Quantcast
Channel: Blog – Blast Analytics

Evaluate Customer Data Platforms For Your Business

0
0

If you’re like most people in the business world, you’ve probably been hearing a lot lately about customer data platforms (CDPs). With this much hype, it can be difficult to differentiate between key functionalities that you need for your business and the exciting buzzwords that attract attention but don’t add much business value. So where should you start?

What’s a Customer Data Platform (CDP)

teammates evaluating the different functionalities of cdps on a computer

Let’s start with the basics. CDPs have found an important place in the modern data ecosystems because they centralize customer first party data from across your various customer touchpoints. They’re focused on providing a full 360-degree view of your customer so that you can engage with them in the right ways.

Simply put, a CDP does the following:

  • Aggregates all your customer first party data from various sources
  • Integrates the data to tell a singular customer journey
  • Enriches the story by creating meaningful classifications and potentially
    importing external sources
  • Allows you to operationalize what you know about your customers in meaningful and
    highly monetizable ways while providing true digital experience optimization

What CDP Functionality Should I Focus On?

There are some key things to focus on when evaluating the capabilities of CDP technology to ensure that it fits the needs of your business:

  • Aggregation – an efficient and secure pipeline to import your data into a centralized system
  • Integration – identifying like users across your various data streams through customer stitching
  • Enrichment – classification and segmentation of your customers to drive relevancy and engagement
  • Operationalizing – leveraging connectors with your content management system to personalize assets and target customers

This is just scratching the surface. Read our full article on How to Evaluate CDPs for Your Business or contact us to discuss your needs.

The post Evaluate Customer Data Platforms For Your Business appeared first on Blast Analytics.


New Roads to Travel Using Google Data Studio and Google BigQuery

0
0

A significant change as Google Analytics 4 (GA4) rolls out is that both paid and standard GA4 properties can now be linked to Google BigQuery. Once linked, BigQuery has accurate event-level data and allows for more robust reporting beyond the GA4 user interface. Google Data Studio (Google’s free dashboard reporting tool) can connect directly to any BigQuery dataset. There are significant, valuable capabilities to be utilized.

Where Do You Get Your Insights?

a woman looks at a map while standing in front of her car; insights

In the world of digital analytics, we all know there are as many data reporting platforms as there are opinions. Many of them are designed out of the box to be extremely compatible with the specific analytics suite you may be using for tracking visitor journey interactions. For this article, I’m focusing on one of those reporting packages, Google Data Studio, which is well-suited to report on the data captured within Google Analytics. While you may find hundreds of articles about this particular reporting package, today I am delving into very powerful and underutilized corners of its capabilities.

Google Data Studio is an online tool for converting data into customizable informative reports and dashboards.

While Google Data Studio has an easy-to-use connector to Google Analytics data, both for Universal and GA4 schemas, there’s also a connector to BigQuery. BigQuery is Google’s data warehouse for the same event-level Google Analytics data. With GA4 now opening the connectivity to BigQuery to all its customers, we anticipate more teams will be planning to link the two and begin a level of user journey exploration that event-level data now offers.

icon - cost

Cost

There are no billing charges associated with exporting data from a Google Analytics 4 property to BigQuery. You can export to a free instance of BigQuery (the BigQuery sandbox), but exports that exceed the sandbox limits will incur charges.

icon - daily export limits

Daily Export Limits

When using the standard (free) version of GA4, the Daily Export to BigQuery is limited to 1 million events. The GA4 360 (paid) version of GA4 can export billions of events per day.

icon - pay by query

Pay by Query

You will need to pay by the queries you execute in Google BigQuery. While storage is very inexpensive, you will be charged $5 per terabyte of data processed beyond the first free terabyte allowance provided each month. Query execution will typically be where the majority of costs could be incurred.

With GA4 now opening the connectivity to BigQuery to all its customers, we anticipate more teams will be planning to link the two and begin a level of user journey exploration that event-level data now offers.

Built-In Data Connectors… or Not!

a forest road forks from one path into two

It’s a simple process to connect Data Studio to Google Analytics data using the built-in connectors. Data Studio accepts your Google Analytics credentials and immediately provides access to a wide list of available dimensions and metrics for you to choose from. My goal, however, is to stretch my reporting to use a “next-level” of control over the selection of the data for use within my charts. For example, let’s assume I have an ecommerce store that sells socks across a handful of departments. I may want the ability to produce a funnel-type report which shows me activity by product views, adds to cart, orders, and overall conversion rates, by department. I would be scratching my head a bit with the standard GA data connector as to how to merge multiple disparate metrics across a single dimension in a single chart.

multiple disparate metrics merged across a single dimension in a single chart

To get this level of control over the data, instead of selecting the GA Data Connector, I can use the BigQuery connector option. At first glance this may not be familiar territory, but it is the same data, accessible to all users that send (link) their Google Analytics data to BigQuery.

Google Analytics and BigQuery information cards

If you look deeper into the BigQuery connector, you have the ability to create custom SQL statements to return exactly the dimensions and metrics of interest. Bingo, exactly the data I want without a lot of extra data baggage. The Data Studio user interface even lets me populate date range variables utilizing the dashboard’s primary date filter.

Populating date range variables utilizing the Data Studio dashboard’s primary date filter

The Hidden Value-Add(s)

a person stands in the sand holding a compass that points north; truth

While it’s easy to use the built-in Google Analytics connector, there are several real and rational reasons to consider connecting to BigQuery instead.

icon - the data in BigQuery is the truth

The Data in BigQuery is the Truth!

When connecting to the data warehoused in BigQuery, we’ll never have to worry about the sampling that occurs in many Google Analytics implementations. If we have the option to get accurate data when building a reporting dashboard, we should vote to get it accurate every time, 100% of the time.

icon - all event level detail

BigQuery has ALL the Event Level Detail

How ready are you for GA4? GA4 is very event-oriented and as you begin to learn how you may need to attach event parameters to various interaction events, BigQuery just might be the only place to see and retrieve all associated event data (or user property data) nested inside the GA4/Firebase structure.

icon - next step analysis

Next-Step Analysis

As we delve into event-level data it is easy to see the possibilities of answering the age-old question of “What did the visitor do NEXT?” While the ability to produce a complete set of end-to-end user-flow diagrams may not be viable, key visitor decision points along the user journey no longer have to be a mystery.

icon - enhance reporting with new data

Enhance Reporting with New Data

BigQuery allows you to import data. Do you have external sources of data at the customer-level that can be mined? Here’s the opportunity to merge by known user and blend the once-offline data with your recent visitors. Think: customer data platform (CDP) data, geo, trend, propensity data, all accessible with any level of complexity of SQL joins handled by the power of BigQuery.

icon - BigQuery machine learning

Access BigQuery Machine Learning Capabilities

With the power of BigQueryML, you now have access to world-class forecasting tools so you can finally get the sales forecast reports you’ve been asking for.

Using BigQueryML's world-class forecasting tools for sales forecast reports

Go Forth and Trailblaze

an SUV drives towards the mountains on a dirt road; trailblaze

As more GA4 implementations are rolled out and more event-level data are captured, extending your reporting capabilities beyond normal approaches will be to your advantage.

As exciting as these dashboard reporting possibilities are appearing to be within your grasp, I am sure it is being tempered with the realization that not everyone knows how to tame custom SQL. My hope and my mission are to first point you to the path, then offer your team a map to navigate to the desired destination. As more GA4 implementations are rolled out and more event-level data are captured, extending your reporting capabilities beyond normal approaches will be to your advantage. We’re here to help you optimize your customer digital experiences and we are clearing the trail ahead. Feel free to reach out to our solutions team to see how we can help blaze a path to the reporting insights you need.

The post New Roads to Travel Using Google Data Studio and Google BigQuery appeared first on Blast Analytics.

Know What’s Possible with Customer Journey Optimization

0
0

The frontline for gaining the competitive advantage lies with the experience that brands provide to their customers. Success on this front is primarily determined by an organization’s ability to meet their customers’ needs and expectations across their customer journey. Therefore, it’s no surprise that teams are prioritizing their customer journey optimization efforts.

