Building Better Data

When I started at iPerceptions five years ago (an eternity in “internet time”), many of the conversations with both prospects and clients revolved around education. Few people knew what Voice of Customer (VoC) was, and fewer still were using it.

Clients were ecstatic to review the data that we presented, as it gave them a novel and deeper understanding of their online traffic.

Change is the only constant when dealing with the internet, and the VoC space is no exception. After a few short years, the questions our clients were asking us had changed. “Tell me about my customers” evolved into the much more interesting, yet more complicated to answer: “Nice charts; so what?” Continue reading


Redefining Business Intelligence

For many years, eCommerce took a mass market, broad strokes approach. Marketers would throw idea after idea against the wall, just to see what would stick. Black hat SEO teams would abuse meta tags, keyword stuff by using white text on a white background, and generally try to game the search engines that were bringing them traffic. More “uniques” contributed to your eEgo, and all levels of the organization focused obsessively on traffic numbers.

In short, internet marketing was taking a telemarketing approach. Conversion percentages were low, but if you called (or served) enough people, eventually you would make a sale. With the ever expanding internet frontier, combined with relatively few businesses selling in the online space, this dragnet or mass-market approach seemed like the best way to go.
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Quicksand, Data Overload and Corporate Concrete

AvinashOne of Avinash‘s favorite expressions is “data puke’, which goes a long way to describing the vast majority of charts, tables and graphs that I’ve seen over my relatively short (but I like to think illustrious) career in web analytics. I’m no “PowerPoint Picasso”, but I believe that I can put together slides in a manner that imparts knowledge, disseminates data and tells a coherent story.

Evidence indicates that I and my colleagues are in the minority. Perhaps this accounts for why many big corporations seem so very keen on the concept of web analytics, yet so very reticent to actually do anything with their data. They’ve either paid big money for an analytics suite that comes with a consultant, or they’ve used a free solution like GA or 4Q and hired someone as a web analyst. You’d figure that they would take advantage of all the available data, considering the money they’ve spent gathering it.

Personally, I think this corporate inaction can be tracked back to three key things.

Data Overload

Data OverloadCollecting a database of all your visitors’ on-site activities generates a pile of records that is nigh on impossible to parse through, even with highly-paid analysts dissecting every keystroke. Even when you think you’ve reached a conclusion, a simple re-segmenting of the data can show you something different. Everyone thinks that all this data will highlight the Yellow Brick Road that the company should follow, but more often than not you simply end up standing at a 4 way crossroads, spinning in place and wondering which way to go.


Related to the above, all this data keeps everyone stuck in the same place, unable to move in on direction or another. You’ll get everyone in the company agreeing that something has to be done, but all the time will be spent trying to figure out what. Typically, you’ll end up with two camps, each with opposing conclusions that are backed up from data drawn from the same source. The net result is that decisions take forever, if they come at all.

Corporate Concrete

Corporate ConcreteCertain things in the corporate world are set in stone. Deployment schedules are one of those things. While they may have been put in place to keep the company running smoothly, they typically ensure that all the company does is play catch-up. When you receive feedback, you have a very short time to respond – yesterday’s news is old news, and if you are forced to wait 2 months for the next release date, an opportunity will pass you by.

Whether its too much data, indecision or corporate procedures, data obtained from various web analytics sources is not acted on quickly enough. Delayed action results in reduced returns and the perception from many corporate higher-ups that web analytics just isn’t worth it.

Deployment schedules need to be thrown out the window for smaller items – with releases coming weekly, if not daily. Quicker turnaround will offer greater rewards, and increased ROI isn’t something that needs to be explained to the HiPPOs upstairs.

For views, thoughts and the musings of other web analysts, why don’t you head over to the “Analytics Blogarama” page (on or after October 6th). Much smarter people than I will be leveraging their personal brainpower and contributing to the online discussion. Check it out, leave a comment on this or any other blog post, and join the conversation!