Tag: data exploration

The Impact of Sustainability on CPG Sales.

by Krina Rathodin Uncategorized

Introduction

In a world where environmental consciousness is rapidly gaining ground, the consumer packaged goods (CPG) industry finds itself at a pivotal juncture. 

Sustainability has emerged as a transformative force, redefining the way CPG companies operate and influencing consumer behavior like never before. 

Today, we delve into the profound impact that sustainability has on CPG sales, exploring the intricate relationship between consumer preferences, brand loyalty, and the quest for a greener future.

The Rise of Sustainability in the CPG Industry

As the global sustainability movement gains momentum, CPG companies are under increasing pressure to adopt sustainable practices. 

Governments, NGOs, and consumers alike are demanding change, prompting the industry to redefine its priorities. 

Companies are recognizing that sustainable business models not only mitigate environmental risks but also unlock new market opportunities and foster long-term growth.


What are the factors which companies need to keep in mind while creating sustainable products?

Consumer Demand:

In an era defined by heightened awareness of environmental issues, consumers are wielding their purchasing power to drive change. A seismic shift in consumer preferences is reshaping the CPG landscape, with sustainability becoming a primary consideration. Studies show that a significant portion of consumers actively seek out products that align with their values, placing sustainability at the forefront of their decision-making process. Whether it’s responsibly sourced ingredients, eco-friendly packaging, or ethical supply chains, sustainability is now a fundamental requirement for discerning consumers.

Here are a few examples of specific consumer demands related to sustainability in the CPG industry:

Eco-friendly Packaging: Consumers are increasingly looking for products that come in recyclable, biodegradable, or compostable packaging materials.

They prioritize packaging that reduces waste and minimizes its environmental impact, such as cardboard, paper, or plant-based alternatives.

Organic and Natural Ingredients: There is a growing demand for CPG products made with organic and natural ingredients. Consumers are seeking products that are free from pesticides, genetically modified organisms (GMOs), and artificial additives. 

Ethical Supply Chains: Consumers are concerned about the ethical practices employed in the supply chains of CPG companies.

They demand transparency and accountability, expecting brands to ensure fair labor conditions, responsible sourcing of raw materials, and traceability throughout the production process.

Renewable Energy and Carbon Neutrality: Consumers are increasingly conscious of the environmental footprint of the products they purchase.

They favor CPG companies that prioritize renewable energy sources, carbon neutrality, and initiatives to reduce greenhouse gas emissions throughout their operations.

Water Conservation: Given the global water crisis, consumers are becoming more aware of water usage in the production of CPG products.

They prefer companies that implement water-saving measures, promote efficient water management practices, and support initiatives that address water scarcity and pollution.

Cruelty-Free and Vegan Products: The demand for cruelty-free and vegan CPG products is on the rise. Consumers seek assurance that the products they purchase are not tested on animals and do not contain any animal-derived ingredients. They prioritize companies that adhere to ethical standards in their product development processes.

Social Responsibility: Consumers are increasingly concerned about the social impact of the CPG brands they support. They look for companies that demonstrate social responsibility by giving back to communities, supporting local initiatives, and engaging in philanthropic activities.

Transparency and Labeling: Consumers want clear and accurate information about the sustainability practices of CPG brands. They appreciate transparent labeling that provides details about a product’s environmental impact, certifications, and eco-friendly attributes, enabling them to make informed purchasing decisions.

Competitive Advantage and Brand Loyalty

Sustainability has become a powerful differentiating factor for CPG brands. Companies that champion sustainability and integrate it into their core values enjoy a distinct competitive advantage. 

These brands resonate with consumers on a deeper level, building trust and forging lasting relationships. By embracing transparency, socially responsible practices, and ethical business conduct, forward-thinking CPG companies foster brand loyalty that transcends the mere transactional nature of commerce.

Regulatory Landscape and Industry Initiatives

Government regulations and industry initiatives play a pivotal role in driving sustainability in the CPG sector. Legislative measures and policies incentivize companies to adopt sustainable practices, encouraging responsible manufacturing, waste reduction, and carbon footprint reduction. 

Moreover, industry associations and organizations collaborate to develop guidelines, share best practices, and foster knowledge exchange. Certifications and eco-labels further contribute to consumer trust and help consumers make informed choices.

Overcoming Challenges and Implementing Sustainable Practices

While sustainability presents immense opportunities, it also poses challenges for CPG companies. Economic considerations and cost implications can deter businesses from fully committing to sustainable initiatives. However, innovative strategies and investments in sustainable technologies can yield long-term benefits, optimizing resource usage, reducing waste, and driving operational efficiency. 

From packaging innovations to responsible sourcing and eco-friendly distribution, CPG companies are trailblazing new pathways towards sustainability.

Measuring the Impact: Data and Metrics

To truly understand the impact of sustainability on CPG sales, data and metrics play a crucial role. Key performance indicators (KPIs) allow companies to track progress, measure consumer perception, and assess the effectiveness of sustainability initiatives. 

By analyzing both quantitative and qualitative data, CPG companies gain valuable insights into consumer behavior, enabling them to refine strategies, make informed decisions, and drive continuous improvement.

This is where tools like Explorazor come into place. With a simple “Google-like” search, analysts and users can search on their data. They can perform root cause analysis to find out the hidden opportunities and best practices that they can do.

Future Trends and Opportunities

Looking ahead, the future of sustainability in the CPG industry holds immense promise. Technological advancements, such as biodegradable materials, renewable energy sources, and circular economy principles, offer exciting possibilities. 

The adoption of a circular economy model, where products and materials are reused and repurposed, can revolutionize the way CPG companies operate. The intersection of sustainability, innovation, and financial performance paves the way for a greener, more prosperous future.

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Category Management: A Key Guide for CPG Companies

by Krina Rathodin brand, brand advocacy, brand commitment, Brand Managers, brand preference, Brand Trust, Branding, CPG, CPG, Explorazor

As a decision maker in the Consumer Packaged Goods (CPG) industry, you’re no doubt aware of the importance of staying ahead of the curve. 

With consumers increasingly demanding more personalized and convenient products, and competition from both established brands and up-and-coming disruptors, it can be challenging to keep up. This is where Category Management comes in.

In this blog post, we’ll explore what Category Management is, its benefits, key elements, steps to implementing a successful strategy, and challenges CPG companies may face in doing so.

