Category: Brand Manager

Data Cleaning Techniques and how to Implement them

by Krina Rathodin Brand Manager

Data cleaning is a crucial step in the data analysis process, laying the foundation for accurate and reliable insights. It involves identifying and rectifying issues within datasets to ensure data integrity and consistency. As data analysts, we understand the value of clean data in driving informed decision-making. 

However, the path to achieving clean data is not without its challenges.

In this blog, we will explore the significance of data cleaning in the data analysis workflow and shed light on the common hurdles encountered during the data cleaning stage. By optimizing our data cleaning techniques, we can streamline our analysis processes, minimize errors, and unlock the true potential of our data.

Understanding Data Quality Issues

To embark on effective data cleaning, it is essential for data analysts to have a solid grasp of the common data quality issues that can undermine the accuracy and reliability of their analyses. Three key challenges frequently encountered during the data cleaning process are the presence of missing values, the existence of outliers, and inconsistencies in data formats and values.

By proactively addressing these issues, data analysts can ensure the integrity of their datasets and lay the groundwork for robust analysis and decision-making.

Identifying missing values and handling them effectively is a critical task in data cleaning. Missing values can occur due to various reasons, such as data entry errors, system failures, or simply the absence of data for certain observations. These missing values can introduce biases, affect statistical analyses, and limit the validity of conclusions drawn from the data.

Data analysts must employ appropriate techniques, such as imputation or deletion, to handle missing values based on the specific context and nature of the data. By carefully considering the implications and leveraging suitable methods, analysts can mitigate the impact of missing values and ensure the reliability of their analyses.

Addressing outliers is another crucial aspect of data cleaning. Outliers are observations that significantly deviate from the typical patterns exhibited by the majority of the data. They can arise due to measurement errors, data entry mistakes, or genuine extreme values.

Outliers can distort statistical measures, affect model performance, and lead to misleading insights. Data analysts should employ robust statistical techniques, such as z-score or interquartile range (IQR), to detect and appropriately handle outliers. By identifying and addressing outliers effectively, analysts can prevent their undue influence and enhance the accuracy and validity of their analyses.

Dealing with inconsistent data formats and values is a common challenge faced by data analysts. In large datasets sourced from multiple systems or data collection methods, inconsistencies can arise in the formatting or representation of data. These inconsistencies can include variations in date formats, inconsistent use of units or scales, or conflicting categorization schemes.

Data analysts must apply data transformation techniques, such as standardization or normalization, to ensure consistency across the dataset. By resolving inconsistencies in data formats and values, analysts can establish a reliable and coherent dataset, facilitating accurate analysis and meaningful interpretation of results.

Streamlining Data Transformation

Once data analysts have identified and addressed data quality issues, the next crucial step in optimizing the data cleaning process is streamlining data transformation. Data transformation involves converting the raw data into a standardized and suitable format for analysis.

Three key aspects of data transformation that data analysts should focus on are standardizing data formats, converting variables into appropriate data types, and handling categorical variables using effective encoding techniques. By mastering these techniques, analysts can enhance data consistency, improve analysis efficiency, and ensure accurate interpretations of their findings.

To achieve consistency and comparability across the dataset, data analysts must standardize data formats. This involves ensuring that data values adhere to a uniform structure or representation. For example, if the dataset includes dates, they should follow a consistent format such as YYYY-MM-DD.

Similarly, numeric values should have a consistent decimal or thousand separator. Standardizing data formats minimizes ambiguity and facilitates seamless analysis, enabling data analysts to make accurate comparisons, calculations, and aggregations.

Converting variables into appropriate data types is another critical aspect of data transformation. In many cases, variables are initially imported or stored as generic data types, such as strings or objects. However, to perform meaningful analyses, it is essential to assign the appropriate data types to variables.

Numeric variables should be converted to numeric data types (e.g., integers or floating-point numbers), while categorical variables should be designated as factors or categorical data types. By assigning appropriate data types, data analysts can ensure efficient memory usage, enable mathematical operations, and leverage specialized analytical functions tailored to specific data types.

