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Third Party Data in Google Analytics

It is obvious that digital analytics is a means to obtain data-driven answers to digital questions. This is because every page load, impression, click and video view is tracked and analyzed. But what about the relationship between digital platforms and the environment outside the world of digital? For example, what effect does the weather have on your customers? Or the exchange rate? Or the result of last night’s football match?

It is important to identify the potential of linking a company’s internal data with data provided by third parties. Organizations that use third-party data and successfully integrate a broad range of external data into their operations can outperform other companies by unlocking improvements in productivity and risk management. The COVID-19 crisis provides a perfect example of just how relevant external data can be. The pandemic has disrupted everything from consumer behavior to supply chains, increasing business unpredictability, making it harder to use past data to predict future behavior. Companies can only go so far with their own internal data and can benefit from using external sources to guide them.

Unfortunately, this data is not readily available within standard digital analytics tools. This is why we have compiled this blog post, in which we examine what data can be added to GA, how this data can be used to answer business questions and how these answers can be used to improve your business.

What Data can you add into Google Analytics?

External-data sources offer immense potential however they also present several practical challenges. For example, simply gaining a basic understanding of what’s available requires considerable effort. Given that there is a broad range of data sources available, and these can inform business decisions in a variety of ways, It is important to have a clear definition of the business problem for value to be generated.

The first step when adding third party data is taking a good look at your business and considering which environmental factors are most likely to impact it. There is no point adding sports results to a small online shop offering bespoke lighting, as these are very unlikely to be correlated to the business.

The second step that we need to address is the source of the data. The key to our implementation is that the data needs to be available via an API. An API is a means of communicating with another service that has the data we are looking for.

We are not going to go into the technicalities here, but there are a lot of APIs out there offering the sort of data we might be interested in. Some of these are free, some of them are free up until a certain number of calls, while others are exclusively paid for services. Before you start looking to add data, it is important to find the API that you will be using.

What are the risks from using third party data in Google Analytics?

The GDPR (General Data Protection Regulation) has extended the scope of responsibility when it comes to data protection and privacy, meaning it is now required to be significantly more careful about the implications of security incidents caused by service providers. Throughout the process of finding and using external data, companies must keep in mind privacy concerns.

  • Confirm that third-party vendors are GPDR compliant.
  • Clearly define all areas and activities in which the GDPR is in scope, and have third-party vendors agree and provide signed contractual assurances that their processes meet the Regulation’s requirements.

What is the status of third-party data in GA4?

Google Analytics 4 is rolling out at the beginning of a new era of online data collection. Over the next few years, business’ access to third party data on customers will continue to reduce. In response, marketing teams will have to build new conversion strategies to adapt to an internet without cookies.

Online tracking tools will need to work around the decrease in third-party data, fortunately GA4 has an integration with BigQuery that allows for increased flexibility joining analytics data with external data sources. This may increase the technical overheads but will improve the availability of important data.

What questions can you answer with this data?

The use of external data has the potential to be a game changer in a variety of business and sectors. After you have accumulated enough data in your Google Analytics account, you are then able to investigate the correlation between this third party data and traditional digital metrics such as transactions and page views.

To show how this could be used, we have included an example from the fictional online clothing store “E-Clothing”:

Below is a report that combines third party weather data and the product category “socks”. This allows us to answer the question, “in what weather are people most likely to buy socks?”.

Weather Data in Google Analytics - Cold

As we can see from this report, the conversion rate (measured as the percentage of detail views that result in a purchase) is highest when it is raining. This is also when the most socks are sold. The second highest conversion rate is when there is light rain and the third highest when it is overcast. This suggests that people are more inclined to buy socks when the weather is poor.

Below is another report that shows the product category “swimwear” alongside the same weather dimension from the above report.

Weather Data in Google Analytics - Hot

Here we can see behavior that contrasts the “socks” behavior. Unlike socks, swimwear sees the highest conversion rates when the weather is clear.

Finally, we are going to look at an example where third party data in the form of sporting results is combined with product data. In this example we are using football results for all matches involving Arsenal, more specifically the goal difference, and comparing this to product sales of Arsenal merchandise.

This allows us to answer the question of “are merchandise sales related to team results?”.

Football Data in Google Analytics

The result that stands out the most in the above table is the first row, whereby Arsenal won by three goals. Views of Arsenal merchandise directly after this game saw by far the highest conversion rate, and accounted for a large portion of total purchases.

Given more data that follows the same pattern, we can easily conclude that the conversion rate for team merchandise is indeed correlated to that team’s performance.

The Covid-19 pandemic has changed consumer behavior in many ways. One sector that has experienced severe disruption is the service industry. Many  restaurants launched online order platforms for their websites, expanded their third-party delivery partnerships and/or created drive-thru lanes. But with the cyclical nature of the pandemic’s severity it can be difficult to get the balance of what consumers will respond to in advertising.

An example of how third party data could help this would be to modify which offerings – delivery and collection vs dine-in – restaurants are advertising. Of course, the higher the infection rate, the more they should focus on low contact delivery and collection options. Conversely, if infection rates are lower then they can focus again on their dine-in options. By comparing historical infection rate to their actual ratio of dine-in to takeout sales they can see more precisely at what rates their customers are feeling safe to dine-in and when they should stick to advertising their collection or delivery options.

What can you do with these answers?

This data can help to minimize risks and add value to the company; however it requires a mix of problem solving, structured working, and focused execution.

Adapt your advertising strategy

One of the most logical applications of this data is the optimization of advertising campaigns. Given the performance of the “socks” category and the “swimwear” category in the above example, we could easily adjust our advertising for these categories based on the weather. In rainy or poor weather, we could increase the bids for our “socks” campaign while simultaneously decreasing the bids for our “swimwear” campaigns. We could also re-allocate budget so that the budget that is normally allocated to swimwear gets moved to socks. Then, when the weather is clear, we could reverse the process and allocate all of the socks budget to swimwear and re-adjust the bids.

Similarly, given the correlation between sporting results and team merchandise, we could allocate more of our advertising budget towards team merchandise for teams that have recently recorded convincing wins.

These optimizations can either be done manually, but in most cases should be done programmatically. Either way, including these new factors in our optimizations can help us in achieving a higher ROI for our Advertising spend.

Adjust your website content

In addition to PPC advertising, this data could also be used to customize the content on your website. By making use of the same APIs from which the data is collected, we are able to change which products are displayed on the home page of E-Clothing, based on the current weather conditions.

Next Steps

All that is left is for you and your team to brainstorm which external factors are most likely to impact your online performance, find an API that will allow you to access this data and start tracking!

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