Let’s jump into Google Tag manager (GTM) wagon and contribute to solutions we came across our cases.
In our company culture is always present question on data quality we work with. Google Analytics data as primary storage point is crucial to decision making and data analysis. Problem we faced at one point in time was – how to detect breaking steps in sales funnel per user session? Google Analytics can give pretty nice information on selected data summarized and split into steps – but we needed to understand more per user session. Beside this problem we had to be certain that data stored in ecommerce part is in line with internal sales system.
We can not guarantee that data stored in Analytics regarding sales (ecommerce) part is 100% correct, as sales process is not hosted on client servers and in complete client control – that we can analyze server logs and all process steps.
System in place, with multi step goals, different payment processors depending on currency used became serious challenge involving wide range of talents, tools and resources.
Main goals of GTM and GA setup was
- identify steps in process where users stop or generate error (with complete trace per user and session and position in process)
- verify ecommerce data collected on thankyou page with processing system(s) information
Second goal concerning verification of GA ecommerce data and external system was finished with standard reporting set from GA. On the other hand, first request showed to be bigger issue.
Google Analytics is storage engine, that does computation of multiple metrics attaching them to specific dimensions. In this process happens sampling. If you have big set of data, in order for system to be responsive and fast – sampling must occur. We had to deal with specific people and their problems on daily basis in order to debug process and avoid sampling.
In order to be able to analyze anything we had to add additional information using session level custom dimension information. As GA is prohibiting storing any kind of personal information in GA we had to rely on unique session identification provided by server. Using this variable (and including it within data layer pushed to GTM) we were able to follow specific user and his flow through site, identifying steps in sales funnel where user stops or goes further.
On top of other data layer variables – user language, site language, location and pages in sales funnel we were able to detect faulty scenarios in sales process.
In terms of outcome – detecting faulty scenarios in sales funnel worked flawlessly giving us and developers insight where to look for breaking points. This lead to cutting development cycles from more than 24 hours (mostly waiting for processing logs) to less than 2 hours.
For second problem – quick integration with SuperMetrics for Google Docs solution and active follow up on client side provided insights in quality of data stored in Analytics. At the end small difference occurred between Analytics and sales system, showing that root cause of difference exist due to nature of processing system – even if transaction information sent to website cleared as OK in next few minutes payment processor can decline payment and cancel sales.
If you need more details or more graphic explanation of process – respond :).