Project Overview
User Journey Accuracy
Enhanced Predictive Capabilities: Develop AI-powered predictive models to forecast customer behavior, allowing for proactive strategy adjustments.
KPIs & Goals
+20%
+15%
+25%
Operational Efficiency
Operational Efficiency & Decision-Making: Improve decision-making speed by 30% and operational efficiency by 25%, using real-time insights powered by ChatGPT.
CX Optimisation
iPrimus an Australian internet service provider, faced challenges due to departments analysing data in isolation, leading to fragmented insights and missed opportunities. A holistic approach to data was needed, along with tools to improve operational efficiency and decision-making. The company embarked on a strategy to unify journey analytics and integrate AI-powered solutions, such as ChatGPT, to tell a cohesive story through data, reveal trends, and enhance decision-making through predictive analytics.
Data Silos: Departments operated in isolation, causing inconsistent metrics, missed insights, poor collaboration, and inefficient decisions.
Complex Data Management: The company struggled to track KPIs consistently across customer journey stages.
Limited Predictive Capabilities: Lack of predictive analytics hindered proactive strategy adjustments.
Seasonality: Fluctuations from November to January complicated goal-setting and performance tracking.
Data Storytelling: iPrimus needed data to go beyond numbers, providing actionable insights that drive decisions.
Background
Challenges
Data Integration & Unification: Eliminate data silos by centralising journey analytics across Marketing, Sales, Product, and Customer Support departments.
Customer Experience (CX) Improvement: Achieve a 20% increase in journey accuracy and a 15% improvement in customer experience through continual optimization.
Decision-Making Speed
30%
ChatGPT-Powered Unified Journey Analytics
Excel-Based Data Unification: The analytics system was built on Excel, where data from various departments was centralized and analyzed, ensuring consistency across the customer journey.
Automated Data Analysis: ChatGPT was integrated to automate the analysis process, allowing real-time insights to be generated without manual data crunching.
Predictive Analytics for Forecasting: Excel-powered predictive models were developed to forecast customer behaviors and trends, enabling proactive adjustments to strategies.
Real-Time Insights via Excel Dashboards: Interactive Excel dashboards provided real-time tracking of KPIs, allowing teams to make informed, data-driven decisions quickly.
Data Storytelling: Automated insights from Excel transformed raw data into narratives, providing actionable insights that aligned with business goals.
SOLUTION
Real-Time Insights
Data Storytelling
We implemented a ChatGPT-powered Journey Analytics solution built on Excel. The system unified data, automated analysis, and provided AI-driven insights that were easily accessible, enhancing operational efficiency and strategic decision-making.
Data Integration
AI Automation
Predictive Analytics
Excel-Based Data Unification
Key Components of the Solution:
Leading the Unified Journey Analytics Implementation
As the Digital Experience Manager and the lead strategist for this project, I was responsible for overseeing the entire process, from concept development to execution.
My role
Cross-Departmental Workshops: Engaged Marketing, Product, Sales, and Customer Support departments to align on KPI objectives, ensure data integration, and unify data management.
System Integration: Worked with IT to connect all relevant systems—Google Analytics, CRM platforms, BI tools, and customer support systems—to create a seamless data flow.
Predictive Analytics Deployment: Spearheaded the integration of predictive analytics with ChatGPT, transforming how data was analyzed and used for decision-making.
Data Storytelling: Oversaw the generation of monthly reports, which included actionable insights, trends, and data stories to guide strategic decision-making.
Key Contributions:
Journey Analytics Framework
1 - Stages Identification:
The customer journey was divided into specific stages: Discovery, Exploration, Consideration, Decision, Subscription, Onboarding, Service Experience, and Loyalty & Advocacy. Each stage was assigned relevant KPIs.
The customer journey was structured into key stages, with KPIs assigned to each phase to capture critical performance metrics. This approach ensures a clear focus on measuring success factors across the entire journey, enabling data-driven improvements and strategic decision-making.
Framework
2 - KPI Selection:
KPIs were chosen to reflect critical success factors within each stage. For example, in the Discovery stage, KPIs such as Total Search Impressions, Paid Website Traffic, and Click-Through Rate were prioritised.
3 - Realistic Target Setting:
Monthly targets were set based on historical performance data, with adjustments made for the slow months of November, December, and January.
4 - Data Correlation:
Correlations between KPIs were considered to ensure that targets reflected realistic expectations. For example, a decrease in Paid Website Traffic during December would correspond with a reduced Cost Per Acquisition.
6 - Spreadsheet Development:
A detailed Excel spreadsheet was created to track each KPI across the months, with specific columns for targets and actual performance data.
5 - Conditional Formatting:
Color-coded conditional formatting was applied to visually represent performance. For instance, cells turned green if a KPI met or exceeded the target, yellow if it was close, and red if it fell significantly short.
