How to Use Data Analytics in the Consumer Goods Industry

In recent years, data analytics has seen exponential growth across multiple industries. The consumer goods and retail industries are among them.

The implementation of data analytics in this industry is transforming the way product manufacturers operate, allowing them to make operational business decisions by looking at data-enabled insights. 

Compared to different industries, the consumer goods sector invests lots of money in advertising and marketing.

Advertising companies usually have a significant influence on the buying and shopping behaviors of customers, resulting in higher revenue for a CPG company.

In 2023, the universal retail and consumer goods market was estimated at US$21.13 trillion and is projected to hit US$34.44 trillion in 2030 at a CAGR of 7.2% from 2023 to 2030.

Understanding the Importance of Data Analytics

In simple words, data analytics is the process of analyzing, cleaning, converting, and modeling data to find needed information, support decision-making, and make decisions.

In the consumer goods and retail industry, data analytics can be used to study consumer behavior, boost operational efficiency, and improve supply chains.

Data analytics can offer essential insights into competitive analysis, the latest market trends, and customer preferences.

These insights act as a foundation for the evolution of new products, the discovery of new opportunities in the market, and the advancement of present ones. 

The capability of inspecting huge amounts of data in real time enables manufacturers to spot challenges in their processes and take appropriate actions promptly.

This leads to a reduction in costs and an increase in operational efficiency.

The Development of Data Analytics in Consumer Goods

In the past, intuition and experience played an important role in consumer goods decision-making.

Moreover, the advancement in technologies and the availability of large amounts of data have changed everything. 

The advancements in data analytics in the consumer goods industry have been fuelled by various factors, including an increase in the amount of data produced by digital technologies, the evolution of modern analytical tools, and the increasing demand to increase productivity and minimize costs. 

At present, data analytics has become an important part of future growth and strategic planning.

It is now easy to forecast trends for the future and make better business decisions. 

For example, predictive analytics can help manufacturers predict product demand and modify production levels. This can reduce overstock and stockouts and lead to higher customer satisfaction.

With advancements in technology, you can expect more implementation and transformative applications of data analytics in the consumer goods industry.

What are the Challenges Faced by the Consumer Goods Industry?

The consumer goods and retail industries are facing critical challenges that have become critical to evolving customer behavior.

It’s crucial to recognize these challenges and investigate how data analytics can offer productive solutions. 

Consumer goods and retail sector companies struggle with a lot of issues that can impact profitability and operational efficiency.

Some of the major challenges involve:

Forecasting demand

Perfect demand forecasting is a critical challenge in the retail and consumer goods sectors, largely due to fluctuations in the market and changing consumer preferences. 

Supply chain agility

Proper planning and effective re-planning are keys to success.

More and more consumer goods are transported daily, so planning needs to be agile enough to manage unforeseen events. 

Additionally, managing an effective supply chain is complex and crucial, given the universal nature of sourcing, distribution networks, and production. 

Quality control

Ensuring persistent product quality across various manufacturing batches and regions can be a difficult task. 

Inventory management

Understocking or overstocking can result in higher costs, reduced customer satisfaction, and lost sales opportunities. 

Regulatory compliance

The consumer goods sector is made more complex by the strict regulations that are applicable in various regions.  

How Do You Use Data Analytics in Your Company?

To take full advantage of analytics in the consumer packaged goods sector, it’s essential to know how to use data analytics in your organization. 

Steps to Implement Data Analytics

Adopting data analytics in your company needs a methodical and strategic concept. Here are a few steps to keep in mind:

Point out the objectives

Ensure you have clear goals before using data analytics. This can include boosting operational efficiency to strengthen client segmentation. 

Set up a team

Collect a team of professionals who have experience in data analytics, AI, and machine learning.

The responsibility of this team is to execute and handle your data analytics tools. 

Select the valid tools

Select the data analytics tools according to your business goals. This could involve tools for collecting data, cleaning data, analyzing data, and visualizing data. 

Execute and test

Soon after setting up tools, execute them and perform tests to make sure they are working fine as predicted.

Inspect and improve

Insights provided by data analytics tools can be used to make wise decisions and maximize your operations. 

Benefits of Data Analytics in the Consumer Goods Industry

Let’s see how data analytics is shaping consumer goods businesses to accelerate their growth and manage consumer trends for the future:

Improved risk management

Consumer goods firms use data analytics to forecast fraud propensity models using various networks, paths, statistical, and big data concepts.

