In today’s fast-paced digital landscape, businesses are facing unprecedented challenges in meeting the evolving needs of their customers. COVID-19 accelerated the shift towards digital, with even non-digital companies now forced to adapt to the new reality. In such a market context, Quantum Metric has emerged as a leading player, helping companies navigate the complexities of digital transformation and improve their customer experience. The rise of e-commerce, the increasing importance of customer experience, and the growing demand for personalized services have turned this into a table stakes capability.
Quantum Metric’s platform provides a comprehensive solution for enabling businesses to analyze and optimize their digital experiences across all channels. At the heart of Quantum Metric’s solution is BigQuery, Google’s fully managed, petabyte-scale analytics data warehouse with 99.99% availability that enables businesses to analyze vast amounts of data in real-time to make data-driven decisions and have actionable insights to drive better outcomes.
Use cases: Challenges and problems resolved
Use case 1: Retargeting
Sometimes someone lands on your website or mobile app but fails to accomplish what you want them to do, such as adding an item to their shopping cart or creating a new checking account. Frustrated customers don’t convert, open an account, or buy an airline ticket. They just leave.
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 to a potential customer 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 or chat prompt shortly after encountering a problem, so that they could speak with a representative?
Together, Quantum Metric and BigQuery address this problem. With the Quantum Metric and BigQuery integration, you can investigate user behavior, including what exactly happens when a cohort of users (e.g. Android users) don’t convert.
For example, behavioral signals have helped a Retailer personalize retargeting messages for customers who struggled online or saw “out of stock” messages. The retailer’s Marketing Analytics team claimed they were getting more out of retargeting spend with deeper insights into what happened during a customer’s session.
Use case 2: Informing a customer data platform (CDP)
Customer data platforms (CDPs) can 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 company optimizing the digital transformation journey. Most airlines offer loyalty status or programs, and this program is usually built in tandem with a CDP. This allows airlines to get a 360-degree view of the customer from multiple sources of data across 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 to maintain and strengthen your customer’s loyalty and drive conversion.
Use case 3: Personalization
The above CDP example is just the beginning of what you can achieve with the Quantum Metric and BigQuery integration. With a joined dataset, informed by real-time behavioral data, you can start to develop truly impactful personalization programs.
Imagine you are a large retailer that sells mostly commodities and need to perform well on Black Friday. With Quantum Metric and BigQuery, your business has real-time data on product engagement, such as clicks, taps, view time, frustration, and other statistics. When your business combines these insights with products available by region and competitive pricing data, you have a recipe for success when it comes to generating sales on Black Friday.
With these data insights, retailers can create cohorts of users (age, device, loyalty status, purchase history, etc.) and these cohorts receive personalized product recommendations based on business, technical and behavior data. These recommendations will tend to perform better with consumers, since the product recommendations are in-stock and tailored to the customers’ needs.
Solution: Why Quantum Metric built on Google Cloud
Quantum Metric chose to partner with Google Cloud because of its world-class infrastructure, high reliability and scalability. This provides Quantum Metric with access to Google Cloud’s Data Analytics and AI/ML capabilities, allowing for advanced analysis and world-class data privacy. Quantum Metric’s solution also integrates with a variety of Google Cloud products, including Google Cloud’s Contact Center AI platform, to provide an end-to-end customer experience offering.
Quantum Metric’s platform is built and offered exclusively on Google Cloud, allowing for easy integration with BigQuery to unify and coalesce datasets, making it an ideal application to simplify and unlock secure data sharing. For instance, Analytics Hub is a capability in addition to BigQuery that enables secure data sharing and assets across organizational boundaries.
The key features of the solution are:
Data capture: With complete data capture of the customer experience, increase customer empathy across digital, IT, and support teams.
Intelligence layer: We process all that data through our ML engine, using Google’s Data Loss Prevention capability to ensure customer data is correctly classified. Creating automated segments and baselines that drive real-time anomaly detection.
Analysis and visualization: Tools that span the needs of your teams, for example: alerting and monitoring of friction for product/IT ops to helping UX make design optimizations with journeys and heatmaps.
Pre-built industry guides are built on top of that data capture, intelligence, and visualization to automatically surface tailored insights, actions and use-cases categorized by sub-journey.
“Google BigQuery unlocked such vast power and scale that we realized we were limiting ourselves previously using a relational database. Google BigQuery differentiates our analytics products from the competition, because no matter what questions we ask or how much data we put in, we get results in seconds.” – Mario Ciabarra, Founder & CEO, Quantum Metric
Solution Architecture
Below is an architecture diagram of how Quantum Metric operates on Google Cloud.
Data from various sources such as websites, mobile devices, kiosks are ingested into BigQuery using Google Cloud services. This data is in turn processed and analyzed for both real-time and historical analytics and stored in BigQuery datasets. Quantum Metric Platform provides out of the box dashboards to cater to multiple audiences ranging from Marketing, Product Managers to Analysts. In addition, the raw datasets can be shared securely with the client so they can query in their BigQuery instance or even coalesce with other datasets to develop more insights using Looker.
Benefits & Outcomes
Some of the notable outcomes from the solution are around:
Product — Automatically alert on customer friction, size the revenue impact of issues, and gain immediate visibility into experiences like promotions or payments.
Analytics — Enable business stakeholders to self-serve insights. Understand the why behind friction, and measure the impact of releases without manual tagging.
Technology — Identify technical issues with out-of-the-box anomaly detection, gain real-time monitoring of the front end experience, and prioritize the backlog based on impact.
Optimizing the retail experience with advanced analytics
Canadian Tire Corporation uses Google Cloud and Quantum Metric to understand its digital engagement with its massive loyalty program and provide exceptional online and in-store customer experiences. Here are a few of their results:
Increases omnichannel sales by up to 15% through more tailored online and in-store experiences.
Democratizes access to insights about customer behavior to improve sales, merchandising, and marketing.
Reduces friction across the digital customer journey for better shopping experiences and brand loyalty.
Quantum Metric’s integration with BigQuery enables Canadian Tire to respond to customer needs, demands, and preferences faster and smarter. Canadian Tire also takes advantage of our unique ability to offer ungated access to all its data in Google BigQuery, as it merges data sets from Google Analytics, transactional information, and Quantum Metric itself.
Click here to learn more about Quantum Metric.
The Built with BigQuery advantage for ISVs and Data Providers
Google is helping companies like Quantum Metric build innovative applications on Google’s data cloud with simplified access to technology, helpful and dedicated engineering support, and joint go-to-market programs through the Built with BigQuery initiative. Participating companies can:
Accelerate product design and architecture through access to designated experts who can provide insight into key use cases, architectural patterns, and best practices.
Amplify success with joint marketing programs to drive awareness, generate demand, and increase adoption.
BigQuery gives ISVs the advantage of a powerful, highly scalable data warehouse that’s integrated with Google Cloud’s open, secure, sustainable platform. And with a huge partner ecosystem and support for multi-cloud, open source tools and APIs, Google provides technology companies the portability and extensibility they need to avoid data lock-in.
Click here to learn more about Built with BigQuery.
We thank the Quantum Metric and Google Cloud team members who collaborated on the blog:
Quantum Metric: Kayla Kirkby, VP of Partner Marketing, Mario Ciabarra (CEO)
Google: Tom Cannon, Head of Built with BigQuery
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