Journey analytics is the process of aggregating data along the customer’s journey from both a brand and a customer perspective. That data is then used to extract patterns and behavioral insights. The main goal of journey analytics is to know the alignment between your employee’s and customer’s journey.
Journey Analytics: Fueling Insights Across the Customer’s Journey
Data can only be a game-changer if you use it to gather the right insights. With journey analytics, organizations must go deeper than pulling together a proliferation of digital touch-points. Parsing together various sources to create a more transparent and direct attribution model for the financial bottom line lets organizations operate more intelligently. Aggregating and interpreting the data that will fit into your people, processes and platforms is the first step towards fueling deeper insights across the customer’s journey.
Applying Data to a Customer Journey Analytics Framework
Transformative insights requires that organizations maximize and monetize the insights available. In doing so, these insights will serve as a framework to guide how you harness data and a beacon steering you in the right direction for your projects and decision-making.
Using a data-centric framework, such as the StoryVesting framework, is just one approach that lets organizations bring together critical data from websites, content, creative, and more. You can layer the brand experience with the customer experience and use that alignment to know how back-office shifts impact front-facing experiences.
Visualizing Your Journey Analytics Data
Visualizing journey analytics is no easy feat. The data is deep and robust. With the right tools and implementation of proprietary algorithms to pull out data, organizations can uncover key performance indicators and North Star Metrics. That data can then be visualized to disseminate critical strategic information organization-wide, dropping silo walls, strengthening decision-making, and keeping teams on the same page.
Why Do You Need a Journey Analytics?
Journey analytics are non-negotiable in today’s world of digital transformation. With these analytics, organizations can stay more competitive and aligned with the modern customer.
Dropping Silo Walls
Groupthink and cognitive bias can have toxic outcomes. Journey analytics allow customer-centric data to be disseminated and democratized across the organization. In doing so, teams can move faster on experience and transformation initiatives, dropping silo walls and increasing organizational relevancy to modern consumers.
Humanizing Big Data
Big data doesn’t always mean accurate data. Taming the fire hose of information can be difficult.
Humanizing journey analytics allows companies to take raw numbers and put context to those figures giving organizations an idea of what happening and why. This humanization and looking at numbers as more than just data points can help improve overall experiences.
Gaining Organizational Buy-In
It’s nearly impossible to gain organizational buy-in when your team doesn’t know how their work impacts the big picture. By showcasing journey analytics and telling a story with the data, teams often become more passionate and productive with their work.
Analyzing Empathetically
Journey analytics allows organizations to acknowledge the people at the heart of the facts, figures and statistics. Looking both scientifically and empathetically lets leaders get into the customer’s mindset, delivering more emotional and impactful brand experiences.
Customer Experience (CX) Terms
- 360° Degree View of the Customer
- AI Ops
- Barlow Bands
- Behavioral Triggers
- Bow Tie Funnel
- Brick-to-Click
- Business Impact Analysis (BIA)
- Cognitive Computing
- Cohort Analytics
- Content Mapping
- Conversational User Guidance
- Customer Data Profile
- Customer Experience (CX)
- Customer Friction
- Customer Insights Map
- Customer Journey
- Customer Journey Mapping
- Customer Satisfaction (CSAT)
- Customized Ratios
- CX Intelligence
- CX Led Growth
- CX Metrics
- Data as a Product (DaaP)
- Data as a Service (DaaS)
- Data Culture
- Data Driven
- Data Engineering
- Data Fabric
- Data Governance
- Data Humanization
- Data Hygiene
- Data Looping
- Data Mapping
- Data Mining
- Data Modeling
- Data Monetization
- Data Swamp
- Data Visualization
- Data Warehouse
- Data-Centric
- Descriptive Analytics
- Diagnostic Analytics
- Digital Asset Management (DAM)
- Digital Transformation
- Dirty Data In Dirty Data Out
- Embedded Intelligence
- Empathy Mapping
- Employee Data Profile
- Employee Experience (EX)
- EX to CX Data Mapping
- EX to CX Mapping
- Experience Management (XM)
- Gap Analysis
- Generative AI
- Human-Centered Design (HCD)
- Journey Analytics
- Machine Learning (ML)
- Managed Agile Services on Demand
- Modified Hoshin
- North Star Metric
- Party Data
- Pathway to Purchase
- Predictive Analytics
- Product-Market Fit Mapping
- Real Time Design Looping
- Revenue Acceleration
- RevOps
- S Curve of Growth
- Stack Impact Analysis
- StoryVesting
- Table Stakes Testing
- The 3 P’s
- User Experience (UX)
- User Insights Map
- User Interface (UI)
- Voice of the Customer (VoC)
- Voice of the Employee (VoE)
- World Cloud Generator Sentiment Mining
- X Analytics