Web Analytics in Retail: A look at REI

Recreational Equipment, Inc., more commonly known as REI, is a leading outdoor equipment and apparel retailer. Since 2005, REI has used Coremetrics as a web analytics platform to monitor and analyze the browsing and purchasing behavior of visitors two their two online stores (Demery, 2005). In this post I'll take a look REI's implementation of a web analytics platform, look at one specific case where web analytics were used to improve the REI online shopping experience, and offer some further suggestions on how web analytics might be used to further optimize REI's online stores. Before I begin though, I'll provide a little background on the company itself.

About REI
REI is a leading outdoor retailer founded as a cooperative in 1938 by a group of mountaineers from Seattle, WA. Since then REI has maintained its structure as a cooperative while becoming a nationwide retailer of outdoor equipment and apparel with annual sales over $1.5 billion (Society Consulting, n.d.). It counts over 3.5 million people among it's members and is the nation's largest retail coop (REI History, n.d.). Along with stores nationwide, REI has two online storefronts: rei.com and rei-outlet.com (Demery, 2005). Online sales are a major part of REI's business. And the company has consistently been ranked highly in Internet Retailer's Top 500 list, coming in this year at 64 (Internet Retailer, 2012).

REI's Implementation of Coremetrics
REI uses the web analytics Coremetrics to monitor and analyze the browsing and purchase behavior of customers on their website (REI Coremetrics, n.d.). As I mentioned above, REI has been using Coremetrics since 2005 when it was selected because it was a top of the line web analytics platform for retail sites.

In using Coremetrics, REI offers its customers three different tracking levels: anonymous visitor, total opt-out, and cancel opt-out (REI Coremetrics, n.d.).

1. Anonymous Visitor: This is the default level of tracking access, and the one we are most used to with many other web analytics platforms like Google Analytics that use anonymous tracking. With this setting no personally identifiable information is collected (REI Coremetrics, n.d.). 
2. Total Opt-Out: With this setting Coremetrics will not collect any information about a customers "shopping actions" or personally identifiable information (REI Coremetrics, n.d.). This seems to be an opt-out from all tracking, although the language is vague in its specification of "shopping actions." Does this mean that REI and Coremetrics can track actions not related to shopping? 
3. Cancel Opt-Out: The name of this preference level does not accurately describe it. It is actually the most robust level of tracking. With it Coremetrics can conduct the same tracking as they do under the Anonymous Visitor level, but it can also capture the customer's email address and membership number, if the customer is and REI member (REI Coremetrics, n.d.).

While the opt-in page is not prevalent in REI's website, it does set an example of good practice by allowing a customer to set tracking based on their own privacy preferences. If they succeed in getting Cancel Opt-Out permission from an customer, REI has the ability to collect some very robust data about the customer's interaction with the site and even determine information about preferred brands and activities.

Putting the Analytics to Work

Recently, Seattle-based Society Consulting aided REI in solving a specific problem related to variations and unpredictability in page load times that they found their customers were experiencing as their online sales grew (Society Consulting, n.d.). In observing metrics from their website, REI found that lag time in loading pages had a significant negative impact on sales. Research has suggested that the impact can be as large as a 1% drop in sales for an added delay of just one-tenth of a second (Kohavi & Longbottom as cited in Society Consulting, n.d.). 

Knowing this REI charged Society Consulting with the task of determining the effect of page load time on REI customers and then running a cost-benefit analysis on several load time factors, including conversion optimization, customer experience, outage losses, and fault triage (Society Consulting, n.d.). 

Through their project, Society Consulting created a framework for collecting and analyzing performance data built to work with Coremetrics. With this in place, they created a new model to determine the sales impact of changes in load times. With this model, Society Consulting created maximum load times for each section of REI's website and created a dashboard where REI's analysts could easily monitor the site's performance and the impact on conversion rate (Society Consulting, n.d.). 

As a result of Society Consulting's work, REI was able to determine that nearly two full days of sales each year are lost because of slow page load times. In addition, REI was able to more efficiently direct its resources towards developing features and fixing outages that have the greatest impact of sales.