Handling categorical variables requires careful consideration and the use of effective encoding techniques. Categorical variables represent qualitative or discrete characteristics, such as gender, product categories, or geographical regions.

To analyze categorical variables, data analysts need to transform them into a numerical representation that statistical algorithms can process. Common encoding techniques include one-hot encoding, label encoding, or ordinal encoding, each suited for different scenarios. Proper handling of categorical variables ensures their inclusion in the analysis process, allowing for meaningful interpretations and accurate modeling outcomes.

Automating Data Cleaning Processes

Two key strategies for automating data cleaning are utilizing programming and scripting languages and exploring data cleaning libraries and tools. By harnessing the power of automation, data analysts can streamline their workflows, reduce manual errors, and focus on extracting meaningful insights from their data.

Utilizing programming and scripting languages is a fundamental approach to automate data cleaning tasks. Languages such as Python, R, or SQL provide robust capabilities for data manipulation and cleaning. With their extensive libraries and packages, these languages empower data analysts to write reusable and scalable code that automates repetitive data cleaning operations.

By leveraging functions and loops, analysts can perform complex data cleaning tasks across large datasets efficiently. Furthermore, the ability to create scripts allows for the automation of entire data cleaning pipelines, enabling analysts to apply the same set of cleaning steps consistently to new datasets.

Apart from programming languages, data analysts can also explore data cleaning libraries and tools specifically designed to simplify and expedite the data cleaning process. These libraries and tools offer pre-built functions, algorithms, and workflows tailored for various data cleaning tasks. For example, Pandas and NumPy in Python provide powerful data manipulation capabilities, while libraries like dplyr in R offer a wide range of data transformation and cleaning functions.

Additionally, dedicated data cleaning tools such as OpenRefine or Trifacta Wrangler provide user-friendly interfaces and advanced functionalities for data cleaning tasks, including data profiling, fuzzy matching, and automated error detection. By harnessing these specialized resources, data analysts can accelerate their data cleaning efforts and achieve consistent and reliable results.

Automation in data cleaning not only boosts efficiency but also enhances reproducibility and scalability. By automating data cleaning processes using programming languages or utilizing data cleaning libraries and tools, data analysts can establish standardized and reusable workflows. This ensures that data cleaning operations can be easily replicated and applied to new datasets, thereby maintaining consistency and facilitating collaboration within teams. 

Best Practices for Efficient Data Cleaning

Three key practices for efficient data cleaning include documenting data cleaning steps and decisions, creating reusable data cleaning pipelines or scripts, and implementing version control for data cleaning processes.

Documentation

Documenting data cleaning steps and decisions is crucial for maintaining transparency and traceability in the data analysis process. By documenting the specific actions taken during data cleaning, analysts can keep a record of the transformations applied, the handling of missing values and outliers, and any other modifications made to the dataset.

Additionally, documenting the rationale behind data cleaning decisions provides valuable context for future analysis and ensures that others can understand and reproduce the cleaning process. Detailed documentation helps maintain data quality standards, enables effective collaboration, and aids in identifying and rectifying any issues that may arise during analysis.

Creating Robust Pipelines

Creating reusable data cleaning pipelines or scripts is an effective way to save time and effort while ensuring consistency in data cleaning tasks. By structuring the data cleaning process as a pipeline or script, analysts can define a series of sequential steps that can be applied consistently to different datasets.

This not only reduces manual effort but also allows for the easy replication and modification of the cleaning process for future analyses. Reusable pipelines or scripts also promote collaboration within teams, as they provide a standardized approach to data cleaning that can be shared and adopted by other analysts.

Setting up Version Control

Implementing version control for data cleaning processes is an essential practice for maintaining data integrity and facilitating collaboration. Version control systems, such as Git, allow data analysts to track changes made to datasets, revert to previous versions if needed, and keep a history of the data cleaning process.

By utilizing version control, analysts can easily identify and understand the evolution of the dataset, experiment with different cleaning approaches without the fear of losing previous work, and collaborate seamlessly with other team members. Version control also provides a valuable audit trail, enhancing the reproducibility and reliability of the data cleaning process.

