Category: CPG Jargon Buster

What is Behavioral Segmentation with Real Life Examples

by Krina Rathodin CPG Jargon Buster

In today’s highly competitive business landscape, where customers have abundant choices and ever-evolving preferences, understanding consumer behavior is the key to unlocking marketing success. While demographic and psychographic information provides valuable insights into who our customers are, it often falls short in revealing what drives their actions. This is where behavioral segmentation comes into play. 

Data-driven companies rely on behavioral segmentation as a crucial tool to gain a comprehensive understanding of their customers’ actions, interactions, and engagement with their brand, product, or service. They analyze customer behavior using BI tools such Explorazor, to perform ad-hoc search driven analysis on their data in real time to make business decisions.

Importance of Behavioral Segmentation

Understanding the ‘why’ and ‘what’ drove customers to a particular decision or their behavior around it is crucial for businesses. It is like having a secret weapon in the world of marketing. Imagine if you knew exactly what your customers do, what they like, and how they interact with your brand. That’s the power of behavioral segmentation. 

Unlike basic demographics, this approach goes beyond just knowing who your customers are – it digs deep into their actual actions and behaviors. By uncovering their purchase patterns, website clicks, and product preferences, businesses can gain precious insights to tailor their marketing strategies precisely. 

This means companies can offer personalized experiences, recommend products users would love, and create a bond that keeps them coming back for more. With Behavioral Segmentation, it’s not just about guessing anymore; it’s about knowing your customers and taking action based on the data around it.

What kind of User Behavior one should track?

To implement behavioral segmentation successfully, it’s essential for businesses to collect and analyze the right data. Tracking user behaviors across various touchpoints provides valuable insights that help in categorizing customers into distinct segments. Here are some key user behaviors that businesses should consider tracking:

1. Purchase History: Keeping a record of customers’ past purchases allows businesses to identify high-frequency buyers, infrequent purchasers, and those who abandoned their shopping carts. Understanding these behaviors helps tailor promotions and incentives to each segment, encouraging repeat purchases and reducing cart abandonment rates.

2. Website Interactions:  Monitoring how users navigate through your website, the pages they visit, the time spent on each page, and actions taken (e.g., clicks, downloads). This data enables businesses to optimize website content and design, making the user experience more engaging and conversion-friendly.

3. Email Engagement: Analyzing email open rates, click-through rates, sending personalized, relevant content based on user behavior boosts email effectiveness and strengthens customer engagement.

4. Social Media Engagement: Keep a close eye on user interactions with your social media posts, such as likes, comments, and shares. This data reveals the type of content that connects with your audience and can guide your social media strategy to foster meaningful connections and brand advocacy.

5. Product Interaction: For businesses with Saas or Paas products, tracking user behavior within the product is crucial. Identify which features are most popular, monitor session durations, and identify pain points to enhance app functionality and increase user retention.

6. Customer Feedback: Keep a record of customer service interactions, including support tickets, live chats, and phone calls. This data highlights common issues faced by customers, enabling businesses to address concerns promptly and improve customer service quality.

7. Loyalty Program: If you have a loyalty program, monitor how customers engage with it. Track point redemptions, tier progression, and customer participation. 

Most of this data can be tracked using CRM tools and other analytical tools such as Google Analytics, Mixpanel, Pendo etc.

But for making effective use of the tracked data, Decision makers need BI tools which help them understand this data on the fly and help them make real time decisions. 

With a No-SQL based search interface, Companies such as Danone and other fortune 500 enterprises, leverage Explorazor to make decisions by simply connecting their multiple data sources and asking questions in natural language. This saves them time and helps them make decisions in real time.

What tools do I need to track User Behaviors?

Implementing behavioral segmentation requires the right set of tools and technologies to gather, analyze, and interpret customer data effectively. Here are some essential tools that businesses can leverage to embark on a successful behavioral segmentation journey:

Businesses can export this data into data warehouses like Snowflake or Amazon Redshift and simply connect them with Explorazor to perform cross analysis.

Real-World Examples of Successful Behavioral Segmentation

Let’s take a closer look at how three real companies leveraged behavioral segmentation to achieve remarkable results:

Example 1: Amazon’s Personalized Recommendations

Amazon is a pioneer in using behavioral segmentation to offer personalized product recommendations to its customers. By analyzing customers’ past purchase history, browsing behavior, and interactions on the platform, Amazon’s recommendation engine suggests products that align with each user’s preferences and interests. 

