When we designed ClickDimensions web tracking we put a lot of thought into how best to model the data and what data points to provide. For example,
- We created a first visit field on our web visit records so users can analyze what is driving people to their sites initially
- We created a separate field for entry page so you can easily see which page the visitor landed on including any special parameters you might have added to links from online ads you have purchased (Read more about this)
- We provide the entire referring URL and we parse out the keywords, show the referring host and the referrer type (i.e. direct traffic, email, search engine or social)
By analyzing visits from social sites we have built a social dashboard that is very useful in understanding where you should spend your time and marketing dollars. We'll look at our data from the last few days to do some analysis. First, let's look at a pie chart from the ClickDimensions geographical analysis dashboard below. You can see that our site is receiving traffic from a number of countries and that, as expected, the majority of visits come from the United States.
Next, we filter down the data to show only the visit that have come from social sites. Immediately we see that visits from the United States drops below 50% when looking at just those from social sites. This tells us that our social strategy is helping to bring proportionately more visits from abroad then from the US. For example, ~6% of visits from the US came from social whereas over 13% of visits from the UK and over 15% of visits from the Netherlands came from social sites.
Filtering to the next level we look at the visits from social sites by their referring hosts. This gives us visits from the main social sites; LinkedIn, Facebook and Twitter. This really tells us what's going on; LinkedIn by far and away brings more traffic to our site than Facebook and Twitter. Now, we know that tweets are served through a variety of Twitter clients and sites so those numbers may be skewed. However, there is no doubt in looking at this that we need to pay attention to LinkedIn.
Then, analyzing which countries are visiting from LinkedIn the variety was too great to show a meaningful bar chart. So, we created a 'Top 10' bar chart to see the countries from which LinkedIn drove the most traffic. It quickly stands out that we are getting 1/3 as many LinkedIn visits from the Netherlands as the US. That seems unusual given that the population of the Netherlands is roughly 5% that of the US. One possible reason for this is that our LinkedIn ads are hitting their budget limit early in the day when Europe is awake and the US is asleep. So, once the US crowd gets up the ads are no longer being served. Of course, because the ClickDimensions visit records entry page field clearly tells us which of our LinkedIn visits were generated from an ad vs. from another part of LinkedIn (i.e. a free click), a simple query verifies that indeed we're blowing our budget early on most days before the US is up and about.
However, before we run out and increase our spend, we'll do further analysis on the average quality of these LinkedIn ad visits so we can understand if the traffic is what we want (i.e. engaged). It is very easy to compare the average visit score, duration and pageviews from LinkedIn ad visits vs. other visit sources. Below is an analysis that took less than 5 minutes using ClickDimensions visit data and Microsoft CRM's Advanced Find query tool. Clearly, and as expected, we see that visits that come from Microsoft's own marketplace (Dynamics Marketplace) are very high quality. This makes sense given that people would only find our Dynamics Marketplace listing if they own Microsoft CRM and are searching for a marketing solution. The data also shows us that the visits from LinkedIn aren't bad and are certainly better than those from Google Adwords. So, the data supports the decision to increase the spend in this area.
With Microsoft CRM 2011's dashboard visualizations and ClickDimension's rich set of data the possibilities are pretty expansive. This is just one example. What will you discover when you bring CRM, marketing and web analytics data together into a common data model accessible by a common reporting framework?