Quantum Metric explores retail big data use cases on BigQuery

Editor’s note: To kick off the new year, we invited partners from across our retail ecosystem to share stories, best practices, and tips and tricks on how they are helping retailers transform during a time that has seen tremendous change. The original version of this blog was published by Quantum Metric. Please enjoy this updated entry from our partner.

If you had access to 100% of the behavioral data on the visitors to your digital properties, what would you change? 

The key to any digital intelligence platform is adoption. For this to happen, you need 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.

As such, Quantum Metric is built on Google Cloud BigQuery which enables our customers, many of which are retailers, to have 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. Through the Quantum Metric and BigQuery integration, customers can start leveraging experience data in more ways than you might realize. 

Let’s explore three ways enterprises are leveraging Quantum Metric data in BigQuery to enhance the customer experience. 

Use Case 1: Retargeting consumers when they don’t complete an online purchase

First, we look at retargeting. Often, when a shopping cart is abandoned or an error occurs during a consumer’s online shopping experience, you may not know why the situation occurred nor how to fix it in real-time.  

With Quantum Metric data in Google BigQuery, you can see user behavior, including what happens when a cohort of users don’t convert. As a result, enterprises can leverage those insights to retarget and win the consumer over. 

Use Case 2: Enable real-time decision making with a customer data platform

Next, consider how you can inform a customer data platform (CDP) to enable real-time decision making - the holy grail of data analytics. 

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 CDP, which allows airlines to get a 360-degree view of their customer from multiple sources of data and from different systems. With Quantum Metric on Google Cloud, you can combine customer data with experience data, empowering you to better understand how users are experiencing your products, applications or services, and enabling you to take action as needed in real-time.

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.

Use Case 3: Developing impactful personalization

The above CDP example is just the beginning of what you can achieve with the Quantum Metric and BigQuery integration. To develop truly impactful personalization programs, you need a joint dataset that is informed by real-time behavioral data. With Quantum Metric and BigQuery, customers can access real-time behavioral data, such as clicks, view time, and frustrations, which allows you to develop impactful personalized experiences. 

Let’s think about this through an example. Imagine a large retailer that specializes in selling commodities and needs to perform well on Black Friday. 

Through the Quantum Metric and BigQuery integration, 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 the critical 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.

With Quantum Metric and BigQuery, you can explore these three big data use cases. The exciting part is, this is just the beginning of what you can accomplish when you combine real-time experience analytics data with critical business systems. Read the companion piece to learn more about how companies are making the most of Quantum Metric and BigQuery today.