This personalized approach has been instrumental in boosting sales and customer satisfaction on the platform, making Amazon one of the most successful e-commerce giants globally.

Example 2: Netflix’s Subscription Service Retention Strategy

Netflix has been a trailblazer in utilizing behavioral segmentation to improve customer retention and engagement. 

By tracking user behaviors within its streaming platform, such as the content they watch, their viewing habits, and the genres they prefer, Netflix offers tailored content recommendations to keep users engaged. 

This level of personalization has played a significant role in reducing churn rates and ensuring that subscribers continue to enjoy the service.

Example 3: TikTok’s Social Media Targeting for App Downloads

TikTok, the popular short-form video app, has effectively used behavioral segmentation to drive app downloads and user acquisition. 

By analyzing user interactions, video engagement metrics, and content preferences, TikTok targets its ads to specific demographics and interests. This targeted approach has contributed to TikTok’s explosive growth and popularity among diverse user segments.

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A Deep Dive into Demand Forecasting for Enterprise

by Krina Rathodin CPG Jargon Buster

Demand forecasting plays a crucial role in the Consumer Packaged Goods (CPG) and Pharmaceutical (Pharma) industries. 

Accurate predictions of future demand enable enterprises to optimize their supply chains, minimize inventory costs, and improve customer satisfaction. 

In this blog, we will delve into the intricacies of demand forecasting for these industries, exploring methodologies, challenges, best practices, and future trends.

Understanding Demand Forecasting

Demand forecasting entails estimating future consumer demand for products. 

For the CPG and Pharma industries, demand forecasting serves as the foundation for effective supply chain management. 

By analyzing historical data, market trends, and consumer behavior, enterprises can make informed decisions regarding production, inventory, and distribution.

CPG and Pharma face unique challenges in demand forecasting due to the seasonality and volatility of demand, fragmented distribution networks, regulatory and compliance factors, and product lifecycle dynamics. 

These complexities make it imperative for enterprises to adopt robust forecasting methodologies that account for these variables.

How to perform Demand Forecasting ?

Calculating demand forecasting involves analyzing historical data, incorporating relevant factors, and applying appropriate forecasting techniques. While there are various methodologies available, here is a general step-by-step process for calculating demand forecasting:

Define the Time Frame: Determine the specific period for which you want to forecast demand, whether it’s days, weeks, months, or years. This will provide a clear scope for your forecasting efforts.

Gather Historical Data: Collect relevant historical data on past sales, demand, and any other factors that may influence demand patterns. Ensure that the data is accurate, comprehensive, and covers a sufficiently long time period to capture trends and variations.

Clean and Analyze the Data: Clean the data by removing outliers, inconsistencies, and missing values. Analyze the data to identify any patterns, seasonality, trends, or cyclicality. This analysis will provide insights into the historical behavior of demand.

Identify Relevant Factors: Identify external factors that may impact demand, such as market trends, economic indicators, promotions, seasonal variations, or competitor activities. These factors should be considered during the forecasting process to improve accuracy.

Select Forecasting Technique: Choose an appropriate forecasting technique based on the characteristics of your data and the nature of demand. Common forecasting techniques include time series analysis, moving averages, exponential smoothing, regression analysis, and advanced machine learning algorithms.

Apply the Chosen Technique: Apply the selected forecasting technique to the cleaned and analyzed data. This involves fitting the data to the model, estimating parameters, and generating forecasts for the desired time frame. The specific steps for each technique may vary, so refer to the chosen methodology’s guidelines.

Validate and Evaluate Forecasts: Validate the accuracy of your forecasts by comparing them with actual demand data from the corresponding forecasted period. Evaluate the forecasting accuracy using appropriate metrics such as mean absolute error (MAE), mean squared error (MSE), or forecast bias. This step helps identify any potential discrepancies and refine your forecasting approach if necessary.

Adjust and Refine: If there are significant deviations between forecasts and actual demand, analyze the reasons behind the discrepancies. Consider adjusting your forecasting model, incorporating additional factors, or applying alternative techniques to improve accuracy.

Monitor and Update: Demand forecasting is an iterative process. Continuously monitor and update your forecasts as new data becomes available and demand patterns change. Regularly review and refine your forecasting methodology to adapt to market dynamics and ensure optimal accuracy.