At Blast, we define customer journey optimization as the process in which engagement with your customers is optimized by accounting for their entire journey, leading to a seamless, relevant and impactful end-to-end customer experience. For some teams, when they hear customer journey optimization, they immediately think of customer journey mapping. While customer journey mapping can be an effective method for understanding the customer journey, we see it as just one solution a brand can leverage under the customer journey optimization umbrella.

Below, we’ll highlight a few of the different ways brands take meaningful action with their customer journey optimization efforts.


Customer Journey Strategic Roadmaps

customer journey optimization process - customer journey strategic roadmap

A common pain point we hear from stakeholders regarding the customer journey is that they know where they want to go, but they do not know how to get there. For example, stakeholders understand that they need to improve the digital experience and meet the needs of their customers at the right moment and with the right messaging along their journey, but they do not know how to achieve this given their current state (e.g., siloed data, misaligned teams, lacking a culture of experimentation, etc…).

A good customer journey strategic roadmap lays out a solid path to execution with prioritized next steps that align with both short- and long-term objectives.

For these types of situations, a brand can benefit most by working to create a strategic roadmap. Building such a roadmap is not a simple task and requires collaboration across teams. It can be beneficial to work with an unbiased strategic partner, who works to gain internal alignment on business objectives, assesses the data maturity of the organization, and collaborates with your teams to identify the key customer journeys and KPIs. The resulting customer journey strategic roadmap lays out a solid path to execution with prioritized next steps that align with both short- and long-term objectives. In essence, this type of roadmap answers the question “How do we get there?”.


Customer Journey Analytics

customer journey optimization process - customer journey strategic analytics

A diagram showing different user paths through the ordering process

a diagram showing order revenue for different order sizes

An essential part of being able to drive meaningful business impact with customer journey optimization is the ability to turn your valuable customer data into actionable insights. For many brands, this is easier said than done. In fact, a common request we hear from stakeholders highlights how complex this can be: “Understanding the paths visitors take on a site is a large task to organize all of that data, but I want to see what it tells us.”

With third-party cookies going away, it’s even more important for brands to ensure they have a solid approach in place to establish a unified first-party data foundation. Customer journey analytics is focused on creating this necessary foundation. But teams shouldn’t stop here!

Once this foundation is in place, it’s important for brands to take the next step in analyzing their customer data for valuable insights. This, too, can present challenges, especially since there are many ways to slice and dice data. In these cases, it’s important for teams to have the right expertise and skill set to know how best to approach this type of customer journey analysis, one where data and insights can easily be visualized and communicated across teams.


Customer Journey Mapping

customer journey optimization process - customer journey mapping

As mentioned earlier, most people are familiar with the concept of customer journey mapping. What makes customer journey mapping more complicated is that most customer journeys do not follow a linear path, but involve diverse journeys across channels and devices.

a hand places pins on a map; the pins are networked together with string

Customer journey mapping can be effective in helping teams visualize the most valuable customer paths. However, in creating these paths, it’s important to leverage Voice of Customer to get into the customers’ mind to understand (and not make assumptions about) their expectations and emotions as they navigate their journey.

Similar to customer journey analytics, one key takeaway with doing customer journey mapping is knowing the importance of including insights. Previous studies have shown that the effectiveness of customer journey mapping can be undermined when insights are not incorporated. Another key takeaway is understanding that customer journey mapping cannot be treated as a one and done exercise for the organization. For example, customer journey maps created in early 2020 (pre-Covid) are very likely not relevant to what the current customer journey is post-Covid. The customer journey is constantly evolving and to keep pace, this really requires teams to view customer journey mapping as an ongoing effort for customer journey optimization.


Customer Journey Orchestration

customer journey optimization process - customer journey orchestration

Customer journey orchestration is the pinnacle for customer journey optimization. It speaks to a brand’s ability to act on their data and insights in real-time, and deliver a relevant experience to customers, by presenting the right message at the right time in their journey. The value of customer journey orchestration is that it enables brands to evolve from optimizing individual touchpoints to personalizing the entire customer journey.

Customer journey orchestration speaks to a brand’s ability to deliver a relevant experience to customers by presenting the right message at the right time in their journey.

Being able to execute customer journey orchestration successfully requires a significant investment in time, resources, and technology. An organization needs to have an optimized martech stack in place, a strong foundation with unified data and as well as a solid strategy for execution. That’s why brands do not typically start out of the gate with customer journey orchestration as part of their initial customer journey optimization efforts. Customer journey orchestration is oftentimes the goal they work towards by first, leveraging some of the other solutions highlighted above.


See the Journey Through Your Customer’s Eyes

customer journey optimization process - customer journey strategic roadmap, customer journey analytics, customer journey mapping, and customer journey orchestration

Whether it’s starting with a strategic roadmap or upleveling your customer journey analytics, what’s really at the heart of customer journey optimization is the ability to step into the customer’s shoes and view the journey as a whole instead of focusing on individual touchpoints. When brands are able to embrace this mindset, they are more likely to see a real positive business impact for their efforts and ultimately, maintain that competitive advantage.

We hope this helps you along your journey to deliver better experiences to your customers. Still have questions? We’d love to hear from you! Request a consultation to discuss your customer journey optimization needs.

The post Know What’s Possible with Customer Journey Optimization appeared first on Blast Analytics.

How Customer Data Platforms Can Benefit Marketers

0
0

We’ve written a lot on customer data platforms, or CDPs. But, like many marketers, you might still be wondering, “How would a CDP help me?”

What’s a CDP?

CDPs are central repositories of all the information you know about your customers. They allow you to combine customer data across all your touchpoints, to discover your customer stories, and ultimately to target your customers in highly directed ways.
A person sits at a desk and types using a computer keyboard

What Can a CDP Do For You?

Get to Know Your Customers

By aggregating all your data about your customers in a singular location, CDPs allow you to stitch your known customers together, providing a true 360-degree view of who they are and how they’re engaging with your organization. This allows you to classify them in highly marketable segments and identify the optimal ways to engage them. Going further, together.

CDPs allow you to stitch your known customers together, providing a true 360-degree view of who they are and how they’re engaging with your organization.

But how does a CDP help with this? CDPs are built for ingesting data from multiple disparate sources while identifying common identifiers for individual customers to allow you to merge all the sources together and create real-time segments for targeted marketing, website personalization, or other channel activation.
A man and a woman sit on a sofa while shopping on a tablet device

Optimize Your Customer Engagement

Analyzing the results of customer targeting efforts can help you ensure that you are targeting customers in the right ways. Marketing is an art, but having a CDP helps you to turn it into a science and significantly improve your customers’ experience while increasing the ROI on your marketing spend.

Read our full article on BlastX — our strategic solution leveraging CDPs to deliver optimal digital experiences. Or learn more about our CDP consulting here.

The post How Customer Data Platforms Can Benefit Marketers appeared first on Blast Analytics.

Google Plans to Sunset Universal Analytics – Our Takeaway

0
0

Recently, Google announced that they will finally sunset Universal Analytics in 2023. While for some this has been expected for a while, it may still come as a surprise for many, especially if you’ve been putting off the implementation of Google Analytics 4.

Dates to Know

A person writes in a notebook-style planner or diary
The dates that you need to know are:

  • July 1, 2023 – Free Universal Analytics properties will stop processing hits.
  • October 1, 2023 – 360 Universal Analytics properties will stop processing hits.
  • January 1, 2024 – Data in UA becomes unavailable for reporting purposes.

Google Analytics 4 Enhancements and Features

Google’s new analytics platform, Google Analytics 4 (GA4), is an exciting step forward in architecture and capabilities. The move away from the Page tracking model to an Event tracking model, which allows you to unify your data across web and applications, has been welcomed by the industry. Now, GA4 has almost reached a level of feature parity with Universal Analytics (UA), with many enhancements planned before UA is sunset in 2023.

GA4’s move away from the Page tracking model to an Event tracking model allows you to unify your data across web and applications.

Google Analytics 4 brings with it a host of new features more appropriate for today’s modern world of privacy-focused users with multiple devices. It offers more granular controls over data collection than its predecessor, no longer stores IP addresses, and has additional data-driven attribution capabilities to help you understand user behavior through your website funnel in more detail.

