Contextual traffic is very popular for online marketing. Big data is playing an important role in helping contextual advertisers optimize their campaigns.
Overview of contextual marketing
Around the beginning of the century, digital advertisers utilized very narrowly defined marketing strategies. They predominately depended on banner advertisements and PPC campaigns with Google AdWords. Over time, their marketing strategies became increasingly diverse. Social media and native advertising were highly popular. Another effective type of digital marketing is contextual advertising.
Outside of affiliate marketing, contextual marketing hasn’t received the same level of attention as search PPC, organic search marketing, social media and other types of traffic. However, it is still incredibly effective and scalable. The Google display network is perhaps the most widely used contextual advertising platform. Over 80% of all Internet users engage with Google Display ads. They are even commonly used to reach customers and non-English speaking regions. Propel Media, RTX, Rapsio and Advertise.com are also popular contextual traffic sources.
Many digital marketers take a shot in the dark approach to running contextual marketing campaigns in international markets. They would have much better success if they understood the potential big data provides to cut their initial testing costs and optimize their campaigns for a much higher ROI.
Significance of big data with contextual Marketing in international verticals
There are a number of ways that big data can be helpful with any form of marketing, including contextual advertising. However, in my experience, improving the targeting of your advertisements is by far the most important. When I first began using contextual traffic, one of my mentors pointed out that targeting was the key to profitability.
How do you use big data to optimize the targeting of your contextual ads? That is going to depend on the nature of the contextual advertising medium that you use.
You can begin with the top down or bottom up targeting strategy with contextual advertising. Some networks and traffic sources allow both approaches, while others only allow one.
With many in-text and PPV advertising networks, you need to select keywords that trigger on various webpages within their network.Marketers that try reaching an audience in smaller regions where English isn’t the primary language need to take a very broad keyword targeting approach. When my mentor in the Netherlands ran campaigns in his home country, he needed to use keywords such as “men,”“dating,” “rooms” and “food.”
The obvious problem with this approach is that adds will appear on thousands of irrelevant websites with poor conversion rates. He would need to spend a tremendous amount of money collecting data on these sites and blocking them through his blacklists before he could create profitable campaigns.
I realized that I could improve on his model by using big data to improve targeting at the onset of the campaign. There are a number of tools that allow you to scrape list of websites throughout the Internet. This allows you to blacklist domains that are unrelated to your offer or wait list those that are likely to have a solid conversion rate.
One of the approaches I took was using the Alexa Million list (unfortunately it has been retired). This was a list that Alexa used by crawling websites throughout the Internet. It allowed me to immediately blacklist all of those sites and then remove sites that didn’t convert well. This left me with a highly filtered list that would have a high conversion rate.
There also a number of search engine scraping tools that you can use in a similar way. Some of these tools like GrabzIt allow you to even scrape content that is dynamically generated with JavaScript. These are highly effective for filtering irrelevant websites for your contextual campaigns in foreign countries. You can quickly build a list of new sites and adult sites in those areas by using the right search keywords.
Another approach is using a business listing extraction tool, which allows you to build lists of businesses along with their respective websites. Many of these tools are available in different languages and allow you to extract business listings from other countries. You can follow a similar process by building a list of websites to either whitelist or blacklist on your contextual marketing campaigns.
All of these approaches will help significantly reduce the amount of money that you have to spend at the beginning of your campaign. You will also have that are targeted data at the beginning, which will help you optimize your creative side landing pages more easily.
