CPG = Consumer Packaged Goods

We can't LIVE without CPG

Consumer Packaged Goods (CPG) are products that are sold quickly and at a relatively low cost. Examples include non-durable goods such as food, beverages, toiletries, over-the-counter drugs, and other consumables. CPG companies face several challenges in today's competitive and dynamic market, such as changing consumer preferences, increasing competition, rising costs, and environmental regulations. To succeed in this industry, CPG companies need to adopt innovative strategies that leverage data, technology, and customer insights to create value for their customers and stakeholders.

The consumer packaged goods (CPG) market is a dynamic and competitive industry that produces and sells products that are consumed on a regular basis, such as food, beverages, personal care, household goods, and apparel. According to various sources , the global CPG market size was valued at around $1.8 trillion in 2022 and is expected to grow at a compound annual growth rate (CAGR) of 4.5% from 2022 to 2028. Some of the key drivers of this growth are the increasing demand for convenience, health and wellness, sustainability, and personalization among consumers, as well as the rapid adoption of digital technologies and e-commerce platforms by CPG companies. However, the CPG industry also faces several challenges, such as rising inflation, supply chain disruptions, labor shortages, changing consumer preferences, regulatory uncertainties, and environmental and social issues. To succeed in this rapidly changing environment, CPG companies need to adopt innovative strategies such as market share expansion, product differentiation, creative transformation, data-driven supply chain optimization, and environmental, social, and governance (ESG) integration

Revenue Management Model (RMM)

A revenue management model is a framework that helps businesses optimize their pricing and inventory strategies to maximize their revenue potential. Revenue management models typically involve analyzing historical and forecasted demand, customer behavior, market conditions, and competitive actions to determine the optimal price and availability of products or services for different segments and channels. Revenue management models can be applied to various industries, such as hospitality, airlines, retail, entertainment, and more.

It can help increase revenue by capturing more demand and reducing customer churn.
It can help improve profitability by lowering costs and increasing margins.
It can help enhance customer satisfaction by offering personalized and dynamic offers.
It can help gain competitive advantage by responding quickly and effectively to market changes.

A clear understanding of their business objectives and constraints.
A reliable and accurate data collection and analysis system.
A robust and flexible optimization algorithm that can handle complex scenarios and trade-offs.
A well-defined and consistent pricing and inventory policy that aligns with the business goals and customer expectations.
A continuous monitoring and evaluation process that can measure the performance and impact of the revenue management model.

RMM is an acronym for Revenue Management Modeling, which is a method for estimating customer demand and optimizing pricing strategies based on customer choice behavior. RMM is also the name of an R package that implements this method using various models, such as the conditional logit (CL) model and the robust demand estimation (RDE) method. The RMM package allows users to fit revenue management models using a simple formula syntax and provides functions to calculate choice probabilities, no-purchase rates, and standard errors. The RMM package can be installed from CRAN or GitHub and requires R version 3.1.0 or higher. The RMM package is useful for researchers and practitioners who want to apply customer choice-based revenue management models to real-world data and scenarios.

Media Mix Model (MMM)

A media mix model is a statistical tool that helps marketers measure and optimize the impact of their marketing activities on various business outcomes. A media mix model uses historical data on marketing spending, media exposure, and business performance to estimate the contribution of each marketing channel to the overall business objective. A media mix model can also help marketers simulate different scenarios and allocate their budget across different media channels to maximize their return on investment (ROI).

ROAS stands for Return on Ad Spend, which is a metric that measures how much revenue a business generates for every dollar spent on advertising. ROAS is calculated by dividing the revenue generated by the ad spend, and multiplying by 100 to get a percentage. For example, if a business spends $1000 on advertising and generates $5000 in revenue, the ROAS is ($5000 / $1000) x 100 = 500%. ROAS is an important metric for marketers and advertisers because it helps them evaluate the effectiveness and profitability of their campaigns. A high ROAS indicates that the campaign is generating more revenue than it costs, while a low ROAS indicates that the campaign is not performing well or is losing money. ROAS can also be used to compare different campaigns or channels and optimize the budget allocation accordingly. However, ROAS is not the only metric that should be considered when evaluating the performance of a campaign. ROAS does not account for other factors such as the cost of goods sold (COGS), the lifetime value (LTV) of customers, or the profit margin of the business. Therefore, ROAS should be used in conjunction with other metrics such as Return on Investment (ROI), Customer Acquisition Cost (CAC), or Cost per Acquisition (CPA) to get a more comprehensive picture of the campaign's impact on the business's bottom line.

Marketing mix modeling (MMM) is a technique that aims to quantify the effects of different marketing activities on sales and profits. MMM can help marketers optimize their marketing budget allocation and evaluate the return on investment (ROI) of their campaigns. However, MMM faces several challenges, such as dealing with nonlinearity, endogeneity, multicollinearity, and heterogeneity of marketing effects. Moreover, marketing effects may vary over time due to changes in consumer preferences, competitive actions, or environmental factors. One way to address these challenges is to use a Bayesian time varying coefficient (TVC) model, which allows the coefficients of the marketing variables to change over time according to a stochastic process. The TVC model can capture the dynamic and nonlinear relationships between marketing activities and sales outcomes, as well as account for the uncertainty and variability of the coefficients. The TVC model can also incorporate prior information from experts or historical data to improve the estimation and inference of the model parameters. The TVC model can be applied to various types of marketing data, such as panel data, cross-sectional data, or time series data. The TVC model can also handle different forms of marketing variables, such as continuous, discrete, or categorical variables. The TVC model can be estimated using Bayesian methods, such as Markov chain Monte Carlo (MCMC) or variational inference (VI), which can provide posterior distributions of the coefficients and other quantities of interest. The TVC model can also be extended to include other features, such as hierarchical structure, spatial dependence, or interaction effects. In this paper, the authors present a general framework for the TVC model and its applications to MMM. They review the literature on TVC models and MMM, and discuss the advantages and limitations of the TVC approach. They illustrate the TVC model with a simulated example and a real-world case study of a fast-food chain. They compare the performance of the TVC model with other models, such as linear regression, random coefficient model, and state space model. They also provide some practical guidelines and recommendations for applying the TVC model to MMM problems.