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Gotta Go with the O.

      Since 1997, Overstock.com has positioned themselves as a cost-conscious online retailer based out of Cottonwood Heights, Utah, near Salt Lake City. The concept for Overstock.com is simple: acquire the surplus of other retailers and sell it to consumers at a lower cost. Because the entire business is based online, the web experience is paramount in everything Overstock does—including its successes and failures. While Overstock.com certainly uses a number of different approaches to the sales process, behavioral targeting via web analytics has become a central focus of Overstock’s approach. Three key areas have been optimized through web analytics that help Overstock remain engaged on the consumer level: design, predictive analysis and key performance indicator measurement. These three tools help the company consistently look to improve their performance on the consumer and financial level.

Design

For a company that is almost completely dependent on conversions on its website to generate revenue, the design and functionality of the site is paramount. From the layout, to the checkout, every step must be user-friendly and seamless. The industry-standard measurement of e-commerce checkout rates is studied by basically every company and classified as an abandonment rate. Overstock clearly studies that rate (which will be addressed later as a key performance indicator, or KPI) looks to constantly make performance-related adjustments. “Overstock’s site design staff discovered shoppers were adding to carts only X% (the company declines to reveal the number) of every 100 times the shoppers viewed product pages. ‘We thought it should be X plus 3%,’ [CEO Patrick] Byrne says. Using analytics data, Overstock realized that product pages forced shoppers to scroll down to the bottom half of pages to get the information they needed to make a purchase decision-a design that caused many shoppers to move on without carting products” (Demery, 2007). Using web analysis in this case helped Overstock realize that they had a simple design flaw in their process. By seeing that they had a significant problem in the consumer funnel towards a successful sale, Overstock was able to address the issue of the layout and design of the page in order to more actively cater to their consumers. One of the most basic tenets of the aforementioned consumer funnel is the ability to see where consumer conversions hit a snag and cause consumers to leave. In this instance, it appears that Overstock has done a good job in isolating that funnel snag and adjusting.

Predictive analysis

The concept of predictive analysis is certainly not singularly a web analytics tool. Many companies use the concept of predicting where or what a consumer would want and then try to cater to that need. What makes Overstock different is their use of predictive analysis based on attributes that the consumer provides. In this case, Overstock is able to use their prior sales records and commerce results to create a more accurate suggestion of potential product choices for their customer. By using a “Bayesian attribute-based technology”—which basically means predictive analysis, the company is able to employ mathematics to help consumers find more aptly-suited products to their needs in Overstock’s GiftFinder application. “The application asks consumers to specify the occasion, the age of the gift recipient, the relationship to the recipient, and the interests of the recipient, and then recommends specific products. Overstock’s CEO notes that the GiftFinder drives 2.5 times the average purchase revenue for the site compared to when customers don’t use it” (Demery, 2007). Again, here is another example of the Overstock group’s ability to employ a metric like prior sales and synergize it with web analytics to help improve their sales results.

Key Performance Indicators

The ability to study results and target customers for future sales is a great use of on-hand resources to try to generate more sales. By studying this data and others, Overstock is constantly monitoring their own key performance indicators and efficiencies. From the growth of revenue as a result of the GiftFinder application to even using event-driven email marketing to customize offerings, Overstock is able to constantly monitor their own data to try and drive larger order sizes and more consistent conversion rates. All of this falls under the concept of trying to reduce abandonment and create sales. Furthermore, it also underscores the dedication to true synthesizing of analytics data for sales purposes by Overstock.

Suggestions

While it is clear Overstock uses behavioral analysis to create recommendations for consumers, it is unclear if they use it for targeting new consumers. By potentially employing this same level of analysis to referring websites and upstream and downstream visitors, Overstock might be able to see exactly who is bringing in the most traffic to their site. In the previously addressed concept of abandonment, Overstock would be able to not only see where the consumer left the conversion funnel, but also where they went to complete the sale and why. If, for example, via downstream analytics the Overstock group was able to deduce that consumers were leaving the checkout process at the shipping subtotal page and going to Walmart’s site, one might imagine that Walmart might be offering a better shipping alternative. While more analytics would need to be run to confirm this, perhaps testing a free shipping (or at least reduced-cost) alternative might be beneficial. Additionally this would also help them in studying which visitors are converting with a higher propensity and where visitors are going after abandoning Overstock’s site. All of this analysis might certainly be already occurring within Overstock’s operation, but the data is clearly there and must be synthesized correctly to accomplish the goals of the company.