7 - Data Analysis Automation:
ChatGPT was integrated into the Excel workflow, enabling it to automatically analyse the KPI data at the end of each month.
8 - Trend Identification:
ChatGPT identified trends by comparing current month data with historical data, highlighting significant changes, seasonal effects, and anomalies.
9 - Actionable Insights Generation:
ChatGPT provided actionable insights based on the analysis, including recommendations for optimising underperforming areas and capitalising on positive trends.
10 - Report Generation:
A monthly report was automatically generated by ChatGPT, summarising key findings, trends, and suggestions for the team to review.
AI Driven Journey analytics
The Journey Analytics system is an Excel-based platform that integrates data from all departments. It offers real-time dashboards and insights, helping iPrimus improve decision-making and customer journeys.
Total search impressions
Click-Through Rate
Paid website traffic
Referral website traffic
Organic website traffic
Direct website traffic
Display website traffic
Social website traffic
Email users
Cost Per Acquisition (CPA)
Total users
Stages & KPI's
Final Output
Target/mth
Discovery
900,000
3.5%
45,000
18,000
90,000
30,000
10,000
15,000
8,000
$45
190,000
JUL FY25
AUG FY25
Key Trends & Insights (Generative AI)
869,440 3.3% 43,184 18,088 97,292 31,109 10,409 16,201 7,508 $47 174,043
Exploration
SEP FY25
815,557 3.5
48,686 18,714 94,563 30,729 10,746 16,047 7,896 $46 199,297
884,612 3.5
45,343 18,078 87,636 29,418 10,732 14,266 8,341 $49 186,914
Page Views - NBN Product
Number of Users - NBNP
Time Spent on NBNP
Product Carousel Clicks
Clicks on 'Compare'
Clicks on 'Things to Consider'
Clicks on 'How it Works'
Clicks on FAQ Section
Scroll Depth
Exit Rate
110,000 75,000 2:25 11,000 8,500 7,000 5,000 7,000 70%
20%
112,503 80,134 2:28 11,028 9,260 6,770 5,416 7,279
65%
22%
109,954 70,236
2.25
11,392 8,714 6,754 5,075 7,5916772%
21%
112,000 76,500 2:27 11,200 8,700 7,000 4,800 7,200 68%
20%
Consideration
Decision
Subscription
Onboarding
Service Experience
Loyalty & Advocacy
Total Search Impressions and Total Users are significantly higher in magnitude compared to other KPIs, indicating that they are major drivers in the discovery process.
There are visible declines in Organic Website Traffic and Direct Website Traffic from July to September, which aligns with the earlier findings of reduced user engagement through these channels.
Cost Per Acquisition (CPA) shows an increase in September, suggesting inefficiencies in converting traffic into users during this period.
Page Views - NBN Product and Number of Users - NBNP show similar trends, with a decline in August followed by a slight recovery in September, indicating user engagement fluctuations.
Metrics like Clicks on Things to Consider and Clicks on How it Works have declined steadily, pointing to possible content or visibility issues.
Scroll Depth peaked in August, suggesting that users were more engaged with content during that period, while the Exit Rate consistently decreased, indicating improved user retention.
Please Note: This case study reflects the actual processes and methodologies used in the project, but all data, figures, are fictional to protect the privacy and confidentiality of the organisation.
Predictive Analysis Unlocked: Guiding User Engagement with Data-Driven Precision
Building on the Journey Analytics foundation, we harness Linear Regression models to forecast user behaviour trends. This approach refines engagement strategies, transforming data into actionable insights that elevate digital experiences and strengthen customer connections.
Predictive Analytics
Unlocking Insights: AI’s Impact on Customer Journeys
The successful implementation of AI ChatGPT-powered unified journey analytics at iPrimus demonstrated how effective data management and analysis could lead to improved decision-making, increased operational efficiency, and more accurate user insights. The integration of predictive analytics allowed iPrimus to take proactive action based on trends, improving customer journey optimisation and fostering a data-driven culture.
By leading this transformation, I contributed to creating a cohesive, AI-powered strategy that aligned departments and helped iPrimus leverage the full power of its data. The case study showcases how holistic data management, enhanced by AI, can revolutionise business processes and customer experience.
User Journey Accuracy
Real-time insights helped iPrimus improve customer experience by 15%, boosting satisfaction and retention while reducing churn.
Performance Summary & Results
+25%
+15%
+30%
Decision-Making Speed
Centralised data and ChatGPT automation increased operational efficiency by 25%, shortening time-to-market and streamlining marketing adjustments.
CX
Optimisation
Excel dashboards improved decision-making speed by 30%, enabling quicker responses to market trends and customer behaviors.
15%
Operational Efficiency
Predictive analytics improved journey accuracy by 20%, allowing iPrimus to anticipate customer needs, adjust strategies, and reduce churn.