Performance Optimization in Data Cleaning

To enhance efficiency and reduce processing time, data analysts should employ techniques such as managing memory usage, implementing parallel processing, and utilizing indexing and optimization strategies.

These approaches help data analysts tackle the computational challenges associated with data cleaning, enabling them to process data faster and handle larger datasets with ease.

How to optimize memory usage?

Managing memory usage is crucial when working with large datasets that can consume significant system resources. Optimize memory usage by loading data in smaller chunks, selectively loading only the necessary columns or rows, or leveraging memory-efficient data structures. By efficiently managing memory, analysts can avoid out-of-memory errors and ensure smooth execution of data cleaning operations.

Parallel processing data techniques

Implementing parallel processing techniques is another powerful method to boost performance in data cleaning. By dividing the cleaning tasks into smaller, independent units, analysts can leverage the processing power of multi-core or distributed systems.

Parallel processing frameworks, such as Apache Spark, offer efficient ways to distribute workloads across clusters, significantly reducing the time required for data cleaning tasks. Utilizing parallel processing techniques allows data analysts to harness the full potential of their computational resources and expedite the data cleaning process.

Indexing and Optimization Techniques

Creating appropriate indexes on frequently accessed columns can expedite data retrieval, especially when filtering or joining datasets. Additionally, employing optimization techniques like query optimization or caching can improve the overall performance of data cleaning operations.

By optimizing data access patterns and leveraging indexing and optimization strategies, analysts can minimize computational overhead and accelerate the data cleaning workflow.

Practical Use case Example.

In this example, we will take an example on how a financial services  company called BetaKube improved their risk analysis process by implementing robust data cleaning techniques. 

To address these challenges, BetaKube undertook a comprehensive data cleaning initiative. First, they implemented advanced techniques to identify and handle missing values in their datasets.

By employing imputation methods such as mean imputation or regression-based imputation, they effectively filled in missing values with reasonable estimates, minimizing data loss and maintaining the integrity of their analyses.

Next, BetaKube focused on detecting and addressing outliers in their data. They employed statistical techniques like z-score or interquartile range (IQR) to identify observations that deviated significantly from the normal distribution.

By carefully examining these outliers and considering the context of the data, they made informed decisions on whether to correct, remove, or investigate the outliers further. This process ensured that extreme values did not unduly influence their risk analysis models and improved the accuracy of their risk assessments.

Inconsistencies in data formats were another challenge that BetaKube encountered. They found variations in the representation of dates, currencies, and other numerical formats across different datasets. To address this issue, they implemented data transformation techniques to standardize data formats.

They converted dates into a uniform format, ensured consistent currency symbols and decimal separators, and verified that numerical values adhered to the expected formats. By achieving consistency in data formats, they eliminated potential errors and ambiguity in their risk analysis process.

The implementation of optimized data cleaning techniques yielded significant improvements for BetaKube. The cleaner and more reliable dataset resulting from their efforts enhanced the accuracy of their risk models and enabled more precise risk assessments.

As a result, they observed improved decision-making processes, reduced instances of false positives or false negatives in risk predictions, and better alignment of risk mitigation strategies with actual risk levels.

By prioritizing data cleaning and investing in robust techniques, BetaKube showcased the direct impact of optimized data cleaning on their risk analysis process. They successfully harnessed the power of clean and reliable data to drive accurate risk assessments, enabling them to make informed decisions, mitigate risks effectively, and maintain regulatory compliance.

Explorazor helps users create forward-looking dashboards and ease their daily data exploration, accelerate hypothesis testing rates, gain independence in conducting ad-hoc queries, and ultimately take the best decisions based on all data points, within an acceptable time frame. 

Be sure to check out our blogs where we discuss everything related to Brand & Insights Managers and how they can ease their data interactions, making them faster and better. 

Explorazor is a product of vPhrase Analytics, which also owns Phrazor.

