Making a more strategic analysis

I have probably spent a good four nights looking for substantial case studies for Web analytics tactics used effectively by companies, and I can tell you that there is a lot of garbage out there. There are many trite, unsubstantiated facts and fewer intensive examples that offer much more than shiny marketing.

But there still are those who don't mind providing in-depth information on their strategies to success. One that I found to go give a deeper than normal analysis is the case study on Amari Hotels and the company's experience with multi-channel funnels (Google Inc., 2011). The report could have contained much more information, I am sure, but the company probably does not want to give all of its secrets away.

Multi-channel funnels is a more sophisticated indicator than typical broad analytics categories such as pages/view or visitors/month, so this example gives us a chance to see the far-reaching power of Web analytics (Google Inc., 2011). This company is using Google Analytics, which as we know is free, thereby making this analysis available to any company interested in taking the time to understand and use analytics to gain market share and build customer engagement. In the end, this means the company can make more money.

Multi-channel funnels reporting is a newer way to show the channels that a customer used in the 30 days prior to making a purchase (Google Inc., 2011). Channels can include clicks on paid and organic searches, display ads, social networks and affiliates.

The company is made up of 13 hotels in Thailand and is a member of the Onyx Hospitality Group, which manages 40 existing or planned hotels in Asia (Google Inc., 2011). First the company's marketing team analyzed the data. They noticed in the Path Length report that 58 percent of visitors made their purchasing decisions in one visit. The assumption had previously been that most customers made their purchasing decisions over several visits, taking time to research deals.

The marketers made the decision to add more information on sales offers to landing pages to assist these customers in their decision making (Google Inc., 2011).

Here are before and after views of an Amari landing page:

Notice the sales listings that were added to the modified page. Within a month of these modifications, the company saw a 44 percent increase in booking rates (Google Inc., 2011).

Later when the marketers looked at the Path Length and Top Paths reports, they saw that 57 percent of transactions resulted from “multi-path” interactions (Google Inc., 2011). Most of the customers went to the site, left to think about the decision and came back to book a room in one of the company's hotels.

The marketers decided to increase the company's coverage on the Google Display Network to “better connect with these visitors after they had visited the site” (Google Inc., 2011). The company tested expanded campaigns for the Amari Palm Reef Samui hotel and saw an 11 percent increase in bookings in the next month.

Here is the link to Google Ads'Display Network. The ads can exist in a variety of online media and show up on websites that are “relevant to what you're selling.” To me, this means that as the customer is searching on other sites, they are being reminded of your company and its (hopefully) attractive offers through display ads.

The company did not ignore search engine optimization in its assessment (Google Inc., 2011). Marketers used the Assisted Conversions report to look at “the relationship between generic and non-brand keywords.” The use of generic keywords led to increased bookings, increased the average value of bookings and lowered the cost per conversion. This report can be found under the Multi-Channel Funnels tab in Google Analytics.

Though the bulk of this effort was analysis and hard work, there was some non-scientific human feeling involved, too (Google Inc., 2011).

Here is what the Onyx Hospitality Group Vice President of E-commerce Chetan Patel said of the decision to take this new direction in marketing:

Understanding cross-channel influences was a matter of hunch. Due to how much we invest in digital marketing across all our brands, the lack of tracked insights made me uncomfortable. Multi-Channel Funnels now gives us visibility into how customers reach our site and convert—from all our channels. It allows us to do attribution analysis on our online marketing activities, looking at them as combinations of channels and steps of varying success, rather than last-clicked channels in isolation. (Google Inc., 2011)

The idea of going beyond “last-clicked channels in isolation” is a powerful one. Sometimes we as marketers think we are taking everything on the whole while still understanding the parts that make up this whole. Often this is not the case.

I was not familiar with the reports that Amari Hotels used in this instance and am interested in using them at my company. According to Google, Multi-Channel Funnels can be extended from any defined goal or e-commerce transaction (Google Analytics, 2012). Most of this information is gathered automatically. Unpaid search from all search engines, referrals from websites and social media sites and direct visits are already detected with Google Analytics. You would have to set up tracking for Google AdWords, non-Google paid search engines and custom campaigns to access data from these sources in the report.

The Path Length report can be found under the Conversions tab and the Multi-Channel Funnels tab. I could not find an explicitly named Top Path report anymore, but if you look in the Path Length report, you will see which path generates the highest percentage of conversions.

Here is that report for my blog:

This next portion of the analysis is a little hypothetical since I am not sure what are some of the other steps the company may be taking. But let us say for the moment that the company is not taking many other in-depth steps analytics to increase sales and maintain a high return on investment.

