Using paid services, such as Omniture, wasn't an option strictly for pricing reasons. We used what we could afford, and at that point we couldn't afford anything commercial (expensive!). So we ended up using Google Analytics for simple traffic reporting and in-house applications built over our own Prism software for everything else.
Not long after, we started acquiring customers at a better rate than we had hoped and the marketing budget started growing. It still wasn't huge, but it was enough to generate quite a lot of marketing data to work with for optimization of existing campaigns and developing new marketing initiatives.
We had a lot of ideas, but we didn't know where to start. So we decided to do what every marketing person should do: we analyzed the marketing data. At least this way, we would be betting our budget on educated guesses instead of clueless luck. Sounds easy enough, but life is never that easy. An innocent desire to make informed marketing decisions turned out to be an R&D nightmare.
I'll give you an example.
The first thing we wanted to determine was which traffic sources generated the quickest ROI. We wanted to understand where customers who take the least time from registration to payment come from. Do they come from particular search keywords? From particular referring sites? Do they even come from the website? This way we could at least have a better risk assessment of our marketing channels.
Well, we knew we have a lot of data in Google Analytics. We could tell which keywords and sites were driving traffic to our website and we also knew how many of them were registering or purchasing online (each defined as separate Google Analytics 'conversions').
But one thing we quickly noticed was that Google Analytics reports were showing us visitor counts, but not the visitors themselves. If we had 3 'registration' conversions on one day and 2 'purchase online' conversions on another, we had no way of telling whether these two groups contained the same people. Stuck.
A quick brainstorming session revealed that we have the customer sales data we need in our billing system's database. But this would turn this marketing initiative to a cooperative effort by both marketing and R&D.
After getting shouted at by the DBA on the mere suggestion of touching the operational billing system, we compromised that the DBA would put a few days into creating an automated process to get the data for us every day in the form of a flat file. Not a great solution, but better than nothing.
A few days later we got the customer data we needed which was basically the customers' personal information and first acquisition date (the billing system did not store the date of registration). All we needed to do now is match this information to the traffic sources data in Google Analytics.
Since Google Analytics was only providing us access to visitor counts and not individual visitors, our only other option of cross-referencing between Google and the in-house billing data was by purchase date. The problem was that multiple customers purchase on the same date making this data link not very usable. We also couldn't calculate the time span from registration to purchase, because the billing data didn't have it. Stuck again.
This experience, and similar heartbreaking attempted marketing initiatives which never got off the ground due to data integration fiascoes, are the main reason why we do not use Google Analytics anymore. But the interesting thing is, that the scenario I described is not limited to Google. It's the same with all the big hosted analytics vendors. It's expensive to store the required amount of data for as many users as Google or Omniture have. Since Google is free, they just don't store it. For some paid services like Omniture, you have to pay extra to access this information.
It's important to understand that if you use any type of commercial hosted analytics solution, you will either have no access to user-level data or you will pay a hefty sum to get it on-demand. Most of the companies I encountered, including my own, moved away from these types of services to internal data collection because of this reason, as well as to have the traffic data physically closer to the rest of the company's data, making it easier to use and access.
By: Elad Israeli | The ElastiCube Chronicles - Business Intelligence Blog