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The Ultimate Guide to Email Marketing for CPG Companies

by Krina Rathodin brand, brand advocacy, brand commitment, Brand Manager, Brand Managers, brand preference, Branding

As the world becomes increasingly digital, the consumer packaged goods (CPG) industry is faced with a critical question: how can companies adapt to the changing e-commerce landscape?

In the fiercely competitive world of consumer packaged goods (CPG), companies are constantly seeking new ways to boost sales and drive growth. With the advent of digital marketing, email marketing has emerged as a powerful tool for CPG companies to connect with their customers and drive sales.

We all know how important it is for businesses to stay ahead of the curve in marketing while remaining competitive.

In this blog post, we will explore the power of email marketing in the CPG industry, its benefits, and how it can help you increase your sales.

By the end of this post, you will have a clear understanding of why email marketing should be an integral part of your marketing strategy, and how you can use it effectively to connect with your target audience, drive sales, and achieve your business goals.

So, let’s dive in and discover the world of email marketing for CPG companies.

Understanding Your Audience

Email marketing has become an integral part of any successful marketing campaign. With an estimated 4.03 billion email users worldwide, email marketing has the potential to reach a vast audience.

Identifying the target audience for your email campaigns

However, to maximize the effectiveness of your email campaigns, it is important to understand your target audience. By creating buyer personas, you can gain insights into your audience’s pain points, interests, and preferences, allowing you to tailor your emails to their specific needs.

As a corporate employee of a CPG company, you understand the importance of identifying and understanding your target audience.

After all, your products are created with your customers in mind. It is essential to know who your target audience is and what they want. Creating buyer personas is a powerful way to achieve this.

Creating buyer personas to understand your audience’s pain points, interests, and preferences

Buyer personas are detailed profiles of your ideal customers. They are based on market research, customer data, and insights from your sales and customer service teams. Buyer personas include information such as demographic data, job titles, goals, challenges, pain points, preferred communication channels, and more.

By creating buyer personas, you can gain a deeper understanding of your audience’s needs and motivations, allowing you to create targeted and relevant email campaigns that resonate with your audience.

To create a buyer persona, start by gathering data from various sources, including market research, customer feedback, and your internal teams.

Look for patterns and commonalities in the data to identify key insights about your audience.

For example, you may find that your target audience is primarily made up of millennials who value sustainability and eco-friendliness.

Once you have gathered your data, it’s time to start building your buyer persona. Start by giving your persona a name and a job title.

This will help you to personalize your persona and make it more relatable. Next, include demographic information such as age, gender, income, and education level.

Then, delve deeper into your persona’s goals, challenges, and pain points. What are they trying to achieve? What obstacles are they facing? What are their biggest frustrations? This information will help you to understand what motivates your audience and how you can help them overcome their challenges.

Finally, consider your persona’s preferred communication channels and content preferences. Do they prefer email, social media, or direct mail? What types of content do they find most valuable?

This information will help you to create email campaigns that your audience will actually want to receive and engage with.

How to Build an effective Email List

In today’s digital age, email marketing remains one of the most effective ways for CPG companies to reach and engage with their target audience. Not only is it cost-effective, but it also provides a direct line of communication between your brand and your customers.

Importance of building an email list

However, to reap the benefits of email marketing, you need to have a quality email list. We will now discuss the importance of building an email list, best practices for building a quality email list, and ways to encourage sign-ups.

You understand that building an email list is essential to the success of your email marketing campaigns. Your email list is a valuable asset that allows you to communicate with your customers, build relationships, and drive sales.

A high-quality email list is made up of subscribers who have opted-in to receive your emails, are interested in your products or services, and are engaged with your brand.

But how do you build a quality email list? It’s not just about collecting as many email addresses as possible.

Instead, it’s about building a list of subscribers who are genuinely interested in your brand and are likely to engage with your emails. To achieve this, you need to follow best practices for building a quality email list.

Best ways to build a quality email list

First and foremost, you should always obtain permission from your subscribers before adding them to your email list. This means using opt-in forms and clearly communicating what they will be receiving from you.