Here are a few suggestions for the company:

• Amari Hotels is looking inward, but they can significantly strengthen their analytics power by comparing their customer engagement with that of other companies in their industries (Kaushik, 2009). This Google Analytics function has changed somewhat since Kaushik wrote about it. Benchmarking used to be a part of the reports, but now if you opted in to anonymous data sharing with the company, you will see certain industry stats in a monthly newsletter (Osam, 2011).
• Kaushik also recommends site search, which you can now find in a tab under the Content section in Google Analytics (Kaushik, 2009). He calls the data in this report possibly the only place “in your entire web analytics data where there is no question about visitor intent.”
• Also, don't forget to look at the bounce rates for your top landing pages. That will give you important indicators about areas of your site that need work (Kaushik, 2009).
• One thing the Amari case study did not mention was ecommerce tracking, though it is possible the company is using it. After setting up this function in Google Analytics, you can look in-depth at metrics such as Time to Purchase and Sales Performance.
• I went to Amari's Facebook page, which has existed since 2009. And while they have lots of pretty pictures and videos of Thailand and the company's hotels, and have a little customer engagement, I think they could do better by publishing more sales-related posts. I think if I were going to Thailand, I would want to like the page so I could find out about deals, not really see beautiful scenes that I already know are there. It is great to have content, but I think they have strayed a bit on their social marketing efforts. Just as they had success with highlighting hotel sales on the main website, I believe they could have similar success on social media outlets.

References

Google Analytics. (2012). Setting up multi-channel funnels. Retrieved from: http://support.google.com/analytics/bin/answer.py?hl=en&answer=1191182

Google Inc. (2011). Case study: Multi-channel funnels. Retrieved from: http://www.google.com/analytics/customers/pdfs/amari-multi-channelfunnels.pdf

Kaushik, A. (2009). Google Analytics maximized: Deeper analysis, higher ROI & you. Retrieved from:

http://www.kaushik.net/avinash/google-analytics-maximized-deeper-analysis-higher-roi-free/

Osam, M. (July 7, 2011). Google Analytics benchmarking is back. Retrieved from: http://www.famousbloggers.net/google-analytics-benchmarking.html

Digging deeper

Since my last blog entry, I took a few actions to further understand and implement Web analytics on my blog:

1. I created a filter on a separate profile to remove instances that my home computer viewed the blog from any data from this point onward.
2. I created two goals in the visit duration and pages/visit sections that consisted of visits of more than one minute and visits to more than one page. These goals seemed like a challenge and achievable since the visit average had previously been about 50 seconds, and I wanted to establish a baseline for pages per visit to gauge future success. Looking at my most recent feedback, I probably ought to raise this pages per visit goal because my average pages per visit is 4.13. I'm still surprised, partly because this is a student blog for class that it is showing a pages per visit figure beyond one.
3. I spruced up the site to make it more attractive but also add more opportunities for engagement such as buttons to allow viewers to share my blog posts, subscribe to the blog via e-mail, or subscribe via RSS feed.
4. I made the site more targeted to the marketing viewer by adding the blogs of my classmates scrolling down the side and adding Mashable news in a feed at the bottom of the blog. When you look at my chart on referral traffic below, you will see that one of my classmates' blogs directed traffic my way. Networking can be a wonderful thing.

I drove traffic to my site again with an invitation on Facebook that looked like this:

 

It followed up from my previous request that asked my family and friends to look at the site and told them I would try to identify their visits.

Filters

I am beginning to feel anxiety from using analytics software that does not look into historical data after the establishment of certain parameters such as filters.

When I established a filter to remove my home computer's IP address, I wanted to make sure my visits used to check the site for formatting problems, comments and my own curiosity were not being counted. I didn't want to overestimate my success, especially when the blog has a relatively small group of visitors. But since I established the filter on June 28, I only have myself filtered out of the data starting at that point and can't go back. It means that my main profile and the filtered one do not match up because the main profile reported its first activity on June 12 and the filtered profile begins on June 28.

On June 28, my full profile showed one visit more than on the filtered one. All of the other days match. This is because I did not visit my blog during the weekend because of a power and Internet outage after a storm hit the mid-Atlantic states. I had the impression that I visit my own blog more, but the filtered profile is so new and the weekend was more eventful than I had intended.

I can see how in a mid-sized company with a smaller audience, it could be valuable to filter out the domain name or IP addresses for employees, especially for those who maintain the website and are constantly checking functionality (Filters in Google Analytics, 2011). In my current job at a university, you wouldn't want to filter out the domain names for the main profile you check often, I am positing. The approximately 8,000 employees at the university have a domain of mail.wvu.edu and the 30,000 students have a domain of mix.wvu.edu. Remove those two and you'd cut out a chunk of the audience our news site is trying to reach. We are looking for outside customers of course, yet our internal customers are also an important audience. They are practically our only audience on some news items.

While you would not want to make all of your decisions based on a profile that doesn't account for all of your customers, it could be valuable to compare behavior by types of customers. One filtered profile could mark only internal customers. Another filtered version could include everyone but internal customers (Filters in Google Analytics, 2011).

For a news site, it is possible that internal customers would appreciate more the student profiles and faculty awards. Yet, outside viewers might be more interested in sports stories, stories about the mascot or stories about research that is applicable to their daily lives. Profiles filtered this way could tell you.

