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Across industries, customers are the source of revenue and profitability. This is clearly true for retail and financial service organizations, but it’s also equally true for healthcare where patients and providers have needs that must be met to achieve corporate objectives. Customer experience (CX) managers from across functional business areas such as customer service, marketing, sales and operations are all looking for proof that the customer marketing strategies they use are working. The only way to do so is through data.
“Without data, you’re just another person with an opinion.”
– W. Edwards Deming
The Evolution of Consumer Insights Analytics
The application of analytics to data to understand customers is the essence of customer analytics. It involves collecting, analyzing and interpreting data where the unit of measurement is the customer.
Basic customer analytics answers these questions:
- How many customers did I have yesterday?
- How many customers do I have today?
Customer analytics today extends well beyond these basic metrics to uncover behavior patterns and motivations. And several factors are driving this evolution.
More Data Volume
Our systems can store and work with more data than ever before. Bringing a wider perspective into behavior means incorporating data at rest (behavior that has happened where the outcome is known, such as a purchase made a month ago) and data in motion (service interactions, web interactions, mobile app use, social).
This broad behavior perspective provides the means to assess the past, measure the present and predict the future. By leveraging advanced analytics, businesses can move from reactive decision-making to proactive strategies, helping to ensure better customer engagement and retention.
More Data Variety
Historically, businesses relied on surveys and market research to understand customer emotions and intent. The advent of digital data has revolutionized this approach, making it possible to extract sentiment from social media and transcripts as interactions are happening.
Ad hoc, scheduled consumer insights research is supplemented and sometimes replaced with transactional, one-question measures such as Net Promoter Score (NPS), journey effort or customer satisfaction (CSAT) captured in the moment. With artificial intelligence (AI), it’s easier than ever to build a multidimensional customer view that brings together behavior, motivation and profiles from a wide variety of customer data sources.
Customer Analytics Unleashed
Organizations gather data through multiple channels, including social media, website interactions, surveys and CRM systems. Unfortunately, these sources are often used in various functional silos.
CX managers from customer support are using the data they collect during product and service interactions. Marketing managers are looking at data captured throughout marketing efforts — from acquisition through to engagement. Sales is looking at customer growth and retention and operations is looking at customer costs.
But to remain competitive in the experience economy, companies must move away from these data silos and think holistically. We’re awash in data that our systems of record (CRM) and systems of engagement (i.e., the Genesys Cloud™ platform) capture. But even with the wealth of data within our many systems, we’re struggling to understand customer behavior with enough clarity to make better decisions.
Our asks have evolved from basic questions that could be answered through simple counts from a single source to questions that require more sophisticated analysis across multiple sources:
- Given how many customers I have today, how many customers will I have tomorrow? What behavior patterns signal interest, engagement or disengagement?
- What customer characteristics belong to my VIPs? Do these VIPs behave differently across my channels? Do their preferred channels mirror the rest of my customer base?
- Are there groups of customers that purchase more than others? Or stay with my brand longer? What are the predictors for purchase?
- What is my customer attrition rate? And what are the characteristics of those who stay versus those who don’t? What’s the inflection point between customer disengagement and attrition?
Bridging the CX Analytics Gap
Customer journey analytics is a specialized form of customer analytics that can help bring together data that focuses on the what — interactions, conversations, agent engagement — and who — profile, intent, outcome) and can be used to get to the why — why is self-service higher for this group of customers? Journey analytics can show us if intent is driving that behavior. It enables the ability to measure progression through a journey and is informed by other customer analytic disciplines.
For example, journey analytics could incorporate surveys or segments as a way to qualify an outcome so analysts can measure the impact of journey complexity, journey friction and other journey features on NPS or customer segment (status, demographic group, etc.).
Skilled data analysts and data scientists are often charged with collecting data from across the organization, including CX, and using segmentation, clustering, regression, feature selection and more to extract meaning, analyze the journey and then provide results through various BI dashboards. This is either scheduled monthly, quarterly, yearly or ad hoc.
In the meantime, the CX professionals work on optimizing their operational metrics by tweaking schedules, queue assignments, intent models and more.
Having a gap between journey analytics and optimization can create inefficiencies and room for errors for both the analyst and the CX professional. If data changes, there are additional lags where the data extraction must be adapted to new data and aggregation. And that means data pipelines have to be updated, too.
The change then must be communicated from the CX professional to the analyst who then adjusts their analysis. Similarly, issues that analysts uncover could reach the CX professional too late — behavior patterns can change quickly and what was true a month ago may no longer be true today.
Embedded Customer Journey Analytics
For customer service professionals, and contact centers specifically, the ability to analyze data from the customer perspective has often been relegated to others — marketing, corporate analytics and other teams that are charged with managing customer analytics at the corporate level. That leaves a lot of insight on the table; this insight is critical to optimizing customer journeys in right-time.
Shortening this time to insight means embedding journey analytics into the system of engagement itself. Rather than relying on sophisticated tools for getting the data out, we’re building the ability to analyze the data in place through flow and journey analysis. This is bringing the insight closer to the point of impact.
Consider the following scenarios:
