Google announced that Universal Analytics will be retired on July 1, 2023. Although Google Analytics 4 will replace Universal Analytics, this change presents an opportunity for companies to identify gaps in their digital insights and reconsider their online measurement strategies.

For example, if you use Universal Analytics or Google Analytics 4, you have insight into the actions consumers take on your web properties – what they’re clicking and where spikes or dips in web traffic occur. But do you know why consumers take certain actions? If not, you have a digital experience insights gap that could be hindering you from reaching your customer experience (CX) and digital conversion goals.

To make the most of your website and mobile app data, you need to add context to your existing web analytics solution, whether it’s Google Analytics or another solution like Adobe Analytics. Rather than only considering page-to-page behaviors, you should be able to understand how a consumer acts within a page before moving to another or leaving the site altogether. By adding digital behaviors and events into your analysis, you can unravel the meaning behind online behaviors to uncover why visitors and customers convert or abandon their digital journey.

Equipped with this information, you can make more informed decisions about how to improve your digital CX. Then, as consumers increasingly have positive experiences across your digital experiences, you’ll encourage them to become customers and eventually, loyal brand advocates.

Elevating your traditional web analytics with contextual insights

Tealeaf by Acoustic can help fill your digital experience insights gaps by capturing individual actions and analyzing collective group behavior to identify important trends that you can act upon. This is vital because events are not single, linear interactions. Complex and compound interactions must be analyzed to uncover how best to streamline and optimize the experience.

Let’s take a closer look at how Tealeaf helps.

Struggle Analysis

As much as we would love to believe that the design, content, and experience we build for our customers is the most optimal, visitors will have their own unique way of engaging with your site. Anytime a customer “struggles” to experience what you had intended for them as they interact with your website or mobile app, struggle analysis can help you understand why. This could present itself in many ways:

Repetitive clicking on a UI element  
Multiple attempts to complete a form field  
Going back and forth between pages on a site multiple times
Waiting an extended time for a page to load

Struggle analysis allows digital teams to understand why a user is or isn’t converting based on friction they’re experiencing. A struggle analysis report may display the top 10 struggling sessions, the struggle score, the session struggle indicators, and the number of instances for each struggle indicator. With this information, you can course-correct quickly by identifying and prioritizing fixes on your digital properties. From there, you can re-engage the consumer with personalized messaging.

Anomaly detection

Digital and analytics teams are hyperaware that there are issues and events happening across the website and mobile app that they must solve for, but they often don’t have the means to understand what these issues are. Anomaly detection is an alert you can automatically receive when there’s a deviation from what is standard, normal, or expected. Essentially, it identifies atypical patterns in data and pinpoints the issue on your behalf. Some examples of anomalies include:

A customer’s monthly purchases are historically $250. A transaction of $3,500 would be an anomaly and could signal fraudulent activity.  
100 gift card purchases per day is normal during the holiday season. However, this activity may be unusual otherwise.
Your online mortgage quote request form averages over 2,000 form fills each month, but this month, you averaged over 4,000. This increase in conversions is worth reviewing to assess if there are ways to replicate the same results.
A user copying data from a remote machine to a local host may be typical if the user is internal. If this happens unexpectedly with someone outside of your company, it could be flagged as a potential attack on your network.

From changes in traffic to the number of transactions that are processed, anomalies are detected in the context of the customer’s experience so you can take appropriate action.

You can also investigate specific questions or hypotheses you may have. For example, if you want to know when the number of checkouts deviates from the norm, you can select the checkout metric for anomaly detection. Anomaly detection monitors the metric against historical data, calculating and quantifying any deviations. It detects a trend where the typical number of successful checkouts over the weekend is 15% less than on weekdays. If this number drops even further, anomaly detection flags the anomaly and ranks the factors that contributed to the drop in successful checkouts. 

These actionable insights can help you react in real-time to anomalous scenarios, mitigating their impact before they hurt your bottom line. And even better: you are notified of what the problem is, so you don't have to spend time looking for the issue or the reasons behind the spikes.

Session replay

Not every customer and internal stakeholder can explain their issue with your website or mobile app in the most effective manner, and not every issue can be replicated on the spot. This makes your job more challenging when everyone is looking to you for a solution to a problem you don’t fully understand. But with session replay, you can watch each individual session exactly as the user experienced it. By seeing the session from the consumer’s point of view, you can more easily identify issues and save time trying to replicate something you didn’t see firsthand. It also makes solving customer struggle quicker because you don’t need to ask the customer to explain the issue, you can proactively pull up the engagement and address it. You may uncover:

A user repeatedly clicking a button without getting the expected result
Where a user entered a promo code during checkout, but received an error
A broken checkout process due to the credit card field not accepting enough digits

While session replay gives you direct access to the consumer’s experiences, you also shouldn’t have to sift through thousands or millions of replays to identify and resolve CX issues. That’s why Tealeaf offers the combined power of session replay, anomaly detection, and struggle analysis – you can rely on anomaly detection and struggle analysis to provide proactive insights and then use session replay to dive deeper into the experience that has friction and conduct even deeper analysis.

Gain a holistic view of your CX

Consumers prioritize convenient and personalized experiences above all else, which is why digital teams need to understand and mitigate anything that may prohibit a visitor from having their ideal experience. 

Stop relying on traditional web analytics to tell you the full CX story. By adding Tealeaf to your tech stack, you can go beyond surface layer insights, break down the silos between analytics and optimization efforts, and add more context, giving you a holistic view of CX.

To learn more about the digital experience insights gap that traditional web analytics leave, check out our infographic.

How to Close Your Digital Experience Insights Gap