This specific example, shows how REI took one metric (page load time) and found the way it was impacting sales. With the knowledge gathered from their analytics information, REI was able to create a system that could potentially have a very significant impact on future sales. 

Using Analytics to Further Improve Sales

The ways REI could further use web analytics to improve future sales are too many to mention here. While some of my ideas are probably already in uses by REI, I will mention a few. 

1. Use geographic customer data collected by Coremetrics to determine cities for new retail stores.
2. Create funnels from various sales promotion and marketing materials, including emails, catalogs, and coupons.
3. Use data collected from those customers grant full tracking access to tailor individualized marketing messages. This could be emails for new products in the specific activities they participate in, an email highlighting an accessory for a product they recently bought, or sales promotions for the brands they buy most often. 

References

Demery, P. (2005, February 17). REI implements hosted web analytics from Coremetrics. Internet Retailer. Retrieved July 7, 2012 from http://www.internetretailer.com/2005/02/17/rei-implements-hosted-web-analytics-from-coremetrics

Internet Retailer. (2012). Top 500. Retrieved July 7, 2012 from http://www.internetretailer.com/top500/list/

REI History. (n.d.). History & today. Retrieved July 7, 2012 from http://www.rei.com/jobs/rei-story.html

REI Coremetrics. (n.d.). Information collection options. Retrieved July 7, 2012 from http://www.rei.com/shared/help/opt_out.html

Society Consulting. (n.d.). Case study: Web analytics REI. [PDF Document]. Retrieved July 8, 2012 from http://www.societyconsulting.com/case-studies/REI-Case-Study.pdf

Goals, Funnels, and Filters

Tracking simple metrics with Google Analytics is one thing, but to actually start using the tool for analysis that can inform your business and/or communications strategy takes the implementation of more advanced features of Google Analytics. When you dig down deep enough, Google Analytics can provide fairly robust analysis. In order to begin moving you Google Analytics strategy from measurement to analysis some of the first advanced features you should implement are goals, funnels, and filters. In the remainder of this post, I will discuss goals, funnels, and filters individually as I apply them to my own blog.
Goals
One of the most important things you can to maximize the potential of your Google Analytics profile is to set ups goals for your site (Logren, 2012). Setting goals is all about tracking conversions. And as Lars Logren puts it, “Conversions pay the bills” (2012).
Goals are essential to tracking the success of a site. There are four types of goals available in Google Analytics: the URL destination goal, the time on site goal, the pages per visit goal, and the event goal (Google Analytics IQ, 2011). Each of these goals can be used for tracking a specific type of conversion. For this blog, I’ve chosen to set up two types of goals for Adventures in Web Analytics: a URL destination goal and a time on site goal.
I’ll start with the time on site goal since it is relatively straightforward. The time on site goal might be very useful to a site like this blog where the bulk of user interaction comes from spending time on the site reading posts. If a visitor remains on the site for a certain amount of time I can assume that they are interacting with the content I’ve created. With this in mind I could set time on site goal to be reached after a visitor has spent five minutes on the site.
Another goal I’ve chosen to set for this blog is to track when a visitor views an individual post independent of the blog home page. I’ve done this by setting up a URL destination goal with a goal URL that accounts a visit to any post made in 2012 by setting the URL to ^/2012/.* with a regular expression match type (Figure 1). I realize that this will only work for the current year’s posts, but as of right now this seems like the simplest way to capture a view on an individual post beyond the home page.