Getting Started with GA4

three young colleagues work together on a laptop
If you haven’t started making the transition already, now’s the time. As a long-time Certified Google Analytics Partner, Blast has helped many of our clients implement Google Analytics 4 in parallel with Universal Analytics to prepare them for the time when UA is turned off. This is to ensure they have enough rich historical data to make the transition seamless and can perform year-over-year analysis. It has also been a nice opportunity for many to review their tracking, optimize, prune, and add data points where necessary.

Google Analytics Consultants Here to Help

We understand though that many organizations might feel daunted by the prospect of the transition, especially if you’ve made big investments in the deployment of Universal Analytics and integrated it with your wider data-architecture using BigQuery. There may be a lot of other moving parts that require the care and attention of experts in the field to ensure adoption of GA4 causes as little disruption as possible. Blast is here to help. If you want to have a conversation and talk through a Google Analytics 4 migration roadmap, reach out to your Program Manager or contact us.

The post Google Plans to Sunset Universal Analytics – Our Takeaway appeared first on Blast Analytics.

The Importance of Integrating Your Testing Tool with GA4

0
0

Integrating GA4 and OptimizelyX

Anyone working with Google Analytics will now be familiar with GA4, the next generation of Google Analytics, as Google recently announced the upcoming retirement of Universal Analytics. Designed around a streamlined hit-based data model, GA4 is a promising progression from Universal Analytics which relied on a now outdated session-based model. If you work with Google Analytics and have not yet got a parallel instance of GA4 up and running, we highly recommend you tackle this soon. GA4 does not contain historic Universal Analytics data so the sooner your GA4 setup starts tracking data the better!

It’s easy to overlook how many other platforms within your MarTech stack you already have integrated with your Universal Analytics, and not consider how you’re going to connect them all to GA4.

Given that GA4 is relatively new, it’s missing a few features and functions we might expect when comparing it directly to Universal Analytics. Unsurprisingly, GA4 is a little lighter on integrations with other tools within the digital experience space. I’m sure there will be plenty of progress here over the coming months – for example a native integration with Google Optimize was recently introduced. While a lot of brands are focused on implementing GA4, it’s easy to overlook how many other platforms within your MarTech stack you already have integrated with your Universal Analytics, and not consider how you’re going to connect them all to GA4.

With GA4 planning in progress, now is the time to think about how your testing and personalization efforts may be impacted by the implementation of a new analytics platform. Looking at some of the most popular testing tools, there doesn’t appear to be a native integration between OptimizelyX and GA4 yet, while VWO do appear to have one setup for an integration via Google Tag Manager. In this article, we’ll look at the importance of integrating your analytics platform and your testing tool.

Integrating Testing and Analytics Tools

a person with long hair works at a desk with two monitors and a laptop

Companies using testing tools such as OptimizelyX are focused on improving the digital experience of their users. To truly dig into how A/B tests and personalization campaigns are impacting the digital experience of your users, it’s important for analysts to be able to take a broad view of how users seeing a test are interacting with your site, app, or marketing materials.

In an analytics tool such as GA4, you have the ability to create detailed user segments that you can apply to compare and analyze the overall usage of your site, rather than only the specific metrics targeted within your A/B test. While the primary and secondary metrics targeted by your A/B test will form the basis of any A/B testing analysis, the ability to look beyond these metrics to explore user behavior more widely is essential in understanding the digital experience overall.

For example, how often have the users converting on your test visited your site or purchased before? What marketing materials do they interact with most frequently? Is there a difference in how users reacted to the test if their prior site behavior was different and they have, for example, viewed multiple products on the site in the three months leading up to viewing the test? These are the types of questions that are going to be significantly easier to dig into within GA4 than within a testing tool itself, given the scope of data a tool such as GA4 captures.

screenshot of the "ga4 integration test users" screen in google analytics 4

While OptimizelyX and other testing tools provide an intuitive and robust reporting interface it’s impossible to explore your test data in as much depth as it is in an analytics tool, such as GA4. Also, many organizations use their analytics metrics more broadly for reporting out on site/company performance. If you have test data also getting sent to your analytics tool, it means all reporting can be done from the analytics tool, ensuring test data and analytics data aligns, which makes sharing test learnings across a large organization easier. It also makes it easier for all teams to embrace test results if teams can stick with the analytics tool they’re most comfortable with, rather than having to look at data within a testing tool they may not be familiar with.

Hence our recommendations to integrate! No matter the testing tool and analytics platform being used, we always recommend integrating the two of them to allow you to truly dig into your test’s impact on your users’ digital experience.

OptimizelyX and Google Analytics Integrations, A Very Brief History

While we recommend integrating any testing tool with GA4, the non-Google tool we come across the most is OptimizelyX. OptimizelyX provided a solid integration with Universal Analytics. After a relatively simple setup, your OptimizelyX experiments would send data on which Experiment and Variation a user was viewing to a Custom Dimension you configured in Universal Analytics. Unless you got fancy in your setup and customized how data was shared between the tools, you generally needed one Custom Dimension reserved per experiment. For organizations running a lot of experiments, this could sometimes lead to a lot of limited Custom Dimension slots getting used up.

Now that the GA4 Measurement Protocol is out of Alpha and into Beta, we expect it won’t be too long before an official OptimizelyX integration is released.

Currently, there’s no official OptimizelyX and GA4 integration. Our expectation is that now the GA4 Measurement Protocol is out of Alpha and into Beta, it won’t be too long before an official integration between the two tools is released. If you’re like me, you don’t want to have to wait around for this to happen; you want to start exploring your OptimizelyX test data in GA4 right now!

Custom Approaches To Integrating GA4 and OptimizelyX

miscellaneous web scripting code on a computer screen

There are a couple different ways you could approach a custom integration between the two tools. Roel Peters has an excellent solution that involves you deploying tags via Google Tag Manager that query the OptimizelyX API, before sending the data on to GA4. For any organizations out there comfortable using Google Tag Manager, I certainly recommend this approach.

The approach we’ve used at Blast addresses the integration from a different angle, with a focus on flexibility and supporting a high testing velocity. Our integration approach can vary from client to client. but it doesn’t always need tag manager updates, so it’s effectively tag manager agnostic.

Our approach can also handle experimentation programs that run a lot of experiments or campaigns at once. Importantly, it won’t run into issues if a user sees a personalization campaign and an A/B test on the same page of your site, which is something that may require some additional custom code if you’re querying the OptimizelyX API from a tag manager. We work with teams to evaluate their short- and long-term objectives for experimentation and personalization to decide which integration approach will work best for them.

Using A Custom Approach To Integrating OptimizelyX and GA4

There are a number of ways to integrate GA4 with a brand testing and personalization tool of choice, and we’re careful to recommend an approach that will work for each individual client. There are a number of factors that weigh into our decision, including whether human-readable or API number names are desired in GA4 reports; which (if any) tag manager a client uses; whether the client makes extensive use of the data layer already; how many tests the client will run at once; and whether the client runs A/B tests and personalization campaigns at the same time.

The amount of customization possible here highlights the importance of working with a strategic partner that can provide a custom approach to meet your specific needs, both for your current experimentation program and your planned future program.

Generally, our approach is relatively straightforward and doesn’t require a heavy development lift for either the client or our developers – fundamentally, we trigger events from your experiment Control and Variations. How these events are triggered and what data is included in the event (alongside how this data is gathered) depend on the client’s individual setup. The amount of customization possible here highlights why it’s important to work with a strategic partner that can provide a custom approach that meets your specific needs, both for your current experimentation program and your planned future program.

The general approach we use can work for tools beyond OptimizelyX. If you’re in the midst of setting up GA4 for the big switchover next year and have an active testing program (or a planned testing program!), let us know and we can help integrate GA4 and your testing platform so it’s set up to work well for you now and in future.

