Visitor Insights

Tealium is proud to have accomplished the Google Analytics accreditation process to become a Google Analytics Authorized Consultant (GAAC). This is in addition to our recent accreditation as a Google AdWords Consultant.

The combined certifications allow us to extend our web analytics and online marketing expertise to clients of Google Analytics and Google AdWords.

You can find out more information about our Google Analytics services via this link.

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.

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.

Today’s major search engines let you show your ads both through their search (keyword advertising) and content networks (contextual advertising). One common mistake that we see happening in the industry is that companies run both their search and content advertising the same way: same keywords being targeted, sames ads and same landing pages. By doing so, they also compare their search and content network performance side-by-side and typically see better results through their search network.

Does this mean that you’re better off discontinuing the contextual advertising and putting your budget towards search advertising? Not necessarily. The reality is that these two networks serve very different audiences and with proper strategy, you can leverage them both optimally. We’ve even seen customers who do much better within content network compared to search networks.

In order to explain this, we’re going to look at the customer sales cycle first, which is shown in the figure below. This is a simplified version of the AIDA model (Attention > Interest > Desire > Action).

Within the online world, the sales cycle consists of three key stages:

• Awareness: clearly the first stage is that future customers will have to be aware of you or your company. How can someone do a search for you or your products if they don’t even know your industry or what you do? Awareness is mostly created through channels such as press releases, news sites, blogs and many of the social media outlets.
• Influence: once people are aware of your product or industry then you can influence their sales decision. In the online world, this is done through the traditional online marketing channels such as banner ads, PPC advertising, email and newsletter campaigns.
• Action: this is the step of actually converting. This component can either be an e-commerce transaction, an online lead generation, a newsletter registration or else.

From here, we can now see the different audiences that search and content networks serve. The search network is serving those who are doing an actual search. That means that they’re already aware of the industry or the product and as a result, search generates high click-through numbers and high ROI.

The content network on the other hand has to be treated differently. You cannot assume that those who see your contextual ads are already aware of your products and you will very likely see much lower click-through numbers within content networks. So for many companies with complex sales they’ll be well served to use content networks as a venue to generate or increase awareness. We’ve even seen companies serving new industries that have seen much better performance on contextual advertising. This is because they’ve been able to leverage the content networks to increase customer awareness about their products.

This also means that you cannot rely on click-through and conversion rates to compare your search and content network performance. Instead, you’ll have to look at the effect of your content network at increasing your search click-through and ROI.

An unscientific approach!!!

One of my favorite tools to see trends, patterns and seasonality associated with search terms is Google Trends. It lets you see trends associated with specific keywords and compare up to 5 keywords together.

With all the buzz around the democratic primaries, it was only fitting to use Google Trends to see if we could see patterns that could shed some light into the outcome. The results, although not scientific are pretty revealing. First off, here’s a comparison of searches for the terms “barack obama” and “hillary clinton” over the last 12 months. We’ve obviously filtered out the international traffic and the results are shown in the figure below.

The interesting trend here is that Obama was behind Clinton in terms of searches till January of 2008. January 3rd happened to be the date of the Iowa caucuses which showed a surprising win by Obama and one can speculate put him on the map as far as the general audience is concerned. To test the hypothesis, lets look at a similar comparison, but this time between “huckabee” and “mccain” and interestingly, we see a similar pattern.

Within the GOP front, we see a spike in interest for Huckabee prior to the Iowa caucuses and a decrease after McCain gains momentum from consequent wins.

How accurate is this?

So you’re wondering, how accurate is this? While Google Trends is a great tool for search engine marketing, it is simply not built to forecast elections and markets. For example, if you look at the breakdown by states, you can see that the term “barack obama” gets higher traffic than “hillary clinton” in all states, including Pennsylvania, Kentucky and Ohio - where Hillary Clinton won by a large margin. This is shown in the image below.

In fact, in terms of popular vote, both candidates were neck and neck. And you can make the argument that most people did not know much about Obama before the primaries began and therefore what we’re seeing is people educating themselves about the candidates. But it’s certainly fun to see the trends in terms of peaks and valleys around some specific events such as the start of primaries and caucuses.

The purpose of this post is not to endorse any one candidate or make market predictions, but rather showcase what you can get from Google Trends. The information can be very useful in determining peaks and valleys in user interest associated with specific events of relevance to your business.