Goals
- Gain a better understanding of how the referrer type and keyword data provided by Click's Web Tracking can help assess a visitor's value as a prospect.
Search Engines vs Direct Traffic
Most of us have seen this graphic before. It is from Google analytics and shows the breakdown of your traffic based on whether it came from search engines, direct (i.e. the visitor typed in your URL or used a bookmark) or referring sites. This is deceiving at best because most of us our search engines as a substitute for typing in the URL.
Instead of bookmarking www.clickdimensions.com, we go to our Google or Bing search boxes on our browsers and type in clickdimensions or something similar. Using our own solution we can see the keywords that are used when people visit us from search engines. Since we classify each visit based on whether it came from a search engine, social site (we keep an extensive list), email link click or direct/bookmark link we are able to query all visits classified as coming from search engines and then inspect the keywords.
NOTE: Google no longer provides anyone with organic search terms or keyword data.
The result of this analysis is that we see a lot of visits that are technically search engine visits but, since they used branded keywords (i.e. our name or our product name) we can really think of them as direct traffic because the visitor clearly knew about us. This is great because it allows us to clearly see visitors that came upon us from non-branded keywords like 'marketing automation'. When we see a combination of a growing lead score from a visitor that used non-branded keywords we can quickly assess their value as a prospect.
Making Use of Keywords
Below is a great example. We have one visitor that came to us from a search on 'powered by windows azure'. Now, while our solution is powered by Microsoft Azure, people buy it because it provides marketing automation functionality. So, there's probably no point in spending time on this visitor and, as you can see, he/she didn't accumulate a high score.
In contrast, the visitor below came to us from keywords that related perfectly to our solution and, thus, was very interested and built a high lead score. In addition, because Click aggregates all traffic from IP addresses, once we identified one person from this prospect, we could see that several others had also been on the site expressing strong interest. At that point, we knew our odds were good and this tempered the sales cycle. They are now a satisfied customer.