Data-led Decisions and the Benefits and Limitations of Google Analytics

[Man 1] A fundamental benefit of using analytics as part of your digital marketing efforts is that it helps you make informed data-led decisions, like when to increase media spend or to stop running activity on certain online channels. This in turn should help you run effective campaigns and drive better commercial performance. Other benefits include the ability to understand your users, the ability to gain insights from real customer activity, the ability to get closer to the customer,

the ability to forecast using trends and patterns in customer behavior, accountability of media spend and resource allocation,

higher conversion rates and enhanced return on investment,

stronger focus, prioritization of resources.

By measuring metrics over time in your web analytics software and noting percentage changes in metrics across areas such as channels, devices, and locations, you can demonstrate that digital activity is working when compared to historical results when the results of the comparison show a positive change. From there, analysts can speculate and theorize about why these changes are occurring using the comparative data available to them. These hypotheses can be validated or disproven through testing by modifying conditions such as spend level, messaging, and price.

[Man 2] Now we’re going to look at ways to use analytics to unearth insights. So our first step for this process is to choose our objective and to measure our data source. So our data source, obviously, in this instance, is Google Analytics, just using the demo account. What we’re interested in, what our objective is, is to understand what’s happening with e-commerce conversions and e-commerce transactions. So our first thing we need to do now that we have our objective set and we’ve decided on our data source is to choose what segment or report we are going to be interested in. So for the majority of digital marketers, it will be the traffic report.

So let’s just go to Acquisition, All Traffic, and then Channels, and that gives us the report that we’re interested in. Next, we need to set our date range. So, by default, in things like Google Analytics, it’s the last seven days, but maybe let’s take a longer date range. Let’s look back to September and see what that looks like. So we can see over that period, there has been $250,000 worth of revenue generated across all these channels, bearing in mind that our objective that we are measuring against is revenue or indeed e-commerce transactions. So we’re just going to sort by that just to see, is there anything of interest or note here?

So what we can see is that organic is driving by far the highest number of visitors, not the same level of revenue. So now that we have a little bit more insight to what’s going on in our report, we need to start looking at comparable datasets. So these are things that are of interest to us in terms of another dataset that we can compare this data against to try and see where the friction is, see where the insight is, and let’s look at a comparable date range because it’s always interesting to look at date range, to look at seasonality. So what I’ve done is I’ve selected compare to previous year, and apply.

And we can see across all our channels here, we are down 33% in visitors, we are down almost 90% in revenue, 85% in transactions, and our e-commerce conversion rate is down 78%. Interestingly, because this is the outlier, again, all of our engagement metrics are up. So our bounce rate is down significantly, our pages per session is up almost 20%, and the average time on site is up almost 40%. So people are engaging far more with our content. There are fewer people engaging with our content, but they’re engaging far more so that would indicate that the quality of visitors has increased over the period. However, we have not managed to turn these quality-engaged visitors into similar levels of revenue.

So we might speculate that the conversion journey is a little bit more difficult this year, that maybe there aren’t enough CTAs on the pages or ways to get to the cart, ways to add things to the cart. So that’s our first hypothesis. And why this is important is if engagement is up and all things were even, conversion rate will remain the same because we’re driving more engaged visitors, but conversion rate has gone down, too. So we have to speculate that there is an issue in getting to the cart. So now we’ve decided what the input may have been that is causing this outcome. The input is what causes this data to change.

We’re thinking it’s something around the cart, it’s something around the ability to convert. And now, we’re going to test to see if modifying this input either up or down will drive predictable outcomes based on what we expect to happen. So if we’re thinking let’s make it easier to get to the cart, we predict that adding more CTAs, that adding more ways to add things to basket, and things like that will result in more conversions. So that’s our hypothesis. That’s our testing roadmap. We’ve also looked at date range as a comparable data set to see what exactly does this data look like over comparable periods.

And we have unearthed that that engagement has increased, visitors are down, but unfortunately, e-commerce conversion rate is also down, resulting in lower transactions and revenue. We speculate that because engagement has gone up but conversion rate has gone down, that it’s more difficult to convert. So we are going to test adding more CTAs or different ways to get to the cart to see, does that affect our quality visitors turning into quality customers? - Google Analytics is the most commonly used free analytics tool, and it’s an excellent choice for most marketing needs.

Some benefits of the standard free version of Google Analytics include the following, it’s free, it’s easy to implement and easy to install, it has a user-friendly interface, it offers customizable reports, dashboards, and data collection, it includes basic and advanced options, it offers seamless integration with other Google products, it offers post-click integration with non-Google traffic sources, including Facebook, Yahoo, and Bing, it provides extensive valuable insights into web behavior. However, Google Analytics also has a number of limitations. It doesn’t provide real-time data and downloadable historical reports.

The free version is limited to 10 million hits per month, it offers limited customization, it defaults to less-click attribution, it’s not always 100% accurate, it’s not as comprehensive at measuring the performance of top of the funnel campaign activities, such as premium display and Facebook, where there is a limited user visit and conversion data from these campaigns. However, despite these limitations, Google Analytics is a highly effective web analytics tool for marketers in most instances.