Overstock is clearly not the first company to employ these techniques—nor the last. The key is clearly in using the data available to the marketing team and trying to figure out the best way to make that data work for you. Furthermore, testing each model against a baseline of data is paramount in understanding if there is something positive happening for the company. Behavioral modeling and predictive analytics are certainly great forays into that equation.

References

Davenport, T. (2009). Realizing the Potential of Retail Analytics; Plenty of Food for Those with the Appetite. Babson Executive Education White Paper. Retrieved from http://analytics.typepad.com/files/retailanalytics.pdf

Demery, P. (2007, February 22) How analytics supports site design at Overstock.com. Internet Retailer. Retrieved from http://www.internetretailer.com/2007/02/22/how-analytics-supports-site-design-at-overstock-com

Goals, Funnels & Filters; Oh My!

As Google’s dominance in the media world continues to grow, new areas of their portfolio have become more and more central to businesses and consumers alike. One such example is Google Analytics, the wildly-popular free analytics software used by large firms and small businesses across the globe for a variety of different reasons. Because the basic concepts of analytics involves analyzing and studying data, one of the areas that has become most central to the success of Google Analytics has been its ability to slice and dice the data. Three of the tools that most-easily allow marketers to do this are the Goals, Funnels and Filters tools. These three areas might be grouped together under the header of analytics tools, but each provides the user with a very different set of information and data. Furthermore, each can be applied in a very unique way to help businesses maximize their efficiencies and work on their inefficiencies.  

Companies have long set goals for themselves and their investors. The concept of setting your sights on a target and pursuing it is not foreign to any business. In the context of website traffic, the concept of creating goals for traffic is incredibly important and can have a tremendous impact on the bottom line of a company. Examples of the importance of tracking goals might include wanting to get people to sign up for your catalog or even request a sales call. All of these goals are set-up and executed with the intention of trying to get consumers to complete an actionable item. Many point to this one action as one of the most valuable areas of the software. “One of the best features in Google Analytics is the ability to set up goals and [analyze] the goal funnel [to] understand how to improve the user experience and website offer” (Nir, 2009). This concept of setting up goals works for large sites looking to study traffic, as well as for a small personal blogger looking to understand his or her traffic.

In the case of the Scitylana Analytics blog, setting goals certainly won’t be commerce and transaction related because of the nature of the content—but that doesn’t mean goals can’t be set.  In this instance, one such goal might be based around the interaction and length of time on the site. For example, Scitylana set up a goal set that executes successfully if a consumer spends 5 or more seconds on the site. While that number might seem arbitrary, a desire to figure out if consumers are truly spending time on the site or leaving is important to really understand the traffic. To take this example to the commerce world, imagine if there was a concern about consumers watching a 10-second splash video of the company at the beginning of loading the page. By creating this five second goal set, the marketer can study this data. In addition to the goal set of visit duration, there is also the concept of URL destinations as goals. While many companies use these unique URLs as goals to represent portions of a transaction, there is also room for this type of tool on a blog. On the Scitylana blog, one of the goals is to try and get other blogs to link to Scitylana for SEO purposes. Therefore, Scitylana created a blogroll that shows other blogs that are available. By setting these URL destinations as goals, the Scitylana marketers are able to determine which blogs on the blogroll are their most important and popular links. From this data, one could imagine how impactful this process would be in a commerce setting as businesses look to promote unique campaigns, products and offerings.