What is Category Management?

At its core, Category Management is a strategic approach to managing product categories. It involves analyzing and understanding customer needs, assessing the competition and market trends, and developing and executing a plan that maximizes the value of a particular category to the business.

The goal is to increase sales, profit margins, and market share by offering the right products to the right customers at the right time, all while minimizing costs and improving operational efficiency.

Why is Category Management Important in the CPG Industry?

Category Management is particularly important, where margins can be tight and competition is fierce. By adopting a Category Management approach, CPG companies can:

Gain a better understanding of their customers and what they want, which allows them to tailor their product offerings and marketing strategies accordingly.

Increase the effectiveness of their promotions and pricing strategies, leading to increased sales and revenue.

Optimize their product mix and inventory levels, reducing waste and lowering costs.

Identify new growth opportunities by analyzing market trends and identifying unmet customer needs.

Benefits of Category Management

Some of the key benefits of Category Management include:

Increased Sales and Profitability

By analyzing consumer needs and buying behavior, Category Management can help CPG companies create more effective product assortments, promotions, and pricing strategies. This, in turn, can lead to increased sales and profitability.

For example, consider a CPG company that sells laundry detergent.

By using Category Management techniques to analyze customer needs, the company may discover that customers in certain regions prefer products with natural ingredients. By offering a natural detergent option in those regions, the company can increase sales to that particular customer segment.

Improved Operational Efficiency

Category Management can help CPG companies optimize their product mix and inventory levels, reducing waste and improving operational efficiency.

By focusing on the most profitable products and minimizing slow-moving or unprofitable items, companies can reduce costs and improve their bottom line.

Better Understanding of Market Trends and Competition

By analyzing market trends and assessing the competition, Category Management can help CPG companies identify new growth opportunities and stay ahead of the curve. This can include identifying emerging product categories or analyzing consumer behavior to identify new target markets.


In the next section, we’ll take a closer look at the key elements of a successful Category Management strategy.

Understanding the customer and their needs

One of the key elements of Category Management is understanding the needs and preferences of your target customers. This includes identifying the products and services that your customers are looking for, as well as the features and benefits that they value most. 

By understanding your customers’ needs, you can create more targeted and effective Category Management strategies that address those needs and differentiate your products from your competitors’.

Assessing the competition and market trends 

Another important element of Category Management is assessing the competitive landscape and market trends.

This involves monitoring the performance of your competitors, understanding their strategies, and identifying the strengths and weaknesses of their products and services. You should also stay up-to-date on the latest market trends and changes in consumer behavior that could impact your Category Management strategies.

Developing and executing a Category Management plan

Once you have a solid understanding of your customers and competition, you can develop and execute a Category Management plan. 

This plan should outline your Category Management goals and objectives, the strategies you will use to achieve those goals, and the tactics you will use to implement those strategies. 

It should also include a detailed timeline and budget, as well as metrics for measuring the success of your Category Management efforts.

Steps/Guide to Implement a Category Management Strategy

Conducting a Category Assessment: 

Before you can develop a Category Management strategy, you need to conduct a thorough Category Assessment. This involves analyzing the performance of your products and services, identifying any gaps in your product portfolio, and determining the key drivers of customer behavior in your category.

Defining Category Roles and Strategies: 

Based on your Category Assessment, you can define the roles and strategies for each of your product categories.

This involves determining which products should be prioritized, how to position those products to maximize sales, and which promotional tactics to use to drive customer engagement.

Implementing Category Tactics: 

Once you have defined your Category Roles and Strategies, you can implement specific tactics to achieve your goals.

This may include launching new products, optimizing pricing and promotions, and investing in marketing and advertising campaigns.

Evaluating and Adjusting Category Performance: 

Finally, it is important to regularly evaluate the performance of your Category Management strategy and make adjustments as needed.

This may involve analyzing sales data, conducting customer surveys, and monitoring market trends to ensure that your strategy remains relevant and effective.

Challenges of Category Management

While Category Management can offer significant benefits to CPG companies, there are also a number of challenges that must be addressed. Some common obstacles that companies face when implementing Category Management strategies include:

Data management challenges: With the increasing volume and complexity of data available to CPG companies, it can be difficult to effectively manage and analyze that data to inform Category Management strategies.

Siloed organizational structures:

Category Management requires collaboration and coordination across multiple departments and functions within a company. However, siloed organizational structures can make it difficult to achieve that collaboration and coordination.

Lack of resources: 

Implementing effective Category Management strategies requires significant resources, including time, money, and personnel. Smaller CPG companies may struggle to allocate those resources effectively.

Resistance to change: 

Finally, some employees may be resistant to changes in Category Management strategies, particularly if they have been successful with existing strategies in the past.

To overcome these challenges, CPG companies should focus on building a strong data management infrastructure, fostering a culture of collaboration and innovation, and investing in the resources and training needed to implement effective Category Management strategies.

How Explorazor helps Fortune 500 Companies with Category Management.

Explorazor is a data exploration tool that helps CPG companies optimize their categories by providing real-time data-driven insights. Here’s how:

Combining all datasets: We combine all datasets, including Nielsen, Kantar, Primary Sales, Secondary Sales, Media, and more, into one harmonized dataset into a single source of truth, eliminating the need to run around data custodians or extract pivots from multiple excel files.

AI engine: An AI engine, trained on data of Fortune 500 CPG companies, sends alerts and suggests action items. This helps brand managers make informed decisions based on real-time data.

Natural language processing: Once brand managers look at the performance, they can ask Explorazor questions in simple language, without troubling the insights team. This makes data-driven insights accessible to everyone in the organization.

Drill down: Losing market share? Brand managers can drill down across dimensions to figure out if the problem is in distribution or trade promotion and what exactly is the problem. This helps them identify the root cause of issues and take corrective action.

In conclusion, Category Management is a data-driven process that involves managing product categories to increase sales and profits. 

By using data-driven insights, CPG companies can optimize their categories and gain a competitive advantage. 

Explorazor’s data exploration tool is designed to help brand managers achieve this goal by providing real-time data-driven insights. With Explorazor, CPG companies can optimize their categories, improve customer satisfaction, and increase sales and profits.

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The Power of Influencer Marketing in CPG Industry

by Krina Rathodin Brand Managers, Branding

In today’s digital age, social media has become a powerful tool for businesses to connect with their target audience.

