Understanding shoppers has never been more important for retailers, and Google’s latest update to its analytics product aims to help the cause.
On Wednesday, the tech giant unveiled a new update to Google Analytics that uses machine learning to uncover important insights for retailers. The goal, it said in a blog post published Wednesday, is to help businesses zero in on customer trends, so they can be more responsive in real time when making decisions.
“By applying Google’s advanced machine learning models, the new Analytics can automatically alert you to significant trends in your data — like products seeing rising demand because of new customer needs,” wrote Vidhya Srinivasan, Google’s vice president of measurement, analytics and buying platforms. “It even helps you anticipate future actions your customers may take.”
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In one example, Srinivasan lays out how Analytics can shed light on churn probability so that stores under pressure to cut marketing budgets can make more efficient investments in customer retention. She also highlights predictive metrics, explaining how the tool can predict potential revenue from certain customer groups.
“This allows you to create audiences to reach higher-value customers and run analyses to better understand why some customers are likely to spend more than others, so you can take action to improve your results,” she continued, adding that Google Analytics’ deeper integration with Google Ads makes it easier for stores to message and bring more relevant, helpful experiences to customers “wherever they choose to engage with your business.”
One of the major changes is the way it breaks down the walls between channels. The tool not only takes into account conversions from YouTube alongside paid and organic Google search, social and e-mail — regardless of device or platform — it can also cover both app and web interactions. So Analytics can draw from YouTube activity, whether people watch in the mobile app or browsers.
“[Google Analytics] has machine learning at its core to automatically surface helpful insights and gives you a complete understanding of your customers across devices and platforms,” Srinivasan wrote in the blog post. Perhaps most crucially, the tool can do that with or without the use of cookies or web identifiers. And that matters, particularly amid heightened concerns over data privacy.
Cookies are bits of data used to identify a computer or browser, so web sites can customize the experience based on the user’s activity, geography and other details. Companies of all stripes use them, whether to promote products that are popular in a particular region or deals for an item that the consumer has previously looked at, among many other use cases — from helpful to downright creepy.
The web data packets have become a hot topic in the privacy debate, and not just among privacy advocates.
In a recent conversation with WWD, Laura Kennedy, senior lead analyst at CB Insights, explained, “That is what comes to the top of the discussion a lot, whether it’s with corporate [or] with retailers, brands, start-ups within our own analyst team.
“Any use of the word ‘personalization’ is like, ‘Well how anonymized is this?’ And, you know, ‘I want the rewards, but I don’t want them to know so much about me’ or, ‘Oh, it’s creepy how Amazon knows how old my kid is.’ And it’s from maybe your baby registry seven years ago,” she continued. “So that’s the area that I would say I watch the most when it comes to retail and in tech.”
Europe began enforcing its General Data Protection Regulation in 2018, and this summer — as retailers reeled from the coronavirus pandemic — California state entered into the enforcement phase of its California Consumer Privacy Act.
Google apparently believes machine learning’s predictive capabilities can help sidestep the issue, at least to some degree, while still offering a way for retailers to understand shoppers.
“Because the technology landscape continues to evolve, the new Analytics is designed to adapt to a future with or without cookies or identifiers,” Srinivasan said. “It uses a flexible approach to measurement, and in the future, will include modeling to fill in the gaps where the data may be incomplete.
“This means that you can rely on Google Analytics to help you measure your marketing results and meet customer needs now as you navigate the recovery and as you face uncertainty in the future.”