Session on "Use Cases and Risks of ML in Capital Markets" | 23rd Dec at 4pm Hi everyone! The AI and Risk Mitigation project is well underway and for the third session, we will be joined by Rachna Maheshwari, Associate Director at CRI… more
The 2023 Monsoon edition is curated by:
- Nischal HP, Vice President of Data Engineering and Data Science at Scoutbee. Nischal curated the MLOps conference which was held online between 23 and 27 July 2021.
- Sumod Mohan, Founder and CEO at AutoInfer. Sumod curated Anthill Inside 2019 edition, held in Bangalore on 23 November.
- AI and Research - covers research, findings, and solutions for challenges on building models in various areas such as fraud detection, forecasting, and analytics. This track delves into the latest methodologies for handling challenges such as large-scale data processing, distributed computing, and optimizing model performance.
- Industrial applications of ML - covers implementation of AI in the industry, with more focus on the AI models, the issues in training, gathering data so, and so forth. ML is being used at scale in industries such as automotive, mechanical, manufacturing, agriculture, and such domains. This track focuses on the challenges in this space, as we see innovation coming out of these industries in the pursuit of using ML on a second-to-second basis.
- AI and Product - covers strategies for building AI products to scale and mitigating challenges. This track provides insights on incorporating AI tools and forecasting techniques to improve model training, developing a working model architecture, and using data in the business context.
There are three phases in the lifecycle of an application - research, application and aftermath of the application.
- Assess capabilities, determining the new frontiers for AI.
- Find a use for the application.
- Learn how to run it, monitor it and update it with time.
The three tracks at the 2023 Monsoon edition of The Fifth Elephant will cover this lifecycle.
The Fifth Elephant 2023 Monsoon edition will be held in-person. Attendance is open to The Fifth Elephant members only. Purchase a membership to attend the conference in-person. If you have questions about participation, post a comment here.
- Data/MLOps engineers who want to learn about state-of-the-art tools and techniques, especially from domains such as automobile, agri-tech and mechanical industries.
- Data scientists who want a deeper understanding of model deployment/governance.
- Architects who are building ML workflows that scale.
- Tech founders who are building products that require AI or ML.
- Product managers, who want to learn about the process of building AI/ML products.
- Directors, VPs and senior tech leadership who are building AI/ML teams.
Sponsorship slots are open for:
- Infrastructure (GPU, CPU and cloud providers) and developer productivity tool makers who want to evangelise their offering to developers and decision-makers.
- Companies seeking tech branding among AI and ML developers.
- Venture Capital (VC) firms and investors who want to scan the landscape of innovations and innovators in AI and who want to source leads for investment in the AI and ML space.
Analytics in Pricing for CPG Industry
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/
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.
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.
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:
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.
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.
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.
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.