Recent shifts in customer demand have left companies scrambling to make sure they are meeting customer wants and expectations. As the need for companies to nurture relationships with their customers escalates, the role data analytics plays in Quality Monitoring is rapidly evolving across industries and lines of business. We sat down with Bryan Gillis, Executive Practice Lead of Quality Solutions & Customer Analytics at Northridge, to discuss the importance of analytics in driving actionable business insights and how a strong data framework represents the next frontier of Quality Monitoring solutions.
Question: How is Quality Monitoring evolving?
Bryan: Traditional Quality Monitoring processes typically give businesses a snapshot of certain aspects of their customer experience, but they often don’t provide the full story. The data might only provide a check-the-box diagnostic on an agent’s performance to a certain behavior or the organization’s adherence to a corporate or regulatory compliance measure. While valuable, the insights that can be gleaned from this dataset can be limiting. In order to maximize the value of Quality Monitoring, the process should be integrated into a more holistic advanced data analytics framework that pulls in both internal and external data sources to drive correlations between the voice of the customer, influencing market forces and trends, and internal metrics.
Question: Can you talk more about the advanced data analytics framework?
Bryan: Instead of focusing solely on internal customer satisfaction metrics like NPS or Post-Call Survey Scores, best-in-class companies are now opting to integrate and analyze a combination of datasets including Voice of the Customer and omni-channel programs, market stats and trends, as well as operational and performance data to drive actionable business insights that impact key business outcomes like revenue, product performance, operational efficiency, and compliance – in addition to customer satisfaction and overall experience. By looking at the data more holistically and identifying what correlations exist to KPIs, the data becomes more meaningful and senior leaders are able to make better, more informed business decisions.
Question: Can you give some examples of these data sources that are used as inputs into a data framework?
Bryan: For Voice of the Customer and omni-channel programs, this could be data from any company’s customer contact channels like phone, email, chat, text, social media and others. Data from your omni-channel quality monitoring program, as well as any customer survey data would also be integrated.
For internal data sources, this might be any operational, financial or performance information. This includes: CRM/ERP data, account rep performance and training data, customer demographics, metrics and scorecards, and other internal data sources.
Lastly, for market statistics and trends – this includes anything from economic factors such as GDP growth and disposable income to raw material prices, government regulations and technological advancements.
Question: What value does an advanced data analytics framework provide the business and their customers?
Bryan: Once a company has established a data framework, the program is equipped to generate a steady stream of actionable business insights that are customized to each company and grounded in the firm’s strategic objectives. This process can be applied across all lines of business and to a myriad of industries, enabling a steady flow of actionable improvements to all types of businesses. The bottom line is that linking Voice of the Customer / omni-channel quality data to internal and external data presents an opportunity to probe beyond the numbers associated with traditional Quality Monitoring techniques to find the solutions that will deliver the greatest impact to your business, as well as your customers. Conversely, businesses that focus solely on Quality Monitoring data, may miss the big picture and fail to reach actionable solutions that truly drive business outcomes.
To learn more about how Northridge has applied a holistic data framework for measurable business outcomes, please read our case study.