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Conversion optimization is a little different if you’re in B2B. You’ll still need to do the same types of conversion research, persona building, and experimentation that is common across conversion optimization, but let’s talk a bit about how and why B2B is different. Longer Sales Cycles and Micro-Conversions.
Another thing that became increasingly useful was to have thousands of custom stats aggregated over thousands of servers graphed. Almost all the command line tools take line separated, space delimited 2D arrays as input, with implicit numeric conversions as necessary, so you don’t usually need to do backflips to pipe programs together.
Working with this dataset can be valuable in terms of understanding the underlying structure of Google Analytics data and experimenting with a number of advanced statistical and data mining techniques that can’t be applied when the data is in aggregate form (which is the norm with standard Google Analytics.). Let’s have a closer look.
Massive Scale with Unprecedented Speed BlazingDB offers a massively distributed, “cloud first”, high performance SQL database that achieves performance gains on the largest workloads and datasets at extraordinarily competitive costs. Transformations, aggregations and joins at massive scale is where BlazingDB wins!
For example, while the data aggregation process in Google Analytics seems like a “normal” feature, it might be a hurdle if your business needs to process data at the hit level instead of by sessions or campaigns. Remember that you have to feed this model with regular traffic and conversion data for the results to be representative.
After a few hours playing around with SQL , I was already able to deliver insights I never could have with aggregated Google Analytics reports. What’s the difference between raw and aggregated data in Google Analytics? Google Analytics, in the free version, provides only aggregated data. Where do my users come from?
11: Close to zero aggregated analysis exists, everything's segmented. #10: 11: Close to zero aggregated analysis exists, everything's segmented. All data in aggregate is crap. Site Conversion Rate. SQL and web analytics is your wheel house." 12: Almost all reporting is off custom reports. #11:
The problem is further compounded by our reliance on last-click conversion tracking. Your dashboards don't need more wiz-bang graphics or for them to be displays of your javascript powers to sql your hadoop to make big query cloud compute. I recommend a shift to Profit Per Click and Avinash Kaushik's custom attribution model.
But customer paths are non-linear and pretty complicated— sooo many things influence conversions. . When you’re working in a multichannel business and a big chunk of conversions happen offline, it gets even worse. Use soft conversions to measure intent with event tracking. Aggregates. But they can’t make sense of it all.
which leads will convert—however you define conversion. The other half have a mix of data sources, which inevitably include an offshore SQL database (or ten) managed by an external vendor whom no one can track down. That disconnect still thwarts even the most fundamental business cases for real-time predictive analytics. The results?
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