Welcome to the big leagues! The Analytics hot shots! If you are interested in this article it means you are asking yourself some fairly advanced web analytics questions and you should reward yourself with a pat on the back just for reading these lines. What is the difference between using Google Analytics Goals to measure a conversion versus using, let’s say, a Google AdWords Conversion Pixel? Asking this question suggests you are well on the way to becoming an analytics samurai!
Two weeks ago, I made a purchase that I’ve been dying to make for a long time: I bought an Apple iPod Nano! And, for the record, it wasn’t I that broke down and bought it, it was my first generation iPod Nano from 2006 that broke down, leading to my buying my new mini-toy.
Boy, do I love my new iPod Nano! It’s 8GB, which for me is more than enough. It’s smooth, slick, and user-friendly, which is a staple of most Apple products. It even has a built-in FM radio tuner, which allowed me to listen to the end of the Yankees / Rangers game last night while biking! Yeah, I’m <3’ing it. 🙂
Naturally, being in the web analytics industry, it got me thinking about my journey from start to finish in purchasing this item, and how all of it would be tracked in Google Analytics, WebTrends, or any web analytics platform. Clearly, if I were in charge of Apple’s web analytics efforts, I could easily pull up a report within a matter of seconds which would show me the exact purchase scenario and tell me exactly what I would need to do next. Right? I mean, how hard could it be for CoreMetrics or NetInsight to show me that I:
– Saw the Apple iPod Nano television commercial five times across two different cable channels;
– Went directly to Apple’s website, found the iPod Nano section, and took a long hard look at its technical specs and color options;
– Listened to my friend ramble on in glowing terms about how great the new iPod Nano was; and
– Played with the iPod Nano at the Apple Store and purchased it (but not before discovering that the Apple Store was out of the 8GB silver, which forced me to choose the 8GB blue one).
For those of you who caught on to the sarcasm in the previous paragraph, you know that we are nowhere near being able to call up such a report from our favorite web analytics vendor. In fact, the only piece of information from the four points listed above that you would be able to access would be:
– Went directly to Apple’s website, found the iPod Nano section, and took a long hard look at its technical specs and color options
The information on the other three points is not in Google Analytics, Oracle CRM or SalesForce. The data isn’t anywhere online, other than in this blog post and in my memory banks. As far as Apple is concerned, I made a single, very-long visit to their website without purchasing something via their online shopping system. On the surface, my visit to Apple’s website could be considered largely unsuccessful, since I never performed an important action, like making a purchase on their website. However, if it wasn’t for the Apple website, I would have never visited the brick and mortar Apple store in the Boca Raton Town Center Mall and purchased the iPod Nano in person in the first place!
To further elaborate upon this point, I wanted to show you what a possible report would look like in an imaginary world where I was Apple’s only website visitor, using the four points previously outlined as purchase influencing factors. Using an arbitrary value of 100 to score a purchase for an individual visitor (myself), a possible report listing the influencing factors for a purchase and their scores could look like this:
3 TV Commercials on Channel A: 0 pts.
2 TV Commercials on Channel B: 0 pts.
Website Visit: 0 pts.
Customer (Friend) Testimonial: 0 pts.
Apple Store in-store Demo: 0 pts.
Color / Model Availability: 0 pts.
Apple Store Purchase: 100 pts.
But in actuality, as far as my particular purchase is concerned, credit is spread across the board, as all of these factors had their own contributions in their own right. I would score my purchase influencers this way:
3 TV Commercials on Channel A: 10 pts.
2 TV Commercials on Channel B: 15 pts.
Website Visit: 35 pts.
Customer (Friend) Testimonial: 5 pts.
Apple Store in-store Demo: 30 pts.
Color / Model Availability: 5 pts.
So, how does Apple (and how do you) really know when a visit to a website is a successful one? How do you really know what source is deserving of the official credit for a purchase (or a conversion)? Unfortunately, there is no perfect answer or one-size-fits-all solution to this very complex problem that all marketers and web analysts face on a daily basis. In my single example alone, I am crediting six different factors as influencers into my purchase of a couple of weeks ago. Using the first scoring model, you would conclude that the website is doing a poor job of selling iPod Nanos. In actuality, in the second scoring model, I gave it 35 points – the biggest slice of my pie – which would indicate that the website is actually doing a great job toward the purchase of iPod Nanos!
My message to you today and the point of this blog post is for you to take a more critical, less cynical approach to the data that your web analytics program is spitting out at you – especially when you are measuring conversions, sales, or purchases. Not only are there going to be multiple influencing factors involved other than what your web analytics package is showing you, but the website that you are in charge of measuring and the online marketing campaign you are running can and will have an impact at your brick and mortar, physical store location or your offline advertising efforts. It did for me.