The sales funnel is another area within Google Analytics that has completely altered the way marketers can interact with consumer data. The most classic example of the successful sales funnel is the checkout process on a commerce-based website. In this particular case, the marketer is able to visualize the steps taken by the consumer on their way to a sales conversion. If step one requires filling out personal information, step two involves shipping and step three involves credit card information, it is quite easy to see where so-called “drop-off” is occurring as consumers navigate the sales process. Through this technology, businesses can manage and make adjustments to their efforts to try and enhance the experience for the consumer. If drop-off within the consumer funnel continues to happen on a subtotal page, one can infer that the consumer isn’t happy with the size of their purchase and the price. One workaround in this instance would be to offer a percentage off the total sale on purchases over a certain amount. This is just one example of optimizing the process via a consumer funnel and how Google’s technology helps marketers execute this reality.

The ability to segment data is central to the concept of filters—one of the main benefits to Google Analytics. Through this technology, marketers are able to decipher information about the types of consumers that are visiting the site and run reports on the content. From excluding certain IP addresses, to including specific subdirectories, Google Analytics filters are useful for understanding the patterns of consumers who meet (or don’t meet) certain qualifications. One area of filtering that is incredibly valuable to marketers is regional and location-based filters. There are numerous examples of information and data that is useless to marketers and the filters created in this scenario help to make this data easier to interpret. “Say if you’re a community college then maybe you only care about local traffic.  Maybe you are targeting a specific country where you have a sizable audience?  Or maybe you just know that International traffic is meaningless to you and you just want to filter it out all together? (James, 2008). By employing filters in this capacity, it becomes clear the value and significance of this data. This point is only underscored more when a marketer is paying for the traffic in a Google AdWords scenario and can filter out underperforming traffic.

While Google Analytics and these three particular optimization techniques are incredibly valuable, it is important to note that there are alternatives to this product. From in-house analytics tools to some of Google’s competitors, other options do exist in this space. The simplicity of the Google software has certainly made it the industry standard, but businesses have found a way to exist without Google’s approach being employed. That being said, Google’s technology platform has made the job of a marketer easier by using tools like goals, funnels and filters to help optimize the consumer experience.

References

James, K. (2008, Sept. 15) 7 Google Analytics Filters To Help Understand Your Visitors. Eduguru.com. Retrieved from http://doteduguru.com/id629-google-analytics-filters.html

 

Nir, R. (2009, March 4). 10 Must Track Google Analytics Goals. Web Analytics World. Retrieved from  http://www.webanalyticsworld.net/2009/03/10-must-track-google-analytics-goals.html

26 B!$#$#$ Visits?

When the Scitylana blog was launched in early June 2012, the reality was it was just another anonymous website in the vast world of the Internet. Whether it held some of the world’s best secrets or was simply a repository of random thoughts (or something in between), no one knew about it. In order to begin generating traffic, a two-pronged attack was launched that would hopefully drive traffic to the website: using referral traffic from Facebook and referral traffic from Twitter. The results show staggering differences in a number of areas. Based on the analytics data gleaned through just three weeks of data, four areas emerge that help classify, organize and understand the traffic: the location of the users, the browsers and devices used to access the site, the mobile versus non-mobile divide and, of course, the referring sites linking to the website.

            Overall 23 unique visitors have visited the site since its launch. A few multiple visits from the same user make the total number of visits 26. Within those twenty-six visits lies a wealth of data about the website and its traffic. One area that represents a very interesting set of data is the location of the users accessing the site. First off, all the users are from the United States and that is an important thing to note for a number of websites that have an e-commerce function. If you are selling a product in the United States with no international shipping, but you are receiving visits from all over the world, it is important to start thinking about expansion. In this case, with no international traffic, the analysis can focus on the more regional level. The analytics show that 13 of the 26 visits occurred within the Washington, D.C. area. This percentage (50%) is staggering considering that exactly half the visits in the world are from this one area. From this data, marketers, content-producers and more could look to build on the regional dominance that the D.C.-area has on this blog. Perhaps local D.C. retailers would be interested in this site strictly because of the proximity of users to this area.

            While regional demographic data helps one decipher exactly where traffic is coming from, browser data can help deduce the types of devices used to access the site. In the case of Scitylana, 14 of the 26 visits (nearly 54%) came from Windows computers and only 9 visits coming from Macintosh/Apple devices. What is significant in this data is the difference in coding and Web 2.0 technologies that must be present on these sites. While Mac computers can certainly see Flash technologies, for example, there are some differences in how that technology is rendered in both Mac and PC scenarios. Therefore, if a marketer is noticing traffic to their website coming in both Mac and PC avenues, it is important to test and guarantee that the user experience is clean and usable in both scenarios.