With the rise of social media influencers, influencer marketing has become an essential part of marketing strategies in the CPG (Consumer Packaged Goods) industry.

In this blog post, we will explore the benefits of influencer marketing in the CPG industry, successful influencer marketing campaigns, key strategies for influencer marketing, as well as challenges and risks associated with this type of marketing.

What is influencer marketing?

Influencers have a large following on social media platforms and can help brands reach a wider audience.

By partnering with influencers who have a similar target audience, CPG companies can tap into their followers and gain new customers.

According to a survey by Influencer Marketing Hub, 63% of consumers trust influencer recommendations more than brand advertisements.

This shows that influencers have a strong influence on consumer purchasing decisions.

By partnering with the right influencers, CPG companies can improve their brand reputation and credibility among consumers.

In addition, influencer marketing has higher engagement rates compared to other marketing strategies. Influencers have built a loyal following of engaged fans who trust their recommendations.

This means that sponsored content from influencers is more likely to be seen and engaged with by their followers, resulting in higher engagement rates for the brand.

Successful Influencer Marketing Campaigns in the CPG Industry

There are many successful influencer marketing campaigns in the CPG industry that have helped brands reach a wider audience and increase sales.

One example is the partnership between beauty brand Glossier and beauty influencer Emily Weiss.

Weiss founded Glossier and used her social media presence to promote the brand. Today, Glossier has a loyal following and has become a household name in the beauty industry.

Another example is the partnership between food brand HelloFresh and food blogger Damn Delicious.

HelloFresh partnered with Damn Delicious to create sponsored content featuring their meal kits.

This helped HelloFresh reach a wider audience and increase sales, while also providing Damn Delicious with a new source of income.

Finally, wellness brand Nike partnered with fitness influencer Kayla Itsines to promote their workout gear.

Kayla created sponsored content featuring Nike products and shared it with her followers. This helped Nike reach a new audience and improve their brand reputation among fitness enthusiasts.

How to implement a successful influencer marketing strategy?

To implement a successful influencer marketing campaign in the CPG industry, it is important to follow best practices.

One key strategy is to set clear goals for the campaign. This could be to increase brand awareness, improve engagement rates, or drive sales.

By setting clear goals, brands can measure the success of the campaign and adjust their strategy accordingly.

Another important strategy is to identify the right influencers for the campaign. 

Brands should partner with influencers who have a similar target audience and share similar values. It is also important to look for influencers who have a high engagement rate and a loyal following.

Creating engaging content is crucial for a successful influencer marketing campaign. Brands should collaborate with influencers to produce content that resonates with their followers and showcases the brand in a positive light. Additionally, many websites are now using text-to-speech generated videos to reach a wider audience by adopting AI technology.  

This could be through sponsored posts, videos, or social media takeovers.

Finally, measuring the success of the campaign is essential to ensure its effectiveness.

Brands should track metrics such as engagement rates, website traffic, and sales to determine the ROI of the campaign.

This information can be used to improve future campaigns and adjust the strategy accordingly.

Things which we need to keep in mind during Influencer Marketing

While influencer marketing has many benefits, there are also some challenges and risks associated with this type of marketing.

One challenge is the cost of partnering with influencers. Popular influencers often charge high fees for sponsored content, which can be a significant expense for brands.

Another challenge is ensuring that sponsored content is disclosed properly.

In the US, the FTC (Federal Trade Commission) requires influencers to disclose their partnerships with brands in their content.

Failure to disclose partnerships can result in fines and damage to the brand’s reputation.

There is also the risk of negative publicity if an influencer’s behavior or actions come under scrutiny. Brands need to ensure that the influencers they partner with have a clean reputation and align with the brand’s values and mission.

Finally, measuring the success of influencer marketing campaigns can be a challenge. While engagement rates and website traffic can be tracked, it can be difficult to determine the actual impact on sales and ROI.

This is where Explorazor comes in.

As a data exploration tool, Explorazor helps brand managers harmonize their different datasets and ask important questions to uncover insights that can drive growth and improve their influencer marketing campaigns.

With Explorazor, brand managers can easily deep dive into their data and identify root causes of issues, making it easier to optimize campaigns and improve ROI.

To see how Explorazor can help you unlock valuable insights from your data, request a demo today.

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Market Basket Analysis: A Guide to Understanding Consumer Behavior in the CPG Industry

by Krina Rathodin ACV

Understanding consumer behavior is crucial to make effective business decisions for any CPG company. Market Basket Analysis (MBA) is a widely used technique in the CPG industry to analyze consumer purchasing patterns and gain insights into their behavior. In this blog, we will explain what MBA is, its importance in the CPG industry, and how it can be used to improve business decisions.

What is Market Basket Analysis?

Market Basket Analysis is a technique that analyzes customer purchase behavior to identify relationships between products. It is a data mining method that helps identify which products are frequently purchased together and which are not. MBA can reveal correlations between products that may not be immediately apparent, providing insights into consumer behavior and preferences.

The basic methodology of MBA involves analyzing transactional data to identify frequently occurring product combinations. The analysis is based on the concept of Association Rules, which identifies the co-occurrence of items in transactions. MBA utilizes three important metrics: Support, Confidence, and Lift.

Support measures how frequently an itemset appears in the transactional data. It is the proportion of transactions containing a particular itemset.

Confidence measures the likelihood that an item B is purchased when item A is purchased. It is the ratio of transactions containing both item A and B to the number of transactions containing item A.

Lift measures the strength of association between items. It is the ratio of the observed support to the expected support if the items were independent.

Why is Market Basket Analysis important in the CPG industry?

MBA is essential in the CPG industry as it can provide valuable insights into consumer behavior, preferences, and buying patterns. By analyzing consumer behavior, companies can identify which products are often purchased together and which are not.

This information can help companies create more effective marketing strategies, optimize product placement, and improve product bundling. For example, if a CPG company finds that customers who buy chips are likely to buy soda as well, they can place these two products next to each other to increase sales.

Moreover, it can help CPG companies in making pricing decisions. By analyzing customer buying patterns, companies can identify which products are price-sensitive and which are not. They can then optimize pricing to increase sales and maximize profits.

For example, if a CPG company finds that customers who buy bread are likely to buy milk as well, they can offer discounts on milk to increase its sales and maximize profits.

