A Bullseye View of Web Analytics

Target is an international discount department store retailer that now has 1,797 brick-and-mortar stores spread across the United States, an additional 124 stores in Canada, as well as locations in the growing market of India (Corporate Fact Sheet, 2013).  Additionally, the company has thirty-seven distribution centers, 361,000 team members around the world, and of course its online ecommerce site, target.com (Corporate Fact Sheet, 2013).

Since the first store opened in Rossville, Minnesota in 1962, Target has grown to become one of the top retailers in the United States.  The brand has made a pretty good name for itself over the years by providing high quality merchandise at discounted prices, which has attracted a customer base consisting mostly of middle-class families with an annual household income of roughly $64k (Corporate Fact Sheet, 2013). 

With technology continuing to develop at a rapid rate in the U.S., shopping online has become less frightening than when it was first introduced to the consumer market.  The popularity of online shopping has given rise to numerous online-only retailers such as Amazon.com and has also led to the vast majority of brick-and-mortar retailers expanding their brands via an ecommerce site.  But even with all of the competition, especially is the department store industry, Target has managed to become the second largest general merchandise retailer in the United States, with its ecommerce site, target.com, being ranked as one of the most visited websites (Corporate Fact Sheet, 2013). 

Target launched its first online website in 1999 and partnered with Amazon to help perfect the customer shopping experience online.  But, in 2009, Target decided to end its decade long relationship with Amazon, despite the fact that Amazon had spent years developing technology to help perfect the online customer experience (Zmuba & Patel, 2011).  Because the company was growing at such a substantial rate, Target decided it would be best for it to develop its own ecommerce technology and create and manage its own website (Zmuba & Patel, 2011). 

First Target Website

So, instead of using Google Analytics or other Analytics tools as a primary way to track its web analytics, Target decided to do things a little differently and develop its own website and tracking systems.  To help Target in its endeavor to gain control for the first time over its online website and sales, the company recruited help from more than 20 vendors, which included IBM, Endeca, Infosys, and appointed SapientNitro as its partner and lead integrator (Zmuba & Patel, 2011). 

At the beginning of this new venture, things didn’t seem to go exactly as Target might of hoped.  According to the AdAge article published shortly after the redesigned target.com was launched, Target's Site Plagued by Glitches, Friction Between Marketing and Tech Teams, the company faced a long list of problems only six weeks after the site launch which included broken links, missing registries, and shopping carts that seemed to be doing more of the shopping than the actual customers. 

Although the company may not have been up to speed on how to create a website and use analytics in a timely fashion to gain a better understanding of where on the site customers were having the most problems, Target was already ahead of the game when it came to tracking customers data online.  Target statistician, Andrew Pole had already developed a system for how Target would collect customers (or “guests” as Target refers to them as) information in order to effectively market to them efficiently as possible. 

According to a blog post from Avinash Kaushik, the diagram created and presented by Pole at a predictive analytics conference showed “the variables in play that Target collects (across online and offline touch points, mobile and desktop) and ties to the Guest ID (you) in order to do better marketing.”  Below is Pole’s diagram titled Bringing It All Together: Guest ID:

As you can see, this model has various paths that data can take in order to link a user to their unique Guest ID. 

If you ask me, that’s a pretty incredible amount of data Target is able to collect about each customer and store it under each customers own Guest ID.  This model allows the company to analyze every point of interaction the brand has with every customer and combining customer behaviors both online and offline to determine the best way to market to them.  Back in 2010, Pole also stated, “that Target was able to associate half of its in-store sales, nearly all of its online sales and about a quarter of all online cookies with specific Guest IDs” (Hill, 2012). 

In his presentation at the predictive analytics conference, Pole explained the personal information Target would collect for each customer in order to associate them with his or her Guest ID.  “It starts with name, address and tender (the credit card or debit card you use) and expands from there to a history of your store purchases, online purchases, mobile phone ID, actions taken in response to Target emails, and Internet browsing activity if you click on a link in one of those emails” (Hill, 2012).  Additionally, according to Hill (2012), Target uses its combined analytics system and location data to steer customers living near a competitor to shop at target.com instead. 

Target also has a potential value model that allows the company to determine how much each customer could potentially spend.  Also in his conference presentation Pole explains, “With data mined from past years’ spending and demographic databases — showing whether Antonia’s married, whether she has children, what her job is, the average income for her neighborhood — ‘we think she should be spending $5000’” (Hill, 2012).  So, if Antonia’s data reveals she was only spending $1,000 a year, Target will continue marketing towards her.  However, if a customer is spending $1,000 a year and the data leads Target to believe that is all he or she can spend based on discretionary income, then Target will stop marketing towards that customer in order to save the company money (Hill, 2012). 

Target also uses customer data both online and offline to determine the coupon value they will offer to each customer.  If the data for a specific customer from both online and in-store coupon redemptions reveals that a $1 coupon is enough to get that person to make a purchase, then the company will likely continue offering coupons for that amount. Target personalizes customer coupons so the value is equal to the amount that the data predicts will push them to make a purchase.

In its current Privacy Policy, Target states that the company may automatically connect information they already have about you in order to identify your Guest ID.  Below is the Automated Information Collection section of the Privacy Policy:

Automated Information Collection

We may connect information collected automatically with information we already have about you in order to identify you as a Target guest. If we are able to identify you as a Target guest we may, for example, link your activity on our website to your activity in a Target store. This allows us to provide you with a personalized experience regardless of how you interact with us – online, in store, mobile, etc.

Automated Information Collection

We and our service providers use cookies, web beacons, and other technologies to receive and store certain types of information whenever you interact with us through your computer or mobile device. This information, which includes the pages you visit on our site, which web address you came from, the type of browser/device/hardware you are using, purchase information and checkout process, search terms and IP-based geographic location, helps us recognize you, customize your website experience and make our marketing messages more relevant. This includes Target content presented on another website or mobile application, for example, Target Weekly Ad. These technologies also enable us to provide features such as storage of items in your cart between visits and Short Message Service (SMS)/text messages you have chosen to recieve. We also use Flash cookies for fraud prevention purposes.

In order to provide the best guest experience possible, we also use these technologies for reporting and analysis purposes, such as how you are shopping our website, performance of our marketing efforts, and your response to those marketing efforts.

Overall, Target has developed and implemented a sophisticated methods and technologies to track customers behaviors both online and offline to determine the most efficient and effective marketing efforts.   In terms of creating a better experience online for customers (and that I’m sure Target is already doing) A/B testing could be used to help with effective site design.  I would also suggest using a heat mapping tool because from my own personal experiences I think the homepage can become a little cluttered, especially during the holidays, making it harder to navigate. 

References:

Hill, K. (2012, February 24). Target isn't just predicting pregnancies: 'expect more' savvy data-mining tricks. Retrieved from http://www.forbes.com/sites/kashmirhill/2012/02/24/target-isnt-just-predicting-pregnancies-expect-more-savvy-data-mining-tricks/

Kaushik, A. (2012, April 12). Retrieved from https://plus.google.com/ avinash/posts/f5K1ueN9Tk1

Target. (2013). Corporate fact sheet. Retrieved from http://pressroom.target.com/corporate

Target. (2013). Privacy policy. Retrieved from  http://www.target.com/spot/privacy-policy 

Zmuda. , & Patel (2011, October 06). Target's site plagued by glitches, friction between marketing and tech teams. Retrieved from http://adage.com/article/news/target-faces-hurdles-site/230188/