I recently saw a funny video on big data that I just had to share. The concept of big data is that meaningful information can be extract from voluminous data — the kind that is too big for a standard database.* Nonetheless, big data is valuable in that it can predict what customers want (before they know that they actually want it!):
Big data is so hot right now primarily because (A) there’s lots of data sources (eg: Google Analytics, eCommerce transactions, in-store/catalogue orders, social media, etc.) and (B) data can be cheaply processed. But it seems that marketers believe that big insights only come with big data! To that point, it also seems that small data is all but useless! But is that really true?
Well, while bigger may seem better I recently
was reminded learned in my Data Analysis MOOC (aka online class) that big insights can also be found in smaller data sets! It just isn’t always feasible to have a super-sized data set — client may not have a large amount of data. And fortunately for them and for data heads, small data along with inferential data analysis (along with random sampling) can deliver big insights.
To get started, you just need a business problem, a theory and data to prove (or disprove) your idea! So don’t let the lack of big data stop you from doing the analysis.
*Big data is more than just voluminous (see O’Reilly).
For the past 5+ years, Google Analytics has been my go-to web analytics tool. While it isn’t as robust as Omniture or WebTrends, the pricing model (Free!) and easy installation & simple configuration processes have resulted in widespread adoption. According to the technology adoption and trends tool BuiltWith, Google Analytics is used by more than 44% of the top 1 Million website. In comparison, Omniture is used on only 1.35% of the top 1 Million websites.
I’ve been spending significant time with Google Analytics over the past few months and I seem to be running into more limitations each and every day. Here are the 3 things that I would expect to be able to do with Google Analytics (but I can’t):
- Advanced segmentation isn’t really advanced. After I wrote my post about about the robustness of advanced segmentation, a colleague asked me to help her with creating a report on user behavior using internal and external visitor segments. Knowing that I’ve set up Views where I’ve excluded traffic using an internal IP address, I began searching for the “Filter by IP” field but could not locate it under any of the available options. Once I realized that this was not possible, I resolved to creating the 6 essential Views that we set up with all client accounts so that she could report on this behavior in the future.
- Custom Dashboards aren’t really customizable. I met with another colleague before the Thanksgiving holiday break to discuss the buildout of a custom dashboard. He sought my help with creating one since I had built a custom dashboard for my blog. We attempted to create a basic widget that displayed on a daily basis the total visitors vs. new visitors vs. returning visitors — a graph that a marketer would commonly look at. While the visitor metric could be added to the graph, we couldn’t add the visitor type dimension. To work around this limitation, we added the % new visitor metric. Furthermore, we could only add a single metric!
- API’s aren’t really open. I add an annotation every time that I publish a new post on this blog. Since my blog posts get automatically pushed to social media networks when they are published, I began investigating whether I automate the process and cut the manual step of adding these annotations. I looked into the Google Analytics API’s and while Google has multiple API’s — API’s for data collection, account management, and data reporting – none could be used to create annotations on the fly.
Google does have a support forum where new features can be requested so I posted my suggestion for automating annotations. While the features that I want are not currently available in either the standard or enterprise version, I’ve noted that Google has invested significantly in the platform this year. I fully expect the product development team to continue to release more valuable functionality in mid to late 2014.
What features do you think are missing in Google Analytics?
Seeing that tomorrow is Thanksgiving, I am thinking about (what else): dessert! Since I am a data geek, I wanted to post these three interactive graphs from Google Trends:
Most searches for pie recipes occur during November, followed by July (see spikes in search volume).
Among searches for pie recipes (apple, pecan and pumpkin), pumpkin pie recipes are most frequently searched for during Thanksgiving. However, apple pie recipes are searches for year-round. This is likely due to apple pie being a traditional July 4th dessert.
Regionally, pecan pie is more popular in the central southern half of the US (see states in dark blue).
And in Georgia, pecan and apple pie recipes are both tied in second place in search volume. Personally, pecan and apple pie are my favorites!
I hope that you and your family have a Happy Thanksgiving!
Photo by Jo.