Despite its potential, adoption of the feature has been slow; likely due to a lack of technical know-how or concerns about cost. Even those who have set up the connection are often not utilising it to its full potential.

The BigQuery integration can be a game-changer for businesses, providing several benefits that are not available in the GA4 reporting interface. Here are a few reasons why you should be using BigQuery today:

Soup Up Your Looker Studio Reporting

Have you noticed that your Looker Studio GA4 reports have started to break? Google recently introduced analytics data API quotas, which have impacted even the most basic reports. If your report is still intact, you may have to deal with poor performance and lots of buffering.

BigQuery’s connector for Looker Studio allows you to create GA4 data visualisations that won’t break and won’t buffer. What’s more, the connector actually queries the underlying dataset using SQL, so you don’t need to know any code to get started.

Eliminate Sampled Data and Cardinality

As has always been the case with Google Analytics, filtering large datasets leads to data sampling. With BigQuery, you have access to the raw data, so your reports will always be unsampled. This means you can draw more accurate conclusions.

Cardinality is also an issue in GA4. If a dimension can take many unique values, GA will often group several of these into a new value called “other”. This means the tables you view in GA4 are not always giving you the full picture. Using BigQuery eliminates this problem completely.

Aggregate Data From Multiple Sources

As a data warehouse, you can use BigQuery to aggregate data from many disparate sources. You can combine your GA4 data with data from your CRM, and start seeing how marketing activity really affects key business metrics, such as customer retention and profitability.

We are leveraging this feature for a number of our clients, to conduct some advanced lifetime value analysis. This simply wouldn’t have been possible without GA4’s integration with BigQuery.

Get rid of your costly marketing data hub

The integration of GA4 with BigQuery is a cost-effective solution for marketers. As compared to other data storage hubs that may require hundreds or even thousands of pounds per month, BigQuery offers an almost free alternative. While other platforms may still have their use cases, if your primary focus is analyzing Google Analytics data, switching to BigQuery can significantly reduce your costs.

Retain Your Data Forever

GA4 only retains some data for a maximum of 14 months (or two months if you haven’t changed your data retention settings). In BigQuery, your data is stored indefinitely, which is vital for longer-term reporting and data analysis.

What’s Next?

Setting up and using GA4’s BigQuery export is a fantastic way to get more from your marketing data, but it isn’t the most straightforward process. If you need any assistance setting up a BigQuery export from GA4, don’t hesitate to reach out for help.

The post Why You Should Be Using the BigQuery Export for GA4 appeared first on Grapevine.

My recommendation to clients was always this: Why bother installing Google Ads conversion tracking, when you can simply import your conversions from Google Analytics?

It seems like the obvious choice. The import feature is simple and works seamlessly, so why would you install two separate sets of tracking tags, when you can do everything with Google Analytics?

There are some notable benefits of this method of conversion tracking too. By using Google Analytics tracking in Google Ads, you can be sure that you are reporting using a consistent method of attribution. This is especially useful if you are using GA as your primary reporting source for your cross-channel marketing efforts. It’s also true that Google Analytics tracking is the most accurate for channel-agnostic reporting, as many of the ad platforms will claim 100% credit for conversions they only had a small hand in.

Despite the benefits of the GA import, Google Ads conversion tracking will definitely improve the performance of your ad account, and here’s why.

Last Google Ads Click vs Last Non-direct Click

The main difference between the two methods of conversion is the attribution model. Google Analytics uses a last non-direct click model, which means a conversion is attributed to the last touchpoint in the journey before conversion (except if that touchpoint is direct). For example, a user who visits a site via a paid ad, and then returns via an organic listing before converting, will give 100% of the credit for the conversion to the organic listing.

Google Ads’ attribution model will look for the most recent Google Ads click and will give credit to that click. This means that in the example above, Google Ads would claim credit for this conversion.

In the days of cookie opt-ins and GDPR, the more data we can gather the better. Why would we only pass last-click conversions through to Google Ads, when we could be capturing every relevant conversion? After all, the automated bidding strategies can do the heavy lifting in terms of account optimisation, and feeding in more data will make those strategies more efficient.

Attribution models in Google Ads make no sense with GA tracking

Google Ads first started suggesting the use of their attribution models years ago. Models such as time-decay, position based, and the crown jewel data-driven attribution, have become a staple of most paid search strategies.

The thing is though, these models mean next to nothing if you are only sending last-click conversions through to Google Ads. Think about it this way – how can Google Ads attribute credit to any earlier click, if non-paid conversions are never being sent to the platform?

The result is, an attribution model that can only credit earlier clicks, if the last touchpoint before conversion is either a Google ad, or a direct visit. So much for intelligent attribution!

With Google Ads conversion tracking, all relevant conversions are ingested by the platform, and attribution modelling can work to its fullest extent. As with smart bidding, this means Google Ads will be optimising your account correctly.

Cross-device tracking and website call conversions

The main reason for switching to Google Ads conversion tracking comes down to attribution: more tracked conversions, more mature data, better account performance and results.