Using OptimizelyX Data In GA4

a woman works at a desk with two monitors and a laptop

When using OptimizelyX data in Universal Analytics, we’d often configure a Custom Report and build it with filters in place so we could see only data associated with the experiment we were interested in reviewing. GA4 functions fairly differently to Universal Analytics given the new hit-based data model that leads to use looking at data at the user level more than the session level.

There are many different ways to surface the new OptimizelyX data you’re sharing with GA4. I’d recommend building a new report in the Explore section of GA4. Create User or Session segments for your Experiment Control and Variations. Apply these segments and start adding in the Dimensions and Metrics you want to investigate to see how your test impacted. Given GA4’s event-based model, we recommend using the Control and Variation Segments, as these allow you to investigate your test’s impact across metrics and dimensions by users who saw your test.

Given that Universal Analytics is still up and running for the next year or so, it’s the perfect time to explore GA4 and this new test data you’ve shared with the tool. Get familiar with how this data is usable within GA4 so that you’ll be ready to make the switch to GA4 from Universal Analytics when the time comes!

The post The Importance of Integrating Your Testing Tool with GA4 appeared first on Blast Analytics.

Thinking of Integrating Google Optimize with Google Analytics 4? Here’s What to Expect.

0
0

sun iconALERT: Google just announced it’s sunsetting Google Optimize. Here is what you need to know to be prepared!

In October 2020, Google launched the next generation of Google Analytics, Google Analytics 4 (GA4). GA4 introduces many new features, including a heavy emphasis on events and a completely new integration with Google Optimize.

With Google’s latest announcement that Universal Analytics 360 will no longer process data starting October 1, 2023, there is no better time than now to prepare for the switch. And as you transition, you’ll want to ensure that you integrate Google Optimize with GA4 properly, so you can take advantage of the detailed reporting that the native integration offers.

The straightforward native Google Optimize and GA4 integration allows for deeper analysis within GA4, allowing you to dig into your test’s performance and how it impacted user behavior across your site or app.

While we often review test data within Google Optimize, taking advantage of the confidence calculations Optimize automatically runs, our deeper analysis often takes place within an analytics tool. The straightforward native Google Optimize and GA4 integration allows for deeper analysis within GA4, allowing you to dig into your test’s performance and how it impacted user behavior across your site or app. But first, let’s take a look at what’s different about the Google Optimize and GA4 integration, which I’ll detail below.

What’s New With the Google Optimize-GA4 Integration

A man uses his laptop while drinking tea

Unsurprisingly, like GA4, the native integration introduces several changes that you should be aware of before proceeding. Here’s what you can expect once you’ve completed the integration.

The native integration is seamless.

Since Google Optimize and GA4 are in the same ecosystem, connecting the two is a breeze. To join the two platforms, check out this article in the Optimize Resource Hub that highlights what you’ll need before you begin and how to link the two properly. Beyond this simple setup we recommend configuring two additional Custom Dimensions in GA4, read on to find out what data they should capture and why!

Google Optimize can use GA4 audiences.

Previously only available to Universal Analytics 360 customers, you can now use GA4 audiences in your experiments.

There are new limitations to running simultaneous experiments.

With Universal Analytics, you can run up to 24 simultaneous experiments per view. However, this has been reduced to just ten concurrent experiments when Optimize is linked to GA4. If you’re looking to run more experiments than this concurrently then exploring an enterprise level testing solution beyond Optimize may be worthwhile.

Fewer experiment objectives are available.

Traditionally, a host of different objectives were available in Universal Analytics, including:

  • Pageviews
  • Session duration
  • Bounces
  • Transactions
  • Revenue
  • AdSense impressions
  • AdSense Ads Clicked
  • AdSense revenue
  • Analytics goals

Currently, only four objectives are available for use in GA4:

  • Purchases
  • Purchase revenue
  • Pageviews
  • Conversion objectives

It’s also important to consider that you must define revenue-based objectives in US dollars.

While there are a limited range of objectives available in Optimize when linked to GA4, deeper analysis across a range of objectives can be done within GA4 itself. There’s no limit to the number, or type, of objectives you can review in GA4 itself, while pulling in Optimize Experiment and Variant ID so that you can review these objectives for each group within your experiment.

Variant impressions are handled differently.

When you activate a Google Optimize experiment, default optimize_personalization_impression and experiment_impression events populate in the GA4 reports. On the other hand, Universal Analytics uses a non-interactive event to report user impressions in an experiment variation.

Experience durations are shortened.

The duration of an experiment has been reduced from 90 days with Universal Analytics to just 35 days with GA4. It’s relatively rare that we look to run experiments longer than 35 days, so the 35 day limit in Optimize isn’t a huge problem for us in terms of experimentation.

AMP experiments are unavailable.

AMP experiments are possible with Universal Analytics properties, but this feature isn’t available with GA4 properties.

Reporting has changed.

With this integration and the updated hit-based GA4 data model, you can expect a few differences in reporting:

  • With GA4, you can now generate reports on individual users across many sessions, while you can only report on individual sessions with the Universal Analytics integration.
  • Real-time reporting isn’t an option for the GA4 integration.

A man looks at some documents with a look of uncertainty on his face

There are no experiment dimensions in GA4 by default.

In Universal Analytics, experiment data automatically populated custom dimensions such as ‘Experiment ID with Variant’. In GA4 these dimensions aren’t available or populated by default. However, setting up new custom dimensions in GA4 so you can easily access experiment data in GA4 reports is relatively straightforward.

As noted above, Google Optimize automatically triggers default optimize_personalization_impression and experiment_impression GA4 events when users see an experiment. These events have useful event parameters attached including experiment_id and variant_id. You can easily expose these parameters in GA4 as custom dimensions by configuring event-scoped custom dimensions in the Custom Definitions portion of GA4. We definitely recommend taking this step so that you can easily see which users have viewed which variant within your experiment in GA4 reports.

Latency times have increased.

The Google Optimize-GA4 integration introduces lengthier lag times between interactions on the site and when the data is visible in Optimize:

  • After launching an experiment, impression counts can take up to 12 hours to populate, while you can view active user counts within minutes with Universal Analytics properties.
  • GA4 audiences can take as many as 30 hours compared to just a few hours with Universal Analytics.

Final experiment results are available sooner.

With the Google Optimize-GA4 integration, you can view the final test results up to 24 hours after an experiment concludes, down from 72 hours with the Universal Analytics integration.

You can target Google Ads accounts.

When you’ve linked Google Optimize and GA4, you can target your experiences at your Google Ads accounts, campaigns, ad groups, and keywords.

You can only export Optimize report data, but Analytics data isn’t available.

Universal Analytics enables you to export Optimize reports and Analytics data, but you’re limited to exporting Optimize reports with GA4.

Integrate Google Optimize and GA4 Today

A person enters code into a text editing program

We believe there is more to come from the Google Optimize and GA4 integration. As updates continue to be made to GA4, we expect to see updates to the scope of the integration. We recommend setting up a GA4 property soon to ensure it starts collecting data. This way, you’ll have some historic data within GA4 to work with when Universal Analytics stops collecting data later in 2023.

If you’re nervous about switching all your Google Optimize reporting to GA4, you can hold off until nearer the time Universal Analytics retires. However, we recommend making the change relatively soon so you can get familiar with how to report on tests from GA4. If you do this you’ll be well-prepared ahead of the Universal Analytics end date, so you can rest easy knowing you’re ready to take advantage of everything the integration offers.

We recommend setting up a GA4 property soon to ensure it starts collecting data. This way, you’ll have some historic data within GA4 to work with when Universal Analytics stops collecting data later in 2023.

If your organization would like assistance integrating Google Optimize and GA4, or if you’re interested in learning how you can use the tools to take your experimentation program to the next level, be sure to connect with us today. Our experts will guide you towards crafting an extraordinary digital experience that is sure to delight your users.

The post Thinking of Integrating Google Optimize with Google Analytics 4? Here’s What to Expect. appeared first on Blast Analytics.