It’s important to note that demand forecasting is both a science and an art, and there is no one-size-fits-all approach.

To help analysts get to their insights, in a simple way, Explorazor comes in.

Explorazor helps analysts to harmonize multiple datasets, in such a way that they can ask queries in natural Language and get insights from a single source of truth.

Danone and other fortune 500 companies are using Explorazor to increase sales and Market Share. Try Explorazor today!

The choice of technique and level of complexity may vary based on industry, product type, data availability, and specific business requirements. 

Experimentation, experience, and domain knowledge play a significant role in developing effective demand forecasting capabilities.

What are the things that we should keep in mind while studying Demand Forecasting?

Several factors influence the accuracy of demand forecasting. Market trends and consumer behavior analysis provide insights into changing preferences and purchasing patterns.

Seasonal variations and promotions impact demand fluctuations, while economic factors and market competition play a significant role. Additionally, product launches and recalls necessitate careful consideration in demand forecasting models.

Case Studies: Successful Demand Forecasting Implementations

Examining real-world case studies highlights the efficacy of demand forecasting in the CPG and Pharma industries. For instance, a leading CPG company faced challenges due to demand volatility. 

By implementing advanced machine learning algorithms, they achieved a significant improvement in forecast accuracy and optimized their supply chain.

Similarly, a Pharma company utilized predictive analytics to mitigate risks associated with product launches, resulting in streamlined operations and increased customer satisfaction.

Best Practices for Effective Demand Forecasting

To enhance demand forecasting capabilities, enterprises should adopt best practices. Collaborative planning with stakeholders fosters alignment and shared insights. 

Continuous monitoring and adjustment enable agility in response to changing market dynamics. Scenario planning and risk management help address uncertainties effectively. Additionally, evaluating forecast accuracy and implementing improvements is crucial for long-term success.

Future Trends in Demand Forecasting

The future of demand forecasting holds promising advancements. Predictive analytics and artificial intelligence will continue to evolve, enabling more accurate predictions. Integration of demand sensing and real-time data will provide enterprises with valuable insights for proactive decision-making.

Enhanced collaboration with supply chain partners will foster efficient coordination. However, ethical considerations and privacy concerns surrounding data utilization will also become crucial in the coming years.

Conclusion

Demand forecasting is a critical component of success for CPG and Pharma enterprises. By leveraging historical data, advanced methodologies, and a data-driven approach, companies can enhance forecast accuracy, optimize their supply chains, and meet customer demands effectively.

Embracing best practices and staying abreast of future trends will ensure enterprises remain competitive in an ever-evolving market landscape. Implementing robust demand forecasting strategies is a strategic imperative for the CPG and Pharma industries.

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Why Price Elasticity Matters for CPG enterprises

by Krina Rathodin CPG Jargon Buster

If you’re in the consumer packaged goods (CPG) industry, you’re likely familiar with the concept of price elasticity of demand. 

This refers to the degree to which changes in the price of a product affect its demand in the market. 

Understanding price elasticity of demand is crucial for setting pricing strategies that maximize profits, as well as maintaining market share and staying competitive in the market. 

In this blog post of the CPG Jargon series, we’ll explore the concept of price elasticity of demand in the context of the CPG industry, as well as the implications it has for pricing decisions.

What is Price Elasticity of Demand?

Price elasticity of demand is a critical concept for businesses, particularly those in the consumer packaged goods (CPG) industry.

It is a measure of how much the quantity of a product demanded changes when the product’s price changes. The concept is based on the principle that when the price of a product goes up, the demand for that product generally goes down. 

However, the extent to which demand changes in response to a price change varies depending on a number of factors. 

If demand changes significantly in response to a price change, the product is said to be “price elastic.” 

If demand changes only slightly in response to a price change, the product is said to be “price inelastic.“

How is Price Elasticity of Demand Calculated?

Price elasticity of demand is calculated by dividing the percentage change in the quantity demanded of a product by the percentage change in the price of that product. 

The resulting number is a measure of how sensitive the quantity of the product demanded is to changes in its price. 

The Formula for calculating price elasticity of demand is:

Price Elasticity of Demand = (% Change in Quantity Demanded) / (% Change in Price)

For example, let’s say a company increases the price of a product by 10%, and as a result, the quantity demanded decreases by 20%. The price elasticity of demand for that product would be:

Price Elasticity of Demand = (-20% / 10%) = -2.0

A negative result indicates that the product is price elastic. In this case, a 10% increase in price led to a 20% decrease in quantity demanded, indicating that the product is relatively sensitive to changes in price.