However, as if that wasn’t enough, Google Ads also throws in a couple of additional features that prove really useful. The first is cross-device tracking. Google Analytics cannot track conversions over multiple devices, but Google Ads uses a combination of data from signed-in Google accounts, and a healthy dose of modelling, to report on conversions that span more than one device.

Secondly, call conversions & Google’s call forwarding feature are only possible via Google Ads. Google dynamically changes your site’s phone number to a Google forwarding number. When a call is connected, Google Ads can report on this. You can even change the settings for different lengths of calls, if you want to qualify your leads. This tracking also works for call extensions within ads, which would never be tracked via the website.

A final word

Google Analytics tracking definitely has its place. It’s still the most robust way to measure your site’s performance across every channel, and should form the basis of any website performance reporting.

Within the Google Ads platform though, why not give some fit for purpose conversion tracking a go? We’re confident you’ll see results!

The post You aren’t reporting conversions correctly in Google Ads. Here’s why appeared first on Grapevine.

With Google recently announcing the sunsetting of Universal Analytics, now is the time to get to grips with GA4 attribution, and what it means for your business. If you want to read our thoughts on the positives and negatives of GA4, you can do so here.

Universal Analytics – Last click till we die

For the entirety of its 10+ year lifespan, only one attribution model was available in Universal Analytics – last non-direct click. Last non-direct attribution works by giving credit to the last user touchpoint before a conversion, unless that touchpoint is direct, in which case the touchpoint before is used etc.

In practice that means the following: Let’s say a user clicks on a paid ad, then an organic listing, and then converts. The organic listing receives 100% of the credit for the eventual goal or sale. That hardly sounds fair in today’s multi-device, multi-touch world. Luckily, Google have taken this into account, and have finally brought out proper attribution modelling with their newest product GA4.

GA4 – Attribution front and centre

Not only is last-click attribution now one of several options in GA4; it isn’t even the default. In its place, we have the new Cross-channel data-driven model.

Let’s see what Google have to say about their newest model:

“Data-driven attribution distributes credit for the conversion based on data for each conversion event. It’s different from the other models because it uses your account’s data to calculate the actual contribution of each click interaction.”

Not convinced? Let’s unpack what that actually means.

What Google is doing, in their own words, is “algorithmically assigning fractional conversion credit to marketing touchpoints”. In essence, Google examines all session paths of both converting and non-converting users, to see which paths are most likely to convert. Paths are then compared against one another for small changes that impact conversion rates. Additional signals such as time from conversion, device type, ad interactions, order of ad exposure, asset type etc. are also used to inform conversion probabilities.

Here’s a simple example. Google analyses that your site has a conversion rate of 3% for all users that click a paid ad, and then a social media post. Conversely your site has a 2% conversion rate for users that click a paid ad, and then an organic listing. The 50% uplift is then attributed to social media, with the result being social media deriving higher conversion value than organic across all relevant conversions.

Other attribution models are available

Google Ads users will be familiar with the other available attribution models. Position-based, time decay and first click models are all available, although the use cases for these are probably quite narrow.

If you really want to make your Google Analytics data match your Google Ads, you can also use the ads-preferred last click model, which is the model Google Ads has used for years. This model simply finds the last Google Ads click, and apportions 100% of the conversion to that click.

What attribution will mean for your business

We all know that users do not convert in the seamless way we’d like them to. Last click attribution is no longer fit for purpose, and I for one am glad we are seeing a shift in focus from Google. The hope is that better attribution and modelling will lead to less wasted marketing spend across the whole funnel. Google Ads will handle the change automatically, and begin to optimise your campaigns to modelled conversions. Third-party platforms will need manual intervention, but the data coming out of GA should be more representative of the real-world value of these platforms. In theory, that will mean more efficient marketing campaigns.

The Google black box gets bigger

My major concern with the new data driven attribution settings (and this same concern goes for most of Google’s recent updates) is that they are taking power and visibility away from the user.

You will never know how Google decides to apportion conversions in your account. The optimist in me hopes that Google’s intelligent data models and machine learning will make your reporting more accurate than ever; finally giving the correct credit to early funnel interactions.

The sceptic in me realises that Google have always used their analytics platform as a means to make more money via Google Ads. Don’t be surprised if data-driven attribution apportions more conversions to your Google Ads account, and, when combined with automated bidding strategies, leads to increased spend.

The truth is, Google is automating more and more of the marketing process whether we like it or not. Most of that automation has proved to be beneficial, and I’m sure better attribution modelling will help businesses meet their goals. The trouble is, even if it doesn’t, you won’t know a thing about it!

The post GA4 Attribution Settings appeared first on Grapevine.

Like it or not, GA4 will be the default analytics product moving forward. Google recently announced that they will be sunsetting UA in July 2023, and given the reception to GA4 in its first years, I’m sure this announcement was not positively received by many marketers.

It’s not all doom and gloom though. GA4 comes with some notable improvements, which could be transformative for your business’s data collection and reporting. In this article I’ll be unpicking the good and the bad, so you can decide which analytics product to use over the coming months as you transition to a GA4 only world. Spoiler – it’s both!

