Three big data use cases for experience data.

The Quantum Metric platform is all about adoption. But for this to happen, your team needs data – big data. Our most advanced customers are using Quantum Metric data outside the walls of the UI and exploring big data use cases for experience data.

So far, we have focused on delivering a robust out-of-box dataset, with over 60 indicators. These range from behavioral errors like possible frustration or force reloads, to technical issues like API errors and long running spinners, as well as plenty of ways to visualize this data with tools like session replay, customer journey analytics, and heatmap tools.

Quantum Metric is built on Google BigQuery. This means that each customer has access to their raw data. They can leverage this data directly in BigQuery or stream it to any data lake, cloud, or other system of their choosing.

So how should you get started leveraging your experience data? It’s easier than you think, and more valuable than you could imagine.

Here’s a look at 3 big data use cases.

Big data use case one: Retargeting.

Sometimes someone lands on your website or mobile app, but fails to accomplish what you want them to do, such as add an item to their shopping cart or create a new checking account. The frustrated customers don’t convert, open an account, or buy an airline ticket. They leave – and that’s that.

Oftentimes, we don’t know why the error happened, or what we can do to fix it. Wouldn’t it be great to reach out with a nice message to say, “Sorry, but we understand what happened and we want to make it right”? How might customers feel if they received an email shortly after encountering a problem, so that they could speak with a representative?

Together, Quantum Metric and Google BigQuery address this problem by leveraging big data. With the Quantum Metric and BigQuery integration, you can see user behavior, including what exactly happens when a cohort of users (e.g., Android users) don’t convert.

For example, Canadian Tire learned that a large segment of users were not converting because they were experiencing “promo code errors.” Quantum Metric helped them quantify and identify the friction. To drive people back to purchase, this retailer emailed the frustrated users with a promo code that actually worked so that they could complete their transactions.

Big data use case two: Informing a customer data platform (CDP).

Customer data platforms, or CDP’s, enable real-time decision making, which is one of the major benefits of big data analytics.

Experience data adds a layer of activation, especially if it’s delivered in real time.

Imagine you are an airline undergoing digital transformation. Most airlines offer loyalty status or programs, and this program is usually built in tandem with a customer data platform This allows airlines to get a 360-degree view of their customer from multiple sources of data and from different systems. When you combine customer data with experience data, you can better understand how important segments of your audience are navigating through your website and mobile app.

For example, you can see when loyalty members are showing traits of frustration and deploy a rescue via chat, or even trigger a call from a special support agent. You can also send follow-up offers like promos to drive frustrated customers back to your website. The combined context of behavior data and customer loyalty status data allows you to be more pragmatic and effective with your resources. This means taking actions that rescue frustrated customers and drive conversion rates.

However, a few of the big data use cases such as real-time chat triggers only work with real-time data.

Big data use case three: Personalization.

The above CDP example is just the beginning of what you can achieve with the Quantum Metric and Google BigQuery integration.

With a joined dataset, informed by real-time behavioral data, you can start to develop truly impactful personalization programs.

Let’s think about this through an example. Imagine a large retailer that sells mostly commodities needs to perform well on Black Friday.

With Quantum Metric and BigQuery, they have real-time data on product engagement, such as clicks, taps, view time, frustration, and other statistics. When they combine these insights with products available by region and competitive pricing data, they have a recipe for success when it comes to generating sales on Black Friday.

With these data insights, retailers can create cohorts of users (by age, device, loyalty status, purchase history, etc.) These cohorts receive personalized product recommendations based on all three sources of data. These recommendations are compelling for consumers, since they are well priced, popular products that shoppers know are in stock. This approach to personalization will become more important as supply chain inventory challenges continue into 2022.

Where will big data take you?

With Quantum Metric and BigQuery, you can start to explore these three big data use cases. And this is just scratching the surface of what you can accomplish when you combine real-time experience analytics data with other critical biz systems.

Are you a customer? Learn how to take control of your experience data today. Visit our community page.

Not yet a customer? Learn about the Quantum Metric platform and our 1st-party data set. Request a demo today.