It's funny because this conversation happens again, and again, and again, and again, with every new business as they grow big enough to start caring. And someho

It's funny because this conversation happens again, and again, and again, and again, with every new business as they grow big enough to start caring. And somehow, multiple attribution (let's not even discuss cross-device attribution, offline marketing, or more advanced ML-based customer identification and fingerprinting) is presented yet again as a freshly discovered Holy Grail. The first time I heard about it was when Rocket Internet told me it was invented at Zalando. Google Analytics is two things: - a tracker. That is, people do things on your site and what they do gets sent to a server. - a data warehouse [1]. This is the part that gets people. Google stores the data, and what you see is the processed stuff that comes out of it after it's been sliced, by Google. - I said two, but a subset of the data warehouse is the data mart that Google offers on top, which is the slick Google Analytics UI in orange with your gmail login. That thing is so easy and intuitive to use that people take a very long time to switch and have very high expectations when they do so (and usually, deeply entrenched manual processes that they try to replicate with the new solution, causing countless wasted hours). The tracking part is pretty much sorted out with all competitors in the market. For the small sum of $150,000 + around $10,000 for BigQuery per annum, you can access a few layers of your "raw" data with a few days lag. This includes the famed multiple attribution, because of course GA free pulls out only the last channel (plus or minus a few rules, such as ignoring direct traffic). The API will drive you nuts and the data is not in a relational format. I would say the sampling limit is the biggest drawback of using GA Free, particularly if you track things like product impressions on a search (which might add up to 100x product views). Once you have the raw data [2] in a decent relational database, it's very easy to get multiple attribution models of your choice, and to link them up to specific discount and customer rules, and do whatever else you want. And of course, if you didn't bother thinking through your tracking codes, you won't get much out of Google. Google encourages you to think of the UTM parameters as "tags" as opposed to giving structure to your marketing tracking, and that results in some pretty messy schemas and workarounds afterwards (not their fault, but when you're the guy who has to clean up afterwards, you grind your teeth). As far as I'm concerned, a much saner approach is to host the "data warehouse" part yourself - as part of your production backend, fed by the tracker directly (let your DBA figure out the specifics). Then, you get the data live, and you don't pay the Google tax. More importantly, you don't separate your customer, product and order information - which sits in your backend - from your web analytics information - which would sit in Google's DWH - requiring extraordinary ETL efforts. For the Google tax, you can hire a decent DBA and have enough spare change left for a very decent Postgres box on AWS RDS. I'm sorry for the rant on your post, I know first hand how hard early stage sales are in SaaS (currently between contracts actually), but needed to get the last few years experience off my chest in the hope that it will save some pain to other people. I also think you should make it clearer how you differ from GA. I'd love to know a. whether your tracker is any different b. whether your data warehouse, API, and so on are different c. whether your front end is different/more limited/more intuitive and of course d. whether your biggest USP is price. [1] you can read the wiki page, but really, on a model, not implementation, level, it means "the single source of truth that contains all the data my company will ever have". You cannot have multiple DWHs running in parallel. As another HN poster once said, businesses are resilient to application errors, so correctness isn't strictly necessary; but it is a massive help and an enabler of profit generation and as such a very desirable goal. [2] https://support.google.com/analytics/answer/3437719?hl=en https://support.google.com/analytics/answer/3437719?hl=en