How Target Uses Web Analytics to Optimize Business

Target.com

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.

Big Data in Retail - Examples in Action from David Pittman

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

Google Analytics App Spotlight: Quill Engage

Natural Language Reports

This week we were tasked with selecting a Google Analytics application and explain how this application can be useful to marketers. I selected Quill Engage, because as a  new marketer, the ability to use an application to create incisive, natural language reports is very appealing to me.

Below is Quill Engage's promotional video that explains the basic premise and functions of the service.

My Experience

I wanted to get firsthand experience with Quill Engage, so I downloaded the free version of the app from their website. I was able to quickly and easily connect this blog to Quill Engage. I was able to choose which View from my Google Analytics account to connect, which is an attractive feature for organizations wishing to create separate reports for separate View groupings.

Unfortunately, because Quill Engage automatically creates weekly and monthly reports, I will not be able to get a report for at least a week. However, it will likely be longer, as the service requires a minimum of 20 visitor sessions in order to generate a report; if your domain falls below that threshold you will not receive a report for that period. This is a low threshold that should not pose a problem for commercial websites or organizations that use social media and paid advertising to draw traffic to their domain.

Quill Engage Sample Report

Because I had no personal report to available to share, I found a sample report shared by Douglas Karr of Marketing TechBlog.

As you can see from the sample report above, Quill Engage aggregates your website's data into a plain language report that explains the relationships between the changes in your data and the contributing factors (such as stronger referrers, more paid search traffic, etc.). This can help marketing teams connect the "what" to the "why," making it faster and easier to find the cause of positive and negative trends.

This sample monthly report compared the most recent month's numbers to the average of the previous 12 months, which uses a larger window of time for comparison in order to account for months that performed as outliers. The report succinctly provides information about number of sessions and page views, referrer performance, paid search, device type, visitor location, and ecommerce performance. While this report might be convenient for its clear organization alone, the natural language descriptions below the infographics provide quick and clear explanations for data trends that can help guide marketers to the strengths and weaknesses of their current marketing tactics. This natural language report is also valuable because it can help non-marketers who are involved with the organization to easily understand traffic and ecommerce trends with less jargon and frustration.

If your organization is an ecommerce organization, the bottom line and the ROI might often be the most important reports to management teams. The final page of the sample report, melds an insightful pie chart with succinct explanations that outline (1) change in revenue, (2) change in number of transactions, (3) change in average transaction total, in addition to (4) conversion rate, (5) referrer performance, and (6) the highest grossing product.

Quill Engage Free vs. Basic and Premium Options

While this report would likely be a strong tool for many organizations to avail themselves, larger brands might need more targeted insights. In this case, investing in the Basic or Premium versions of the application might be a worthy investment.

Quill Engage Basic costs $19.99 per month and increases the amount of reports generated from weekly and monthly reports for 1 Google Analytics view or website to 3 Google Analytics views or websites. This would allow a user to generate different reports for 3 separate views, which could be valuable if those views were created to pinpoint different, but equally valuable insights. The Basic version also allows access to Events, Goals, and ecommerce report elements, which are not available in reports generated for Free version users. It also enables 5 recipients for email reports, KPI settings, and links for PDF downloads of reports.

As you might imagine, Quill Engage increases the maximum allotments for many of the aforementioned functions for the cost of $49.99 per month. Users can use up to 10 views or websites and receive separate reports for all of them. They also can select 10 email recipients and have access to custom branding, colors, and segment analyses.

These costs are still modest compared to many other analytics services, but it should be noted that this service is a supplementary application with an added cost on top of whatever your organization pays for Google Analytics (unless only the free version of Google Analytics is enabled). Additionally, for any ecommerce organization, the Free version of Quill Engage would likely not be as attractive an option, due to no ecommerce insight in the free reports.

Final Summary

Quill Engage is highly rated on the Google Analytics Partners website and appears to provide useful insights into Google Analytics data. I predict that it could be useful for new and self-taught marketers, organizations without a designated e-marketer or marketing team, or even just marketers who are looking for clearer, quicker ways to report web marketing trends to their co-workers. As a student who is relatively new to the world of digital marketing, Quill Engage's reports provide me with a strong example of how to organize and report marketing data using natural language and a succinct approach.

