Our assignment this week revolves around selecting an etailer and assessing how that company uses big data and web analytics to shape their web strategy. I selected Target because it is a large, multi-national brand with a well established eCommerce site. I also thought it would more interesting to look at a "challenger" brand like Target, as opposed to a hyper-ubiquitous presence like Amazon or Walmart.
Background
Target began online operations in 1999 (Target, 2016). According to sales numbers from Q1 2016, web sales make up about 3.5% of target total revenue (though this number has surged to 5% during the holidays) (Guy, 2016). The 2015 holiday season saw Target.com eclipse $1 billion in sales for the first time; While this is a strong accomplishment, it should be noted that Amazon.com's ecommerce sales are about 20 times greater than Target.com's (Ziobro, 2016).
Target's Analytics Strategies
Target has a team of 50+ employees who work to analyze big data and leverage consumer trends to propose and acquire higher sales from those consumers who have been selected (Moylan, 2012). If you do a quick web search about Target's analytics, you will likely find links to news outlets spotlighting a story about Target algorithmically predicting a young woman's pregnancy before her parents even knew, based on changes in her purchasing habits. Charles Duhigg, a journalist for The New York Times, reported that Target.com used analytics that showed that women who begin buying scent-free soaps, certain kinds of vitamins and supplements, and cotton balls frequently begin buying baby products soon after. A mailer with targeted coupons was sent to her home, which her parents discovered.
Viral one-offs aside, Duhigg gleaned some revealing information from Target statistician, Andrew Pole. Duhigg reported:
Also linked to your Guest ID is demographic information like 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 Web sites you visit. Target can buy 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 coffee, paper towels, cereal or applesauce, your political leanings, reading habits, charitable giving and the number of cars you own... all that information is meaningless, however, without someone to analyze and make sense of it. That’s where Andrew Pole and the dozens of other members of Target’s Guest Marketing Analytics department come in.
This information suggests that Target gains information from consumers who establish an account with Target (which could include a Target credit card, a target.com account, or both). Once a guest ID is attributed, personal demographic, psychographic, and behavioral information may be acquired or purchased, and the customers' purchasing habits are monitored. Because Target is a retailer and an etailer, it is capable of recording consumer behavior across both channels, gleaning more information about that customer's habits, schedules, and preferences.
Web Analytics, Specifically
Taking a closer look at web analytics, I decided to research a common way etailers collect visitor information in order to push towards conversions with e-shoppers.
Sentiment Analysis. Sentiment analysis is the process of implementing algorithms in order to gauge the "buzz" around consumer products and trends. According to Forbes.com, this regularly involves monitoring the popularity of topics on social media and certain search terms on search engines. These numbers help etailers predict which products will be popular in the coming months (or years), and plan their sales strategies and product mixes accordingly (Marr, 2015).
Below is a slide presentation from IBM Big Data Hub that helps explain how this information is gathered and utilized.
You can see that web browsing patterns, movie releases, social media trends, relevant industry advertising, and enterprise data all make up a part of these predictive algorithms (slide 14). After this "at large" data is collect, an etailer can look at its clientele and discern which individuals and groups are most likely to purchase certain items, based on their demographics (age, sex, location, income) and shopping history.
Etailers can also use software to consider demand and track competitor pricing in order to match or beat key competitors and attract customers to purchase from them instead of the other guys. Ad buys are also initiated which target key demographics at relevant locations across the web (i.e. video game websites, forums, etc.).
My Observations at Target.com
I decided to record my own observations after using Target.com. I did not have an account with the website, so I created one to see how the site would track my browsing patterns and adapt to predict my preferences. I clicked on the "home decor" icon on the homepage and followed it to a page with product categories. I selected "rugs," and clicked through to an item I liked.
I enjoyed seeing a linked roll of "more items in this collection," as coordinating decor is a common desire among many consumers. I was not surprised to see two other link rolls that Amazon has popularized - the "guests also viewed" and "guests ultimately bought" rolls. While I was not surprised, I do feel that these are smart inclusions for any etailer, because they save the visitor time they would be searching for coordinating items and best alternatives. Also included was guest reviews, which is also known to have a large impact on item performance. There were no discernible "picked for you" or "based on your shopping habits" sections on Target's website, which is a strong feature Amazon.com uses.
References
Guy, S. (2016, May 18). Target's Q1 e-commerce sales account for a growing share of revenue. Retrieved from https://www.internetretailer.com/2016/05/18/targets-q1-e-commerce-sales-account-growing-revenue-share
Marr, B. (2015, November 10). Big data: A game changer in the retailer sector. Retrieved from http://www.forbes.com/sites/bernardmarr/2015/11/10/big-data-a-game-changer-in-the-retail-sector/#6dbf7f9e678a
Moylan, M. (2012, March 7). Target's deep customer data mining raises eyebrows. Retrieved from https://www.mprnews.org/story/2012/03/07/target-data-mining-privacy
Pittman, D. (2013, Jan 24). Big data in retail: Examples in action. Retrieved from http://www.slideshare.net/davidpittman1/big-data-in-retail-16163341?ref=http://www.ibmbigdatahub.com/presentation/big-data-retail-examples-action
Target (2016). Target through the years. Retrieved from https://corporate.target.com/about/history/Target-through-the-years
Ziobro, P. (2016, February 24). Target's sales driven by online traffic. Retrieved from http://www.wsj.com/articles/target-swings-to-a-profit-1456318856
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