Examples of Market Basket Analysis in CPG industry

Market Basket Analysis has several applications in the CPG industry. Here are a few examples:

Amazon.com: Amazon.com uses MBA to identify which products are often purchased together and recommends products based on the customer’s purchase history. This helps Amazon increase sales and improve customer satisfaction.

Tesco: Tesco, a UK-based supermarket chain, uses MBA to improve store layout and optimize product placement. By analyzing customer purchase data, Tesco can identify which products are often purchased together and place them close to each other to increase sales.

Coca-Cola: Coca-Cola used MBA to identify which products are often purchased together and launched a new product line based on the analysis. Coca-Cola found that customers who bought coke were likely to buy popcorn, so they launched a new product line that combined coke and popcorn.

Advantages and Limitations

MBA has several advantages that make it an essential tool in the CPG industry. It is easy to use, provides valuable insights into consumer behavior, and can help improve business decisions. However, MBA has some limitations that need to be considered. The results of MBA are based on transactional data, which may not be representative of the entire customer base.

MBA also does not provide insights into why customers purchase certain products together, which can limit the usefulness of the analysis.

How to perform Market Basket Analysis

Performing MBA involves several steps, including data preparation, data analysis, and interpretation of the results. Here are some factors to consider while performing MBA:

Choose the right data: MBA is based on transactional data, so it is essential to choose the right data source. The data should be clean, reliable, and representative of the entire customer base.

Define the scope: Determine the scope of the analysis and the products or product categories to be analyzed.

Set the metrics: Set the metrics to be used in the analysis, such as support, confidence, and lift.

Choose the tool: There are several MBA tools available in the market, such as Excel, SPSS, and R. Choose the tool that best fits your needs and expertise.

Interpret the results: Interpret the results of the analysis and draw insights from the data.

Market Basket Analysis is a powerful technique that can help CPG companies gain valuable insights into consumer behavior and preferences. However, performing MBA can be a complex and time-consuming process that requires expertise in data analysis. This is where Explorazor comes in.

Explorazor is a data exploration tool that can help CPG enterprises quickly and easily perform MBA and other types of data analysis. With Explorazor, you can ask a query in seconds and get insights on your data, without the need for extensive data science knowledge.

Moreover, Explorazor can also perform root cause analysis to help you identify the pain points in your data and take corrective actions to improve your business operations. By using Explorazor, CPG companies can gain a competitive advantage by making data-driven decisions based on reliable insights.

Try Explorazor today and discover how it can help you gain valuable insights into your data.
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5 Ways Artificial Intelligence (AI) like ChatGPT is Revolutionizing CPG Industry

by Krina Rathodin Brand Managers, data consolidation, Data Exploration, data exploration tool, Uncategorized

Artificial intelligence (AI) in the CPG industry has turned out to be a game-changing technology for businesses and enterprises.

With its ability to analyze large amounts of data, identify patterns and make predictions, AI is revolutionizing the way CPG companies operate and serve their customers.

The impact of AI can be seen across the entire CPG value chain, from production to marketing, supply chain management, and customer service.

This blog post will examine the various ways in which AI is transforming the CPG industry, including its benefits, challenges, and the future outlook.

By the end of this article, you will have a better understanding of the role of AI in the CPG industry and how it can help your business stay ahead of the curve.

Predictive Analytics and AI in CPG Industry

The use of artificial intelligence (AI) in predictive analytics is transforming the Consumer Packaged Goods (CPG) industry by providing insights that help companies make data-driven decisions. Predictive analytics is a process that uses data, machine learning, and statistical algorithms to make predictions about future outcomes based on historical data.

The benefits of predictive analytics include increased efficiency, cost savings, and improved decision-making. By using AI to analyze vast amounts of data from multiple sources, CPG companies can identify patterns and trends that would be difficult or impossible to detect manually.

Benefits of Using AI in Predictive Analytics in the CPG Industry

One of the primary benefits of using AI in predictive analytics is the ability to improve accuracy. With traditional methods, predicting outcomes based on historical data can be challenging due to the complexity of the data and the need to analyze multiple variables.

However, with AI, it is possible to analyze vast amounts of data quickly and accurately, allowing companies to make predictions with greater confidence.

Successful Implementations of AI in Predictive Analytics in the CPG Industry

One of the most successful implementations of predictive analytics using AI in the CPG industry is the use of AI-powered algorithms to predict demand for certain products during peak seasons or promotional periods.

By analyzing historical sales data and external factors such as weather patterns, these algorithms can accurately forecast demand and optimize inventory levels to ensure that products are available when customers want them.

For example, a CPG company might use AI to predict the demand for a particular product during a specific promotional period.

Based on the forecasted demand, the company can adjust production schedules and inventory levels to ensure that they have sufficient stock to meet customer demand. This can help to reduce waste and improve efficiency, as well as increase customer satisfaction by ensuring that products are always available when customers want them.

Another benefit of using AI in predictive analytics is the ability to identify patterns and trends that would be difficult or impossible to detect manually. For example, a CPG company could use AI to analyze social media data and identify emerging trends in consumer preferences.

By analyzing data from multiple sources, including social media, online reviews, and customer feedback, companies can gain a more comprehensive understanding of consumer behavior and preferences. This information can then be used to develop new products and marketing campaigns that better align with customer needs.

Personalization and Targeted Marketing with AI in CPG Industry

Personalization and targeted marketing are becoming increasingly important in the Consumer Packaged Goods (CPG) industry. With so many products available on the market, consumers are looking for brands that cater to their specific needs and preferences. This is where personalization comes in.

Why is Personalization important in CPG marketing

By personalizing their offerings, brands can create a unique customer experience that is tailored to each individual’s preferences. This can lead to increased customer loyalty, higher engagement, and ultimately, increased sales.

Benefits of Using AI in Personalization and Targeted Marketing

One of the ways that brands are achieving personalization and targeted marketing is through the use of artificial intelligence (AI). AI can help brands analyze vast amounts of customer data to identify patterns and trends that can inform targeted marketing campaigns.

For example, AI-powered algorithms can analyze customer purchase histories to identify which products they are most likely to buy in the future. Brands can then use this information to create targeted marketing campaigns that highlight these products and offer personalized promotions and discounts.

How are CPG companies adopting Personalization using AI in Marketing?