References

Karr, D. (2015, August 24). Quill Engage: Transform Google Analytics into natural language reports. Retrieved from https://marketingtechblog.com/quill-engage/

Narrative Science [username]. (2014, August 5). Quill Engage: Google Analytics Partners - Apps. Retrieved from https://www.google.com/analytics/partners/company/4812220005351424/gadp/5741031244955648/app/5649050225344512/listing/5639274879778816

Narrative Science [username] (2015, August 18). Quill Engage for Google Analytics [video file]. Retrieved from https://www.youtube.com/watch?v=D8iL_zjW340#action=share

Comparing Google Analytics and Adobe Analytics

Background

I am currently learning to use Google Analytics, but I am aware that there are many types of analytics software on the marketing. This week, I want to look at a major competitor - Adobe Analytics. By researching Adobe Analytics and reviewing online tutorials, I have come up with some important distinctions between the two tools that I would like to share.

Major Differences at a Glance

The first difference prospective users will notice between Google Analytics and Adobe Analytics is price - Google Analytics offers a free version whereas Adobe Analytics does not. The premium version of Google Analytics is advertised to start at $150,000 per year, whereas there is no readily available price quote from Adobe.

Another important difference is in required customization. Because Adobe Analytics is a more expensive, involved platform, it requires that panel and data collection customization be completed in order for the software to function (Ingle, 2015). Additionally, Adobe Analytics allows for the tracking of 250 total variables, while Google Analytics Free only allows for a total of 40.

The last immediate difference between the tools lies in the ability to export reports and data to associated programs. Google has a large array of associated websites and programs, such as Data Studio, Tag Manager, Docs, Surveys, Google +, etc. This web of free services helps augment Google Analytics functionality and exportability for easy sharing. However, it should be noted that Adobe also has its own grouping of associated software that can be used for importing and formatting data into digestible reports; some examples include: Adobe Marketing Cloud (for backing up, storing, and sharing data), Adobe Acrobat Reader, Adobe Campaign, etc. It stands to reason that while Adobe offers professional grade, integrated suite of software and solutions, the cost to an organization to purchase the associated software would likely be prohibitive for all but large organizations.

Calculative Differences 

While style, organization, and interface differences are likely to be the most noticeable at first, differences in data calculation will actually result in different data. Below are some examples of ways that data might vary between Google Analytics and Adobe Analytics.

1.) "Time per visit" data can vary between the tools due to a difference in how the final page of the session is calculated. Analytics software cannot track the amount of time spent on the final page of the session, as no more clicks are made within the domain and the software cannot track the closing of browsers or browser tabs. For this reason, the final click made in order to access the last page of the session is the final reliable data point the software can collect. 

One important difference between the tools is that Google Analytics calculates the time on the final page of the sessions as "0," while Adobe Analytics does not calculate any time for the final page (Kavanagh, 2015). This affects the data because Google Analytics would produce lower "time per page" data for the page that is the last page of any user's session. This could highlight a negative trend accurately; if a page is broken or not engaging, it could be a common place for visitors to end their session. However, the page could be very helpful and provide the visitor with all the information they need, negating the need for the visitor to traverse the domain any further. This difference in methods would also affect time per visit data in the case of bounces; Google Analytics would record the start of a new session and record a new visitor, but the time for that visit would be recorded as "0," even if the visitor spent 5 minutes on the entry page before leaving. Adobe Analytics would simply not record any time for that visit, but still record the visitor and bounce data.

2.) Another important distinction I found deals with the calculation of natural search referrals vs. direct traffic. Melissa Kavanagh for Fueltravel.com did some great research and was able to ferret out the reason for differences in these numbers between Google Analytics and Adobe Analytics for the same web domain. It seems that Google Analytics registers a higher percentage of natural search visitors and sessions due to the way it collects cookie data. The cookie data regarding the referrer for that visitor's first visit is used for all visits within a six month period. An example would be if a visitor first visited a website through a Google search for throw pillows. Let's say the visitor revisits the same website four more times over a period of months by typing in the URL to the site's homepage - if the analytics team is using Google Analytics, they will record a higher number of "natural search" visits, due to the original cookie information. If the team is using Adobe Analytics, each visit will be categorized based on the referrer or direct traffic status of each visit/session.

Takeaway

This was just a quick analysis of some important differences between both analytics tools I found through research. I have limited experience with Google Analytics, personally, but I have never had the privilege of using Adobe Analytics. It should be noted that there are several other free, affordable, and paid analytic services available, such as Woopra, Clicky, Mint, and KISSmetrics. Working within your organization's budget and ascertaining the depth of data collection and analysis you need is key to finding the best fit.

References

Ingle, S. (2015, September 23). What's the difference? Comparing Google Analytics and Adobe Sitecatalyst. Retrieved from http://www.paceco.com/insights/analytics/google-analytics-adobe-sitecatalyst-comparison/

Kavanagh, M. (2015, September 30). Two surprising ways Google Analytics differs from Adobe Analytics. Retrieved from http://www.fueltravel.com/blog/two-surprising-ways-google-analytics-differs-from-adobe-analytics/