Data Science in FMCG Analytics

Data Science is a science that uses scientific procedures, processes, algorithms, tools, strategies, and technologies to extract knowledge and information from massive volumes of unstructured and untuned data for fmcg analytics. Machine learning, data mining, and big data are all connected to data science.

It combines data analysis, statistics, and informatics, as well as related approaches, to learn and analyze real-world events using data. It draws on theories and methods from a variety of fields, including statistics, mathematics, computer science, and information technology.

Data scientists look into the future. They begin with big data, which is defined by the three Vs: volume, variety, and speed. The data is then used to feed algorithms and models. Working in machine learning and AI, the most cutting-edge data scientists create models that automatically self-improve, recognizing and learning from their failures.

According to a report, the worldwide data science industry is expected to reach USD 115 billion in 2023, with a CAGR of 29%. According to a Deloitte Access Economics survey, 76 percent of organizations aim to raise their fmcg analytics spending over the next two years. Data science and analytics can aid almost any industry. However, some industries are better positioned to benefit from data science and analytics than others.

To have a better understanding of the value of data in the FMCG analytics, consider the following use cases that are now in use at several leading FMCG companies:- 

• Projection and Forecasting in FMCG analytics
  To ensure a trickle-down impact across departments, businesses must predict sales. To stay up with the firm’s evolution, the forecasting process necessitates merging FMCG analytics with business and product expertise, as well as a constant focus on improving outcomes. Companies with strong analytical capabilities may address each challenge from the various perspectives necessary, including product, customer, retail structure and complexity, and supply chain interdependencies.
• Marketing in FMCG analytics
  With such significant expenditures in trade promotion operations, FMCG firms are finding it difficult to make informed judgments that prompt suitable actions and position them to succeed in both emerging and developed markets. FMCG Analytics can assist firms in becoming more sophisticated in controlling pricing across the supply chain in such situations. This would cover shelf-based pricing, pricing to distributors and retailers, and optimizing,  promotional, spending, which is a huge expense for CPG firms.
• Supply Chain Analytics in FMCG analytics
  The supply chain is one of the most important aspects of the FMCG industry. FMCG companies are leveraging big data in supply chain analytics to optimize delivery networks, for example. Organizations throughout the industry have started combining different delivery networks utilizing analytics to produce a faster, more efficient process.
  This not only improves service accuracy but also reduces the time it takes to get from one station to the next. Furthermore, by focusing on warehouse management, big data analytics in the FMCG business may produce a more efficient supply chain. Real-time examination of warehouse facilities and procedures is now possible thanks to technological advancements. Identifying delivery mismatches, inventory levels, and income deliveries are all part of this process.
• Inventory Optimization in FMCG analytics
  Many businesses are attempting to strike the correct balance between shelf availability and inventory levels. The “raising bar” of consumer expectations and company objectives, as well as the increasing complexity of FMCG supply chains, are forcing businesses to confront more complicated issues regarding inventory management. Service levels, inventory, and asset utilization are just a few of the key performance variables that FMCG Analytics can uncover.
• Consumer satisfaction in FMCG analytics
  In today’s hyper-competitive market, a company’s ability to retain consumers and acquire their loyalty can be the difference between success and failure. Data analytics is being used by businesses to keep their important clients by identifying and avoiding weak areas. Companies can study consumer behavior and experience in order to enhance impact purchase behavior, customer experience, and customer retention by identifying significant opportunities.

An FMCG firm has several decision-making issues at every point of the process, from production to delivery to marketing and sales, and data is the only resource that can be depended upon. The firm begins collecting data from the moment it is founded, including POS data, customer demographic data, market research data, promotional campaign data, financial data, Twitter data, Facebook data, weather data, and so on. This information is frequently loud, unstructured, dispersed, and available in a variety of formats. How does a business use this information to develop action items that not only address the current situation but also plan for the future? Data Science is the answer. And Python is an important programming language in Data Science, learn more about Python vs Java here.

Statistical techniques such as ANOVA can assist a shop to pick the best shelf space investment. An FMCG firm may coordinate its efforts on a regional basis to grab customers and cope with regulatory compliances with the aid of shop clustering. The FMCG firm may use RFM (Recent Frequent Monetary) analysis and category scorecard analysis to develop a consistent sales plan to gain and maintain loyal clients who buy their products on a regular basis rather than only once.