There are many successful examples of AI-powered personalization and targeted marketing in the CPG industry.

One such example is Coca-Cola’s “Freestyle” vending machines. These machines use AI-powered algorithms to offer customers personalized drink options based on their previous purchases. The machines use a touchscreen interface that allows customers to select from hundreds of different drink combinations, and they even offer suggestions based on the customer’s past choices.

Another example of AI-powered personalization is Amazon’s recommendation engine. By analyzing customer purchase histories and browsing behavior, Amazon is able to suggest products that are highly relevant to each individual customer. This not only improves the customer experience, but it also leads to increased sales for Amazon.

By using AI-powered algorithms to analyze customer data and identify patterns and trends, brands can create personalized customer experiences that lead to increased customer loyalty and sales.

AI-powered Supply Chain Management

Supply chain management is a critical function in the CPG industry. Ensuring that products are delivered to customers on time and in the right quantities is essential to maintaining customer satisfaction and maximizing profitability.

However, managing a complex supply chain can be challenging, particularly when dealing with large volumes of data and multiple stakeholders.

How AI helps improve Supply Chain Management for CPG Companies

By using AI-powered algorithms to analyze data from across the supply chain, brands can identify areas where efficiencies can be gained and costs can be reduced.

For example, AI can be used to optimize inventory levels, reducing the risk of stockouts and excess inventory. It can also be used to optimize transportation routes, reducing the time and cost of shipping products to customers.

Successful Implementations of AI in Supply Chain Management

One successful implementation of AI-powered supply chain optimization is PepsiCo’s “Smart Scan” program. This program uses AI to analyze data from across the supply chain, including sales data, inventory levels, and production schedules.

By analyzing this data, PepsiCo is able to identify areas where efficiencies can be gained, such as optimizing production schedules and reducing transportation costs. As a result, PepsiCo has been able to reduce its operational costs by millions of dollars each year.

Another example of AI-powered supply chain optimization is Nestle’s “WMS Vision” program. This program uses AI to optimize warehouse operations, including inventory management and order fulfillment. 

By analyzing data from across the warehouse, including product location and movement, Nestle is able to optimize its warehouse operations and reduce the time and cost of fulfilling orders.

By using AI-powered algorithms to analyze data from across the supply chain, brands can identify areas where efficiencies can be gained and costs can be reduced.

Quality Control and Assurance using AI

Quality control and assurance are essential aspects of the CPG industry. Consumers expect products that are safe, reliable, and consistent, and brands that fail to meet these expectations risk damaging their reputation and losing customers.

How can AI play a role in Quality Control and Assurance

This is where AI can be particularly helpful. By using AI-powered algorithms to analyze data from across the production process, brands can identify potential quality issues before they become major problems.

For example, AI can be used to monitor the production process in real-time, identifying any anomalies or deviations from the norm that could indicate a quality issue. AI can also be used to analyze customer feedback, identifying common issues or complaints that could indicate a quality problem.

Corporate Usage of AI in Quality Control and Assurance

One successful implementation of AI-powered quality control and assurance is Johnson & Johnson’s “CaringCrowd” platform. This platform uses AI to analyze customer feedback from across the company’s various product lines.

By analyzing this feedback, Johnson & Johnson is able to identify potential quality issues and take corrective action before they become major problems.

Another example of AI-powered quality control and assurance is Coca-Cola’s “QualityWise” program. This program uses AI to analyze data from across the production process, including ingredients, production methods, and packaging.

By analyzing this data, Coca-Cola is able to identify potential quality issues and take corrective action before the products are shipped to customers.

By using AI-powered algorithms to analyze data from across the production process and customer feedback, brands can identify potential quality issues and take corrective action before they become major problems.

AI-powered Customer Service and Support

Customer service and support are crucial aspects of the CPG industry. Consumers expect prompt and helpful support when they have questions or concerns about products, and brands that fail to meet these expectations risk losing customers and damaging their reputation.

AI to analyze customer inquiries and support requests

By using AI-powered algorithms to analyze customer inquiries and support requests, brands can provide more efficient and personalized support to their customers.

For example, AI can be used to provide automated responses to common inquiries, reducing the workload on customer support teams and allowing them to focus on more complex issues.

AI can also be used to analyze customer sentiment and feedback, identifying areas where products and support services can be improved.

Examples of AI in CPG industries for customer service and support.

One successful implementation of AI-powered customer service and support is Unilever’s “U-Studio” program. This program uses AI to provide personalized support to customers across the company’s various product lines.

By analyzing customer inquiries and support requests, U-Studio is able to provide more efficient and personalized support to customers, reducing the workload on customer support teams and improving overall customer satisfaction.

Another example of AI-powered customer service and support is Procter & Gamble’s “P&G Everyday” program. This program uses AI to provide personalized product recommendations and support to customers based on their individual preferences and needs. By analyzing customer data and behavior, P&G Everyday is able to provide more personalized and effective support to customers, improving overall customer satisfaction and loyalty.

How successful has the Adoption of AI been in the CPG industry?

To sum it up, the CPG industry is going through a significant transformation, and AI is playing a critical role in this evolution. With AI-powered solutions, CPG companies can optimize their supply chain, improve quality control and assurance, and deliver personalized marketing and customer support.

However, to achieve these advancements, businesses need a robust data exploration tool like Explorazor.

By providing quick and easy access to data insights, Explorazor empowers businesses to make informed decisions that can drive growth and customer satisfaction.

As the CPG industry continues to evolve, Explorazor will remain an essential tool for businesses that want to leverage the power of AI and stay ahead of the competition.

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A Pre-Built Brand Overview Dashboard and RCA Across Datasets – Explorazor Product Updates

by Krina Rathodin Explorazor product updates, Marketing

Last month, we discussed dynamic KPIs and saving filters as groups, along with various updates to our super Root-Cause Analysis (RCA) feature. In Explorazor’s latest update, we’ll take a look at the newly introduced features Brand Overview dashboard, ability to conduct RCA across datasets, and 5 other helpful updates.

Let’s explore:

1. Brand Overview Dashboard

Any manager wants to have a quick and reliable overview of his important KPIs. Time is of the essence, as well as accuracy.

Explorazor now provides a pre-built Brand Overview Dashboard on request – for doing just that.

This dashboard provides key insights on important KPIs, patterns, and trends – all in simple language.

