Revolutionising FMCG Industry with Data Science: A Comprehensive Analysis

Artificial Intelligence (AI), is an emerging area of computer science that is focused on automating processes that normally call for human involvement. By simulating human intelligence in robots, it is possible to reduce the amount of repetitive work that needs to be done by people. Additionally, AI facilitates future analyses and forecasts that would otherwise be challenging, if not incorrect, for humans to complete.

The primary AI technologies that are transforming the FMCG business include Data Science, Machine Learning, Natural Language Processing, Deep Learning, and Computer Vision. Some of the popular AI use cases in the FMCG and CPG sectors are listed below.

The FMCG industry's present landscape has been expanding quickly for a while. Despite the industry's bright future, trends are always shifting due to unpredictable customer behaviour. FMCG organisations must adjust to a fast shift in strategy choices depending on shifting consumer wants and demands. Currently, the FMCG sectors have been transformed by Data Analytics, which has greatly contributed to innovation.

What is the FMCG Industry?

The FMCG industry is the 4th largest industry in India. Consumer packaged goods are distinguished by their high turnover, which includes the rapid production, distribution, marketing, and consumption of their products. FMCG products including detergents, toiletries, toothpaste, cosmetics, and others rule the market. The FMCG industry in India also includes pharmaceuticals, consumer electronics, soft drinks, packaged foods, and chocolates. Due to the wide variety of products in the FMCG industry, several businesses control the market in various sub-sectors. The top three FMCG firms in India are Hindustan Unilever (54%), Colgate (54.7%), and Dabur (60%) respectively.

India's FMCG market is split into rural and urban Indian demographics. A total of 60% of the FMCG market's consumer revenue in India comes from the urban market. In 2017, the market for this sector was worth $29.4 billion. The FMCG sector in India has had the most rapid expansion in urban areas, although semi-urban and rural areas are also expanding rapidly. Over 40% of all FMCG sales in India come from the semi-urban and rural markets. In India, rural areas have seen greater growth for FMCG enterprises than metropolitan ones. Investors cannot afford to overlook the Indian rural FMCG market because around 12.2% of the world's population resides in India's villages.

Almost everyone in the world utilises fast-moving consumer goods (FMCG) on a daily basis. These are modest consumer purchases done at produce stands, grocers, supermarkets, and wholesalers. Examples include milk, gum, fruits and vegetables, toilet paper, cola, beer and over-the-counter medications like aspirin. More than half of all consumer spending goes into FMCGs, although they are often low-risk purchases. A new car or a smartphone with a stunning design is more likely to be flaunted by consumers than a $2.50 energy drink from the corner store.

Should AI be adopted in FMCG Industry?

Emulating India's high levels of production and manufacturing require a lot of effort. This necessitates a greater urgency to automate any operations that can be made easier. Whether it's decision-making, machinery, or CCTV cameras, AI is being incorporated into many operations. As this happens, new, intelligent positions are being developed. Everybody is being urged to become more tech-savvy, and every nation is being urged to grow technologically. 

All FMCG companies will soon realise the value of implementing AI into their operations and start investing heavily in its implementation. FMCG companies have already started employing AI extensively. AI for marketing, analysis, and automation in FMCG industries may soon be accepted as normal practice in consumer product and commodities organisations. 

Use of Data Analytics in the FMCG Industry

Data Analytics, which uses real-time predictive intelligence, may help the FMCG business make the best decisions about inventories, supply chains, and consumer experiences. The content will just focus on the ways Data Scientists and Data Analysts assist corporations in dealing with consumer preferences and comprehending the target market.

Sales Forecasting

By forecasting consumer purchases, a company can plan production in accordance with its knowledge of the sales that will occur soon. One may make wise company decisions and foresee short- and long-term performance with accurate sales projections generated by the application of AI. If the prediction is unfavourable, proper precautions can be taken.