Embracing Change: GA4 is Here to Stay

0
0

As Google announced the sunsetting of Google Analytics Universal in March, many companies are now facing questions they haven’t had to face in many years. How fast can we get this new platform up and running? Do we stay with Google Analytics or move to a new platform such as Adobe Analytics? The number of questions only grows as they find out that GA4 isn’t a simple migration but a full new implementation, meaning no historical data and the changing of terminology, reporting interfaces, and more. Companies now have to wonder: How much will the switch cost? Will Google Analytics 4 (GA4) support my analytics needs? Is the new implementation of GA4 worth the cost and effort versus going with a different analytics vendor?

These are all great questions, and while the answers to most will depend on the company, one thing is clear: things are about to change. When considering all the factors involved in the questions above, here are five reasons the switch to GA4 can be a positive one.

One: Take advantage of Google Analytics 4 (GA4) Parameters to Enhance Your Analytics

Hands typing on a laptop keyboard

Event category, action, and label were the bread and butter of tracking the custom interactions your users have on your website or application. These were great, as they provided you a way to capture what the user is doing and details about that action; however, this hierarchy is very limiting in how much data you can truly collect. The naming conventions were also a catch-all for too many actions and details, making it hard to distinguish which details corresponded to which actions without intimate knowledge of the site and analytics tracking architecture. To help, many people turned to applying custom dimensions to the various reports, using custom code to string two or more values into the event action or label (separated out by various characters such as dashes, colons, semi-colons, etc.), or both. This could make the data hard to read in the reporting interface, with many choosing to export the data to a separate system to parse the values out into separate line items. As a result, someone new to the company or reporting tool won’t always know what the values represent – since event category, action, and label could be a multitude of various data points based on the tracking setup and user interactions.

A common mistake we sometimes see is taking GA universal tracking and mirroring it to GA, using the exact same tracking methods and sometimes even using event_action or event_label as a parameter name.

GA4 is doing away with event category, action, and label, and instead using custom naming conventions that users can tailor based on their business, website, and/or application. Event category and action are now replaced with custom-tailored event names. For example, an event category of “top navigation” with an event action value of “click” in GA Universal would simply become an event name of “top_navigation_click” in GA4. These new data fields are called parameters and are noted in the GA4 interface as custom dimensions with tailored names for the detail you want to see. For example, what might have been an event label value of “about us” in GA Universal, will become a parameter (or custom dimension) called “navigation_item” with a value of “about us”. If you think of events as actions your user takes on a site or app, parameters are all the details you might want to know about that action.

A common mistake we sometimes see is taking GA universal tracking and mirroring it to GA, using the exact same tracking methods and sometimes even using “event_action” or “event_label” as a parameter name. While your current tracking may be working well for your business objectives, doing things this way means you won’t be taking advantage of what GA4 can bring and are missing out on an opportunity to go from good to great reporting.

Why are custom event names and parameters in GA4 better than using event category, action, and label in GA Universal? Aside from a more tailored name that can make data literacy of the analytics much quicker to achieve and cleaner to report on, you have much more flexibility with how you view and report on the data.

While the out-of-the-box hierarchy of event category, action, and label were nice to have in GA Universal, GA4 offers more flexibility when it comes to viewing all the details of a user action. With the ability to add up to 25 parameters for each event (100 for the paid version), you can see a wider range of data points, and can slice and dice them however you like in the reporting.

A diagram comparing GA Universal custom dimensions with GA4 event names

For example, imagine a scenario where you have an interactive map and you want to see which place a user zoomed in on most, and which zones were zoomed in on the most. In GA Universal you’d most likely start by either:

  • Creating an event with a category of “interactive map”, an action of “zoom”, and a label of “the place”, then creating a custom dimension for the zone numbers, or…
  • Having the zone and the place pushed into the event label (zone.place)

For reporting you’d need to either go to two different places to see the data (events for the places most zoomed, and a custom report for zones) or you’d need to use filtering in the event table to see what you’re looking for. With GA4, by using an event of “interactive_map_zoom” and parameters of “place” and “zone” you can now see in one report the top places zoomed in on and the top zones zoomed in on – without the need to parse things out or use filtering.

While the out-of-the-box hierarchy of event category, action, and label were nice to have in GA Universal, GA4 offers more flexibility when it comes to viewing all the details of a user action.

This isn’t to say you should throw your current analytics implementation out the window; but use it as a base for your GA4 implementation rather than as the playbook. Take your current implementation and build from it, taking what’s working for your company and enhancing it with purposeful collection of user interaction details.

By allowing users to go deeper into the data with a larger understanding of the data presented to them, GA4 parameters give companies the ability to become their own analysts. While there may be a learning curve to understanding what parameters are and how to best utilize them, they allow companies to get more information about user behavior than ever before – all while still allowing the company to tell Google just how much of that information they want to look at and digest at one time.

Two: GA4 Offers Greater Flexibility for Reporting and Richer Analysis Tools

A woman presents information to a colleague

Those who are familiar with GA Universal know the out-of-the-box reporting was a major selling point for the platform. However, its visualization and ability for quality analysis was less than ideal, forcing most companies to bring the data into other visualization tools to help with analysis. GA4’s explorer reports give you deeper abilities to slice and dice your data as you need. With the ability to use custom templates for visualizations or use a more free-form option, GA4 allows companies to answer more of their burning questions right in the reporting interface. Even more, while some of these visualizations were previously available only to GA360 customers, GA4 allows standard (non-paid version of the platform) users to create these visualizations as well.

Want to see how many users viewed a certain page, then made a purchase at some point in the future? Create a segment overlap using one grouping of users who viewed that page and another grouping of users who made a purchase. From there you can see how many users of the two groups intersect, how many users viewed the page but didn’t make a purchase, and how many users purchased but didn’t see the page.

The graph below shows a segment overlap of those that viewed the clearance page and those who eventually made a purchase (clearance purchase or non-clearance purchase).

A venn diagram showing segment overlap between clearance page viewers and users who made a purchase
Example from GA4 – Google Merchandise Store

Want to see a funnel of users who worked through an application process? Create a custom funnel that allows you to see how many users flowed through each step and the number of users dropping from each step of the funnel. Further tailor the funnel to see users who joined the funnel from step 3 instead of step 1, or tailor the funnel to see only users who accessed the funnel from a mobile device. You can even add an “elapsed time” metric to see how long it took a person to move from one step to the next.

The funnel below shows a custom 4-step process from when a session was started to when a purchase was completed.

A funnel analysis diagram showing the process from session start to completed purchase
Example from GA4 – Google Merchandise Store

Additionally, GA4’s visualization tools allow for things that aren’t possible in GA Universal. One awesome feature is the ability to see backwards pathing. You can now see a user journey from the viewpoint of the destination rather than only from the starting point..

The flow below shows the landing page the user accessed, then where they went from there.

Flow diagram showing user journeys from landing pages to other content
Example from GA4 – Google Merchandise Store

The flow below shows the pages a user viewed before they got to a 404-error page.

Flow diagram showing user paths to 404-error page
Example from GA4 – Google Merchandise Store

Three: Great Perks that Aren’t Just for Paid Users

Colleagues work at a laptop together

In the free version of GA4, Google is introducing some great features that were either previously limited to paid Google Analytics users, or are new but with a premium feel to them.

Previously only available to paid users, the ability to connect to BigQuery now allows users on the free version of Google Analytics to take the advantages offered by parameters even further. While there are limitations to the number of parameters you can register as custom dimensions in the GA4 interface and the number of parameters you can link to an event, BigQuery allows you to send even more parameters of data to be queried and processed for reporting. This is a huge benefit to those who may want more details about a user’s action than what the GA4 interface allows for, but who can’t afford to pay for the upgraded data collection limits allowed for in the paid version.

Analysts now have the power to change report titles for clearer meaning, create a custom collection of reports that can be added to the navigation for quick access, and even manipulate the metrics displayed and visuals used in some of the canned reports.

In addition to the BigQuery connection, another great feature of GA4 is the ability to tailor the left-hand navigation of the out-of-the-box reports, and even some of the reports themselves. This wasn’t an option in GA Universal. Analysts now have the power to change report titles for clearer meaning, create a custom collection of reports that can be added to the navigation for quick access, and even manipulate the metrics displayed and visuals used in some of the canned reports. This flexibility allows analysts to tailor the experience in the GA user interface to what works best for their company, whether by creating clearer report titles or tailoring reports to show users the data they want in the format they want it in. GA4 may require some additional setup beyond what was required by GA Universal, but the benefits of these custom setup options will be well more than worth the time it takes to set them up. This feature is available on both the paid and free versions of GA4, allowing everyone greater flexibility and an upgraded feel.

