I Know Who You Are and I Know What You Like

Web analytics have steadily increased in richness and complexity. Today, most online e-tailers, no matter the size, use some type of web analytics to track customer shopping behavior and attract new customers. In fact, it is nearly impossible to shop online without sharing intimate details about your consumption patterns. Many e-tailers are studying those patterns to figure out what you like, what you need, and what marketing techniques will make you buy more.

One of the first e-tailers to offer sophisticated online shopping targeted to specific customers was Amazon.com. It seemed to know more things about you than you knew about yourself. Amazon truly was the trailblazer for putting web analytics into practice, having collected information longer and used it more proactively than any other e-tailer before (Associated Press, 2005).

Today, many e-tailers are mirroring Amazon by offering customized online shopping experiences by merging web analytics data with data acquired from other sources. Almost every major retailer, from grocery chains to investment banks to the U.S. Postal Service, has a predictive analytics department devoted to understanding not just consumers’ shopping habits but also their personal habits (Duhigg, 2012).

One such company that uses its customer data well is Target. For decades, Target has collected vast amounts of data on every customer who shops at it stores or buys something online. Whenever possible, Target assigns each customer a unique code — known internally as the Guest ID number — that keeps tabs on everything he/she buys (Duhigg, 2012). The Guest ID becomes a bucket that stores a history of everything customers buy and any demographic information Target has collected or bought from other sources (Hill, 2012). For example, if you shopped at Target, Target would know if you use a credit card or a coupon, fill out a survey, mail in a refund, contact the customer help line, visit the website, or open an e-mail Target sent you. All of those actions are recorded in your Guest ID (Duhigg, 2012). Also linked to your Guest ID is demographic information such as your age, whether you are married and have kids, which part of town you live in, how long it takes you to drive to the store, your estimated salary, whether you’ve moved recently, what credit cards you carry in your wallet and what websites you visit. Target also buys data about your ethnicity, job history, the magazines you read, if you’ve ever declared bankruptcy or got divorced, the year you bought (or lost) your house, where you went to college, what kinds of topics you talk about online, whether you prefer certain brands of products, your political leanings, reading habits, charitable giving and the number of cars you own (Duhigg, 2012).

Target uses this massive collection of data to provide more targeted marketing to its customers. Its marketing is so “on target” that Target recently came under-fire when it started sending coupons for baby products to a pregnant teenager before her father even knew she was pregnant (Hill, 2012). As it turns out, Target developed a very sophisticated predicative analysis program to determine when women who shop at Target become pregnant. The program allowed Target to reach pregnant women at a pivotal time in their lives before any other retailers know a baby is on the way (Duhigg, 2012).

To implement the program, Target analyzed the data it already had in it’s a baby-shower registry and observed how shopping habits changed as a woman approached her due date, which women on the registry had willingly disclosed (Duhigg, 2012). Next, they analyzed the data and some useful patterns emerged. It was observed that women on the baby registry were buying larger quantities of unscented lotion around the beginning of their second trimester. It was also noted that sometime in the first 20 weeks, pregnant women loaded up on supplements like calcium, magnesium and zinc. They also suddenly started to buy lots of scent-free soap, cotton balls, sanitizers and washcloths (Hill, 2012). These findings lead Target to assign each shopper a “pregnancy prediction” score based on if they had purchased some of the 25 products flagged. More importantly, Target could estimate her due date to within a small window, so coupons could be sent that were timed to very specific stages of her pregnancy (Duhigg, 2012; Hill, 2012).

Target has clearly demonstrated an ability to use its extensive collection of data to predict buyer behavior. It has a solid understanding that all data collected, no matter the source, needs to go into one bucket – in Target’s case, the Gust ID number. According to a web analytics expert, predicting behavior based on web data alone does not work because data collected on the web is mostly anonymous, usually incomplete, and is really unstructured (Kaushik, 2007). It is important for web data to be supplemented and merged with other data across multiple channels. Knowing all of the customer touch points both online and offline makes it easier to understand current behavior and predict future behavior (Kaushik, 2007).

According to the company website, Target (2012) is already using cookies, web beacons, and other technologies to receive and store certain types of information whenever customers interact with its website. Target uses this information, which includes the pages visited, which web address the customer came from, and the type of browser they are using, to customize website experiences for its customers (Target.com, 2012). However, Target could do a better job of customizing its online shopping experience by employing a strategy similar to Amazon whereby Target suggests products to customers that it thinks they will like.

References

Associated Press. (2005). Amazon knows who you are. Wired.com. [Web log post]. Retrieved July 3, 2012, from http://www.wired.com/techbiz/media/news/2005/03/67034

Duhigg, C. (2012, February 16). How companies learn your secrets. http://www.nytimes.com/2012/02/19/magazine/shopping-habits.html?_r=2&pagewanted=1&hp

Hill, K. (2012, February 28). How Target figured out a teen girl was pregnant before her father did. Forbes Magazine. Retrieved July 3, 2012, from http://www.forbes.com/sites/kashmirhill/2012/02/16/how-target-figured-out-a-teen-girl-was-pregnant-before-her-father-did/

Kaushik, A. (2007, September 11). Data Mining And Predictive Analytics On Web Data Works? Nyet! [Web log post].Retrieved July 4, 2012, from http://www.kaushik.net/avinash/data-mining-and-predictive-analytics-on-web-data-works-nyet/

