Introduction Consumer Packaged Goods:

  • Data Analytics for Consumer Packaged Goods (CPG) industry, the integration of data into marketing strategies is vital for maintaining a competitive edge. 
  • Leading brands adopt a data-driven approach, utilizing insights and patterns derived from data to inform strategic decision-making. 
  • By leveraging CPG data analytics, businesses can establish a continuous cycle of improvement in the industry, facilitating adaptability and growth. This blog aims to provide an overview of CPG data analytics and explore how statistical models can assist organizations in the CPG sector.

1. Understanding CPG Data Analytics:

  • CPG data analytics encompasses the collection and interpretation of data related to promotional and marketing activities undertaken by companies in the CPG industry. 
  • By leveraging data, CPG brands can transform the information they gather into valuable insights by analyzing it and identifying patterns. 
  • To visualize this process, imagine data points plotted along a line, and analytics as the knowledge derived from analyzing the resulting graph. 
  • In CPG data analytics, brands typically concentrate on three primary types of data analytics: observational data, behavioral data, and revenue data.

2. Recent research has focused on models pertaining to consumer packaged goods in Data Analytics:

i) In the current economic landscape, businesses must leverage all available resources to thrive in the market. In order to make informed decisions, each sector should base its actions on comprehensive analysis. Data analytics science presents an ideal avenue for harnessing internal and external knowledge within an organization, enabling accurate predictions of outcomes through various processes. The field of marketing has evolved to encompass the study of the marketing mix, a fundamental principle that explores the key influences shaping a company’s product trajectory, as emphasized by Mantzoufa [1].

The marketing mix comprises the crucial elements of Price, Product, Promotion, and Location, which hold significant importance across industries. These elements serve as the foundational pillars that every company must invest in. The research employed the R programming language and SAP Expert data Analytics to facilitate the study, utilizing multiple linear regression algorithms for modeling purposes. Through the “plot(model)” command, we generated four residual plots to extract valuable insights from the outcomes of our study.

ii) The annual growth rate of real GDP per working worker (SDG 8) serves as a metric to evaluate labor productivity and provides insights into the quality and utilization of human resources within the manufacturing process. In a study conducted by Adeniji and colleagues [2], attention was given to leadership factors, workforce motivation, and work performance. The research encompassed a descriptive report and involved distributing survey questionnaires to 422 employees across various Nigerian consumer packaged goods (CPG) companies.

The findings of the study revealed a holistic interplay among three variables: leadership aspects (Transformational, Transactional, and Laissez-faire), employee engagement, and job efficiency. These Data analytics relationships were examined using the Partial Least Square (PLS) path modeling process.

The model demonstrated a significant and moderate connection between transformational leadership, transactional leadership, and employee engagement. Conversely, a negative association was observed between laissez-faire leadership and employee involvement. Consequently, an increase in laissez-faire behavior among leaders would ultimately lead to a reduction in job efficiency. 

Additionally, a noteworthy correlation between employee engagement and work success was identified. Transformational leadership proved to be more effective in fostering higher levels of worker efficiency compared to transactional and laissez-faire leadership styles. Therefore, managers within the selected companies significantly influence employee behavior, motivation, and work efficiency of Data analytics, all of which have implications for the actual GDP per working individual—serving as a measure of labor productivity in SDG 8.

iii) Low-involvement product purchases are characterized by consumers spending less time and gathering less detailed information, leading to a lack of consideration in their buying decisions. This is primarily due to the low cost and low risk associated with such goods. In many cases, particularly with commonly purchased consumer packaged goods, little deliberate decision-making takes place, and uncertainties prevail. Consequently, in such scenarios, a stochastic model that accounts for the random nature of decisions is more suitable than a deterministic solution.

To address this, Atanu Adhikari developed a stochastic model for customer purchasing decisions concerning low-involvement goods [3]. This model takes into consideration the agitations experienced by buyers during the buying process. These agitations possess intrinsic energy that stimulates the consumer’s mind. As these energies are chaotic, the resulting force is random, leading to decisions being made in a random manner.

Numerous research studies have employed statistical techniques to tackle CPG (consumer packaged goods) problems effectively.


  • Data analytics forms the foundation of efficient consumer packaged goods (CPG) businesses. 
  • Each strategic objective is rooted in insights derived from customer data analytics and organizational data. 
  • Gone are the days when decisions about shelf positioning, sales strategies, and pricing were based on instinct and guesswork. 
  • To thrive in today’s market, CPG firms must adopt a comprehensive, data-driven perspective encompassing their operations, supply chain, customer knowledge, and demand forecasting. 
  • By embracing this approach, businesses can cultivate innovative ideas, explore potential opportunities, and develop sustainable solutions to maintain their market share as they progress toward becoming intelligent enterprises in the future.

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