Additionally, you should never purchase email lists or add email addresses without explicit consent. This not only violates anti-spam laws but also leads to low engagement rates and high unsubscribe rates.

Another best practice for building a quality email list is to segment your list based on your subscribers’ interests and behavior.

By doing so, you can create targeted email campaigns that are more likely to resonate with your audience. For example, you may want to segment your list based on purchase history, location, or engagement level.

Ways to encourage sign-ups

Encouraging sign-ups is also an essential part of building a quality email list. One way to do this is to offer something of value in exchange for their email address.

This could be a discount code, a free e-book, or access to exclusive content. You can also include opt-in forms on your website, social media profiles, and in-store signage.

Social media is another effective way to encourage sign-ups. By promoting your email list on your social media profiles, you can reach a wider audience and drive sign-ups. You can also use paid social media advertising to reach even more people.

By following best practices for building a quality email list, such as obtaining permission, segmenting your list, and encouraging sign-ups, you can create targeted and relevant email campaigns that resonate with your audience.

So, take the time to build a high-quality email list for your CPG company. It’s an investment that will pay off in the long run.

How to Craft an Effective Email

Now that you have built a quality email list, it’s time to craft effective emails that will engage your subscribers and drive results for your CPG company. The components of an effective email include a strong subject line, engaging and relevant content, a clear call-to-action, and personalization.

In this section, we will discuss tips for creating attention-grabbing subject lines, writing engaging and relevant content, and personalization techniques to make your emails more appealing to your audience.

As a brand manager, director, CXO, or VP of a CPG company, you know that your subscribers’ inboxes are inundated with countless emails every day.

So, how to make your email stand out and get opened?

The first step is to craft an attention-grabbing subject line. A strong subject line should be concise, descriptive, and compelling. It should entice your subscribers to open your email and find out more.

One way to create attention-grabbing subject lines is to use personalization.

Tips for creating attention-grabbing subject lines

This involves incorporating your subscriber’s name, location, or previous purchases into the subject line. Personalization can also extend to the content of your email, making it more relevant to your subscriber’s interests and behavior.

Once you have captured your subscribers’ attention with an attention-grabbing subject line, it’s time to focus on the content of your email. Your email content should be engaging, informative, and relevant to your subscribers. It should provide value to your subscribers and inspire them to take action, whether it’s making a purchase or visiting your website.

Writing engaging and relevant content

One way to create engaging and relevant content is to segment your email list based on your subscribers’ interests and behavior. By doing so, you can create targeted email campaigns that speak directly to your audience’s needs and preferences. For example, if you have subscribers who have purchased your products in the past, you can send them emails about new products or special promotions.

Another way to create engaging content is to use visual elements such as images or videos.

Visual content can help break up long blocks of text and make your email more visually appealing. It can also help convey your message more effectively

How personalization plays an important role in content creation for email marketing

Finally, personalization is key to making your emails more appealing to your audience. Personalization can take many forms, from using your subscriber’s name in the subject line to providing personalized product recommendations based on their previous purchases.

Personalization can help your subscribers feel valued and connected to your brand, which can lead to increased engagement and loyalty.

By understanding the components of an effective email, such as attention-grabbing subject lines, engaging and relevant content, clear call-to-actions, and personalization, you can create email campaigns that resonate with your audience and drive results for your CPG company.

So, take the time to craft effective emails that provide value to your subscribers and inspire them to take action. It’s an investment that will pay off in the long run.

Steps to Measure your Email Campaign Performance

We have now understood how the usage of both captivating subject lines and pers email marketing can be a powerful tool for reaching your audience and driving results.

However, to get the most out of your email campaigns, it’s important to track their performance and make data-driven decisions.

Key metrics to track in email marketing

We will now discuss key metrics to track in email marketing, tools for tracking email campaign performance, and strategies for improving email campaign performance.

One of the most important aspects of email marketing is tracking key metrics to evaluate the effectiveness of your campaigns.

These metrics include open rates, click-through rates, conversion rates, unsubscribe rates, and bounce rates.

