Visitor Insights

A while back we published a post on using Google to predict elections. A similar post has recently been submitted by Jeremiah Owyang on the use of social networking stats for similar analysis. Obviously, in both cases, one cannot heavily rely on these numbers to predict elections, as they’re a reflection of interest and one cannot guarantee that interest turns into votes. But in both cases, there’s a great deal of entertainment value associated with the data.

However, a recent article in New York Times reveals a much more powerful side of Google Trends. In this case, Google is using its search data ot track the spread of flu within the United States. More specifically, deta from last year revealed that Google saw a spike in number of searches for terms such as “flu symptoms” about two weeks before the CDC (Centers for Disease Control) reported regional outbreaks. The graph below shows the comparison between the two data sources.

I must admit this is one of the most clever uses of Google Trends as it captures real data in a manner that can be used to actually predict trends. Congratulations to the folks at Google.

I always enjoy what the guys at Common Craft do. They have a way of simplifying things for everyone to understand. This is another fantastic piece of work by them. It explains social bookmarking. Many people miss the social aspect of bookmarking.

One of the most popular KPIs for lead generation sites is “Cost per Lead”. It lets marketers know whether they’re spending the right amount on marketing campaigns. However, a better KPI for such customers is “Cost Per Qualified Lead”. It provides a more accurate picture of the campaign performance.

The following is what a customer has recently shared with us regarding their use of WebToCRM.

Note: the numbers have been revised in this post.

After running a number of online campaigns, they were able to use their web analytics solution to determine high-level performance metrics such as campaign clickthroughs, conversion rates and cost per lead. The sample data is shown in the first graph below.

The campaign conversion rates are mostly within the same range. However, because of the higher conversion rates and lower cost per click (CPC), newsletters end up generating a much better cost per lead (CPL). Based on this data, it would make sense to take some budget away from search engines, which have the highest cost per lead and put that money towards more newsletter sponsorships.

However, the customer did not stop there. Thanks to WebToCRM, they integrated their online campaign data into their salesforce.com implementation and decided to break down their leads into two classifications:

• Qualified leads: these are defined as leads with complete and accurate information, including the person’s full contact information, job title and decision making timeline.
• Unqualified leads: these are leads with incomplete or inaccurate information such as fake email addresses, etc.

How do the numbers hold up when you look at qualified leads instead of just the raw number of leads? The picture turned out to be quite different and is shown below.

With a simple “Cost per Lead” KPI model which is what many web analytics practitioners use, the company would have diverted money from search engines into newsletters. However, the more relevant “Cost Per Qualified Lead” KPI shows that the customer would be well served doing the exact opposite. Although search engines provided fewer leads per click than newsletters, they also provided a far higher percentage of qualified leads. The client is therefore going to continue its investment in search engine marketing.

Still wondering about the value of cross-channel analytics? Think it’s expensive? Take another look at WebToCRM. It lets you integrate your online campaign data into your CRM application, regardless of what web analytics or CRM tool you use. Best of all, the Free edition give you the same level of data that you see in this example.

Ever wonder what all the buzz is about social media? The folks at Common Craft have done the best job I’ve seen explaining social media. Enjoy.

Media for the people, by the people.

Ever tried measuring the return on investment of your press releases? Although seemingly simple, the process turns out to be more complex than one might think.

Recently we published a press release announcing the WebToCRM product. For those of you interested, it can be found here.

The purpose of the press release was to introduce WebToCRM, but also to generate traffic and leads to the tealium.com Web site. So how did the press release do? It turned out to be quite successful as shown in the chart below. It ended up generating a tremendous boost in traffic.

The site traffic on May 20 increased exponentially on the day of the PR launch. Given the fact that Tealium is a startup and the site does not get much traffic today, clearly the increase can be attributed to the press release.

But drilling down into the data reveals a hidden value of PR that cannot be detected by web analytics solutions. To analyze this more, we’ve broken down the traffic by source. The categories analyzed are “Bookmarks or Directly Referred” (typically direct traffic to the site or emails), “Search Engines”, “PR Sources” (compilation of referring URLs associated with the news stories), and others (not fitting any of these mentioned categories). The results are shown in the figure below.

We’re analyzing two seven-day periods, one before the release and the other after the release. The results are definitely revealing. Obviously, the PR traffic increased since there was no PR the previous week. But more interesting is the fact that both the bookmark and search engine traffic increased dramatically as well. In other words, there’s a hidden value to PR that previously hasn’t been mentioned in the Web analytics world. Because of the press release, more people came to the site directly and more people searched for “Tealium” on search engines.

If you’re a marketing veteran, the results make sense. Press releases are a great medium for generating awareness. Also, by creating a marketing mix that includes multiple touch points (PR, search, banners, trade shows, etc.), you’ll get an overall result that exceeds each one of the touch points combined. In other words, 1+1+1=4.

However, it also reveals the challenges associated with PR measurement. It shows both the effectiveness of online PR, as well as the difficulty of measuring it. In our case, this was not a difficult task, because of the reasonably low traffic that we were getting before the press release. But if you’re a high traffic site, measuring the effectiveness of PR becomes much more challenging and certainly an area that has not been addressed by Web analytics solutions.

The field of PR measurement today consists of understanding your Outputs, Outtakes and Outcomes. Outputs and Outtakes are fairly simply to measure, but Outcomes (the business results of the PR - site visits and conversions in the online world) are much harder to measure, and ironically more valuable. We’d love to hear back from you about your experiences in this field and what you’ve done in your organizations to solve this problem.