Future of data analytics - tools to use in analytics

When Google announced a sunset date for Google Analytics 3, many of us in the industry panicked. Google Analytics 4 was far from being a stable analytics solution. GA4 had a ton of features missing and the user interface changed drastically from its predecessor.

When Google Analytics 4 was first introduced, it was recommended to continue using Google Analytics 3. There was a short period to make the switch. GA4’s architecture is a complete revamp with new ways to track data, where everything is an event. GA4 also introduced a ton of new metrics such as the daily average user (DAU), better e-commerce tracking, and improvements to what data can be tracked.

The new GA4 architecture also takes us to a future where web analytics will be the basis of a holistic analytics strategy. Other analytics solutions exist that leverage Machine Learning and Artificial Intelligence to assist with real-time optimization.

Analytics Technology

Data Warehouse

Data Warehouse solutions such as Google BigQuery and Amazon Redshift will become the new standard of storing and analyzing web data. Google Analytics 4 allows you to send your analytics data to BigQuery (with a bit of setup work). Data warehouse platforms such as BigQuery allow you to import other tables for cross-analysis. The implications of this for marketers are powerful. How many times have you wanted to view social media data, CRM data, email marketing automation, or paid advertising data alongside your Google Analytics data?

Now you can see the big picture of your marketing efforts and make decisions using multiple data points. Collecting and moving data through extracting, transforming, and loading (ETL) has always been a complicated process. Take advantage of third-party APIs to collect data and move the data into a database. Google’s tight integration with its own products makes a lot of what used to be hard to do in the ETL process really simple.

Real-Time Streaming Analytics

Many large enterprise-level companies use real-time streaming analytics to make decisions and provide decisions in real-time. Again, machine learning and artificial intelligence are at the backbone of real-time analytics. Industries that benefit are e-commerce brands, transportation-focused companies (delivery companies), over-the-air (OTT), and the healthcare industry.

There are many use cases for streaming analytics data such as making real-time decisions in cyber security and fraud detection. However, real-time analytics does have a significant cost.

Natural Language Processing

If you really think about how search engines work, they are algorithms. The foundation of those algorithms is rooted in Natural Language Processing, which is technically a type of analytics. BERT and GPT-3 are really popular at the time of this writing. GPT-3 has proven that it can write human-readable content just with a prompt.

Natural language processing is also being used to understand the context and sentiment of online content. Text-to-speech engines such as AWS Transcribe are another foundation of NLP. For many data scientists and analysts leveraging the power of NLP will open up tons of possibilities in analyzing written content.

Audience Engagement Analytics

General Data Protection Regulations (GDPR) have changed the way we analyze user data on websites. Free audience data is a thing of the past. It’s time to really dig down into understanding the audience on a deeper level. Brands that leverage first-party are delivering better value to their customers with better content recommendations, offers, or whatever drives audience engagement.

Audience engagement analytics segments your data down to the core users who drive the core of your business success. For eCommerce brands audience engagement could be Lifetime value metrics (which customers are spending the most and how often). OTT video content is king, but who’s watching what, at what time, delivering video recommendations based on past watch history, surfacing new genres to watchers. For companies like Netflix whose business model is driven by retaining and increasing subscriptions, these metrics are extremely important. Audience engagement data also helps Netflix create new content that can increase watch retention rates.

Content brands (publishers) also use engagement metrics to monetize. Every brand wants to drive engagement. Social media platforms have engagement algorithms to keep people individuals on their sites. Creating engaging content is an art form.

Customer Journey Analytics

A customer can take many journeys. But capturing that journey has been one of the hardest things in analytics. Capturing and visualizing those data points can get overwhelming. That’s why Customer Journey Analytics will be a part of the evolution of analytics in the future. Google Analytics gives a great view of the user journey, but the data is too aggregated to know individual user journeys.

Many analytics tools currently lack deeper exploratory features and place customer journey analytics as a simple dashboard. But if you really think about the user journey, it tells us a story of how a person arrived at the final conversion goal. The journey could have taken months with many interactions (audience engagement analytics), which should be explored in more detail. Customer journey analytics is about a single user.

Personalization Engines

Personalization engines need analytics data to drive decisions. Having information about your user’s habits will be critical to delivering personalization in applications and on websites. There are many ways to create a personalization from using Amazon Personalize or using other technologies like Elastic Search. The possibilities are endless. The goal for data analysts should be how can the data collected be used to drive real-time decisions and deliver personalized content to users on the web. First-party data will be the backbone of a personalization engine. The segment is a great tool to use for optimizing advertising campaigns and activating your audience to take action.

Recommendation Engines

Recommendation engines can be powered by both personalization engines and behavioral analytics. Again an indexing and search engine like Elastic Search can power a recommendation engine. Over the next few years, larger websites will need to incorporate search and index technologies to deliver more personalized recommendations to users. Behavioral analytics, customer insight data, and psychographic data will be the three keys to surfacing content that will keep users engaged with your website or application.