Four: Google Has Enhanced the Data Quality for Some Key Metrics

A person types on a tablet device

There are three metrics in GA Universal that always had some issues: bounce rate, time on page, and sessions.

Bounce rates are generally used to see how many users aren’t actually interacting or engaged with your content. However, they have to be taken with a grain of salt. A high bounce rate isn’t always an indicator of bad design or bad traffic. Depending on the type of content, a high bounce rate can be completely okay. Consider the example of a state government website where people can subscribe to email alerts that indicate if the state flag should be at full- or half-staff. These email alerts contain a link to a webpage that details why the flag may be ordered to be at half-staff. A user’s purpose in visiting that webpage is to get the information and then resume their normal daily activities. This type of page is bound to have high bounce rates, but that’s perfectly normal when we consider the type of content and the objective of the page.

You’ll need to resist the urge to compare the metrics between GA Universal and GA4, which will never be an exact match as the systems fundamentally collect the metrics differently.

In GA4, Google is taking these use cases into consideration. Bounce rate is no longer a metric in GA4 (although it could possibly be added back in at some point in the future). Instead, you’ll find an engagement rate, which is essentially the opposite of a bounce rate. This engagement rate is based on the number of sessions that were deemed “engaged sessions”. An engaged session is determined by one of three criteria:

  • The user stays on the page for a certain amount of time (defaults to 10 seconds, but can be adjusted to as high as 60 seconds in 10-second intervals)
  • The user has more than one pageview, or…
  • The user completes a conversion event (configured by the business based on business objectives).

This means that in the flag status scenario, those users who actually read the content of the page (generally taking longer than at least 10 seconds to read), would no longer be considered a “bounced user”. This makes the data more accurate by taking in all scenarios that the previous metric of bounce rate missed, while still allowing the company to determine parts of its configurations.

A couple uses their digital devices

Time on page was another metric that wasn’t always accurate. Due to the way Google was calculating the metric, they were unable to get a time stamp on when a user actually left the website, meaning the last page viewed didn’t actually get a “time on page” metric associated with it. This won’t just skew the results for each page, but also your overall “time on page” metric.

GA4 solves this by using a unique event called “user_engagement”. The “user_engagement” event is triggered by two different interactions from the user:

  • When the user moves from one page to another, or…
  • When they close or move away from the browser or tab the website was on

The latter gives them the final time stamp that was missing from the calculations in GA Universal, making the “time on page” metric more accurate. This leads to better quality data, which in turn means more accurate analysis of the data.

Sessions in GA Universal came with one particularly controversial trigger: changing a session at midnight. While this trigger can help make sure the user’s session always makes it into reporting, it also leads to inaccurate data since it may show two sessions for a user who in fact only had one session. Google has done away with this trigger in GA4. This may have some small drawbacks, but it will lead to more accurate data – especially when looking at sessions per user.

As with any change, it will take time to get used to these new ways of seeing the data you’re accustomed to. You’ll also need to resist the urge to compare the metrics between the two systems, which will never be an exact match as the systems fundamentally collect the metrics differently. However, the updates to these three metrics will make the switch to GA4 much more compelling and the data in GA4 more accurate.

Five: Opportunity to Change What’s Not Working and Take the Good to Great

Colleagues viewing a laptop together

An analytics platform change like this one is also the perfect time to take a good look at what is and isn’t working for your company’s analytics needs.

All too often, companies notice that they haven’t truly looked at their analytics tracking, and realize that they forgot to evolve their analytics tracking as their website or application evolved. This leads to data that’s either inaccurate or unimportant. With GA4 implementation, companies are now forced to take a harder look at their data and have a great opportunity to fix these issues. Find data that’s no longer needed or important to know for the business? Don’t track it in GA4. Find data points that are no longer reporting, reporting the wrong information, or not firing appropriately? Update the tracking for GA4 and look for ways to “future proof” the vital data. Find important user actions that never got tracked or were always pushed lower in priority? Ensure it’s captured in GA4.

While an analytics implementation can be time-consuming, it can also provide you with a good opportunity to step back and reflect on your tracking needs. Analytics isn’t a set-and-forget task; it’s a living breathing entity that needs to grow and change as your company, websites, and applications change.

Conclusion

Colleagues look at printed reports together

GA4 is different from what the Google Analytics user base has become accustomed to. It requires a mental shift in how you think about your data collection and data structure. However, embracing this change is key to getting the most out of GA4. Use GA4 as an opportunity to evaluate what is and isn’t working in your current analytics implementation.

While GA4 has been out for over a year and a half, this news on sunsetting Google Analytics Universal is prompting many customers to ask, ‘do I need to switch?’ followed quickly by a statement: ‘I have to switch.’ Embracing the change and delving into all of what GA4 has to offer is the best way to understand your data needs, keep your data in tip-top shape, and continue driving towards better quality data. Still have questions? We’d love to hear from you! Reach out to us for a consultation to discuss your needs.

The post Embracing Change: GA4 is Here to Stay appeared first on Blast Analytics.


Do You Really Need to Track That? Understanding the Difference Between Analytics Wants and Analytics Needs

0
0

All too often when a client is asked what they want to track, the answer is “everything.” Clients often don’t really know what they want or will need to achieve business goals, and the default response is to track everything that can possibly be tracked. However, the “track everything” mindset often leads to too much data, most of which is rarely looked at—let alone used. In addition, having too much data can easily lead to data clutter, making it hard to see what’s really important and keeping teams from delivering valuable insights to their organizations. The “track everything” mindset is a classic example of prioritizing wants over needs.

Why the “Track Everything” Mindset is So Common

A team works at a computer

There are many reasons why people default to the “track everything” mindset. Here are four of the most common ones:

  • Lack of key performance indicators (KPIs)
  • Not understanding the difference between nice-to-haves and crucial data
  • Being worried about not capturing enough data
  • Not knowing how the data can help them or what analytics capabilities are available to them

Lack of KPIs and the Necessary Time to Create Them

Most organizations have a range of desired business outcomes, yet many struggle to translate these into KPIs that the organization can use to measure whether they’re achieving those outcomes. This is especially true in the initial steps of identifying what data they need to wrangle from their digital touchpoints.

Of course, running a business or otherwise being involved in an organization’s daily operations leads to some very busy schedules. Teams are often too busy and have too much on their plates to slow down and focus on writing KPIs for the business. This inevitably leads to headaches: not doing the appropriate analytics prep work in the beginning can lead to larger workloads later—usually in the form of messy reports that need to be filtered, sorted through, or even redone due to inaccuracies.

Two people discuss what data should be tracked

Nice-to-Haves vs. Crucial Data

Curiosity is great and is a particularly desirable trait when trying to glean valuable information through data analysis, but it’s important to rein it in when deciding which information to track. Too much curiosity can quickly muddy the waters, leading teams to collect more data than necessary on topics with little or no bearing on an organization’s KPIs.

“Nice-to-have” data is, well, nice to have—but it isn’t always needed right away and can sometimes go without ever being looked at. As companies change and the digital functionality evolves, these “nice-to-haves” tend to become obsolete in the future. This sort of data can easily create more problems than helpful insights, especially when there may be potential cost implications in tracking, processing, and storing the additional data.

Not Enough Tracking vs. Over-Tracking

Not having the proper data when you need it can feel like an organization’s worst nightmare. The thought of not being able to answer critical business questions can give stakeholders anxiety, so they resort to tracking everything “to be safe.” As a result, massive amounts of data are captured and subsequently ignored due to the high level of effort required to understand them. This typically either leaves the business questions unanswered or creates a skewed perception of what’s going on with the data.

It’s important to maintain a balance between not tracking enough, which can hinder your data analysis abilities; and tracking too much, leading to information overload and ultimately analysis paralysis.