Figure 1. Setting up a URL Destination goal to track the view of any individual post from 2012.
Funnels The URL destination goal provide a good segue into the concept of funnels. Funnels allow you to define a step-by-step progression through your site that leads up to your specified conversion goal (Google Analytics IQ, 2011). There isn’t a need to setup a funnel on a time on site goal, but a URL destination goal is a perfect place to do this because the destination URL could be a confirmation or thank you page that results from the completion of a sale or an email subscription.
Setting up funnels allows you to see how visitors are progressing through your site, and where exactly they might be exiting the funnel (Google Analytics IQ, 2011). For instance, you might find that 50 percent of your visitors exit your site after step 2 of 6, but that less than 10 percent exit if they make it through step 2. It would stand to reason then that you should find a way to improve step 2 to reduce the exit rate.
On my blog, I’ve set up a simple funnel along with my URL destination goal for a visit to an individual blog post (Figure 1). You can see that the funnel only has two steps. The first is a visit to the homepage and the second a visit to an individual post. This is nothing to complex. However, by setting up a funnel, I’ll be putting myself in a position to better analyze how visitors are interacting with my site and whether it’s a way I’ve intended for them to interact with it our not.
Funnels are much more powerful than I’ve made them seem with this example. The best execution of a funnel will have more steps and a conversion goal with a set value (Google Analytics IQ, 2011). This is something that I haven’t institute on my blog as of yet.
Filters
The final feature of Google Analytics that I’ll explore is filters. Filters allow you to do exactly what the name implies. They allow you as the account owner to set parameters on what data is displayed in a specific profile and how exactly it is displayed (Google Analytics IQ, 2011). With filters properly implemented you can do things like exclude internal traffic to your website, thus eliminating an inflation of metrics by your own employees. You can also change the way data is displayed so that you can more easily read your reports (Google Analytics IQ, 2011).
Before you implement any filters on your site, it is absolutely crucial that you set up a separate profile that maintains the raw data collected from your site. Once you apply a set of filters to one of your profiles, you are unable to go back and recapture that raw data after it’s been processed (Google Analytics IQ, 2011). Lars Logren even recommends setting up one further additional profile to test out new goals and filters without risking the integrity of your main profile(s) (Logren, 2012).
Needless to say, my first step in setting up a filter for my blog was not setting up the filter itself, but actually setting up an additional profile for the raw data. With this second profile in place I proceeded to set up a filter to exclude any traffic from my own IP address (Figure 2).
This simple filter of internal traffic should be the first filter you setup, especially with a blog. It is likely that you will visit your own blog over and over again, thus inflating your metrics. By excluding your own traffic, you can make sure that you get the most accurate portrayal of traffic to your site.

Figure 2. Filter setup to exclude traffic from my local IP address.
References
Google Analytics IQ. (2011). Google analytics IQ lessons. Retrieved June 20, 2012 from http://www.google.com/analytics/iq.html
Lofgren, L. (2012, April 2). The 8 Google Analytics features every site must have enable. Retrieved July 2, 2012 from http://blog.kissmetrics.com/8-google-analytics-features/

Google Analytics: Metrics and Reports for Bloggers

Google Analytics is a powerful tool for any business, organization, or person with a web presence. It’s a free tool set up to spit out a large quantity of data about your site, its visitors, and those visitors’ interactions with the site. However, unless you have clear goals and know what metrics to look for and what reports are valuable, Google Analytics is nothing more that data for data’s sake.

I installed Google Analytics on this blog about a week back, and while I don’t have much traffic to report (2 visitors and 3 visits), I can already see the metrics and reports that could prove useful to any blogger. Before I get into which metrics and reports a blogger should pay attention to, I’d like to explore the goals of a blog.

A Short Note About Goals
As I mentioned in a previous post, before we can put web metrics and analysis to use we have to have goals. It’s a common mantra I hear at web conferences: Goals before tools! While many may approach blogging as a hobby without any monetary or business goals, that doesn’t mean that goals can’t be set. While every blogger might have different reasons for blogging their primary objective tends to be the same: get ever increasing number of people to read what you’ve written.

Goals can grow from this primary objective. They might include creating engaging content, increasing repeat visitors, increasing frequency of visitors, increased traffic to the blog from external sources, and improving organic search engine traffic to the site. As you can see there are many possible goals a blogger might have, but notice that they grow from the primary objective of gaining (and maintaining) readership.

With a well-defined objective and goal set, you can use Google Analytics as a tool to not only evaluate the performance of your blog, but also to inform the future direction of it. In the remainder of this post I’ll discuss a few metrics and reports in Google Analytics that ought to help any blogger achieve their goals and reach the objective of increasing their readership.

