Goal: To enable clients to efficiently use the marketing insights dashboard with minimal manual intervention, leveraging the automation and predictive analytics capabilities built into the system.

Create a platform that aggregates data from various marketing channels and provides

actionable insights, including:

• Customisable Dashboards: Visualise key metrics and performance indicators.

• Predictive Analytics: Use AI to forecast trends and customer behaviour, helping businesses to plan their marketing strategies more effectively.

• Data Aggregation Tools: To gather data from multiple marketing channels.

• Business Intelligence (BI) Tools: Like Tableau or Power BI for data visualisation.

• Predictive Analytics: Using AI and machine learning to forecast trends.

• Customisable Dashboards: To allow users to create their own views of key metrics.

How Clients Use the Dashboard

1. Accessing the Dashboard

• Login: Clients access the dashboard via a secure login portal provided by your company.

• Interface: Once logged in, they see an intuitive interface with customisable widgets and sections tailored to their business needs.

2. Real-Time Data Updates

• Automatic Refresh: Data from various sources (e.g., Google Analytics, CRM, social media) is automatically updated in real-time or at scheduled intervals, ensuring the dashboard always reflects the latest information.

• Notification Alerts: Clients receive alerts for significant changes or anomalies in

data, helping them stay informed about critical metrics.

3. Customisable Dashboards

• Drag-and-Drop Interface: Clients can customise their dashboards using a drag-and-drop interface in Power BI, allowing them to add, remove, or rearrange widgets based on their priorities.

• Pre-Set Templates: Offer pre-configured templates for common use cases, such as campaign performance, customer segmentation, and sales funnel analysis.

4. Viewing Key Metrics

• Visualisations: The dashboard includes various visualisations (charts, graphs, heat

maps) to display key metrics like customer acquisition cost, lifetime value, conversion

rates, and engagement metrics.

• Filters and Drill-Downs: Clients can apply filters and drill down into specific data

points to gain deeper insights into particular segments or campaigns.

5. Predictive Analytics Insights

• Forecasting: The dashboard uses predictive models to forecast trends and customer behavior, helping clients plan their marketing strategies more effectively.

• Actionable Insights: Predictive analytics provide actionable insights, such as identifying at-risk customers (churn prediction) or high-potential leads (lead scoring).

6. Generating Reports

• Automated Reports: Clients can schedule automated reports to be generated and sent via email at regular intervals (e.g., weekly, monthly).

• Ad-Hoc Reporting: They can also generate ad-hoc reports on demand, customized with the specific metrics and time frames they need.

7. Support and Customisation

• Training Sessions: Offer initial training sessions to help clients understand how to use the dashboard effectively.

• Ongoing Support: Provide ongoing support through a helpdesk or dedicated account

manager for any customisation requests or troubleshooting needs.

• Consultation Services: Optional consultation services for deeper analysis, strategy

development, or custom model tuning.

Example Client Use Case

Scenario: A mid-sized e-commerce business wants to optimize its marketing efforts across

different channels.

1. Login: The marketing manager logs into the dashboard.

2. Real-Time Overview: They see a real-time overview of all marketing channels, with

key performance metrics displayed prominently.

3. Customisable View: They adjust the dashboard to focus on recent social media

campaigns and email marketing performance.

4. Predictive Insights: The dashboard shows predictive insights, indicating that a

particular customer segment is likely to respond well to upcoming promotions.

5. Report Generation: The manager generates a detailed report on campaign

performance over the past month and schedules it to be emailed to the executive team.

6. Alerts: They set up alerts to notify them of any significant drop in website traffic or

engagement rates.

7. Support: For any additional customisation or in-depth analysis, they reach out to the

support team for assistance.

What We Need to Do:

1. Initial Setup and Integration:

• Set up API connections and Power BI Data flows.
• Clean and unify data from multiple sources.

2. Predictive Analytics Configuration:

• Configure Google BigQuery and AutoML for predictive models.
• Deploy and automate predictions.

3. Dashboard Development:

• Design and build the Power BI dashboard.
• Integrate predictive analytics data.

4. Customisation and Training:

• Tailor the dashboard to meet specific needs.
• Conduct training sessions and provide documentation.