5 Reasons Why Google Analytics Isn't Enough
While there is no shortage of data flowing into your business, getting the most out of it can be challenging. Reliance on free or native tools gives marketers a view – but it’s often not enough. Here, Pavel Šima explains why Google Analytics is good, but comes up short as a stand-alone solution for most marketing pros.
1. Naive Marketing Attribution
Google Analytics offers a range of statistics and capabilities, but only a very basic model of attribution. The most-used is “last click,” which awards all the credit for the order to the last source the customer clicked on before ordering.
Aside from last click, in Google Analytics you can find first click (all the praise goes to the first click), linear (every channel involved in the conversion path gets the same equal credit), and time decay (biggest award goes to the channels near the end of conversion path) metrics.
There are numerous issues at play, but the most-important (for now) with these attribution models is that they arbitrarily decide in advance which click has which importance (weight) for you. Attribution modeling is complicated, because every business has different marketing mix, products, and competition. It can be difficult to get everything into one view – a view that lets marketers focus on making decisions rather than aggregating diffused streams of data.
Another issue with Google Analytics is that it cannot evaluate the influence of the ads that were only viewed. Advertisers who want to include the banners which were only seen, no clicks or other interactions, to the attribution paths are left wanting.
Most businesses cannot see margins in Google Analytics and therefore have no way of optimizing strategies toward profits. This is an issue especially for businesses with several products with large differences in margins (one product has margin of 5% percent, while another might have 80%, for example). Optimizing investment without the right (or incomplete) data can be a gamble which can cause a bigger part of the campaign to be non-profitable and even loss-generating.
Missing margins are dealt with in different ways:
• Calculate mean margin from all the revenue, for example for the past year
• By arbitrarily stating a PCO (Percentage of Cost per Order)
• By dividing groups of products into margin categories and tracking the mean margin
• By not dealing with it at the marketing level at all!
There are a multitude of issues here, but the central result of all of these cases is that marketers do not know if they are creating profit or loss.
In order to be able to work with margins, you need to connect every ordered product to its acquisition costs plus delivery cost, payments, and discounts). Roivenue gathers this data from your ordering system and compares the marketing costs of the orders in relation to the gross profit they bring.
Consider transitioning to data-driven attribution modeling - statistically based operations that compute the real benefit of a given marketing channel relating all conversions and all of the possible combinations of conversion paths. Data-driven attribution models are offered by several premium solutions for web analytics, such as Google Analytics Premium or Adobe Analytics Premium. Their issue is that they are so-called “black-box” applications, meaning front-end users do not know the calculations that lead to the results.
Roivenue does not impose an attribution model you should choose. Instead, it shows you three typically-used models (our math is public!), lets you compare them, and helps you choose the most-relaible one. Roivenue can compute attribution based on only-seen banners, for example, for a fraction of the price of big suppliers of attribution solutions.
In this case, the solution is simple - Roivenue never works with samples. Data gets transferred to another server and analysed – all of the data. Not 2%, not 10%...all of it.
Because Roivenue is feed-connected to ordering system, discrepancies are nearly impossible. Roivenue knows, thanks to the ordering system feed, about 100% of transactions. Even if you miss 5% of transactions in Google Analytics (a common problem) you can see all of them in Roivenue.
4. Returned Goods
Awesome. You have succeeded in buying cheap traffic which converts fantastically. Hold your horses!
If you look only in Google Analytics, you can see web conversions but miss any data telling you if the customer has paid for and accepted the goods. What if the goods from this cheap traffic have 3x higher returns than usual? In other words, what if three times more customers do not accept the parcel and do not pay for it?
Logistics cannot advise you because they cannot connect the returned orders with the campaigns. Google Analytics may erroneously make you think that you have found a gold mine but all you do is put more budget in to losing campaigns.
Google Analytics ends with a conversion (or order) and does not track product delivery and money transferred.
As in the previous example, the solution is to connect web and marketing analytics with your ordering system and to get that data talking and working together.
That is exactly the reason for an extra step in Roivenue between conversion and revenue. Conversion —> Delivered conversions —> Revenue.
Google Analytics is great tool for beginners and for evaluating events on web pages. If your business grows quickly, however, you also encounter its limits:
- Inability to calculate margins and profit
- Inability to analyze delivery rate (and/or closed sales from leads)
- Data sampling and missing orders
- Arbitrary attribution models
- Inability to compare and report on the total performance of your business.
The solution can consist of custom integrations and buying premium solutions for web analytics. Roivenue is an alternative tool which can solve all of the integration processes for you, with almost zero demands on your IT and for a fraction of the price of global solutions.