Consequently, effective data analytics implementation can help organizations in protecting their financial, physical, and intellectual assets from external and internal risks. 

Enhanced fraud risk management techniques arise from efficient data management and timely reporting of different fraud incidents. 

Improved agility

CPG businesses can respond to changing consumer demographics and resist quick fluctuations in the market.

Data avoids the requirement of guesswork in decision-making, and professionals who build strategy can concentrate on extracting precious insights from it. 

Customer retention

The consumer goods industry is renowned for being highly competitive. A business’s capability to retain consumers can break or make it.

Data analytics can help organizations in finding strategies to encourage consumer loyalty in different markets.

Real-world Use Cases of Data Analytics in Consumer Industry

Let’s dig deeper into the real-world use cases that indicate the power of data analytics in the consumer industry.

These examples showcase the importance of consumer goods analytics in driving effective business results.

Use case 1: Analysis of market baskets for cross-selling opportunities 

Market basket analysis is an effective technique used to recognize the purchasing behavior of customers by analyzing the mixtures of products repeatedly purchased together. 

CPG companies can use transactional data to find out cross-selling possibilities and product associations. 

For instance, via market basket analysis, a CPG business identifies that consumers who purchase designer clothing are also likely to buy perfumes. 

Based on this insight, the organization can place such items on store shelves and develop bundled promotions to increase the chances of consumers purchasing all three products. 

This strategy improves the customers’ shopping experience along with sales. 

Use case 2: Customer segmentation for focused marketing

The retail and consumer packaged goods sectors cater to a wide range of consumers with varying preferences and demands. 

Companies use customer segmentation techniques to divide their focused market into different groups according to psychographics, demographics, and purchasing behavior. 

CPG businesses use data-driven segmentation approaches to understand their customers more deeply and create powerful, focused marketing plans. 

For instance, a CPG business uses data analytics to find various customer segments and locate a group of health-conscious people who buy organic food items repeatedly. 

With this information, the business customizes its marketing campaigns for this segment, concentrating on organic certifications, health benefits, and sustainable sourcing of its items. 

By successfully focusing on this niche market, the business experiences a better return on marketing investments and a rise in brand loyalty. 

Use case 3: Demand prediction for optimizing the supply chain

Error-less demand prediction is crucial for CPG organizations to improve their supply chains, control inventory levels, and guarantee effective production and distribution. 

By examining past sales data, market trends, and exterior factors to predict demand for its favourable snack items for the next summer season. 

The analysis discloses a constant rise in demand during this time because of outdoor activities and vacations.

Based on this insight, the business improves its production schedules, acquires raw materials suitably, and guarantees effective distribution to match the anticipated increase in demand. 

Use case 4: Sentiment analysis for managing brand reputation

In the modern digital era, brand perception and customer sentiment can break or improve the reputation of a CPG company. 

Sentiment analysis, the field of natural language processing, enables data analysts to track and inspect consumer emotions and feedback expressed via online reviews, social media, and other digital channels.

This concept enables them to improve their brand’s reputation, make data-enabled enhancements, and interact with consumers in a better way to build long-lasting loyalty. 

For instance, a consumer goods organization uses sentiment analysis techniques to track social media conversations and online reviews of its new personal care line.  

The sentiment analysis discloses a common complaint regarding a specific ingredient resulting in infections and burns. 

Based on this information, the firm rapidly inquires about the issue, rebuilds the product, and communicates the changes to customers.

Emerging Consumer Goods Industry Trends to Watch in 2024

Below are some future trends that can transform consumer goods markets in 2024:

Revolution with a purpose

The persistent revolution has enabled businesses to generate new products; this tendency will remain in the future.

For instance, in the last 7 years, the number of SKUs in the US grocery channel has grown by 50%. Soon, many products will be obtainable in the consumer sector. 

Investment to nourish foundations

To grow rapidly, consumer packaged goods (CPG) companies will be investing more in expanding globally at all levels of the value chain such as manufacturing, sales and marketing, procurement, R&D, and distribution. 

Perish or perform

In the upcoming decade, the consumer sector will discover simply as many winners as losers.

The below-mentioned are the top 5 causes of this:

1. Unlimited new middle-class customers in the developing markets
2. The growth of consumers who purchase products online
3. The effects of changes in demographics that involve consumption and aging patterns
4. Rise in fluctuating input costs due to scarcity of natural resources and emerging major suppliers

Consumer packaged goods that develop value-based products will have larger markets than others.