Open rates measure the percentage of subscribers who opened your email, while click-through rates measure the percentage of subscribers who clicked on a link in your email.

Conversion rates measure the percentage of subscribers who completed a desired action, such as making a purchase or filling out a form.

Unsubscribe rates measure the percentage of subscribers who opted out of receiving future emails, while bounce rates measure the percentage of emails that were undeliverable.

Best Tools for tracking email marketing campaign performance

To track these metrics, you can use email marketing tools such as Mailchimp, Constant Contact, or Campaign Monitor.

These tools provide analytics dashboards that allow you to track your email campaign performance in real-time.

They also allow you to segment your email list, A/B test your campaigns, and automate your email marketing efforts.

Different ways through which we can improve the email campaign performance

To improve your email campaign performance, there are several strategies you can implement. 

One strategy is to optimize your email content for mobile devices. With more and more people accessing their emails on their mobile devices, it’s essential to ensure that your email content is easy to read and navigate on small screens.

Another strategy is to use A/B testing to test different variations of your email campaigns. A/B testing involves creating two versions of your email campaign and sending them to different segments of your email list. 

By comparing the performance of the two versions, you can determine which one is more effective and optimize your future campaigns accordingly.

As discussed above, Personalization is also key to improving email campaign performance.

By using your subscriber’s name, location, or previous purchases in your email campaigns, you can make them more relevant and engaging to your audience.

Personalization can also extend to the timing and frequency of your email campaigns, ensuring that your subscribers receive your messages at the right time and at the right frequency.

By tracking key metrics, using email marketing tools, and implementing strategies such as optimizing for mobile devices, A/B testing, and personalization, you can create email campaigns that resonate with your audience and drive results for your CPG company. 

So, take the time to track your email campaign performance and make data-driven decisions that will help you achieve your marketing goals.

Email Marketing Examples:

Email marketing has proven to be an effective tool for increasing sales and building brand loyalty. Many CPG companies have embraced email marketing as a key component of their overall marketing strategy, with impressive results

Examples of CPG companies that have successfully used email marketing to increase sales

For instance, Starbucks – In celebration of their 40th anniversary launched a personalized email campaign that used customer data to generate unique and personalized messages for each recipient.

The campaign resulted in a 10% increase in sales as per a report by Campaign Monitor.

Another example is Coca-Cola. They launched a holiday-themed email campaign that featured a virtual Santa Claus who delivered personalized messages to customers.

The campaign generated a 20% increase in open rates and a 73% increase in click-through rates.

Nestle launched an email campaign that featured personalized recipe suggestions based on customers’ dietary preferences and product purchases.

The campaign resulted in a 15% increase in sales.

Procter & Gamble launched an email campaign to promote their Tide PODS product line. The campaign used bold, eye-catching visuals and a simple, clear message to generate a 40% increase in click-through rates.

One of the key strategies that CPG companies use to maximize the effectiveness of their email campaigns is to create compelling content that resonates with their target audience.

For example, Johnson & Johnson’s BabyCenter creates email campaigns that provide valuable information to expectant and new parents.

Their emails contain tips on child-rearing, product recommendations, and other useful content that helps build trust and loyalty among their subscribers.

Similarly, Nestlé Purina’s email campaigns focus on pet care and provide valuable content that pet owners can use to improve the health and wellbeing of their pets. By providing valuable content that their subscribers find useful, these CPG companies are able to build strong relationships with their customers and increase the likelihood of repeat business.

How does Explorazor a Data Exploration Tool help Marketing Teams to get the Required insights?

In addition to compelling content and personalization, CPG companies also use data analytics to measure the effectiveness of their email campaigns and optimize their strategies accordingly.

For instance, PepsiCo uses A/B testing to determine which subject lines, images, and content are most effective in driving engagement and sales.

They also use analytics to track customer behavior and preferences, and then use this information to create more targeted and effective email campaigns.

Trusted by leading CPG & Pharma companies such as GSK, DANONE, Sanofi, Abbot, ALKEM and Olem, Explorazor helps combine all the datasets (Nielsen, Kantar, Primary Sales, Secondary Sales, Media, and more) into one harmonized dataset making it the single source of truth.

