October 5, 2020

The world of digital marketing is ever-changing, with big tech companies altering the way they operate their services seemingly every day. As marketers, this can complicate how we keep track of results and find answers to our problems. With this in mind, we must rely on one of the most despised and evil concepts ever known, responsible for the emotional demise of millions of children and adults around the world: *math*. That's right, we're talking numbers and formulas today, so buckle up... this article is about to get nerdy!

The reason such formulas will have to be used more frequently, aside from it being a good refresher for some, is that ever since __September__, search term reports now only include terms that a “significant” number of users searched for, even if a term received a click. This results in less terms being included in the report. Citing their attempt to maintain standards of privacy and strengthen protections around user data, Google offers no clarification on what will be considered “significant.” With big tech companies facing continuing scrutiny for their use of users data, as well as government bodies taking steps into not only breaking up big tech but regulating the industry, it is reasonable to assume that the data available to digital marketers will only continue to become more and more limited.

As a result, PPC and SEO managers will have to focus more on large-scale account impact versus granularity in pieces like keywords. Regardless of how much data Google decides to hide from advertisers, there will always be ways to measure campaign success. We have compiled a list of useful __formulas__ that will help you do just that but remember these formulas have to be adjusted in order to account for your business model.

But first, we must focus on our good old KPIs, such as conversions or revenue, and keep track of the metrics that drive them, such as clicks and conversion rate. If your conversions have different values, and you aggregate conversion counts across the entire account, you will get a lot more page view conversions than leads or sales. Which are usually less valuable to the business so they shouldn’t receive equal weight in optimizations. The key is in evaluating your attributions to understand the role paid search is playing throughout a user’s buying process and adjusting accordingly. Ideally, a big advertiser with a complex sales cycles would have a larger CRM that takes in all users’ interactions and micro-conversions, so an attribution model can help guide each channel’s spend allocation.

This is a fairly simple calculation if you’re accurately tracking conversion value, especially when considering that Google Ads actually provides ROAS under “All conv. value / cost”. In this instance, take the amount dedicated to ad spend and multiply it by how many products or leads were converted, depending on which category you would like to analyze. This will give the total conversion value for that specific category, which you will then divide again by the total cost of advertising. For example, lets say your website sold 350 blue shirts out of the 1200 sold this previous month, with an ad spend of $5,000. The blue shirts were sold for $25 each, meaning that the conversion value was $8,750. The ROAS would then be 1.75, so you would be able to report back to your client and say you’ve had a 175% return on ad spend!

Profit Margin= (Revenue - costs)/ revenue

We can take this a step further by determining the break-even point of our ROAS. First, we must take our average profit from sales, revenue minus expenses, and divide it by total revenue. This will give us our profit margin, which is then divided by 1 to give us our break-even ROAS. When analyzing your results, it is important to remember that anything over the breakeven ROAS, means you are making money, if it is less then it means you are losing money. It is critical to establish with your client what should be included in profit margin calculations, because as we previously alluded to, one size does not fit all. In general terms, cost is the number of clicks multiplied by the cost of clicks (CPC). Revenue is the number of conversions multiplied by average order value (AOV), multiplied by the number of clicks.

One of the most popular ways in which conversions are tracked is through lead form submission. However, attributing a value for this conversion can be difficult since some leads don’t represent any profit or revenue for your client, or if so, the amounts can vary drastically. In order to find the true cost per form conversion, you will have to take the average cost per conversion given by Google ads, and divide it by the form conversion rate. Remembering that the average forum conversion rate is the percentage of forms that turn into sales. The result gives you the true average cost per conversion, based on the percentage of forums that turn into sales.

But in order to find out how many cents for every dollar is made from advertising, we must find the ROI. Some get confused about the differences between return on investment (ROI) and return on ad spend (ROAS) and may use both terms interchangeably. But it is important to remember that ROAS tells us if we made back more money in revenue than we spent on ads, while ROI accounts for the costs of the products or service, as well as the cost of advertising, and tells the company if they made a profit in total. To do that we must take the known value of average profits per sales, same value used for the first half of the profit margin formula, and subtract the true cost per conversion. The result will then be divided by, once again, the true cost per conversion.

Continuing on the subject of forums, in order to know the maximum amount that you can spend on a form submission without losing money, you will need to use a break-even formula once again. You do this by multiplying the known average profit from sales, same value used previously, with the form conversion rate. The result will tell how much you can pay per form submission on average before you start losing money.

If you don’t have a formal background in statistics or marketing, these calculations and frameworks will save you from making mistakes in your analysis. Understanding the math and being able to use it to achieve long-term results will in turn make you stand out as an efficient media buyer.

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