Visitors Overview

The first set of metrics and report I suggest every blogger look at is the Visitors Overview report found under the Audience tab in Google Analytics (Google Analytics Help). The Visitors Overview report is just that, an overview of the visitor traffic to your site over the specified time period. In this report you’ll find a number of useful metrics for measuring the volume of traffic to the site. These include unique visitors, visits, pageviews, new and returning visitors, pages per visit, average visit duration, bounce rate, along with a host of demographic and technical user data.

This report provides at quick overview of what’s happening on your site (Google Analytics Help). You can tell at a glance how many people have visited your site, how many times it’s been visited, how long visitors are staying on the site, and how many of those visitors are new or returning. All of these metrics are valuable not because they actually give you the ability to increase traffic to your blog or to inform at strategy that would help achieve that objective, but rather they serve to provide a pulse check and baseline against which to compare previous traffic measurement.

By knowing how a blog is doing currently compared to how it has performed in the past can give a blogger some low level insight into the success of their blog.

Content Reports

A blog is all about the content, so it would make sense that Google Analytics content reports would play a role in measuring the success of a blog in achieving it’s goals. Specifically, the content reports can help you determine just how engaging your posts are down to the level of each individual post. The Site Content reports under the Content menu provide this sort of information (Google Analytics Help). Under the All Pages portion of this report you can find some repeat information like total pageviews and bounce rate, but you’ll also find a page by page breakdown of these metrics. For each page in the site, you are able to see the metrics mentioned above along with average time on page, entrances, and exit rate (Google Analytics Help).

Among the metrics provided in the Site Content report, the most important one for a blogger will likely be average time on page. This metric in particular can help you measure just how engaging your posts are. If you notice that people are spending a relatively short amount of time on each page, it could be a reason to change your content strategy.

In looking at Site Content report, you might also discover that certain types of posts perform better than others not only in terms of average time on page, but also number of unique pageviews and even entrances since that indicates a source of incoming traffic. By identifying trends in which content is successful, you can replicate that success by creating a content strategy that utilizes the elements that made those specific posts successful with readers.

Traffic Sources

While I could discuss several more Google Analytics reports that might be useful to achieving blogging goals and measuring success, I will only focus on one more in this post: the Traffic Sources report. Since the overall objective of blogging is to expand the blog’s readership, it makes sense to look at where your current readership is coming from. Knowing where your current readership it coming from might help identify blogs that are linking to your own that might be worthwhile to comment on, it might indicate strengths or weaknesses in search engine optimization (SEO), or it might highlight some other unique trend that could tell you more about your readers’ interests and habits.

The All Traffic report under the Sources menu of the Traffic Sources tab provides you with a list of sites and other sources that have directed traffic to your blog (Google Analytics Help). It will also show you what percent of new visitors come from a particular source and what the bounce rate for that source is (Google Analytics Help). Knowing the bounce rate can be specifically helpful because while you might have a high level of traffic coming from a specific source, it may be that that same source has a high bounce rate as well, meaning that those visitors are not finding what they were looking for (Google Analytics IQ). Having this knowledge can direct you to take further action where that’s with SEO or some other promotion strategy for your blog.

As I mentioned above, Google Analytics gives anybody with a web presences a massive amount of data about their site. The power of the tool is all in knowing how to put it to good use by always remembering that objectives and goals come before any set of metrics or report. If you keep this one thing in mind, you’ll be achieving your blogging goals with the help of Google Analytics in no time.

References

Google Analytics Help. (n.d.). Google analytics help center. Retrieved June 25, 2012 from http://support.google.com/analytics/?hl=en
Google Analytics IQ. (n.d.). Google analytics IQ lessons. Retrieved June 20, 2012 from http://www.google.com/analytics/iq.html

The State of Web Analytics (in Higher Ed)

The practice of web analytics is constantly evolving in step with the evolution of the web itself. Avinash Kaushik outlines the development of the practice from the early dates of the web on through the semantic web in his book Web Analytics 2.0.