The Brand Overview Dashboard can help Brand Managers:

a. Benchmark brand performance

Get quick answers to your questions such as “How am I performing against my competition?” and “What does my performance look like in comparison to the category?”

b. Diagnose issues quickly

Looking at the trends of the data points within the dashboard, managers can drill down further to identify areas of concern, or potential areas of improvement, and zone in on that to find out more.

c. Save time

As all important KPIs are at one place, time to decisions is accelerated

2. RCA Across Datasets

Root-cause analysis is nothing but identifying where exactly a particular problem lies. Currently, conducting this analysis on multiple datasets can be time-consuming and possibly frustrating, as it requires constantly switching between them.

RCA across datasets eliminates the need to switch between multiple datasets where Explorazor takes care of transitioning between the datasets, as required, behind the scenes without you having to bother about it.

If you want to understand root cause analysis in Explorazor in detail, we recommend you read the drill-down via point-and-click article on our blog.

Other Updates: Explorazor March 2023

a. Table Slicer

You might have certain pre-set criteria based on which you would prefer your data to be displayed. Table Slicer allows you to do exactly that – insert your criteria and choose which sections of the data you want to view specifically.

b. Date Range Filter

Explore the events between any two time periods of your choice by applying the date range filter. This is another convenient feature that lets you be more precise in your searches

c. Group Data Sharing

Now you can share data with multiple people in your group, at the same time. Group Data Sharing helps managers collaborate better

d. Dynamic Period Filters

Dynamic Period Filters such as ‘Last 2 months’, ‘This Year’, etc. are auto-update filters where the values keep updating as the data updates

For example: If we pin a response created by using dynamic filter “this month” in the query, the values will be updated to the latest month every time the data refreshes.

e. Improvements to Group Filter

The option to manage all your group filters is now available on the left panel of your Explorazor screen.

Interested in having a look at all of Explorazor’s features live? If yes, then take an interactive product tour of Explorazor today!

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Retaining Sales Talent is Becoming a Challenge. Here’s What You Can Do About It

by adminin sales, sales talent

In a November 2022 report, Gartner’s Chief of Research Craig Riley raised a pressing issue – sales talent attrition is on the rise, and retaining them will be harder than ever in 2023. His comments were based on an August 2022 survey conducted amongst 900+ B2B buyers, which concluded that a staggering 89% felt ‘burned out from work’. It’s not just the USA. The Great Resignation debate is live and raging across the world.

Forbes echoed the same sentiment as Gartner, recognizing the growing talent crisis in sales and cautioning managers of the various harms that come along with high attrition rates, one of them being damaged customer relationships. Every sales manager reading this bit will resonate with the word ‘catastrophic.’ Forbes research also indicated that more than half of employees feel overworked by their employers.

OF SELLER DRAGS AND ‘COGS IN THE MACHINE’

One of the key causes that leads sales talent to this level of exhaustion is termed ‘seller drag’, a phenomenon that causes employees to procrastinate on work and deliver lower output. One of the roots of this ‘seller drag’ lies in employees having to undertake non-value-adding administrative tasks, and a consistent emotion of being just a ‘cog in the machine.’ 

To tackle this problem, many organizations opt for tools and/or platforms that help departments achieve their objectives. We find advocacy for this approach from Stephen Diorio (Executive Director of the Revenue Enablement Institute and an author, among other things) who discussed the need to reconfigure the daily workflows of sales professionals by simplifying their technology stack. 

However, there’s just a slight problem with this strategy:

No one is particularly interested in using these technology stacks. 

SIMPLICITY – THE ROOT OF TECHNOLOGY STACK ADOPTION

Greg Munster, Global Sales Operations Director of Canonical, quips 

“After years of supporting sellers with sales enablement systems at IBM, Red Hat and Lenovo, I’ve learned the key differentiator – and driver of value – always comes down to simplicity, intuitiveness, and user adoption in the eyes of the sales user of process or tool.”

Technology stacks carefully curated by Insights Teams, for example, for their sales managers, are perceived as ‘complex’ and capable of only compounding the daily data-related struggles of these sales champions. As such, they steer clear of such tools right from the get-go – our own research at vPhrase Analytics found that out, when we interviewed senior Brand Managers from globally-renowned firms such as HUL, Marico, Godrej, and others. While tools were available in abundance, their usage was next to none.

RETAINING SALES TALENT – WHAT YOU CAN DO ABOUT IT

If you’ve kept up till now, you would’ve noticed we 

  • Recognized a very pressing issue in the form of higher sales attrition in 2023 
  • Pinpointed the tiring daily workflows of sales professionals as a core contributing cause to the attrition, and 
  • Spoke about the well-intended approach of concerned departments like Insights Teams in building technology stacks, but the approach being ill-received due to the stacks being too complex to adopt and use

It’s a focused discussion we’re having. Let’s continue:

A SIMPLE AND EFFECTIVE TOOL FOR YOUR TECHNOLOGY STACK

We dove into the heart of the matter and found the ideal solution: consolidating data from multiple Excel files. Sales Managers have multiple datasets at their disposal and have to constantly shift between and examine multiple Excel files to extract a data pivot or test out a hypothesis. They face multiple challenges en route; access to some files is missing or delayed, standardizing and updating these files is a consistent, time-consuming process.

Sales Managers revert to Insights Teams to help them with ad-hoc analysis. Help does arrive, but often late, rendering the insights to a great extent, valueless. Can’t blame the Insights Team, either. They’ve got tasks other than conducting ad-hoc analysis for Sales.

We’ve developed Explorazor, a simple data exploration tool that integrates multiple datasets into 1 standardized dataset and provides unified data access to users. Users query the consolidated dataset via a simple search interface and extract instant data pivots. Explorazor is powered with double-click, or point-and-click drill-down for instant root cause analysis. The idea is to ensure on-time and independent data analysis for managers, and these features prove pivotal in helping them identify market opportunities and internal and external issues. Explorazor contributes to revenue growth and employee satisfaction simultaneously.

There are many features we haven’t talked about here, like the ability to download desired data pivots as CSV files and take them to Excel, or the super-clean user interface that makes managers want to work on Explorazor. You can also read the blog we’ve written on how Explorazor differs from Power BI.