Pro – Combined app and web view

Gone are the days of having two sources of data for your web traffic and your app traffic. GA4 was built with cross-platform tracking in mind. The new event-based data structure in GA4 is perfect for handling hits from both webpages and applications, and even the terminology used in the platform is more universal to accommodate both types of measurement.

After installing a consistent user ID, you’ll be able to stitch your users’ web and app sessions together, giving you even more insight into user behaviour. If you have significant app traffic, GA4 will definitely be the platform for you.

Con – No more hierarchical events and strict parameter limits

Many publications and blogs I’ve read have talked about the positives of the new event data model. I’m not so sure. For the everyday drop-in and drop-out analysis that most marketers undertake, the hierarchical structure of event category, action, and label was a simple one to understand.

My major gripe though, is the lack of parameters allowed with the new model. Only 50 event-scoped custom dimensions can be registered in a GA4. While only 20 custom dimension slots were available in UA, the event structure actually allowed you to send unbounded information into the event reports. The new data structure is far more limiting.

It’s now crucial that you map out your analytics solution, so you don’t run out of space in GA. Decide ahead of time which dimensions and metrics you wish to measure, and make sure you aren’t sending any data into GA that is redundant, or not useful for your reporting.

Pro – BigQuery integration

The BigQuery integration for UA would have set you back $150,000 a year – hardly a snip. The same integration for GA4 is now completely free. The BigQuery data warehouse gives you the ability to join your GA data with offline data sources, such as your CRM. If you’re really smart, you could even run your GA data through statistical models, or develop your own attribution method. Whilst complex, the benefit for medium to large organisations is marked.

Con – Fewer predefined reports

The in-built reports in GA4 are by far its worst feature. The overwhelming positive of UA were the predefined reports that provided so much value for so little effort. The majority of that reporting has been stripped back, and in its place we have been presented with Analysis Hub. The Analysis Hub allows users to create their own reports, by combining dimensions, metrics and segments in whatever way they wish.

The problem is we used to get 80% of the reports we needed without having to configure them. Now almost every report you want will have to be built from the ground up, which could be a big time sink. Framing this in a more positive light, GA4 forces the user to become more data literate, by making them define their own reports. Maybe this will be a good thing in the long run.

Pro – Intelligent audience creation

For the day-to-day analytics user, this is the feature to focus on. Firstly, I want to talk about audience triggers. With an audience trigger, you can now fire an analytics event every time a user is added to that audience. This means you can create complex audiences and easily monitor how many people are matching that pattern of behaviour.

Secondly, predictive audiences. Predictive audiences use GAs machine learning to predict the future behaviour of your users. This allows you to create audiences such as ‘Likely 7-day purchasers’ or ‘Likely 7-day churning users’. The latter is particularly important for re-engaging iOS users who might otherwise have their GA cookies wiped from their Safari browser. I envisage that predictive audiences will be a cornerstone of any good remarketing campaign in the years to come. Google’s algorithmic power is getting better all the time, and tapping into it will be key.

Con – No views

I’m a major believer in creating useful views in GA, as they help to keep your data clean and orderly. Well configured Universal Analytics accounts should have a view for reporting, a staging site view, a raw data view, and a test view as a minimum. Views have been totally scrapped in GA4, with the only possible segmentation now coming in the form of web and app data streams. Yet again, the analysis hub must be configured by the user if you wish to simulate views in your reporting.

Pro – Consent mode and modelled conversions

I won’t go into too much detail on consent mode in this article, but more can be found here.

In essence, consent mode is Google’s privacy feature that helps to eliminate the use of cookies when users do not give explicit consent. With Universal Analytics, any data from non-consenting users is lost, but GA4 ingests this data in the form of anonymised and cookieless pings. This data is then used to model conversions, meaning you can report on all of your user interactions; not just interactions from consenting users.

In a post-GDPR world, we will need to rely on this kind of modelling, and Google’s solution will help to bridge the data gap caused by the increased focus on privacy in recent years.

Should I be using GA4 now?

July 2023 sounds like it might be a while away yet, but it’s crucial that you get GA4 running as soon as possible. There is talk of Universal Analytics properties being deleted six months after this date, so GA4 will be the only place to view historical data. If you wish to have year on year comparisons, you’ll need to get GA4 installed by this July.

GA4 brings some great new features to the table, but is missing some of the best parts of Universal Analytics. In my opinion, the best solution is to run both GA4 and UA in parallel (while you still can), and take the best parts from each. Create your audiences in GA4, make use of the BigQuery integration, but use UA for your day-to-day reporting and analysis. With Google’s recent announcement regarding Universal Analytics, now is definitely the time to start focussing on GA4, but I see no reason why you shouldn’t make the most of UA while it’s still available.

Final note

I’ve spent the last few months working with clients on Adobe Analytics. I can tell you that a lot of GA4’s functionality seems to be lifted directly from their playbook. Like Adobe Analytics, GA4 has the potential to be incredibly powerful, but can be a minefield to navigate. Whereas UA was built with the layman in mind, GA4 exists for the professional web analyst, data scientist, or business intelligence unit. If that isn’t you, perhaps you should get in touch!

The post The Case For and Against GA4 appeared first on Grapevine.