            An easy transition to another area that is affected by additional technological differences is the mobile versus non-mobile arena. Much like PC versus Mac, there are areas here where traffic must be handled differently in the mobile versus non-mobile space. In the case of Scitylana, 23 of the 26 visits came from traditional, non-mobile devices and while that symbolizes nearly 90% of the traffic, the other 10% was mobile and must be addressed. For example, going back to the example of Flash, it is a piece of software that allows consumers to see video and other elements and many mobile devices don’t support its use. The wildly-popular iPhone has trouble with Flash because, “many (if not most) current Flash games, menus, and even video players require a visible mouse pointer. They are coded to rely on the difference between hovering over something (mouseover) vs. actually clicking. This distinction is not rare. It’s pervasive, fundamental to interactive design, and vital to the basic use of Flash content” (Lazar, 2010).  So imagine you made a great video about your product (or blog) in Flash and you want the world to see it, but all of your users are accessing from an iPad—well, you are out of luck! Understanding where the consumer is coming from and on what device is paramount if you want to optimize their experience online.

            Perhaps the most valuable area of analysis to study is the referral traffic to the site. As mentioned earlier, two main mechanisms were used to try and drive traffic to the site: Facebook and Twitter. While both of these services are free, each provides users with something entirely different. In this particular case, it appears that Facebook was a much stronger source of referring traffic. Of the 20 referrals, eighteen were from Facebook and two were from Twitter. The remaining visitors came from direct traffic. While 26 visits isn’t a tremendous amount, it is clear that the referral traffic from Facebook was strong and drives consumers to the site actively. This reality has been seen in the news media as more and more sites are being sent referral traffic via Facebook. “A big priority for any media property on the Web right now: Trying to figure out how to take advantage of the traffic Facebook is sending to their site, and trying to figure out how they can get more” (Kafka, 2011). As more traffic is sent to the site, understanding which referrers are sending it and how it is performing is essential. In an e-commerce scenario, this is incredibly important, especially if you are paying for the traffic.

            Whether it’s a small blog like Scitylana or a giant site like The Huffington Post, understanding where your consumers are coming from, what devices they are using, whether or not they are using a mobile device and how they got there is incredibly important. In the corporate climate that exists today, every dollar is precious and if money is being spent, executives are going to want to see what it is bringing in. Analytics is where that reality begins and studying and understanding your consumers is where this process takes you.

References

Kafka, P. (2011, May 9) Facebook isn’t the new Google…Al Things D. Retreived June 23, 2012 from http://allthingsd.com/20110509/facebook-isnt-the-new-google-but-its-a-very-big-deal-for-media-sites/

Lazar, L. (2010). The real reason iPhone and iPod won’t ever support Flash. Cult of Mac. Retrieved June 23, 2012 from http://www.cultofmac.com/31097/the-real-reasons-iphoneipad-wont-ever-support-flash-they-cant/

Facebook vs. Google

 “If it ain’t broke don’t fix it” is the adage we’ve all heard when it comes to making changes to something good just for the sake of change. In the case of advertising on Facebook and Google, both have seen what works and seem to mimic each other in their usability in many ways. There are a number of similarities between the two applications that are evident when a user begins using the respective services. Two areas that stand out are the similarities in handling money and transactions, while the other area has more to do with targeting and demographics.

                When a marketer wants to advertise on television, radio and other traditional offline media, there is a life-cycle to the process that takes time and money. The spot must be produced, the media must be placed and then the media runs and then it ends. There aren’t a lot of places within that cycle that can start and stop quickly or even be ignored altogether. The reality is the Internet—and Facebook and Google specifically-- have turned that model upside down. With these two Internet tools, marketers can begin a marketing campaign and stop on a dime. While there are some minimum thresholds to begin a campaign, the dollar values are incredibly low and aren’t really barriers to entry. Furthermore, if the media isn’t working it can be stopped immediately. That is absolutely not the case with offline media, where campaigns can be stopped, but certainly not immediately, as inventory has already been placed and will run until traffic departments can replace or pull the spots.