CONCLUSION

Retain sales talent by easing their day-to-day challenges. Explorazor can help simplify the daily data exploration activities of Sales Managers, and it’s a very simple tool to understand and adopt, and effective to boot. While it’s not the only tool you may ever need, it certainly is the perfect complement to Excel, and therefore a must-have tool in your tech stack.

Why not take a quick look at Explorazor? Here’s an introductory video to get you started, and you can schedule a call with our solutions consultant for the full demo. 

Take an Interactive Product Tour of Explorazor!

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Interested in Becoming a Brand Manager? Know Your Kantar Data!

by adminin Brand Managers, Data Exploration, Explorazor, Kantar, Uncategorized

As part of our 3-blog series to educate professionals wanting to become Brand Managers, we’ll be introducing you to some columns within the Kantar data that Brand Managers receive, and interact with, on a regular basis. 

The other two blogs in the series are:

For now, let’s look at some columns in the Kantar data and their brief explanations. We’ll be continuously updating this blog as well as the Nielsen blog over time, so be sure to bookmark and check them out once in a while!

 Let’s begin:

DATASETS THAT BRAND MANAGERS DEAL WITH – KANTAR DATA

  1. Households  

Households (HH) indicates the total number of households in the target market. This informs the Brand Manager of the total market potential that his/her brand can ideally target and reach

  1. Penetration 

Household Penetration is the number of households in which a brand is being used. Large brands such as Coca-Cola and Maggi rely heavily on increasing the HH penetration of their products. For this, they develop increasingly robust logistical networks, especially in high-potential countries like India where the majority of the population resides in rural areas. This data point also helps decide the allocation of billions of dollars of investment into advertising and promotions.

  1. Volume 

Vol, or Volume, is the total sales made. Volume could be sliced and analyzed, for example, in a particular time period or for a particular geography. This, of course, is one of the cornerstone data columns needed for progress – What are my sales figures? Which were my highest-selling areas? Which areas showed degrowth? are the fundamental questions that every Brand Manager starts with

EXPLORAZOR – FOR OBTAINING THE FUNDAMENTAL ANSWERS, FAST

Our data exploration tool, Explorazor, is built solely for Brand Managers to obtain answers from data at accelerated speeds. How? Brand Managers view a single, combined dataset on Explorazor, which they query using simple keywords, and obtain data pivots instantly. No switching between files at all.

You can view the 3 Types of Analysis Brand Managers can Perform Super-Easily on Explorazor.

BMs are also able to drill down and drill across to arrive at event root-cause, conduct ad-hoc analysis (independently, without support from Insights teams), and test out more hypotheses than ever before. There’s so much more to Explorazor as it plays its part in complementing Excel perfectly, so users do not have to leave Excel entirely, yet do away with some of Excel’s ‘rougher edges’, if we might call them that.

  1. Volume share

Volume share is the part of the market your brand has captured as against the total category share. This is a broad metric that lets a Brand Manager understand where s/he stands with respect to competition. Necessary remedial/preventive steps can then be taken to overtake the competition and increase the volume share, be it hiring more on-field forces or a from a completely different angle, say, increasing media spends to raise brand awareness in specific regions.

Explorazor again comes in handy when it allows Brand Managers to get all their queries answered at a single place, in addition to drill-down into a particular metric via simple clicks. 

  1. Avg Trip Size

The average trip size is understood as the average number of units bought by a consumer at one time/ in a single go. It is also understood as the average purchase weight per transaction. Since packet weights vary, a Brand Manager can potentially decide on a standardized purchase weight, which can be translated into how many packets of that particular weight were sold to a shopper during his/her visit.

With data on average trip size, a Brand Manager understands the distribution and stocking requirements of a particular store.

SIMPLIFYING DATA ANALYSIS – AND NOT JUST KANTAR

We hope these 5 points gave you a glimpse into the areas that Kantar data focuses on, and how Brand Managers can use these data points to elevate all aspects of their brand, like goodwill and sales. We want to further elaborate on how Explorazor can help Brand Managers achieve all of this in an extremely simplified manner.  

Explorazor holds all the datasets that a Brand Manager works on, and showcases them as a single integrated, standardized dataset on its interface. This includes all the separate Kantar columns we discussed, Nielsen data columns, IQVIA (in case of pharma), primary sales, secondary sales, market research, etc. 

Brand Managers pose queries, and data pivots are generated instantly. This speeds up the data analysis process, allowing Brand Managers to spend more time on strategizing and contemplation instead of conducting the manual labor of standardizing columns and querying multiple data sheets for a single insight. The data pivot on Explorazor can also be customized to produce visually appealing charts and graphs, and exported as CSV as needed.

We are on a quest to help Brand Managers ease their day-to-day data exploration process, relieve them of unwanted manual work and over-dependence on BI/Insights teams, enable them to conduct ad-hoc analysis and hypothesis testing at speed, and ultimately help them arrive at quality, target-smashing decisions.


Also understand how Explorazor differs from Power BI.

Take an Interactive Product Tour of Explorazor!

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3 Types of Data Analysis Brand Managers can Perform Super-Easily on Explorazor

by adminin Brand Managers, data analysis, Data Exploration, Excel, Explorazor, querying

Explorazor is a data exploration tool designed specifically to help brand managers in their day-to-day data analysis and exploration, which they’d otherwise do on Excel.

The Explorazor platform provides Brand Managers with a single view of all their data. With data analysis made easy and fast through this single-view dataset, Brand Managers are also able to accelerate the speed of their hypothesis testing. All they have to do is use a simple search interface to get the answers they are looking for, in the form of relevant pivots/charts.

Explorazor - Making data analysis easy for Brand Managers!

Here are the 3 Types of Data Analysis Brand Managers can Perform Super-Easily on Explorazor:

  1. Category vs Your Brand 

Let’s say you, as a Brand Manager, need to look at your brand’s performance in relation to the performance of your brand category. This is helpful in tracking the market, detecting consumer trends, and comparing how relatively strong a market is, with its overall sales. 

Let’s look at an example of how Explorazor makes it easy and quick to search your data and get answers instantly.

Above is how a search query and the result look on Explorazor. You can see the keyword-based query conducted which, if translated to an interrogative sentence, reads as ‘What is the Market Sales Value of our brand Alpha Supplement and how has it performed with respect to its Category, on a quarterly basis?’ 