Customer Behaviour Analysis

This is the study of consumer purchase patterns, preferred options, least-liked goods, favourite places, buying customs, tastes, and market trends. AI assists in making the necessary adjustments to draw in new clients, satisfy current ones, and stand out in the marketplace.

Analysis of Consumer Sentiment and Decision Making

A company can acquire insight and efficiently respond to its customers by using an AI solution to scrape emotions from the Internet in order to learn how customers feel about a product, brand, or service. This will improve customer experience and engagement. Automating decision-making and customer service is also possible with machine learning.

Tracking Market Trends

The AI-enabled system will tell you of any new trends or items that hit the market and will give you advice on which ones your business should invest in. Machine learning links the pricing to sales and uses that knowledge to optimise the price dynamically. One can supply complete and detailed information for improved marketing strategies by scraping from the numerous websites and social networking sites on the internet and combining this data.

Route Management

Holiday months differ from other months since this is the time of year when buyers anticipate receiving their purchases more quickly. Additionally, when omnichannel shopping becomes the new standard, customers demand outstanding customer service and high levels of convenience for the delivery of their orders. This necessitates that retail and logistics businesses offer flawless omnichannel service. So, using location intelligence for route management is what logistics companies are concentrating on. They can use this to find hidden location points, the ideal last-mile delivery route, and traffic patterns. They are also able to deploy additional workers to the closest delivery centres because they can anticipate the intensity of demand from certain locations, ensuring prompt delivery of goods in these areas.  

Going Paperless

For a very long time, data input and other analogue procedures were highly relied upon by retailers and logistics managers. The varying customer expectations over the festival seasons, present a problem. This process is made much more convenient by tools powered by AI. This promotes transparency across the entire process and offers 360-degree 'factory-to-sale' visibility, all while assisting in the maintenance and analysis of enormous data sets. Companies can get real-time updates using data analytics, such as the distribution of the workforce across segments or the availability of raw materials.

Automated Targeted Marketing

One can use AI to give their marketing system additional intelligence. The system will direct, as to where to market, what medium to utilise, and which social networking site to use so that one can have a precise target audience and prevent irrational marketing expenses. This way the clientele will undoubtedly expand once the correct target is reached. 

Churn Prediction

The likelihood that a consumer may cancel their membership with you can be predicted using data and customer feedback. Churn prediction enables your sector to have a better knowledge of anticipated income. Additionally, being able to predict a customer's prospective turnover rate enables you to target that person more effectively.

Conclusion

According to Subrata Dey, Global CIO at Godrej Consumer Products Limited (GCPL), in today's disruptive and competitive market, every firm has the challenge of boosting its top line. Godrej is no different, as the company is making an effort to boost its top line by utilising data analytics in the FMCG sector. FMCG companies now have the chance to modernise their operations and marketing. FMCG companies can go beyond simply reactive operations and make proactive decisions by utilising data analytics tools.

The FMCG sector is driven by marketing, inventory, seasonal variations, returns, out-of-stock, raw material availability, localised pricing, and other variables. The FMCG sector may use data analytics to find patterns, holes, and opportunities in consumer behaviour and purchase decisions in these unpredictable times. Data analytics enhances the scalability, flexibility, and value creation of FMCG businesses. To keep ahead of trends and give customers useful services, it is a dynamic industry that demands a similarly dynamic strategy. For instance, the company can use big data analytics to create the finest omnichannel purchasing experience.

To successfully apply the data-driven model in the company, it is crucial to know what tools and techniques to utilise. Offshore solution providers step in at this point, giving comprehensive services for establishing, integrating, executing, and overseeing big data analytical tools. The FMCG market is impacted by seasonal variations, socioeconomic circumstances, politics, ideology, raw materials, pricing, and other factors. The right technology must be used to track every part of the firm. Businesses can restructure their systems to match changing market demands and become leaders in their sector with the help of big data providers' data consultation services. Businesses may fill up strategic gaps with data analytics, which will also help them get ready to deal with market movements in a proactive manner.