Driving Growth: Enhancing Sales and Customer Engagement with ML

Client – A Global Automotive Manufacturer

Business Opportunity

The client is an automobile OEM manufacturer in India, entered the Indian market in 2017 and launched its first vehicle in 2019. The brand has been well-received in India. They are currently utilizing SAP for their dealer management (DMS), ERP, and CRM applications, with data distributed across both SAP Cloud and on-premises solutions. They were looking to streamline their data ingestion from SAP into a centralized cloud-based data lake and also improve sales forecasting accuracy, thereby enhancing operational efficiency and decision-making for around 90 dealers.

Niveus created a data lake solution to improve the efficiency of SAP Analytics Cloud, which was previously used to generate and email Excel reports to over 90 dealers. By separating storage from analytics, they achieved better scalability. Additionally, Niveus developed a dealer sales forecasting ML model and a PowerBI dashboard to boost data analytics capabilities. Our solution aimed to predict the probability of a customer purchasing an automobile from the client using a machine learning classification model. By analyzing historical data on customer interactions, we intend to provide insights that can enhance sales strategies and customer engagement.

Technical Challenge

Ensuring data quality and identifying the right predictive model was the key technical challenge here. The data for this project was sourced from Google BigQuery, utilizing three primary tables:

  • Opportunity: Contains detailed information about potential sales opportunities.
  • Leads: Provides the status of customer qualification (e.g., hot, cold).
  • Appointments: Records different types of appointments, such as phone calls and showroom visits.

Our Solution

Niveus built a data lake solution to address the inefficiencies of using SAP Analytics Cloud for generating and emailing Excel reports to over 90 dealers. They separated storage and analytics for better scalability. Additionally, Niveus developed a dealer sales forecasting ML model and a PowerBI dashboard to enhance data analytics capabilities.


Data Preparation and Modeling

Data Integration

We integrated data from the three primary tables—Opportunity, Leads, and Appointments—to create a master table, ensuring a comprehensive dataset for analysis and modeling.

Feature Engineering

Feature engineering was performed to prepare the data for analysis and model building. This involved:

– Consolidating categorical variables.

– Encoding categorical features.

– Handling missing values and outliers.

Exploratory Data Analysis (EDA)

A thorough exploratory data analysis was conducted to understand the relationships between the target variable (customer purchase) and predictor variables. Key steps included:

  • Analyzing distributions and summary statistics of features.
  • Visualizing relationships between features and the target variable.
  • Creating correlation plots to identify potential multicollinearity among predictor variables.

Model Training

Train-Test Split

The dataset was divided into training and testing sets to evaluate model performance.

Initial Model Trials

We experimented with several classification algorithms, including:

– Logistic Regression

– Decision Tree

Final Model Selection

After evaluating the initial models, we selected the Random Forest algorithm for its superior performance. Hyperparameter tuning was conducted to optimize the model parameters, enhancing accuracy and generalization.


Benefits of the implementation 


  1. Enhanced Sales Strategies
    • Targeted Marketing: By predicting the probability of customer bookings, the client can tailor personalized advertisements and marketing campaigns, focusing on customers most likely to convert.
    • Resource Allocation: Sales teams can prioritize leads with the highest potential, optimizing their efforts and resources on high-conversion opportunities.
  2. Improved Customer Engagement
    • Personalized Interactions: Insights from the model enable the client to personalize customer interactions, such as invitations and follow-up phone calls, increasing customer satisfaction and engagement.
    • Timely Follow-ups: The model can help determine the optimal frequency and timing for follow-ups, ensuring timely and effective communication with potential customers.
  3. Data-Driven Decision Making
    • Strategic Insights: Understanding factors influencing customer decisions allows the client to make informed decisions based on data rather than intuition, leading to more effective strategies.
    • Continuous Improvement: Integrating real-time data and feedback loops helps continuously improve model accuracy and adapt strategies to changing market dynamics.
  4. Increased Conversion Rates
    • Focused Efforts: By concentrating efforts on leads with the highest likelihood of conversion, the client can significantly improve their overall conversion rates.
    • Optimized Sales Process: Identifying key drivers of customer behavior helps refine the sales process, making it more efficient and effective.
  5. Customer Retention and Loyalty
    • Understanding Customer Preferences: Insights into customer demographics and behaviors allow for tailored retention strategies, enhancing customer loyalty.
    • Proactive Engagement: Predicting customer needs and preferences enables proactive engagement, reducing churn rates and fostering long-term relationships.
  6. Operational Efficiency
    • Streamlined Processes: Automating the identification of high-potential leads and tailoring engagement efforts can streamline sales and marketing processes.
    • Reduced Costs: Efficiently targeting marketing efforts and optimizing resource allocation can lead to significant cost savings.
  7. Competitive Advantage
    • Market Adaptation: The ability to quickly adapt strategies based on predictive insights provides a competitive edge in responding to market trends and customer needs.
    • Innovation in Sales and Marketing: Leveraging advanced analytics and predictive modeling positions the client as an innovator in sales and marketing practices, differentiating them from competitors.

Power of Partnership

Partnering with a Premier Google Cloud Platform (GCP) partner like Niveus Solutions brings significant advantages, leveraging their deep expertise in GCP services and industry experience to implement robust, scalable, and secure solutions. Niveus Solutions offers access to advanced tools and custom solutions, streamlining cloud adoption and optimizing resources for enhanced operational efficiency. Their continuous support and strategic guidance ensure long-term success, while their focus on security and compliance builds trust. This collaboration fosters innovation, agility, and flexibility, enabling businesses to quickly adapt to market changes and maintain a competitive edge.


The Random Forest model, with an accuracy of 97.48%, provides valuable insights for the client to enhance their sales strategies and customer engagement efforts. By predicting the probability of customer bookings, the client can tailor personalized advertisements, invitations, and phone calls to potential customers, thereby increasing the likelihood of conversion. Insights from the model, such as the importance of test drives, follow-up frequency, and customer demographics, can help optimize sales strategies to focus on high-conversion opportunities. 

Understanding factors influencing customer decisions allows for targeted engagement strategies that improve overall customer satisfaction and loyalty. Based on the findings, the client could further explore integrating real-time data and feedback loops to continuously improve model accuracy and adapt strategies in response to changing market dynamics.

Our collaboration with Niveus has revolutionized our approach to data handling and forecasting. The introduction of the ML model for dealer sales forecasting has provided us with precise, actionable insights that have significantly enhanced our decision-making capabilities. This tool has become indispensable for managing our network efficiently.

Assistant General Manager – IT

Technology Stack

Cloud Data Fusion
ODP Plugin
Cloud Composer
BigQuery ML (ARIMA PLUS model)
Cloud IAM
Cloud Data Studio
Security Command Center

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