Making products obtainable at affordable prices or enhancing the products’ quality will provide higher value to the customers. Both of these approaches yield better results. 

Age of consumers who purchase products online

There has been a significant growth in the number of digital consumers in recent years. In the next decade, almost all businesses will undergo a digital transformation.

Consumer packaged goods businesses must know how to develop a profitable business online. They should be ready with the answers to the following questions:

1. How can they develop a profitable business via online retail mediums?
2. How can they develop categories and brands in a world of social networks?
3. How can they take advantage of technology-oriented opportunities to get to know consumers in detail and interact with them more frequently?

Digital branding

Brand communication will be greatly impacted by digital technologies.

The majority of consumers rely more on referrals; 70% of them look at user reviews before making purchase decisions.  

Understanding consumers more clearly than before

Consumer goods businesses will have to look at unique approaches to match the requirements of aging consumers.

For instance, L’Oreal recently introduced a series of anti-aging moisturizers, skincare, and hair care products to target female audiences in the 21-55 age group. 

Supply chain inconsistencies

For the most prosperous CPG organizations, globalized trading has provided the chance to develop into fresh markets and combine production and supply.

Specialization driven by globalization has also led to a notable rise in the universal irregularities of commodity input prices. 

Global supply chains with a proven track record of producing value can experience more inconsistencies in the future.  

Digital integration

In 2024, you can experience more combinations of digital technology in consumer goods, from laundry detergents to smart home appliances.

Brands that provide products with flawless digital integration are expected to have higher demand and consumer loyalty.  

To implement this trend in consumer products, you have to think about launching products that merge with digital devices like smartwatches and smartphones.

Furthermore, you can try adding marketing strategies that showcase the advantages of products’ digital consolidation, including enhanced functionality or improved convenience. 

Personalization

As per a Twilio survey, 60% of customers say they become frequent buyers as a result of personalized brand experience. 

Lastly, personalization is another trend that will probably continue in 2024.

This trend is observable in the beauty and fashion sectors, where brands provide customized experiences and products. 

Moreover, you can develop marketing strategies to focus more on the advantages of the customized products, including uniqueness or enhanced satisfaction.  

Targeting and segmenting customers with CPG data analytics

To remain active in the CPG sector, identifying and considering particular customer segments is essential for success.

Perfect customer segmentation and targeting are made easy by CPG data analytics solutions. 

Finding client segments

CPG organizations can use analytics to segment their clients according to numerous criteria, including lifestyle, demographics, purchasing behavior, and preferences.

This segmentation is responsible for a targeted and customized concept in marketing and product development. 

Strategies for targeted marketing

After finding client segments, targeted marketing strategies can be developed using analytics.

By realizing the needs and unique characteristics of every segment, organizations can personalize their campaigns and messages for each group. 

Boosting consumer experience

Segmentation and targeting play a vital role in boosting the complete client experience.

By providing customized and applicable experiences, CPG organizations can boost client satisfaction, brand advocacy, and loyalty. 

Predictive targeting

Modern predictive analytics techniques allow CPG organizations to forecast the upcoming purchasing behaviors of various client segments.

Predictive targeting allows for more systematic and suitable marketing campaigns. 

Data Analytics for Optimizing Pricing and Promotion in the CPG Industry    

Data analytics for consumer goods is all about achieving more profits, meeting client expectations, and optimizing pricing and promotion strategies.  

Data-based pricing plans

CPG organizations rely on pricing decisions to set prices according to present market trends and competitor strategies.

This concept results in productive and dynamic pricing, which ends up with a 15% cost reduction and increased market positioning. 

Analysis of promotional productiveness

Analytics is useful in measuring the success of promotional strategies.

By inspecting consumer responses and sales data, organizations can identify ways to connect with consumers and increase sales, resulting in more productive strategies in the future. 

Personalized promotions

CPG companies can look at the insights provided by data analytics and design personalized promotions that target particular client segments.

This personalized concept can boost the efficiency of promotional activities and improve client relationships. 

Maximizing channels and promotion timing

Companies can determine the best channels and times for promotions using analytics. 

ROI analysis

Analytics offers an effective way to calculate ROI and promotional efforts. 

KPIs and Major Metrics for CPG Data Analytics

Knowing KPIs and relevant metrics is important for optimizing the usefulness of CPG data analytics.

Businesses use these KPIs and metrics as a dashboard to evaluate their performance, know market dynamics, and make strategic decisions.

