Enhancing Sales Prediction Models: AI Consulting for Data and AI Models Optimization

Client

The Client is a publicly traded global manufacturing company that specializes in systems that move water and fuel. They manufacture motors, pumps, controls, and electronics and serve many different end markets, including residential, commercial, industrial, municipal, and mining.

Following the corporate strategy to continuously improve the products and processes to deliver valuable and innovative solutions that better serve customers’ needs, the company was looking for AI business consulting services to find ways to better align production with customer demand.

The Client contacted InData Labs, a predictive analytics company, specializing in data analytics,  predictive analytics consulting, and development. The specific request of the company was to improve its advanced predictive analytics and AI capabilities, particularly within sales operations. The key goal was to use AI predictive forecasting to better plan and assess performance of the enterprise based on public macroeconomic data and proprietary data analysis.

By leveraging the expertise of the InData Lab’s AI business consultants, the Client receives access to the strategic knowledge of how to integrate AI effectively into business operations that may not be accessed internally. This external perspective allows for a fresh, expert, and unbiased assessment of the company’s strategies, processes, technologies used, and overall operational approaches.

Our AI consultants agreed to work closely to help analyze existing data pipelines and AI models to identify inefficiencies and recommend actionable changes that can lead to accurate sales predictions and operational efficiency.

Challenge: enhance data analysis and build efficient sales prediction models

The AI sales predictive models used by the Client were not accurate enough, and enhancements were required for both dataset processing and the machine learning approach. One key goal was to align inventory levels with customer demand, improving the existing AI sales prediction algorithms.

Solution: detailed data analysis and advanced feature engineering to improve AI sales prediction models

Our team of data engineers made an in-depth analysis of the Client’s case. We proposed an approach with a series of consulting workshops for the Client’s team to develop a comprehensive roadmap for addressing the current issue of inaccurate sales predictive models. These workshops were focused on two main areas:

1. Data Analysis and Review: Improving dataset processing through better data understanding and feature engineering techniques.
2. AI Forecasting Model Review: Enhancing existing forecasting models with advanced model selection, hyperparameter tuning, and evaluation methods.

The outcomes of the workshops were the following:

• Detailed data analysis, including data correlation and data distribution analysis, data integrity checks, splitting data for separate models, and improved feature engineering
• ML forecasting model optimization, advanced features, target variable scaling (e.g., using price increments instead of prices), and hyperparameter optimization
• Detailed documentation and recommendations.

Result: a comprehensive roadmap for improving sales prediction models

InData Labs, a trusted data analytics firm, has consulted the Client on the prediction of sales using data analytics and AI. We’ve suggested a robust plan to enhance sales performance with data analytics and predictive sales AI.

As a result of our consulting services, the following deliverables were created and provided to the Client:

• The report on comprehensive data analysis related to feature correlations and dependencies. It will help the Client to correctly plan strategies to train AI models
• AI models were built and evaluated for their performance at the specified predictive level. The results were shared to discuss performance improvement areas further
• A report with recommendations to improve data features and the accuracy of the AI sales prediction model.