The thought of not being able to answer critical business questions can give stakeholders anxiety, so they resort to tracking everything “to be safe.”

Information overload can lead to results that are directly opposed to what the organization set out to achieve. Instead of lending itself to a “dream” data framework that helps the organization find and deliver insights to drive better business results, the sheer amount of data becomes overwhelming. This leaves the organization struggling to understand all of the available information and how it might be used to improve business results.

Lack of Analytics Knowledge

The analytics industry is in a constant state of growth and change, so it’s unsurprising that many organizations struggle to keep up. There are also crucial knowledge barriers that prevent organizations from understanding which tools will meet their needs and how they should use those tools. Organizations must take the time to tear down these knowledge barriers, and the first step is to gain an understanding of the organization’s current tools.

Data literacy is key to being able to understand the data your organization tracks, and it can also prove very valuable at the start of the analytics journey. However, many organizations don’t know enough about analytics to confidently claim data literacy. This lack of knowledge can lead organizations straight to the “track everything” mindset; and without knowing how various analytics platforms can help them (tool capability) and what the terminology means (tool jargon), organizations frequently end up under-utilizing areas of their current analytics platforms while over-utilizing other areas.

Separating Analytics Wants from Analytics Needs

Two people have a discussion at a table

The first step to changing the “track everything” mindset is to identify the difference between the data you want and the data you need. Doing so will require a fundamental shift in how you think about data and its relationship to your business.

KPIs

The term “KPI” (key performance indicator) has become increasingly popular in recent years, and for good reason. KPIs are really the heart and brain of an organization: when all is said and done and the dust settles, KPIs show where your organization stands in relation to its goals. Taking the time to write these metrics down is the single most important thing an organization can do when starting to think about analytics tracking.

Starting with objectives, an organization needs to understand and define the main purpose of its digital presence. I usually look for a primary, secondary, and tertiary objective, plus a few others that support the first three. As an example, your list of objectives might include providing a way for users to buy a product, sign up for a government program, or digest information on specific topics.

Once you’ve determined your objectives, you can then outline the goals that those objectives will help fulfill. Examples include increasing the number of online purchases, increasing form submissions, or maintaining a bounce rate within a certain range.

Based on these goals, you can then determine which key metrics will tell you if the goals have been achieved or how close you are to your targets. For example, your key metrics might include the number of transactions, the number of form submissions, or the bounce rate.

Ideally, this process should allow you to narrow down your KPIs to a manageable number. I usually aim for no more than 1-2 metrics per goal and no more than 2-3 goals per objective, with about 5-7 key objectives. This will give you enough data to gain a comprehensive understanding of the purposes your digital presence serves, without suffering from information overload.

A diagram showing how KPIs are created

Prioritizing What Data to Track

Even after you identify your KPIs, it’s important to track additional supporting data that will aid those KPIs and broaden your ability to paint a more complete picture with the data. While too much curiosity can lead to over-tracking, it’s natural to be curious about how users interact with your digital touchpoints. Take the time to write down what you’re curious about, then go back through and prioritize items on your list by sorting them into three buckets: crucial data, aiding data, and curiosity data. Crucial data is generally made up of your KPIs—the data points that drive your organization. Aiding data consists of data points that will complement your KPIs by allowing for deeper analysis and bigger-picture views of those crucial metrics. Curiosity data is made up of “nice-to-haves”—things that you or your team might be curious about, but that won’t put a damper on progress if you don’t track them right away.

Prioritizing this list will help you determine where to focus your tracking efforts first and what can be addressed later. As a result, your organization will avoid spending time and money tracking data points that may not be used or may become obsolete down the road.

Iterative Analytics Tracking

Learn from the past, look at the present, and iterate in the future. When an organization grasps for data perfection right out of the gate, teams default to the “track everything” mindset to ensure they gather all the data they can possibly get. This reasoning typically stems from fear of the unknown. By tracking everything, organizations assume they’re future-proofing their data—but in reality, they’re spending valuable resources gathering data that might be misused or never analyzed at all.

As business needs and digital functionality change, analytics should change with them. Organizations can’t always predict what data they’ll need in the future—and while it’s important to ensure your tracking remains helpful in the long run, it’s equally important to understand that analytics can be iterated on as your organization’s data needs change. By focusing on key areas and iterating on tracking as needed, organizations can avoid the unnecessarily high costs associated with collecting and storing excess data.

An organization isn’t data-driven simply because it tracks everything. In fact, tracking everything indicates a lack of the awareness and effort required to become a truly data-driven organization. To achieve this, organizations must engage with the past, present, and future:

  • Learn from past data to grow in the present and the future.
  • Use data from the present to pivot for maximum future gains.
  • Iterate on data in the future to keep pace with changes in the organization.

Become Data Literate and Get to Know Analytics Tools

You don’t need to limit yourself to core analytics platforms like Google Analytics and Adobe Analytics while on your data journey. These tools may represent the bulk of your analytics implementation and serve as the backbone for your data needs, but there are tons of other tools that can be used in conjunction with these platforms. One example is Hotjar, a tool that can provide heatmaps of your organization’s website pages with screen recordings of the user’s mouse movements to help you better understand the numbers provided by your other systems.

Additionally, while quantitative (numbers-driven) data is vital for data collection, qualitative (user-driven, voice of customer) data can put these numbers in perspective. Numbers can tell a strong story, but direct feedback from your customers will paint the most vivid and realistic picture of how well your organization’s digital touchpoints are performing. Surveys, polls, ratings and reviews, and Net Promoter Score (NPS) questions are just a few ways to gather qualitative data, and there are plenty of tools to help you in the process: options include Qualtrics, Hotjar, and Survey Monkey, among numerous others.

A/B testing and personalization tools can also aid in understanding an organization’s data and its users. A/B testing tools like Optimizely and Google Optimize allow you to run various tests to determine which of a series of designs is most attractive to your users, which copy will encourage the most clicks, and so much more. Personalization tools like Dynamic Yield and CMS systems like Sitecore allow for real-time personalization of site content to different users, ensuring that users get the right information at the right time and helping organizations to optimize positive user experience and conversions.

Numbers can tell a strong story, but direct feedback from your customers will paint the most vivid and realistic picture of how well your organization’s digital touchpoints are performing.

Upon bringing these additional tools into the mix, an organization may find that it’s unnecessary to track quite as much data they had originally planned with just the standard quantitative tracking platforms in mind. Implementing qualitative data tools can help clarify the purpose of the quantitative platform and, in doing so, guide your organization’s focus towards tracking the most important metrics and dropping those that aren’t necessary. These tools can broaden your organization’s ability to view and analyze its data, leading to a stronger understanding of your end users and their experiences with your brand.

Remember, it’s important for your team to learn the terminology used within the platforms your organization employs. Knowing how a given tool calculates certain metrics, and understanding the nuances of how it processes and tracks data, can help your organization judge when it’s appropriate to track certain things and how to interpret the data once it’s in the platform. This knowledge is crucial for long-term success, and for making the most of the data that you’re collecting and reporting on.

Weed Out the Junk: How to Get Over the “Track Everything” Mindset

An exasperated woman at a cluttered desk

Tracking everything usually results in an overwhelming amount of data, leaving analysts wondering where to start and establishing a steep learning curve for new employees trying to understand how and why certain data is tracked. If this scenario describes your organization, it’s time to weed out the junk and bring clarity to your data. Here are some practices that can help you reduce data clutter, filter out the noise, and gain more meaningful insights from your analysis.

Track with Purpose

Be purposeful and strategic in your tracking, and ensure that each metric your organization tracks is tracked for a reason. Ask yourself and your team: how will we use this information, when will we use it, and how can it help us answer key questions or improve the organization? Having a strong understanding of your organization’s KPIs is the first step in creating a robust data profile that will set the organization up for success.

While it’s true that you can always add tracking later, it’s typically not possible to get historical data this way—a fact that further underscores the importance of putting in the necessary time and effort early on to establish a strategic plan for your tracking journey. The point isn’t to track everything “just to be safe”; what matters here is that you’re strategic with your tracking. Devoting time to understanding your organization’s true tracking needs in the beginning will help you deliver what’s needed without opening your team up to data overload.