  1. Competition vs Your Brand 

The next type of data analysis is Competition vs Your Brand. Once you’ve identified your competitors, consistently measuring their performance helps you benchmark your own growth vs. theirs. 

Further to querying, Explorazor allows you to pin your answers to the project dashboard, which means that all pinned answers are updated every time the data refreshes. The need to re-query the same thing is eliminated.

Let’s look at the ‘Competition vs Your Brand’ query here. As you can see, there are more inputs in this search query than in the last one. The query reads as ‘Comparing the average Market Sales Value, Net Spends on TV, average Share amongst Handlers of our brand Alpha Supplement as against other brands, for the last quarter.’

Using the customization options above, one can also convert the table into a chart of their choice. 

One can easily pin the query using the available icon on the top right, and add the particular query to the dashboard.

  1. Compare Primary Sales, Secondary Sales and Market Sales

To compare and analyze primary, secondary, and market sales values in Excel requires separate access to 3 different datasets. The results have to be then collated to get a complete understanding. 

Since all datasets are connected in Explorazor, you can simply access the single integrated dataset and obtain answers swiftly, with a single query.

Here we are comparing the average Market Share Value, Net Spends on TV, average Share amongst Handlers of our brand Alpha Supplement as against competitor brands, for the last quarter.

The default tabular format provides a clean and familiar look for Brand Managers to analyze the data, and is downloadable as a CSV file too, in case it needs to be transported to Excel for further exploration.

Directly Proportional – Quality & Speed 

The quality of decision-making is directly proportional to the speed and convenience of the hypotheses testing process. Systematically investigating the validity and reliability of multiple areas of interest simultaneously serves as a solid foundation for incremental improvements that may have otherwise not been possible. De-cluttering a Brand Manager’s mind space by providing an integrated data view and freeing up their time through data cuts at their fingertips will work wonders for both the brand and the manager – and that is what Explorazor is all about. 

Take an interactive Product tour of Explorazor.

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Modeling Basic FMCG KPIs in Excel

by adminin Brand Managers, Data Exploration, Excel, Explorazor, FMCG

This blog will introduce you to how Brand Managers model basic FMCG KPIs in Excel.

There are a lot of articles that touch upon the life of a Brand Manager and the various responsibilities they shoulder. Here we will put a microscope on just one of the numerous calculations that Brand Managers undertake, and learn how they find business improvement areas through data analysis.

If you are a Brand Manager, we recommend you skip to the end of this blog to ‘Basic FMCG Modeling Made Easy’ or read ‘Complementing Excel – How Brand Managers can Simplify Data Exploration and Analysis’.

Let us understand how to obtain Gross Margin, Net Margin, and Operational Profit. Arriving at these numbers helps Brand Managers analyze where they are losing their margin – is it at the production level, is it the cost of sales and marketing, or is it the head office costs? Brand Managers thus have a sense of direction to initiate further data exploration and make optimal, data-driven decisions.

Let’s begin:

Part 1 – Obtaining Net Margin

  1. Unit Gross Margin 

Unit Gross Margin Depends on two things – 

  1. The average price we are getting from the middlemen, or if we are directly selling to the customers, from them 
  2. Subtracting the unit production cost from this average price 

So Unit Gross Margin = Avg product price (say Rs. 70) minus its production cost (say Rs. 40) = Rs. 30

Note: The unit production cost is again dependent on two things – 

a. The total fixed cost divided by the total quantity produced, plus 

b. The unit variable cost

There are further sub-calculations in each component. For example, Total Fixed Cost (FC) includes salaries to be paid, which is typically generated as: taking the number of full-time employees or full-time equivalents (FTE), setting an average salary per FTE, and assuming some social securities as a percentage of the salary. The salary excludes the bonus earned by the employee.

  1. Gross Margin 

Once we have the unit gross margin and the total number of products sold, we get the Gross Margin easily enough.

Gross Margin = Unit Gross Margin x Total Products Sold

The Gross Margin will be calculated for various channels we are selling through, and a year-on-year, or month-on-month record will be maintained too.

As you can see, such calculations require Brand Managers to be detail-oriented, organized, knowledgeable and possess a deft hand at Excel.  

  1. Sales and Marketing Costs 

Obtaining the Gross Margin has covered the Production Cost. We have yet to factor in the sales and marketing costs, so let’s do that. Sales and marketing costs depend on the size of a brand’s market share. A bigger market share means we are selling more, which means that the costs attached to sales and marketing per unit is lesser. 

Marketing elements would include –

  • Social Media
  • TV ads (computed as the number of campaigns multiplied by the cost of 1 campaign)
  • Outdoor campaigns
  • Loyalty programs
  • Market research
  • Mailing

Components of cost of sales would be –

  • Salaries
  • External services (cars, phones, fuel, etc)
  • Materials & Energy
  • Other related services

These would be calculated for both retail chains where we supply directly as well as for the traditional stores that we reach via wholesalers.

  1. Net Margin

Part 2 – Obtaining Operational Profit

Deducting Head Office costs from the Net Margin gives us the Operational Profit. Head Office costs include –

  • Salaries
  • Material and Utilities
  • Maintenance
  • Rent (for offices and warehouses)
  • Depreciation and amortization of assets

Part 3 – Zooming Out

Converting all numbers into percentages for easier visual view, the final output would be like this:

Basic FMCG Modeling Made Easy

The above KPI modeling and profit calculation require a Brand Manager to continuously switch between multiple tabs and insert various formulae to get the figures. The same process can be augmented through Explorazor, our data exploration tool. 

Explorazor combines and hosts all datasets, for example, market research, internal sales, Nielsen data, etc. in an integrated manner. Brand Managers thus obtain a single view of the entire dataset. From there, they can extract data cuts instantly through a simple search function of using column names as keywords.  

Explorazor also allows 

  • Visualizing pivots as charts
  • Pinning the charts to a pinboard, and 
  • Downloading them as CSV files

Moreover, all data resides on servers and is accessible via a browser. Laptops are thus relieved from the burden of processing huge datasets. Brand Managers are further liberated when their reliance on BI teams is reduced. The acceleration of ad-hoc exploration is experienced immediately with Explorazor.

Explorazor is built for large enterprises, with single sign-on, row and column level security, data encryption, and on-cloud and on-premise availability.

Do you want to see other features added to Explorazor? Write to us at sales@vphrase.com. If you want to see the product in action, take an interactive Product Tour.

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