How to track brands performance to Calculate Price Elasticity?

In order to evaluate the performance of their brand in specific regions, brand managers must create dashboards and analyze multiple data sets. 

This process involves working with various teams, including analysts and the insights team, who utilize data visualization and analytics tools to provide insights, which can take up to 2-3 months. 

Unfortunately, this delay can negatively impact the brand’s market position, potentially leading to consumers switching to other products. 

Fortunately, tools like Explorazor are available to help brand managers obtain real-time, actionable insights from their data by simply asking questions in a user-friendly format. 

With Explorazor, brand managers can perform root cause analysis, drill down into opportunities, and identify potential red flags, enabling them to make swift and informed decisions.

Factors Affecting Price Elasticity of Demand

In the CPG industry, there are many factors that can affect the price elasticity of demand for a given product. 

For example, the availability of substitutes is a key factor. If there are many other similar products available in the market, consumers are more likely to switch to a competitor’s product if the price of the original product increases. 

This means that the product is likely to be price elastic. 

Conversely, if there are few or no substitutes for a product, consumers are less likely to switch to an alternative if the price of their preferred product increases. 

This means that the product is likely to be price inelastic.

Another factor that can affect price elasticity of demand is income levels. 

Products that are considered necessities, such as food and shelter, tend to be price inelastic because consumers are willing to pay whatever price is necessary to obtain them. 

However, products that are considered luxury items, such as high-end electronics or designer clothing, tend to be more price elastic because consumers are less willing to pay high prices for non-essential items.

Brand loyalty can also affect price elasticity of demand. If consumers are highly loyal to a particular brand, they may be willing to pay higher prices for that brand’s products, even if there are cheaper alternatives available. 

This means that the product is likely to be price inelastic. 

Conversely, if consumers are not loyal to a particular brand, they are more likely to switch to a cheaper alternative if the price of their preferred brand’s product increases. 

This means that the product is likely to be price elastic.

What happens if I change the price of my products often?

For CPG enterprises, understanding price elasticity of demand is crucial for setting pricing strategies that maximize profits. 

Products that are price inelastic can generally be priced higher, while products that are price elastic may need to be priced lower to maintain sales volume. By knowing the price elasticity of demand for their products, companies can make informed decisions about how to price their products to maximize revenue.

However, companies should also be aware that price changes can have unintended consequences. 

For example, if a company raises the price of a product too high, it may cause consumers to switch to a competitor’s product, resulting in a loss of market share. 

Similarly, if a company lowers the price of a product too much, it may result in lower profits even if sales volume increases. 

Therefore, it’s important for companies to carefully consider the potential consequences of price changes before implementing them.

In conclusion, price elasticity of demand is a critical concept for CPG businesses that want to optimize their pricing strategies and stay competitive in the market. 

By understanding the factors that affect price elasticity of demand and the implications it has for pricing decisions, companies can make informed decisions that maximize revenue and maintain market share.

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

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How Customer Segmentation Benefits CPG Companies

by Krina Rathodin CPG Jargon Buster

As a consumer packaged goods (CPG) company, understanding your audience is essential to drive better results.

Knowing what your customers want and need can help you create more effective marketing campaigns, develop products that meet their demands, and ultimately increase your revenue.

One way to achieve this is through customer segmentation, a technique that helps you divide your audience into distinct groups based on their characteristics, behavior, or preferences.

By doing so, you can tailor your strategies to each segment’s specific needs, improving your chances of success.

In this blog post, we’ll provide an overview of customer segmentation, including the different types, steps to conduct it, benefits, limitations, and examples of successful implementation.

What are the different ways through which you can differentiate your audience?

There are several ways to segment your audience, and the most common include:

  1. Demographic Segmentation: This approach divides customers based on demographic factors such as age, gender, income, education, and occupation.
  1. Geographic Segmentation: It groups customers by their geographic location, such as country, region, city, or neighborhood.
  1. Behavioral Segmentation: This approach focuses on customers’ behavior, such as their purchase history, loyalty, frequency of use, or response to promotions.
  1. Psychographic Segmentation: This technique groups customers based on their personality, lifestyle, values, attitudes, or interests.

Each approach has its benefits and limitations, and the best one for your business will depend on your objectives, available data, and resources.

