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Google Analytics 360 VS Adobe Analytics: a guide to choosing the right platform

data woman
(Image credit: Future)

The pandemic has driven many businesses to ramp up their online focus. For some, that has meant recognizing the importance of digital and taking their first tentative steps into web-based services. Others moved to launching more advanced offerings — for example, the influx of digital banking outreach programs, remote support, and flexible payment services from the likes of Natwest and HSBC.

For all, however, providing fast, relevant, and frictionless experiences is now paramount, whether via mobile apps, ecommerce sites, or Internet connected devices. In today’s increasingly digital-centric world, customers expect smooth interactions that align with their changing habits and preferences; and have limited patience for anything less.

To deliver on those expectations and ensure future survival, better use of data and analytics is crucial. Every business is going to require smart analytical technology capable of not only giving them a clear and comprehensive view of their data, but also generating rich insights that allow them to harness each new opportunity and quickly resolve problems.

The question is: which tool should they choose?

Selecting an enterprise-level analytics solution is no mean feat. Of course, there is little doubt about the most popular options — Google Analytics 360 (GA360) and Adobe Analytics (AA) — but businesses must make informed decisions by weighing up the advantages and pitfalls of both platforms.

The case for advanced analytics

For most organizations, the main issue with data isn’t availability but usability. While they collate huge volumes of data, it’s often held across various platforms and systems, which makes harnessing it challenging. Many companies — especially those only just beginning their digital transition — are also unsure how to make the best use of their data. When combined with additional difficulties such as internal roadblocks and a lack of tech skills, this can mean existing analysis is flawed and fueled by fragmented, imprecise data.

This can be problematic on multiple levels. To create engaging and high-quality customer experiences, businesses must be able to translate data about interactions across every channel, device and branch of the business into one unified picture, and uncover behavioral insights. The same is true for wider organizational success; with a holistic view of all activities vital to power ongoing operational efficiency, financial performance, and competitiveness.

Analytical tools are key in helping businesses to take control of and understand their data; if they select their tech wisely. 

Two very different flavors of analytics

It’s easy to see where the confusion around GA360 and AA comes from. At a higher level, they are data gathering engines and frameworks that process data with the goal of producing reports on data digital performance and sharing that data with activation tools. But there are many nuanced variations that make a big difference.

Overall, GA360 is the most accessible and user-friendly option; providing easy-to-use data summaries that companies can simply plug back into their likely primary advertising end point – Google Search and Display Network, and use to drive performance; be that conversions or improving the efficiency of ad spend. AA allows organizations to take a deeper dive into data on myriad levels before activating it across a more complete set of touchpoints — from real-world stores to digital channels — but it’s often complex to use and, for certain tasks, may require businesses to switch between various bolt-ons.

How to pick the optimal platform

Ultimately, there is no one-size-fits-all answer for creating the ideal analytics stack. Both GA360 and AA are powerful platforms for instantly producing summarized data and deep insights in close to real time, and the choice for each company will depend on its requirements, development stage, and goals. Organizations need to look carefully before they leap, closely assessing which capabilities are the best fit and whether that might change in the near future; after all, as businesses evolve so do the tools they need.

There are, however, several factors companies can use to decide which platform is likely to bring the most value for them; business model, team composition and maturity level, and size.

1. Type of business model

As a rule of thumb, data models should tally with the complexity of business models; GA360 for those with simpler models, AA for those with more fragmented operations. For example, firms selling physical items via their website on a drop-ship basis — obtaining stock when orders come in and sending products directly — aren’t likely to need anything more than GA360. On the flip side, it’s probable companies with multiple online and offline touchpoints will require the advanced abilities of AA to ensure they can develop a holistic customer journey strategy and deliver consistent cross-channel experiences.

2. Readiness to tap analytics

The next vital element to consider is whether companies are in the position to harness certain tools. For instance, it doesn’t necessarily follow that businesses with a complex model are inherently well-placed to implement sophisticated platforms such as AA. To make the most of AA, businesses must have the right foundations for orchestrating the complete customer journey; and this ideally calls for one cross-functional team that drives organization-wide alignment on customer experience objectives and approaches.

From the maturity standpoint, it’s also important not to assume that smart tech will remove challenges. Analytics might better support companies as they move forward, but it won’t automatically fix problems. Frequently, the best course is to set a clear direction around customer experience and gradually work to bolster company and analytical maturity.

3. Level of digital dependence

Generally speaking, there tends to be a correlation between how much of the business is reliant on digital and the kind of analytics companies should adopt. When digital sales contribute less than £10 million to the bottom line, there is a strong chance they will be amply served by a tool with fewer features, such as GA360. In instances where digital income exceeds this amount, companies will typically have a larger volume of use cases that will be most effectively met by the expanded scope of AA. The key exception to this rule, however, is that if companies are on track to increase their proportion of digital revenue, aiming for the full offering of AA will save them from switching platforms when ratios change.

Honing data mastery is essential in keeping pace with rising customer demands and increasingly multi-faceted journeys. Robust analytical capacity will be an important navigational and survival tool for all businesses. But as they ruminate over the perfect platform, they must appreciate that the leading contenders have fundamentally different propositions; businesses must dig beneath surface similarities to understand the differences that will signal the right solution for them.

Steve Carrod, Co-Owner and Managing Director, DMPG

Steve is the Co-Owner and Managing Director at DMPG, working with organisations to help transform digital journeys and improve digital customer performance. Steve’s main focus is on providing strategic advice and guidance to clients, and ensuring all projects are delivered to the highest level.