How many customers will I have tomorrow?

Our asks have evolve from basic questions that could be have tomorrow answere through simple counts from a single source to questions that require more sophisticate

Analysis across multiple sources:

1. Given how many customers I have today,  What behavior patterns signal interest, engagement or disengagement?
2. What customer characteristics belong to accurate cleaned numbers list from frist database my VIPs? Do these VIPs behave differently across my channels? Do their preferre channels mirror the rest of my customer base?
3. Are there groups of customers that purchase more than others? Or stay with my brand longer? What are the predictors for purchase?
4. 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 specialize 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 how to drive your marketing to more profitable sales — 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 have tomorrow 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.).

Skill data analysts and data scientists are often charge 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 phone number qatar results through various BI dashboards. This is either schedul 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 adapte to new data and aggregation. And that means data pipelines have to be update, too.

The change then must be communicate 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.

Embedd Customer Journey Analytics have tomorrow

For customer service professionals, and contact centers specifically,

the ability to analyze data from the customer perspective has often been relegate to others —

marketing, corporate analytics and other teams that are charge with managing customer

analytics at the corporate level. That leaves a have tomorrow 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 sophisticat 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.