How to perform Customer Segmentation?

To conduct effective customer segmentation, you need to follow a set of steps that include:

  1. Identify the objective: Determine what you want to achieve with customer segmentation, such as improving customer retention, acquiring new customers, or increasing sales.
  1. Collect data: Gather information about your customers through surveys, online analytics, or social media. Ensure that the data is accurate, relevant, and diverse.
  1. Analyze the data: Use statistical tools or software to analyze the data and identify patterns or trends that can be used to segment your audience.
  1. Create segments: Based on the analysis, group your customers into distinct segments that share similar characteristics or behaviors.
  1. Evaluate the segments: Assess the viability and profitability of each segment, considering factors such as size, growth potential, competition, and customer needs.
  1. Implement the segmentation: Develop marketing campaigns, product strategies, or customer experiences tailored to each segment’s preferences, needs, or values.

How does an enterprise improve their process using Effective Customer Segmentation?

Effective customer segmentation can bring several benefits to your business, including:

  1. Improved customer understanding: By segmenting your audience, you can gain a deeper understanding of their needs, preferences, and behaviors, helping you create more targeted and relevant products and services.
  1. Targeted marketing efforts: Segmentation allows you to tailor your marketing campaigns to each group’s interests, pain points, and communication channels, increasing the chances of engagement and conversion.
  1. Increased customer retention: Segmentation helps you identify loyal customers and offer them personalized experiences, rewards, or incentives, increasing their loyalty and decreasing churn rates.
  1. Improved customer satisfaction: By meeting each segment’s specific needs and expectations, you can enhance their satisfaction and loyalty, leading to positive reviews, referrals, and repeat business.
  1. Enhanced product development: Segmentation helps you identify new product opportunities or areas for improvement by understanding your customers’ unmet needs or pain points.

What are some Limitations which enterprises face during differentiating their audience

Despite its benefits, customer segmentation has some limitations that you need to consider, such as:

  1. Cost and time-intensive: Conducting customer segmentation requires significant resources, including time
  1. Limited sample size: If you don’t have a large enough sample size, your segmentation may not be representative of your entire audience, leading to biased results.
  1. Risk of oversimplification: Customer segmentation can oversimplify your audience, leading you to miss out on their nuances, diversity, and complexity.
  1. Difficulty in predicting customer behavior: Even with segmentation, it can be challenging to predict your customers’ behavior, as it can be influenced by various factors beyond their demographic, geographic, or psychographic characteristics.
  1. Inability to capture new trends: Segmentation may not capture emerging trends or changes in your audience’s behavior, making it necessary to update your segments regularly.

To overcome these limitations and gain deeper insights into their customers, CPG companies can use data exploration tools like Explorazor.

Explorazor helps user track their marketing, advertising, and promotional efforts, finding the root cause of their analysis and identifying where they can focus their efforts with respect to Market Share and regions.

Explorazor’s natural language and visual format make it easy for users to find insights by simply asking questions, making it a valuable asset for any CPG company looking to improve their business results.

Lets take a look at how CPG enterprises make use of Customer Segmentation.

Use Cases:-

Many CPG companies have successfully implemented customer segmentation to improve their business results. Here are some examples:

Procter & Gamble: P&G uses segmentation to target specific audiences with different brands and products, such as Tide for families, Olay for women, and Gillette for men.

Coca-Cola: Coca-Cola segments its audience by age, lifestyle, and occasions, creating campaigns tailored to each segment’s preferences and behaviors.

Nestle: Nestle uses segmentation to target customers by their stage of life, such as infants, children, adults, or seniors, offering products that meet their unique needs.

Unilever: Unilever uses segmentation to target customers by their behavior, such as eco-conscious or health-conscious consumers, creating products that align with their values.

In summary, customer segmentation is a powerful tool for CPG companies to better understand their audience, tailor their strategies, and improve their business results.

Learn more about such CPG jargons through CPG Jargon Buster Series

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Point of Sale Data: How CPG Companies Use It to Improve Decision-Making

by Krina Rathodin CPG, CPG Jargon Buster, Data Exploration

The consumer packaged goods (CPG) industry is a highly competitive market, and companies need to make informed decisions to stay ahead. One tool that CPG companies use to make data-driven decisions is Point of Sale (POS) data.

What does POS mean?