Target.com. (2012, May 3). Privacy Policy. Retrieved July 4, 2012, from http://sites.target.com/site/en/spot/page.jsp?title=privacy_policy#collectedonline

Tighten Up Your Measurements with Google Goals, Funnels, and Filters

After a dismal traffic report last week, I was super pumped to discover I had 17 visits and 24 page views last week! After digging deeper into the report, my overflowing excitement was soon tamed. I noticed 14 of the 17 visitors stayed on the site less than ten seconds. This very limited time on sites doesn’t represent a very engaged visitor. They did what Kaushik (2010) says, “I came, I saw, I puked, I left.” Although this was crushing news, there was some more encouraging data. In the traffic referral section of the report, I noticed six of the visitors came from a blogger.com referral and one came from kaushik.net. This tells me that perhaps other blogrolls on my fellow classmate’s blogs are working and maybe my comment on one of Kaushitk’s posts really did generate traffic.

Given this week’s report, there is plenty of room for improvement. In this post, I examine how Google’s Goals, Funnels, and Filters can be used to optimize my Google Analytics data for this blog. I took a look at these three tools and chose which ones were appropriate to apply to my blog.

Goals

When you originally think of the term “goal”, you likely think of it as achieving a desired result. In Google’s world, a goal represents a level of interactivity on your website (Google Analytics, n.d.). There are four types of goals that can be set in Google Analytics (Google Analytics, n.d.; P.I. Reed School of Journalism, 2012):

1.      URL destination goals are achieved when a user visits the defined goal page. A good example is a thank you page after a user has submitted information through a form or completed a shopping check-out process.

2.      Time on site (or visit duration) goals are achieved once the user meets a certain time threshold on the website. You can specify a greater than or less than time in hour, minute, or second increments. Once this time threshold is met, the goal is achieved.

3.      Page views/visits goals are achieved once the user meets a certain number of page views or visits to the website.

4.      Event goals attach a conversion to an event.

Every website should have at least one goal. At a basic level, the goal should answer the question, “why do I have a website?” (Google Analytics, n.d.). A goal for this blog is that the user is actually engaged and reads a post. I don’t want to count visitors who click on the site, don’t read anything, and leave. This tells me they didn’t find what they wanted on the blog. For these reasons, I felt it was appropriate to set two goals for my blog. I set one goal for when the time on site exceeds more than 61 seconds. I felt after 60 seconds, the site visitor actually read a post, or at the very least, skimmed some of the content. The other goal was set for when the visitor views more than one page. This will show me that the site visitor viewed more than one post and had a high level of engagement with the site. All of the other goals offered through Google Analytics seem too in-depth for a shallow blog, but could be appropriate for more in-depth or e-commerce sites.

Funnels

Each goal can have a funnel that defines the desired steps (or path) necessary for converting to the goal (Google Analytics, 2012; P.I. Reed School of Journalism, 2012). Defining these pages allows you to see how frequently visitors abandon goals, and where they go. Funnels are particularly helpful on e-commerce sites because they can show how effective a sites check-out process is. Another example of where funnels are useful is a college’s online admission process. If the site visitor doesn’t reach the URL destination goal (e.g. purchase confirmation page, submits an application), the funnel report will show where the site visitor abandoned the process.

Although I certainly understand the merits of funnels in the examples provided, I don’t think they are necessary for my shallow blog. A goal of mine is for the site visitor to read a post. It would be nice if they continued reading all of the posts, but I don’t consider it a failure if they don’t. I also don’t expect them to travel through the site in any particular pattern or desire them to reach a certain URL destination. For these reasons, I did not apply a funnel to my blog.

Filters

Filters provide a flexible way to determine what data is included in your Google Analytics report and how the data appears (Google Analytics, 2012). Google recommends excluding internal IP addresses from reports but also suggests keeping all of the data because once a filter is applied, the data can no longer be accessed (Google Analytics, 2012). Google offers predefined and custom filters. For the purpose of this assignment, custom filters are too in-depth and unnecessary. I found the predefined filter to exclude traffic from a specific IP address to be helpful because I could at least filter out my own visits to my blog. After learning how to actually find my IP address, I set up a filter to exclude myself from the Google Analytics report. I certainly realize that I will see a decline in site traffic due to this filter, but at least I will be measuring actual visitors to the blog.

My former employer refused to apply an internal domain filter because it would show a significant decrease in site traffic. Their site goals were based on volume and they didn’t care if the traffic came from internal or external visitors. At the very least, they should have applied filters so that they could distinguish between internal and external visitors. This would have provided valuable insight on what types of content these two different sets of visitors are interested in.

Putting Data into Practice

After digging further into Google’s Goals, Funnels, and Filters, I realize these tools provide a more in-depth measurement of results. It is easy to gather data, but interpreting data is a whole different ball game.  You really need to interpret the data, identify and summarize relevant parts, and understand the meaning and implication of this data so you can tweak and improve what you’re doing. Whatever industry you’re in, it is important to fully understand how your site is performing. With this understanding, you can optimize the Web experience for site visitors and get the most out of your site.

References

Google Analytics. (n.d.). Google Analytics IQ lessons. Retrieved June 30, 2012, 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, IN: Wiley Publishing. ISBN# 978-0470529393

P.I. Reed School of Journalism. (2012, May 25). Lesson 6. IMC: 642, Web Metrics and SEO. West Virginia University.