit is Cost-Per-Visit (Marketing Spend) and another is Revenue-Per-Conversion (Revenue). In order to get the right answer we need to convert revenue-per-conversion to revenue-per-visit.

Calculating Revenue Per Visit
The Marketing Spend part of our equation above is easy to solve since we already know our cost-per-visit but Revenue will require some work. We know our revenue-per-conversion, but to turn that into revenue-per-visit we need to give a value to each visit based on the conversions it contributed to. Visits that never led to a conversion are easy to value, they get a value of $0. The difficult visits to value are those that did lead to a conversion. In order to give these visits values we need to come up with a set of rules that determines how to to value them. We call this set of rules an Attribution Model, it’s the key to calculating ROAS.

Attribution Models

Attribution models are a set of rules that determine how we convert Revenue-Per-Conversion into Revenue-Per-Visit so we can fairly compare our revenues to our marketing costs. There are two basic types of attribution models.

Single-Touch – The Old Way
A single-touch attribution model is one that gives credit to only one visit for a conversion. This is the most common way marketers today attribute revenue. It is easy to calculate but not very accurate.

Some common single-touch models are:
– First Click (give credit to the first visit)
– Last Click (give credit to the last visit before conversion)

Multi-Touch – The New Way
A multi-touch attribution model is one that gives credit for a conversion to a number of visits rather than just the first or last visit. This is the new way that smart marketers are using to attribute revenue. It is harder to calculate than single-touch attribution but is far more accurate.

Some common multi-touch models are:
– Uniform (give credit evenly to all visits)
– Parabolic (give credit to all visits but more to the first and last visits)
– Time Decay (give credit to all visits with more credit going to later visits)

Attribution – An Example

In order to understand why multi-touch is the “new way” lets consider a specific example. We’ll take a last-touch attribution model and a multi-touch attribution model and compare the differences in ROAS and channel performance.

Assume we have 5 users who visit our site for the first time on May 1st or May 2nd, these users then each make a $200 purchase on May 9th with a number of other visits in between their first visit and purchase. The user visits look something like this, with each visit channel color-coded for convenience (Facebook is blue, AdWords is green, Retargeting is purple):

Screenshot 2015-05-29 16.20.28

We also have some advertising across different channels over each of those days:

Screenshot 2015-05-29 16.22.23

Single-Touch Attribution – Last-Click Model
Now lets check out what last-touch attribution looks like in this scenario. We’ll ignore dollar values for now and say that each conversion has a value of 1 to make this simple. Let’s see how our different marketing channels are credited here. (Facebook is blue, AdWords is green, Retargeting is purple). If we add up the credit for each channel it looks something like this:

Screenshot 2015-05-29 16.34.13

As you can see, the channel that each user visited last gets 100% of the credit for the conversion. In this case Facebook was the last channel two times, AdWords was the last channel two times, and Retargeting was the last channel once.

Now let’s include the spend and see what it looks like. Remember each conversion is worth $200. The ‘reporting window’ just means we’re only looking at what is happening during those dates. For example, when in Google Analytics you select to look at the last 7 days, those 7 days are your ‘reporting window’ even though there may be other user activity happening outside that timeframe. Since we only have the 5th to the 9th selected as our reporting window we’ll add up all the marketing spend and all the attributed conversions in that time period. The result looks something like this:

Screenshot 2015-05-29 17.30.42

This looks great! From the 5th to the 9th we spent $600 on marketing and made $1000 in revenue on the users acquired from those marketing efforts for an ROI of 1.67x our marketing spend. Based on our last-touch attribution model for this sample of users it appears that AdWords and Facebook are equally good at driving conversions, and each are twice as good at
driving conversions as retargeting is on a per-dollar basis. Before we go tell our boss we need 10x more marketing budget and start allocating more spend to Google and Facebook than Retargeting lets give multi-touch attribution a shot.

Multi-Touch Attribution – Uniform Model
As we did before with last-touch attribution we’ll ignore dollar values for now and say that each conversion has a value of 1. Lets see how our different marketing channels are credited here. (Facebook is blue, AdWords is green, Retargeting is purple). In this case, instead of giving value just to the last click we’ll split it up evenly between every visit that drove a conversion. If we have one conversion for a user and 4 visits as we do in this case, each visit will get 1/4 of the credit for the conversion. The way it works out looks like this:

Screenshot 2015-05-29 17.27.22

Now lets include the spend again and see what it looks like. Remember each conversion is worth $200, so we just multiply the partial credit amount (0.25 for all visits in this case) for each visit by the conversion value for each conversion ($200 for all users in this case). The result looks something like this:

Screenshot 2015-05-29 17.32.26

That is quite a bit different than our last result, not only is the overall ROI worse 0.83x vs 1.67x but the per-channel numbers actually tell a completely different story. Here’s how last-touch and uniform attribution compare overall:

Screenshot 2015-06-16 16.09.12

Our more robust multi-touch model tells us we actually have 50% of the ad performance we thought we did, crazy! Here is how the it looks if we compare by marketing channel:

Screenshot 2015-05-30 14.59.26

Our single-touch model would suggest we move budget away from Retargeting and towards Facebook and AdWords since they have almost twice the ROI (2x vs 1x and 1x respectively), but our multi-touch model actually tells a different story with Retargeting performing 33% better than Facebook and AdWords (1x vs 0.75x and 0.75x respectively). It also suggests that we shouldn’t be expanding our budget at all yet, at least not without more in depth analysis on user lifetime values since we’re still losing money on our advertising.

The takeaway here? The attribution model we select can have large effects on how our spend is performing, using a naive model (or no model at all) can leave us in the dark about how our ad spend is performing, leading us to think we are making money while we’re actually losing it.

Implementing Attribution

Prescribing which specific attribution model is the correct one for your company is out of the scope of this post, but any model that gives credit to each visit along the path to conversion (multi-touch) is going to be much more accurate than one that only gives credit to a single visit (single-touch). Moving from a single-touch attribution model to multi-touch one can have big impacts on spend allocation decisions and can significantly improve advertising ROAS. As we saw in the example above.

It’s a lot of work to build out accurate multi-touch attribution, but there are third-party solutions out there that can simplify the process. My company Interstate makes a pretty good one in my opinion! Regardless of whether you build or buy, if you’re going to use paid marketing as a sustainable growth mechanism you absolutely must have a clear understanding ROAS. In order to that you must move past single-touch attribution, without it you might as well be guessing.

Did you like this post? Share now!
Email this to someoneTweet about this on TwitterShare on FacebookShare on LinkedInBuffer this page

I write 1-2 times a month about Product Marketing and Growth. Sign up now to be notified of new posts!