From the earliest days of the web, we were counting hits on individual sites. But hits don’t tell us much. Hits evolved into page views, and while Kaushik says these are still a reasonable measure of success, they are becoming obsolete. Kaushik says that we’re currently living in the era of visits and/or visitors. And this metric does mean something. A visit means that a real person visited your site and consumed some type of content.

While we may be using visits and/or visitors as a primary metric now in 2012. Kaushik suggest that we’re on the verge of a “transformative moment.” He foresees a move to outcome-based metrics. Not one metric can be described as outcome-based because just as key performance indicators outcome-based metrics can vary depending on the business.

Kaushik suggests e-commerce site focus on conversion rate and revenue trend metrics. He suggests that content sites look at depth of visit metrics. He even has a suggestion for Facebook, “don’t tell me the number of profiles you have; rather, tell me about your Visitor Loyalty metric.”

So here we are poised between a world focusing on visitors and visits and a world of outcome-based metrics. It sounds like a positive evolution, but what is the reality of it. I have a particular interest in understanding where higher education is on track with that evolution as it has always been somewhat behind trends in private industry (at least in regard to marketing and everything that goes along with it).

Fortunately, the results of a survey conducted by Higher Ed Experts were just released today in a white paper to their email list subscribers. The results of the survey confirmed that there is a lot of work to do if higher ed marketers are going to catch up to current trends in industry. Of the 344 respondents, very few indicated that their institution utilized a model that relied on outcome-based metrics. Instead the vast majority are stuck somewhere between steps 2 and 3 of Kaushik’s evolutionary timeline – between page views and visits/visitors.

The top five metrics monitored by respondents were page views (85%), visits (84%), unique visitors (80%), Facebook Page Likes (66%), length of visit (65%). None of these metrics fall into what might be considered outcome-based metrics.

The highest “conversion” metric rated was click stream and conversions for email campaigns (23%).

Respondents were also asked about some higher ed specific conversion metrics. Of the eight asked about, all but one were monitored by less than a third of respondents. The highest-ranking metric in this category was the monitoring of online inquiry for admissions (35%). Only 18% measured online donations and gifts.

Respondents were also asked to consider how they used the insights that gained from web analytics. Responses were not outcome based either. Rather than responses like “increase enrollment” or “increase online donations”, the most common responses were general and content related, like “to improve website” (72%) and “to inform web content strategy” (66%).

These uses for analytics insights are all well and good, but strategy and goals aren’t one in the same. We use strategy attain goals, just as we use web analytics to find out if we’re attaining those goals.

Goals First, Analysis Later: Setting a Web Analytics Measurement Framework

Before you can have tools, metrics, or analytics you must have goals and objectives. Without goals and objectives, web metrics are nothing more than a compilation of interesting data points. You may know how many people visited your site, where they were from, and what pages they looked at, but without goals the data is nothing more than anecdotal.

Avinash Kaushik writes on his blog that before he even looks at any web analytics tool he creates a web analytics measurement framework specific to that business, organization, or project.

This framework consists of five parts or steps, all of which become necessary to check any metrics or analytics information against. The framework puts everything into perspective.

Kaushik’s web analytics measurement framework consists of the following parts:

1. Business objective
2. Goals
3. Key performance indicators (KPIs)
4. Target
5. Segments

For a complete discussion of each of these see Kaushik’s Web Analytics 101 blog post from 2010.

Each piece of the framework builds on the next. Let’s look at an example. Say I work for the admission office at a small liberal arts college in Pennsylvania. My framework might look something like this:

Business objective: Prospects in admission inquiry file
Goals: Increase number of signups for inquiry file via web form
KPIs: Number of signups via direct traffic on website, number of signups via email campaigns, number of signups via social media referrals
Target: 1,000 signups a month via direct traffic, 750 signups a month via email campaigns, 500 signups a month via social media referrals
Segments: Acquisitions: Organic search, email campaigns, Facebook, Twitter
                  Behaviors: Visits to conversions (signups via web form)

This is a simple example, but it shows the need to set goals before employing the use of web analytics just because we think we should or because everyone else is doing it. Using a web analytics measurement framework as Kaushik has outlined, any organization is able to put web analytics platform to good work.