About me

I am currently a Data Science Manager in Revenue Growth Management Analytics at AB InBev, the world’s largest beer company. In this role, I harness the power of machine learning (ML) to drive business success. With my expertise in pricing and mix management, combined with a master’s degree in economics specializing in applied quantitative finance, I bring a unique blend of knowledge and practical acumen to the table.

My passion lies in uncovering hidden patterns within complex data sets and utilizing ML algorithms to transform them into actionable recommendations that fuel revenue growth. I have witnessed the exponential impact that occurs when diverse perspectives merge with ML-driven insights, achieved through fostering collaboration and cross-functional partnerships.

By continuously staying at the forefront of technological advancements and industry best practices, I remain eager to push the boundaries of what is achievable in data science implementation. I aim to showcase the profound impact that ML-powered data-driven insights can have on business success.

Speaker LinkedIn Profile - https://www.linkedin.com/in/aaradhya-dave-238583129/

Problem Statement:

The Consumer Packaged Goods (CPG) industry faces the challenge of effectively navigating a complex pricing landscape to drive profitability and maintain market share. Traditional pricing approaches often fall short in addressing the multifaceted nature of pricing decisions, which encompass factors such as the trade-off between profitability and market share, capturing consumers’ price and income sensitivity, maintaining consistent price architecture, managing cannibalization effects, accounting for changing consumer preferences, assessing macroeconomic conditions, monitoring industry movements, and strategically analyzing overall industry pricing.

To overcome these challenges and maximize value creation, there is a pressing need to explore advanced analytical techniques, particularly the application of machine learning (ML), to enhance pricing strategies within the CPG industry. Organizations can gain a competitive edge, achieve greater agility and precision in decision-making, and ultimately foster sustainable business growth while delivering superior value to customers and stakeholders.

Use Case :

In Dominican Republic alcohol beverage market, there is an increasing shift of consumers from beer to rum and whiskey. This results in a decline in the beer share in the overall alcohol market and hampers the growth of beer manufacturers. The business could aim to use pricing as a lever to stem this volume bleeding and maximize long-term profitability.

Solution Implementation:

To effectively address the challenges of the complex pricing landscape in the Consumer Packaged Goods (CPG) industry, an actionable solution can be implemented through the following steps:

1. Fundamental Analysis: Conduct a comprehensive analysis of pricing data, market trends, and consumer behavior to gain valuable insights into the factors that impact profitability and market share. This descriptive analysis will lay the groundwork for ML modeling and establish a strong foundation for effective pricing strategies.

2. ML Modeling and Statistical Techniques: Leverage machine learning (ML) models such as linear regression, Blasso, GLMboost, Ensemble, GLMnet, and statistical techniques like conjoint analysis or Vickrey auctions. These techniques enable the identification of key business drivers within the pricing landscape and provide insights into consumer preferences and willingness to pay for different product attributes. ML algorithms uncover patterns, correlations, and accurately capture price elasticities and the impact of other features on demand.

3. Prescriptive Price Recommendations: Generate optimized price recommendations by utilizing the insights derived from ML models. Incorporate pricing data, customer segments, product positioning, competitive dynamics, macroeconomic conditions, and the key drivers identified through ML-powered algorithms. These recommendations strike a balance between profitability and market share, enabling organizations to make data-driven decisions and align pricing architectures with customer expectations and perceived value.

By implementing this comprehensive solution, CPG organizations can gain a competitive advantage, enhance decision-making precision, and drive profitability while maintaining market share. The use of advanced analytics in generating prescriptive price recommendations empowers organizations to optimize pricing strategies, effectively respond to the challenges of the dynamic pricing landscape, and deliver superior value to customers and stakeholders.

Conclusion:

In conclusion, the implementation of ML in pricing strategies within the CPG industry offers substantial benefits to businesses. By leveraging ML-powered pricing analytics, organizations can optimize profitability, maintain market share, and deliver superior value to customers and stakeholders.
Through fundamental analysis, ML modeling, and statistical analysis, organizations gain deep insights into pricing dynamics, consumer preferences, and market trends. This enables the accurate capture of price and income elasticities, identification of key drivers, and generation of prescriptive price recommendations that strike a balance between profitability and market share.
ML-powered pricing strategies provide agility, precision, and a competitive advantage in responding to market changes and aligning pricing architectures with customer expectations. By harnessing advanced analytics, CPG organizations can drive sustainable growth and achieve significant value addition in a dynamic industry.
As a data science manager specializing in revenue growth management analytics, my commitment lies in showcasing the transformative impact of ML-powered insights on business success. By leveraging data-driven decision-making and ML algorithms, I aim to revolutionize pricing strategies in the CPG industry and unlock the full potential of pricing analytics.