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SmartData Collective > Big Data > Using Big Data to Optimize Your Media Buying Campaign
Big Data

Using Big Data to Optimize Your Media Buying Campaign

Sean Mallon
Sean Mallon
4 Min Read
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There are endless numbers of advertising mediums to grow your brand over the Internet. Many companies rely on Google Adwords and Facebook advertising, but there are other strategies worth exploring. Media buying is one of them.

Contents
  • Don’t overestimate the importance of click-through rates
  • Make sure data is statistically significant
  • Compare conversions across websites
  • Look at placements on different parts of the site
  • Test an intermediary landing page

Media buying is the purchasing of advertisements from media companies. Over the Internet, it more specifically refers to the purchasing ads based on individual websites. Media buying typically relies on banner adverting.

While banner advertising is an old school approach to advertising, it’s still effective if done properly. Brands will spend an estimated $13.4 billion on banner ads in 2016.

How can you create a successful media buying campaign?

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While the logistics of media buying are different from Adwords and other marketing strategies, the underlying goal is the same. The advertiser needs to get the right message in front of the right audience.

Here are some data based tips to help reach that goal. You can always seek help from Baldyne Digital Marketing and Magfellow if you are struggling to create profitable media buying campaigns on your own.

Don’t overestimate the importance of click-through rates

Your click through rate is calculated by dividing the number of clicks your banner receives by the number of impressions. It’s obviously an important variable because you can’t generate conversions without people clicking your site.

However, your click-through rate (CTR) isn’t everything. Sometimes banners with the best click through rates have terrible conversions. It could be a number of reasons, such as misleading claims, clickbait wording or visual styling that doesn’t match the destination landing page.

Improving your click through rate is important, but shouldn’t be your end goal.

Make sure data is statistically significant

Too many advertisers draw conclusions with insufficient data. They may notice one banner is performing much better than another after they’ve received 2,000 impressions. They immediately pause the banner that isn’t converting as well or has a lower CTR.

This is a big mistake because they don’t have nearly enough data to know which banner is truly better. After split testing for another 10,000 impressions, they may discover that the second banner is the clear winner.

It’s important to make sure you have a statistically significant sample of data while split testing. There are a number of free tools such as this one that you can use.

Compare conversions across websites

Choosing website placement is possibly the most important part of optimizing a media buying campaign. Some websites are simply more relevant to your target audience than others. You can use your intuition, but you ultimately won’t know which work best without testing.

Once you see the types of websites that convert the best, do you want to start looking for similar sites to test?

Look at placements on different parts of the site

Most websites have different advertising options. They may have a spot for a 728×90 ad at the top of the page. The same website may also have a spot for a 125×125 ad below the fold.

Generally speaking, the more visible ads will get much better conversions. However, they also have higher CPM rates. You’ll need to test different placements to determine which yield the best ROI.

Test an intermediary landing page

Depending on your conversion goal, linking to your main landing page may not work. If you’re trying to get the user to purchase a high ticket item, you may want to use an intermediary landing page instead. You could use this page to generate subscribers for your email marketing list.

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BySean Mallon
Sean is a freelance writer and big data expert with a passion for exploring the depths of information that can be extracted from massive datasets. With years of experience in the field, he has developed a deep understanding of how data can be harnessed to drive insights and make informed decisions.

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