Inventory turnover

Inventory turnover rates offer useful insights to describe how well an organization is managing its stock.

Low turnover rates can indicate declining demand or overstocking, whereas high rates can indicate higher sales. 

Metrics for sales performance

Sales data containing value, frequency, and volume are crucial in consumer goods and retail analytics.

Companies can examine these metrics to evaluate their market presence, know sales trends, and pinpoint growth opportunities. 

Customer segmentation metrics

It’s crucial to recognize various customer segments.

Metrics including demographic data, preferences, and purchase history enable organizations to customize their products and marketing campaigns to particular consumer groups. 

Marketing ROI

It is essential to calculate the return on investment for marketing strategies.

Here, you can inspect the impact of various marketing campaigns and channels on improving sales and engaging consumers. 

Efficiency of the supply chain

Different metrics associated with the supply chain, including supplier reliability, delivery times, and transportation costs are important in boosting the distribution process. 

Predictive analytics metrics

Modern metrics, including demand planning signals and predictive sales forecasting, are becoming more crucial.

These metrics enable firms to predict market shifts and modify their plans. 

CPG Analytics Data Visualization and Reporting 

Reporting and data visualization are crucial parts of CPG data analytics, converting complicated data sets into productive and extensive insights.

In industry-enabled data, the capability to properly visualize patterns, trends, and metrics is important for decision-making. 

Data visualization tools and dashboards

A major component of CPG analytics is the use of data visualization tools and dashboards.

Companies can use data to create visual representations using these tools, and effortlessly find outliers, patterns, and trends.

Perfect visualization helps simplify complicated data, allowing stakeholders to rapidly understand the insights. 

Real-time reporting

The fast-growing nature of the CPG market expects real-time reporting potential.

This allows companies to adapt quickly to changes in the market, improve inventory levels, and modify their marketing plans.   

Tracking performance

Visualization tools are useful in tracking metrics and KPIs. The success of operations and strategies can be evaluated using this tracking to fulfil business goals. 

Predictive analysis visualization

Predictive analytics benefits from the use of modern visualization tools.

They play a crucial role in modelling various scenarios and predicting trends for the future, and they offer a strategic advantage in decision-making and planning. 

In other words, reporting and data visualization in CPG analytics are about presenting data and converting data into a story that can direct operations and organization strategy.

The Role of Predictive Analytics in the CPG Sector

Predictive analytics is now a pillar of CPG data analytics, facilitating future-proofing approaches and data-enabled decision-making. 

Forecasting needs

Predictive analytics plays an extremely important role in predictive analytics.

By examining past sales data, consumer behavior patterns, and market trends, CPG businesses can forecast future needs with higher accuracy. 

Predicting consumer behavior

Understanding and predicting the behavior of consumers is crucial to remaining active in the CPG industry.

Companies use predictive analytics to find consumer preferences and the latest trends, enabling them to customize their marketing plans and products perfectly. 

Maximization of marketing campaigns

By inspecting market dynamics and consumer data, predictive analytics can optimize marketing efforts.

This includes figuring out the best channels, timing, and messaging for marketing activities, resulting in higher ROI and engagement. 

Strategies for pricing

CPG organizations use predictive analytics to build dynamic pricing techniques.

Inspecting different factors such as consumer needs, conditions of the market, and competitor pricing enables decisions associated with better pricing that can have a huge influence on profitability. 

Optimization of the supply chain

Predictive analytics is influential in improving supply chain operations.

It assists in pointing out possible disruptions, improving delivery routes, and predicting supplier performance by ensuring streamlined and productive supply chain management.        

Generally, predictive analytics in the consumer goods sector is related to generating a roadmap for endurable growth. 

How Brands Can Drive Growth Using CPG Data

Retail data can be used by CPG brands to increase overall operational efforts for their brand.

Let’s explore numerous ways where CPG data can be used in two buckets, such as chasing performance and making plans. 

Chasing performance 

You shouldn’t ignore some major regions to chase the productivity of your CPG products:

Sales velocity

This metric is used to measure your retail product’s sales speed.

This information can be used to understand the popularity of new products and determine whether the popularity of current products is declining in the market.  

Promotion analysis

How productive are your marketing strategies at improving sales? Use this data to determine which strategies are and aren’t producing sales, so that you can adjust your strategy. 

In-stock performance

This is an essential metric for CPG companies as it shows how successfully you are satisfying consumer demand.