Goals

Here are the results for the two goals I set in Google Analytics for this blog. Goal accomplishments, like filters, also start counting from the day you input your goals into the software and they do not count retroactive data even if it meets the goals set.

 

Since June 28, I've had 13 goal completions, which includes nine visits that went to two or more pages and four visits that lasted more than one minute. This more valuable traffic came from Facebook referrals, blogger.com referrals and direct traffic.

I can drill down and look more closely at where this traffic is coming from to try to attract similar viewers (Kaushik, 2010). In the goals report, you can find out on which pages the goals were completed. With my blog at such an early stage (and considering it is a blog with pages being of roughly equal importance), I haven't gleaned anything too helpful. However, on a news site like the one at my workplace, I could look at goal conversions and discover on which pages the conversions occurred. This will tell me which stories, videos and pages achieved my company's goals.

Google Analytics also has the ability to track specific URLs as goals (Google Analytics, 2011). For example, you could set a goal for each time a customer reaches the “thank you” page at the end of a sales process. If you create events in Google Analytics, you can also register the event completion as a goal.

Google evangelist Avinash Kaushik offered a vignette about how he put a value on his blog based on the value he earns for his time on other pursuits (Kaushik, 2010). I think his time is worth a lot more than mine, and because this blog is more of a test right now, I personally don't put much value on it, seeing as how it draws its audience mostly from my family and friends who want to help me out, not users who are searching for analytics information.

If I were assigning value, I would wait until I blogged more extensively and did so outside of homework before I set a value. Though I have not done so, putting a value on goals is an incredibly helpful tool when it is done as accurately as possible (Kaushik, 2010).

Funnels

According to the Google Analytics IQ Lessons, the company's analytics software can set goal funnels. Funnels record the series of actions a user must take to fulfill a goal (Google Analytics, 2011). As I set my goals, I thought about which goals would tell me the most important information. I'm most interested in visitors who spend a lot of time on the site and visit more than one page.

Step-by-step processes are extremely useful in e-commerce sites where there's a shopping cart step followed by checkout, registration if necessary, payment and confirmation pages. Of course funnels are useful to more than just e-commerce sites. As I examined the usefulness of implementing goal funnels for my blog, I did not find a process that I wanted visitors to follow. I'm pleased if they visit pages in any order. I'd like them to subscribe to the blog, but I don't prefer a particular order for that either.

However, for my company's news site, we could set a funnel for projects that have stories and videos embedded. We want viewers to enter the story and then move to the video. We can also look at funnels for common links, such as links to the university's main page or to the admissions or application page. This can give us a good picture of how our strategy and goals connect.

Once you have defined your funnels, you can see where your site may have technical difficulties, where it may lose audience/customers and the most effective path to goal conversion (Google Analytics, 2011).

Since I have collected more data, I've noticed a few extraneous changes I wanted to note in this post:

Source traffic

I have a keyword to examine now! My one visitor from outside the United States is in the United Kingdom, and this person used a keyword to get to the site and didn't bounce! The visit originated from Little Chesterford. I can safely say I know no one from there. I feel like I have a legitimate blog now.

 

This one visitor came to the site via the keyword shown in the chart below, “facebook vs google ads diagram.” The visit included two pages, though it was a very short visit. I know my blog post on Facebook vs. Google did not have a diagram though this visitor was searching for one, so maybe this contributed to the relatively quick exit.

Analysis of this keyword underscores the large amount of useful information that analytics can provide even though it is anonymous. I have no idea who this person is. But I know what he or she was looking for, the pages that were viewed and the duration of the visit.

Now let's look at traffic as a whole. Most visits are still coming from Morgantown. I've got one more visit from Moscow, Idaho, and from Broward County, Fla. There are no additional visits from West Palm Beach and Lake Worth in Florida.

But one of my old friends from Kansas City has visited presumably since I only know two people who live there.

I have a visit from Manassas and a visit from Oakland, both of which bounced. The two visits from Philadelphia also bounced. The visits from Dallas and Plano did not bounce.

Overall, the blog's reach is increasing, but as you can see below, the site has a majority of new visitors, which is not a good sign, according to Kaushik (2010).

Bounce Rate

I finally have a bounce rate of 27.78 percent! You should not normally be excited about a bounce rate because that means you have visits that consist of only one page. But I'm glad to see the bounce rate because it means that I am getting more visitors, even those who happen across the blog by mistake, and I am also sure that the analytics software is working. It also helps that my bounce rate is relatively low.

References

Google Analytics. (2011). Filters in Google Analytics. Retrieved from: http://www.google.com/analytics/iq.html

Google Analytics. (2011). Goals in Google Analytics. Retrieved from: http://www.google.com/analytics/iq.html

Kaushik, A. (2010). Web Analytics 2.0: The art of online accountability & science of customer centricity. Indianapolis, Indiana: Wiley Publishing, Inc.