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.

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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.

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The pre-defined segments should greatly increase user adoption of this functionality. At the same time, users will be able to further extend the value they get from the tool. We’re going to outline some additional segments that can further add value. For this post, we’re going to concentrate on lead generation site types, whose goal is to generate leads for the sales team that are eventually closed off-line.

Note: These segments are also applicable in other web analytics solutions, even though the examples are provided for Google Analytics.

Some quick win segments that you can get started with are:

1. Form Page Visits
2. Form Abandonment Visits
3. Engaged Visits
4. Highest Value Conversions

Form Page Visits: One of the pre-defined segments in Google Analytics is “Visits with Conversion”. As long as you’ve defined your site goals in Google Analytics, this segment will filter out visits where a goal event has occurred. The segment will let you identify your most effective acquisition sources for example. However, you can take Google Analytics to the next level and define a segment based on visits where your site visitors hit the form pages (just before the conversion). Why? People who make it to the form pages are also considered as qualified visitors, even though they don’t fill out the forms. This segment will also let you identify your best acquisition sources, whether a conversion occurs or not. This segment can be created by selecting the dimension “Page” from the “Content” list and typing in the name of your form pages. If you have multiple form pages, you can add them by adding an “or” statement.

Form Abandonment Visits: This segment will filter out visitors that make it to the form pages but do not complete the process. This segment is valuable because it lets you identify where you’re leaving money on the table. This segment will consist of two filters. The first one is similar to that used for the “Form Page Visits” segment. The second filter is using the metric “Total Goal Completions” and setting the value to equal zero. The relationship between the two filters should be an “and” statement.

Engaged Visits: Regardless of whether your visitors convert or hit the form pages or not, you’re still interested in how they consume your content. The “Engaged Visits” segment lets you filter out the site visitor that are engaged in your content. An easy way to do this is to start by your average number of pages views per visit and time on site. You can then build a segment with two filters. The first filter is by selecting the “Pageviews” metric and making sure that it is set to a value greater than your average. The second filter (“and” statement) uses the “Time on Site” metric, as shown in the figure below. You can take also this segment to the next level by adding recency into the equation. Eric Peterson has recently posted about this very topic, and we recommend his approach for media sites. For lead generation sites a simplified engagement approach should suffice.

Highest Value Conversions: Not all web leads are created equal. For companies that offer only one product or service, this may not be an issue. But for many lead generation sites and companies that offer multiple products, this will be of importance. For example, you may have a free and a paid version of your product. In that case, you would be more interested in visits that result in a lead for your higher-end products.

In some cases, this may be an easy segment to build if your conversion pages for your various products are different. Often times, you’re using the same form page to capture leads for all your products. In such cases, we recommend that you allow visitors to choose which products they’re interested in form within the forms and capture the user selection in Google’s User Defined variable. By doing so, you can then build a segment based on the “User Defined Value” dimension under the “Visitors” group. The new segment will then allow you to determine where to target in order to capture leads for your highest value products.

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We’re specifically interested in connecting with other industry experts and see how organizations are tackling their analytics challenges.

If you’re also attending the event, please drop us a note.

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Apparently, this is not just isolated to web analytics. An analysis of the VP debate by a language monitoring service shows that the phenomenon also applies to the world of politics. Here’s a link to the story.

Highlight KPIs from the VP debate:

Sentences per paragraph: 2.7 for Biden and 2.6 for Palin.

Letters per word: tied at 4.4.

Ease of reading: Biden, 66.7 (with 100 being the easiest to read or hear), versus 62.4 for Palin.

Number of words spoken: 5,492 for Biden and5,235 for Palin.

There is definitely entertainment value in the data, but not much value beyond that.

Why do we bring this up? Because we’ve seen many web analytics practitioners fall into the same trap. Yes, you can measure just about everything online: detailed user interactions, mouse movements, etc. But just because you can measure it doesn’t mean you should. Rather, you should always start with the question “what would I do with the data”.

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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.

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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.

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Media for the people, by the people.

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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.

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