Booyah is agnostic when it comes to tools. Truth be told though, we hold a special place in our agency for Google’s tools. They are virtually indestructible, fast, affordable and come with top notch support. More than 75% of our clients use the Google stack as their primary digital advertising platform. We’ve been heavy users for the past five years and don’t see that changing in the next five.
With such a robust tool, we are able to save a tremendous amount of time on reporting, trafficking, and optimizing. The time we save gets redirected to campaign strategy and expert thinking for our clients.
DoubleClick offers a superb unified approach to digital advertising that pushes our team to become better and better marketers every single day. We are pleased to partner with DoubleClick, and even more pleased to be featured in their video series documenting successful case studies. Check it out at the link below!
Thank you, DoubleClick, the video turned out great! Booyah!
As paid search marketers, we’re always looking for better ways to give our clients the best analytics and most accurate forecasting for their KPI’s and business goals. There are many approaches to forecasting; however, we’ve found that regression analysis has always been extremely effective. In this article, we will take a closer look at simple linear regression, which allows us to find and study the relationships between two variables: one dependent and one independent. We’ll also take a look at how the historical relationship between both variables is able to help in predicting future values. I promise not to get too technical!
Let’s take one of our clients that advertises on TV and also leverages paid search online as an example. They are interested in a few things:
1) Is there any relationship between how much they spend on TV advertising and their paid search brand impressions?
2) If there is a relationship, how strong is it?
3) How much of an increase in paid search brand impressions can be expected if there is an increase in TV advertising by X?
In this example, TV advertising is the independent variable and paid search brand impressions is the dependent variable. In other words, TV advertising doesn’t depend on paid search brand impressions; however, paid search brand impressions depend on TV advertising. To answer the questions above, I’ve created a scatter plot in Excel using sample data:
Before reading too much into the chart, let’s first identify and explain what each element is:
Hopefully I haven’t lost you yet! Now that all of the definitions are laid out, we can now answer the questions proposed earlier.
1) Yes, there is definitely a relationship between how much is spent on TV advertising and paid search brand impressions. How do we know? Read answer number 2!
2) The relationship between both variables in this model is extremely strong. As we saw from R2, it’s at 0.93, which is basically saying that 93% of the variation in the data can be explained by the model.
3) We can leverage the equation at the top of the chart to figure out the incremental lift in paid search brand impressions as TV advertising changes
There are a ton of other great examples that you can apply regression analysis to with paid search – and it’s not just limited to using one variable! We will be following up on multiple linear regression analysis, where there are many independent variables affecting the dependent variable. For example:
For more information regarding regression analysis, I’d recommend checking out the Wikipedia page.
There are also some great introductory videos from a YouTuber.
In a perfect PPC world, negative keywords wouldn’t exist. They wouldn’t exist because advertisers would know exactly which user queries were triggering their ads. This can be accomplished by advertising only on exact match keywords, but the result would be substantially low traffic volume.
Thus, there is a necessity to advertise on broad and phrase match keywords. By allowing phrases within queries or similar terms to trigger ads, a whole slew of unknown traffic can now map to your PPC campaign. Unless you’re satisfied with paying for irrelevant traffic, you must now add negative keywords for every irrelevant product, service, company, person, or thing that is even slightly related to the keywords you’re advertising on.
Adding negative keywords is simple. Using search query reports, you can look and see exactly which queries are triggering your ads to show. From here, any queries that you would no longer like to show up for, simply add them as a negative keyword within that ad group or campaign. Negative keywords also have the same match type options as general keywords do, so you are able to block anything from basic themes to entire specific search terms.
It makes sense why it is desirable to add negative keywords to block completely irrelevant traffic. But there is another angle that is becoming increasingly important…
Imagine an advertiser that is selling teddy bears. They have big teddy bears and small teddy bears. They have white teddy bears and black teddy bears and brown teddy bears. You can be certain that this advertiser is bidding on the keyword “teddy bears”, as well as all the variations above. Now, imagine a user that is looking for a brown teddy bear, and types in ‘soft brown teddy bears’. The advertiser assumes that this will map to the keyword “brown teddy bears” and trigger their ad that drives them to a landing page filled with brown teddy bear options. But in reality, this query could have triggered any broad keyword relating to ‘bears’, as well as the generic phrase keyword “teddy bears”. This user could have now been driven to any of the landing pages the advertiser offers pertaining to bears, even to the black/white teddy bears landing page which would now be irrelevant to this user.
More negative keywords. Organizing style, sizes, etc. into ad groups allows negative keywords to now be used directionally. We know ‘brown teddy bears’ queries are relevant to this advertiser, but it should still be used as a negative on other teddy bear colors and generic terms like “teddy bears” to ensure that it is mapping to the most relevant ad group within the account. When we look at large advertisers with hundreds of thousands of different keywords across all match types (this is not unusual), this is essential to keeping keywords within campaigns mapping to the correct queries.