Point of sale (POS) data is a term frequently used by consumer packaged goods (CPG) companies to refer to the data collected at the time and place of purchase. This data includes information about sales, inventory, and promotions, and it’s a critical component of market research and decision-making for CPG companies.

In this post, we’ll explore what POS data is, how CPG companies use it, and the challenges and best practices associated with collecting and analyzing it.

What is Point of Sale (POS) Data?

POS data is the information collected at the time and place of purchase, typically using electronic scanners or manual data entry. This data includes details such as the item purchased, the quantity sold, the price paid, and the time and date of the transaction.

Types of data included in POS data vary by industry and the needs of the company, but they generally include sales data, inventory data, and promotional data.

How CPG Companies Use POS Data ?

CPG companies use POS data to make informed decisions that can help them optimize their sales strategies. The following are some examples of how CPG companies use POS data:

Market Research: POS data helps CPG companies to monitor market trends, understand consumer behavior, and identify opportunities to improve their products and services. For example, a company could use POS data to identify which products are selling well and which ones are not, and then use that information to adjust their product lineup or marketing strategy.

Inventory Management: POS data can help CPG companies optimize their inventory levels, reducing the risk of stockouts and overstocking. This can help reduce costs and increase sales. For example, a company could use POS data to identify which products are selling quickly and adjust their inventory accordingly.

Pricing Strategy: POS data can help CPG companies determine the most effective pricing strategies for their products, based on market demand and competition. For example, a company could use POS data to analyze the sales performance of a product at different price points and then adjust the pricing accordingly.

What are the challenges which companies face while collecting and analyzing POS data?

While POS data can be highly valuable, it’s not without its challenges. Some common challenges include data accuracy, data timeliness, and data completeness.

Data accuracy can be an issue if there are errors in the data collection process, such as incorrect product codes or pricing information. To address this challenge, CPG companies may use data cleaning techniques to identify and correct errors in the data.

Data timeliness is another challenge, as POS data may not always be available in real-time. For example, if a retailer only reports their sales data once a week, a CPG company may not have access to the latest sales information until that report is available.

Data completeness can also be a challenge, as not all retailers may provide the same level of detail in their POS data. To address this challenge, CPG companies may need to work with retailers to ensure that they are collecting and reporting the data that is most relevant to their needs.

Best Practices for Working with POS Data

To make the most of POS data, CPG companies should focus on data visualization and Exploration tools and optimization strategies.

Data exploration tools can help make sense of the data and identify trends, allowing companies to make more informed decisions. For example, a CPG company could use a graph or chart to visualize sales trends over time or compare sales performance across different products or regions.

This is where Explorazor comes in handy for the enterprises. Explorazor is a data exploration tool that can help CPG enterprises get insights quickly and easily.

With Explorazor, you can ask a query in seconds and get insights on your data, without the need for extensive data science knowledge.

Try Explorazor today and discover how it can help you gain valuable insights into your data.

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CPG Jargon Buster Master Article

by adminin ACV, Average Items Carried, Brand Manager, CPG, CPG Jargon Buster, decision-making, Explorazor, FMCG, Nielsen, Nielsen Database, sales, Sales Managers, Sales per Million, Total Distribution Points, VelocityLeave a Comment on CPG Jargon Buster Master Article

Hello, and welcome to the knowledge hub that is the CPG Jargon Buster Master Article!

Here you will find direct links to many relevant jargon/concepts in the CPG Industry. Each term is explained in brief below, with a link to the detailed blog at the end of it. 

We keep adding more jargon as we write about them, so be sure to bookmark this page and keep learning! We’re also creating a FANTASTIC CPG-specific product for optimal and super-easy data exploration – you might want to check Explorazor out!

Till now, we have covered 

  1. ACV

ACV stands for All Commodity Volume. It is used in the calculation of %ACV (obviously, but the term ‘ACV’ is often used interchangeably with %ACV, so one needs to be mindful of that). 

ACV is nothing but the total monetary sales of a store. Assessing the ACV of a retailer helps suppliers know which outlet presents the best sales potential based on its business health. 

Learn how to calculate ACV using Nielsen data and how ACV relates to %ACV 

Read more: What is ACV in CPG?

  1. %ACV 

A more comprehensive blog than the ACV blog above, %ACV, or %ACV Distribution, helps managers understand the quality of their distribution networks. You might wonder why a product is not selling well in a region despite being apparently well-distributed there. A deep analysis of metrics such as %ACV will help you resolve that. 