Additionally, there is the issue of demographics and targeting. Both of these firms realize that marketers really find targeting to be efficient and effective ways of focusing their marketing message on consumers that meet a certain set of criteria. This again is certainly a part of traditional offline media, but there aren’t any guarantees that every male 18-35 is watching ESPN or listening to urban radio. Facebook and Google can absolutely drill down to specific areas, passions and even zip codes. Having said that, there are some differences between these two behemoths and demographics is a good place to start.

As much as Google is able to create specific traffic funnels for certain consumers, it doesn’t know you and your personal interests as well as Facebook does. This reality is a function of Facebook’s usage of personal data and the valuable information you share about yourself on Facebook. For example, if you like hockey Facebook knows this because you have “liked” the NHL, your favorite hometown team and have shared articles about the sport. Google doesn’t have quite this access. They can see a pattern of your searches, but can’t drill down to ‘31 year old male who likes the Washington Capitals,’ like Facebook can. Additionally, it should be noted that the same technology that Google uses to power their searches is also what makes them more predictive than Facebook. Because of those aforementioned search patterns, Google can more accurately conceptualize where your likely to go and then can serve you ads via AdWords accordingly. The ability for both firms to succeed based on these definable and unique characteristics is profound and central to their growth.

Conversation, Content and Bieber

From the town crier, to megaphones, to the invention of the television, the concept of having a message and then sharing it has existed in some fashion. Broadcasting, as a concept, is basically this same idea. Taking a message and sharing it with a larger group. The actual definition is “to transmit for public use” and clearly this concept can be conferred to the Internet, where producers of content are sharing their message and broadcasting it to others. So how does using the Internet to “broadcast” a message translate to the job of a marketer? To be blunt-- not well. Think of it this way: the Internet has basically made everyone a broadcaster. Someone with a funny baby, a great eyewitness video or a catchy song can become a sensation (see: Charlie bit my finger, the Oakland fight and Justin Bieber) and at the same time, millions of people post something and no one ever sees it. So just broadcasting a message into the Internet ether isn’t going to get you anywhere. The reason Charlie and Bieber worked was they went “viral” and sparked a conversation. This is where the content versus conversation emerges for marketers who are looking to use social media to engage their consumers. It must be a two-way conversation—not just broadcasting a message and hoping for the best.

In hopes of engaging with an audience and eventually finding a relevant foothold, marketers must be able to utilize the messaging and interact with it. Long gone are the days a company can put up a Twitter page and then not respond to questions, comments and solicitations. Companies (and individuals) hoping to be relevant in the social space have to be ready to engage with the consumers they hope to hook. “As conversations are increasingly distributed, everything begins with listening and observing. Doing so, will help identify exactly where relevant discussions are taking place, as well as their scale and frequency…Perhaps most importantly, the process of listening and observing will reveal the cultures of the very communities you may wish to engage” (Solis, 2008). This point by Mr. Solis illustrates that the engagement of the consumer is advantageous for both parties as the consumer gets what they are looking for, and at the same time, the marketer benefits from the exchange of information. Catherine Novak noted a similar point in her look at the conversation versus content discussion when it comes to how important the viral nature of good and relevant content can be. “Users are constantly sending out little bits of information, but the fun really doesn’t start unless those morsels are taken up by someone else and responded to, passed along, or even “liked” (Novak, 2010). The point being that the “fun” Ms. Novak referenced is the core of this argument. Getting information out there isn’t that difficult, (I’m doing it right now!) but finding someone who is willing to take the time to read the work and then engage with it is really the core of this style of communication.

Here is some more Bieber for the masses...

 

References

Novak, C. (2010, July 27). Why conversation, not content, is king. SocialMediaToday.com. Retrieved April 12, 2012 from http://socialmediatoday.com/wordspring/152636/why-conversation-not-content-king

Solis, B. (2008, August). Introducing the Conversation Prism. PR 2.0. Retrieved June 10, 2012 from http://pdf.edocr.com/248786cc9c0c4e3a29ea813ba548b73bc8035cfb.pdf