AI and machine learning (ML) to enhance demand forecasting and understand customer preferences

· AI,GenAI,ML,forecast


Automakers and dealerships face the challenge of accurately predicting demand for specific vehicle makes, models, and colors. Incorrect forecasts can lead to overstocking slow-selling vehicles, lost sales from insufficient inventory of in-demand models, and ultimately lower profits. Traditional forecasting methods often rely on historical sales data, which may not account for evolving customer trends and sentiment.


This solution proposes a cutting-edge system that leverages AI and machine learning (ML) to enhance demand forecasting and understand customer preferences. The architecture will incorporate the following:

Tableau: Visual analysis and exploration of historical sales data.

Google BigQuery: Data warehouse for centralized storage and analysis of structured datasets.

AlloyDB: High-performance transactional database for optimal speed in operational use.

Vertex AI: Comprehensive platform for building, training, and deploying machine learning models.

Data Sources (DMS, CRM, Web Analytics, Social Media)

BigQuery (Data Warehouse)

AlloyDB (Transaction Database)

Vertex AI (Model Development, Deployment)

Tableau (Dashboards, Reporting)


Data Collection and Unification

Customer Inquiries & Lead Data: Collect data from your dealership's CRM, web forms, phone call inquiries, etc., on customer preferences including desired make, model, year, color, options.

Lead Qualification and Conversion Probability: Utilize your CRM or lead management software to track indicators of lead qualification (e.g., contact frequency, demographics, credit score) and historical conversion rates for each lead stage.

Historical Sales Data: Extract sales records from your Dealership Management System (DMS) including vehicle details, purchase date, customer demographics, and location.

Web Analytics: Integrate data on website traffic patterns, pages visited (specific makes/models), search terms used.

Social Media Sentiment: Employ social listening tools to track online conversations, opinions, and trends related to your automotive brands, competitors, and specific car models.


Data Warehousing with BigQuery

Centralize Data: Build a comprehensive data warehouse in BigQuery to consolidate all data sources.

Create ETL Pipelines: Develop Extract, Transform, and Load (ETL) pipelines (using tools like Dataflow or third-party connectors) to move data into BigQuery regularly.

Data Cleaning and Preprocessing: Ensure data quality, handle missing values, outliers, and format data for modeling.


Visualize Historical Trends: Use Tableau to create dashboards highlighting historical sales patterns, popular makes/models/colors, trends over time, and regional differences.

Segment Customers: Visualize customer demographics and purchasing behaviors, identifying distinct market segments.

Correlation Analysis: Examine relationships between lead qualification factors, conversion rates, and vehicle preferences.


Feature Engineering: Identify and create the most relevant features for model training. This may involve:

Categorical data encoding (make, model, color)

Numerical data transformations (lead score, customer income)

Text analysis (customer inquiries, social media sentiment)

Model Selection: Experiment with different algorithms:

Regression Models (for sales volume prediction)

Classification Models (for predicting the likelihood of a customer preferring a specific car)

Time Series Models (for forecasting based on trends)

Model Training and Evaluation: Split data into training and testing sets, tune hyperparameters for optimal performance.

Model Deployment: Deploy the best-performing model(s) as API endpoints for integration.


Operational Integration: Connect your sales systems or inventory management tools to AlloyDB for fast, transactional queries and updates.

Query Stored Procedures: Use AlloyDB stored procedures to integrate predictions directly into operational processes.Live Predictions: Feed new data on lead quality and customer preferences