Read the blog to understand how to calculate %ACV, and the 5 points to consider when performing the calculations:

Read more: What is %ACV?

  1. Velocity

Velocity is another metric to study distribution. Velocity factors the rate at which products move off the store shelves once they are placed there. 

Managers can take charge of sales by utilizing velocity fully, and understanding the two major velocity measures – Sales per Point of Distribution (SPPD) and Sales per Million. Refer to the blog to learn what these measures are, with examples to help. As Sales per Million is a complex concept we’ve also explained it separately in another blog:

Read more: ALL About Velocity / Sales Rate in CPG

  1. Average Items Carried

This is the average number of items that a retailer carries – be it of a segment, brand, category, etc. For example, suppose that Brand X has 5 products/items under its name. Average items Carried would be from a retailer’s perspective – he could be carrying 2 products, or 2.5 products, or 4 products of Brand X, on average. 

AIC is one of the 2 components of Total Distribution Points (TDP), the other being %ACV Distribution. The blog explains the relationship between AIC and %ACV with respect to TDP (Total Distribution Points), using examples to simplify. 

Learn why AIC and %ACV are called the width and depth in distribution, and how to calculate AIC in Excel:

Read more: What is ‘Average Items Carried’ and How Does it relate to %ACV?

  1. Total Distribution Points – Basics

Total Distribution Points, or Total Points of Distribution, is again a distribution measure, considering both %ACV and Average items Carried to produce a TDP score that helps Brand Managers understand things like product distribution and store health, and base their future strategies accordingly. 

There’s also a method for managers to know whether their brand is being represented in a fair manner on the retailer’s shelf, using TDP. Learn how to calculate TDP and the special case of TDP if %ACV is 95 or above:

Read More: Basics of Total Distribution Points (TDP) in CPG

  1. Sales per Million

How do you compare two markets where one is many times larger than the other? Does a manager simply say “It’s a smaller market, thus sales are less” and be done with it? Shouldn’t s/he investigate if the products in the smaller market are moving as fast as they are in the larger market? 

Sales per million helps compare across markets, while controlling for distribution. It accounts for the varying Market ACVs and stabilizes them, so managers can find how each product is doing in each market, regardless of market size.

Learn how to calculate Sales per Million with a cross-market comparison example following it:

Read More: Sales per Million 

  1. Panel Data Measures

Nielsen and IRI provide the numbers for these 4 measures, and even those who do not use Nielsen/IRI need to have an understanding of household-level analysis using these 4 measures.

Here are the one-line introductions:

  1. Household Penetration

How many households are buying my product?

  1. Buying Rate

How much is each household buying?

Purchase Frequency and Purchase Size are sub-components of Buying Rate.

  1. Purchase Frequency (Trips per Buyer)

(For each household) How often do they buy my product? 

  1. Purchase Size (Sales per Trip)

(For each household) How much do they buy at one time?

These 4 measures in table format can be used by managers to understand the consumer dynamics that drive the total sales for their product.

Understand these 4 measures in detail, and how they relate to sales:

Read More: Panel Data Measures

  1. Market Basket Analysis

Market Basket Analysis (MBA) is a powerful data mining technique used in the CPG industry to analyze customer purchase behavior and identify relationships between products.

Learn how Market Basket Analysis can help you gain valuable insights into consumer behavior in the CPG industry.

Read more on: Market Basket Analysis

  1. Point of Sale

The consumer packaged goods (CPG) industry is a highly competitive market, and companies need to make informed decisions to stay ahead.

One tool that CPG companies use to make data-driven decisions is Point of Sale (POS) data.

Learn how CPG and Pharma companies optimize their performance using Point of Sale

  1. Customer Segmentation

Customer segmentation, is a technique that helps you divide your audience into distinct groups based on their characteristics, behavior, or preferences.

By doing so, enterprises can tailor your strategies to each segment’s specific needs, improving your chances of success.

Read more on: Customer Segmentation

  1. Price Elasticity of Demand

Price elasticity of demand is calculated by dividing the percentage change in the quantity demanded of a product by the percentage change in the price of that product. 

The resulting number is a measure of how sensitive the quantity of the product demanded is to changes in its price. 

The formula for calculation Price of Elasticity is:

Price Elasticity of Demand = (% Change in Quantity Demanded) / (% Change in Price)

Check out our blog on how CPG companies take decision on the basis of Price Elasticity.

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