A lower in-stock performance indicates an excessive number of orders and incomplete inventory to satisfy them.

At this time, you have to identify new approaches to boost supply chain operations or enhance production stages. 

Broker management

Brokers can be an integral part of any CPG sales campaign, but if not handled properly, they can be expensive and lead to confusion.

You can use retail data to monitor your broker’s accounts, point out opportunities, and perfectly establish priorities. 

Making plans for the future 

CPG data acts as the foundation for CPG businesses to make plans for the future. Listed below are a few examples:

Product development

When launching a new product, data from current products can help you determine whether or not the fresh product will become popular.

You can use this information to enhance the formula of your products, packaging, and price to make sure you’re making the most out of every dollar, and plan your strategies related to distribution to ensure your products connect with niche audiences. 

Order fulfilment

CPG data can help you plan your shipments and guarantee that all orders are full and delivered on time.

This reduces lost sales, penalty fees, and consumer complaints. 

Inventory planning

CPG data can be used to save your money and time and improve your supply chain.

Accurate and timely data can guarantee you’re meeting expectations without overstocking, which can result in higher waste and carrying costs. 

Planning for demand

Use retail data to inform requirement planning for a specific category or product, enabling you to forecast future operations and staffing demands. 

Distribution growth

If you are planning to increase distribution, CPG data can determine which areas, store formats, and chains have the greatest demand for your items.

How to Solve Business Challenges Using Data Analytics?

The applications of data analytics in the consumer goods industry address these challenges through an effective solution:

Forecasting demand

Data analytics can look at market trends, past sales data, and audience behavior patterns to produce valid demand forecasts. This can minimize waste and boost production planning. 

Regulatory guidelines

Data analytics can monitor and handle compliance information, ensuring compliance with guidelines and minimizing the risk of non-compliance penalties.

Supply chain management

Data-enabled insights can improve supply chain operations, from sourcing and production to distribution, by pointing out inefficiencies and forecasting possible disruptions. 

Quality control

Predictive analytics can point out deviations from quality violations by tracking real-time production data and allowing corrective actions. 

Inventory management

Analytics can reduce overstocks and stockouts, increase cash flow, and maximize inventory levels according to demand predictions. 

How to Transform Your CPG Analytics Approach with AI and Machine Learning

Simple to use

When researching AI-enabled analytics platforms for CPG, you have to seek out a solution with a user-friendly interface and natural language search feature. 

Quick results

With the addition of profitable data visualizations and natural language search, untechnical users have to wait for a few days or weeks to get answers to their queries.

Instead of contacting a very busy analytics staff, your category manager can type their query and obtain a complete report in a matter of minutes. 

This gives your analytics team and business users enough time to enable your CPG organization to remain competitive in the market. 

Actionable insights

Apart from answering the queries of untechnical users rapidly, modern platforms also provide insights.

The level of insights will be different from platform to platform; you have to look for a solution that can compile your data and provide you with crucial takeaways and operative findings. 

The major analytics platforms use machine learning and AI to handle various enterprise-level data origins and sort thousands of data points to find significant opportunities for consumer goods companies. 

AI is an automation of the longest sequence of judgments deriving from prescriptive analytics.

The origin of its intelligence is its capability to offer real-time feedback data to boost prescriptive models.

This guarantees that the next prescribed choice will be superior to the previous one. 

This remarkable capability to adjust and learn allows AI to carry out activities according to automated decisions.

As businesses continue to produce enough data, the analytics skills of AI will propel them to the next level of profitability and decision-making.

Data Analytics’ Future in the Consumer Goods Sector

Moving forward, data analytics will play a significantly crucial role in the consumer goods industry.

Developments in machine learning and AI are covering the way for complex CPG data analytics tools that can offer detailed and more precise insights.

This can result in real-time improvement of inventory levels, better forecasting, and refined consumer segmentation. 

Furthermore, the combination of data analytics with various digital transformation initiatives, including predictive maintenance and automation is expected to see notable enhancements in the consumer goods sector. 

Conclusion

Retail and consumer goods businesses can achieve tremendous growth by implementing modern and big data analytics. The major problem is the collection of data and producing relevant business models, but that doesn’t yield better results.

Instead, companies need to convert data-informed insights into practical action. Implementation of effective strategies for consumer goods data analytics can drive tremendous growth and re-evaluate market positions.

References:

Unveiling Hidden Opportunities: Data Analytics in the Consumer Goods Industry

7 Trends of the Consumer Goods Industry- the way to future