Track with Understanding

In many cases, the people making the tracking decisions aren’t the ones who have to report on the data. Sometimes these decisions might be made by someone who doesn’t truly care about the data points; often, they do care but have only considered the small subset of data required for their needs and may not understand the broader data needs of the organization. Before deciding on what to track, don’t just talk to your own team: talk to other teams within the organization, as well as higher-ups who might be able to offer a different perspective on what the organization needs..

It’s important to understand what the marketing team needs to know in order to improve their campaigns, as well as what the UX design team needs in order to improve the user experience across your organization’s digital touchpoints. By talking to the various teams within an organization, business stakeholders can stay informed about what’s being tracked and reported on. This can also help in weeding out data points that stakeholders previously believed to be important, but which have in fact gone unused in current reporting. Keeping this line of communication open allows stakeholders to get the most out of the analytics tracking efforts from day one.

Illustration showing too much data vs. just enough

Track with Agility

Analytics is an iterative practice: as changes happen within the business, analytics can and should be adjusted to fit the organization’s new needs. With each iteration, stop tracking data points that are no longer important; track only what is crucial to the business right now, and update as time goes on. Reviewing your analytics setup and your KPIs once a quarter is a good way to regularly evaluate what tracking is still needed and what’s no longer applicable, as well as identify things that are broken or otherwise need tweaking to meet your organization’s new needs.

Part of being agile is being proactive instead of reactive. No one wants to open up their reports to find that they’re missing critical data points from the past 6 months, and regularly taking time to review your organization’s analytics setup and KPIs will reduce your risk of running into disasters like these. Doing so ensures that crucial data is collected, while preventing your team from defaulting back to the “track everything” mindset.

Being agile with analytics is the best way to get the most out of it. Analytics implementations aren’t a “set and forget” task; they require regular maintenance and attention. Iterating on your organization’s implementation will help to ensure that you don’t miss out on critical data, while also saving your team from the perils of data overwhelm.

Start Tracking for Optimum Success

A woman laughs during a meeting

Sometimes stakeholders get so caught up in their curiosity about what’s going on that they lose focus on what they really need to see and understand. The “track everything” mindset has become commonplace as conversations around technology advancement, personalization, and privacy laws change. Data is a phenomenally popular buzzword, so there’s a tendency to assume that more is always better. However, this mindset can create more barriers than doors. To find the data tracking “sweet spot”, an organization must define KPIs, use them to prioritize data points for tracking, iterate and adjust regularly, and put real effort into learning more about analytics tools and processes. In other words, organizations need to think strategically if they want to get the most of the analytics tracking tools and methods available to them. It’s time to get out of the “track everything” mindset and start tracking for optimum success.

We hope this helps you along your data tracking journey. Still have questions? We’d love to hear from you! Request a consultation today to hit the ground running and get started on your organization’s strategic tracking plan.

The post Do You Really Need to Track That? Understanding the Difference Between Analytics Wants and Analytics Needs appeared first on Blast Analytics.

Improve Your GA4 Reporting with BigQuery Sessionized Tables

0
0

A few months ago, I wrote an article touting the benefits of using BigQuery custom SQL inside Data Studio—Google’s free visualization suite for analytics data—and explained how this method could be used to produce eCommerce funnel reports.

Shortly after that article was published, a few of my teammates mentioned that they’d love to see how we could use Data Studio with customized Google Analytics 4 (GA4) data. I enjoy exploring interesting use cases for Data Studio and often find myself pushing the envelope as I look for ways to answer complex questions, so I was happy to start experimenting with the possibilities.

Many businesses have heard and begun to act upon the news that “Universal Analytics will no longer process new data beginning July 1, 2023”, so I anticipate attention will soon shift from implementing GA4 analytics to extracting insights from your new data.

In my opinion, Google Analytics (GA4) reporting methodologies—such as the built-in GA4 Reports and Explorations—are still in their infancy, with continued development and improvement plans hopefully in the works. Even though GA4 reporting solutions are available now through the administration pages, they will face limitations when it comes to highly customized reports that reach beyond traditional capabilities. In my book, BigQuery wins for having the most accurate data and the best range of options for event-level flexibility.

Bridging the Gap: How to Make GA4’s Event-Centric Data Work for You

Two colleagues compare notes

It’s easy to recognize that the mandated change to GA4 is a difficult one. As Google Analytics 4 data becomes available, it’s logical for us to want to build reports similar to those used for previous analysis. And perhaps not surprisingly, our clients want to continue to view dashboards and metrics that they’re familiar with after working with Universal Analytics data for so many years.

Out of the gate, GA4 data is completely event-centric—not session-centric. Universal Analytics did a lot of great aggregation for us, and even applied default user attribution rules; but simply connecting Data Studio to GA4 data and expecting similar reporting data is a lot to ask.

As I attempted to construct such reports during my experimentation, I realized I wanted an already transformed table that mapped the data I needed using the raw event-structured GA4 data. It would be ideal to see a schema focused around sessions, as is prevalent in the majority of existing Universal Analytics reports. Unfortunately, GA4 data in this structure is currently not readily available via BigQuery.

My “Aha” Moment: Transforming GA4 Event Data into Session Data with Looker

Two colleagues working at a desk

A recent client project required me to convert a suite of analytics reports from utilizing Universal Analytics data to utilizing GA4 data within Google’s Looker business intelligence platform.

One of Looker’s strengths is strong data governance; and as I began the reporting conversion task, I discovered that Looker had built a transformational GA4 component. Since Looker is built on a series of LookML blocks, they’ve already created a methodology to construct session-based intermediary tables that perform exactly the type of transformation I’d been looking for.

Inside Looker, a clever extraction of Google Analytics (GA4) event data is transitioned to a cached and sessionized internal table. Using this internal table schema, Looker designers can draft and create “Looks.” These “Looks” then become elements in dashboards, which take advantage of the internally transformed data.

Armed with a new methodology for transforming raw GA4 event data into session data, I determined the next course of action in my mission to power commonly requested reports would be to schedule a daily GA4 sessionized table creation process. This step relies on the Looker sessionized view, and establishes a new partitioned table in BigQuery on a daily basis.

Scheduling view in GA4

Here are a few advantages to this approach:

  • You’ll now have a data set organized around a session-per-row schema.
    Subsequent queries for individual metrics can be run against this new daily table, without the need for processing large volumes of raw GA4 event data beforehand. This can lead to a significant reduction in BigQuery processing costs.
  • Since the daily transformation processes event-level data, it can also parse or replicate specific attribution logic like last non-direct click for individual sessions.
  • Many common Universal Analytics-style reports can be built in Data Studio using session-oriented custom SQL statements as connectors, exposing an array of common metrics and dimensions.
  • In contrast to using the GA4/Firebase connector directly, both the raw and transformed BigQuery GA4 data contain no sampling or added estimated visitor data from a machine learning inference model.
  • Re-processing of this sessionized table extraction is possible (at an added cost) if, for example, you wish to introduce a change to the logic or filters.

A dashboard with sessionized data

Creative Solutions: The Key to Uncovering the Story in Your GA4 Data

Colleagues celebrate success together

Many of my teammates recognize my tendency to “nerd out” when it comes to finding creative reporting solutions; but as far as I can tell, this one has a lot of promise for turning event-focused data into coveted session-focused GA4 data. And the way I see it, if a solution’s been built by a Google (Looker) team dedicated to GA4 reporting as a solution to this problem, then why not share the concept?

As an added bonus to those clients in the Tableau or Power BI communities who recognize the need to connect a Google Analytics (GA4) profile directly: this solution of daily scheduled tables works to power your reports too, as both of these reporting suites can connect to BigQuery through the use of custom SQL.

If you’ve just started your Google Analytics 4 (GA4) journey and need a partner who can help you reveal the stories behind the data, contact our data insights team. We’re here to guide you through the challenges of your GA4 transition with solutions tailored to meet your unique needs.

The post Improve Your GA4 Reporting with BigQuery Sessionized Tables appeared first on Blast Analytics.





Latest Images