Once all the Datasets are added to Explorazor, rather than troubling the insights team, ask those questions to Explorazor in simple language and get the desired insights to your queries.

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

by adminin Brand ManagerLeave a Comment on Interested in Becoming a Brand Manager? Know Your Nielsen Data!

If you are interested in becoming a Brand Manager and want to learn more about the kind of datasets Brand Managers deal with on a daily basis, you have landed at the right place. We also plan to introduce you to a tool that is currently making the life of Brand Managers so much easier than before. How? Well, keep reading!

We have written similar blogs for Kantar and IQVIA datasets as well, so open both in new tabs and explore them once you’re through this one.

Brand Managers are champions. They are multi-taskers, owning multiple responsibilities
like using market research data to formulate brand strategies, managing various stages of the brand life cycle, and performing other tasks such as juggling budgets and building a strong rapport with multiple stakeholders.

As someone interested in becoming a Brand Manager, you should first of all warm yourself up to the fact that strong data handling skills will be the backbone of your career and the key to success. Branding, marketing, sales, SCM – everything is data-based. It’s a highly valued, challenging, and rewarding career path to go down – and we wish you all the luck for it.

DATASETS THAT BRAND MANAGERS DEAL WITH – NIELSEN DATA

Nielsen is one of the most prominent names in data and market measurement. It measures media audiences such as TV, newspapers, radio, etc. Nielsen provides Data as a Service (DaaS) which includes access to 60,000 consumer segments, globally, and 300 media & marketing platforms.

Here are some of the common columns present in Nielsen data:

Market
This Column comprises all the individual and combined market i.e. States, Zones, All India, etc.

Geo Classification
This column contains classifications such as Metro, Zones, States, and All India

Brand
Brand includes one’s own brands as well as competitor brand names. Total rows include the Brand, the Category in which the brand operates, and the company to which the brand belongs

Sales Value & Sales Volume
Value comprises the Market Sales Value, while Volume means the Market Sales Volume in Kg

PDO Val Rs.
PDO stands for Per Dealer Offtake. It is the ratio of sales per outlet/store, or volume, to the total number of dealers handling the product

PDO in Units
This is the same as Per Dealer Offtake with number of units replacing total value

No. of Dealers
This is another metric provided by Nielsen, letting you know the total number of dealers in the market, brand-wise

NumD & WtdD
Numeric Distribution is the percentage of stores where a brand is placed out of ‘n’ total stores. Weighted Distribution is the percentage of stores with a good potential for sales of a brand, out of ‘n’ total stores

SAH Val
Suppose you are present in an outlet. Now, what is your brand’s share within the sales of a particular category in a particular outlet? That share would be called Share Among Handlers. For example, the share of Cadbury within the total sales of chocolates that takes place in an outlet.

STR
Sell-Through Rate is the product inventory sold within a period. It is used to predict the demand for a particular product. One method can be studying the STR of similar products by other sellers. Avoiding spending on unnecessary product listings is another reason to study STR and improve cost efficiency

Stock Volume & Stock Units
These are the available Stock Volume at stores and the available Stock Units at stores

SEPARATE FILE FOR EACH, OR JUST 1 INTEGRATED DATASET?

We’re proposing the second!

Explorazor combines not just Nielsen, but also Kantar (if FMCG industry), IQVIA (Pharma), and your primary sales, secondary sales, and more, into 1 integrated dataset available to you on the Explorazor screen. From there,

  • Ask queries via simple search interface
  • Obtain data pivots as tables, in seconds
  • Choose to customize tables into charts, trend graphs, etc.
  • Choose to download as CSV and transfer to Excel
  • The option to pin a query result to the dashboard is also present

Not only this, Explorazor also directly recognizes time-based filters, has an intuitive search query mechanism, supports time-period comparison (such as Sept 2022 vs Sept 2021, or Nov vs April 2021), and allows drill-down and drill-across to facilitate root-cause analysis, through simple clicks.

Features so good, we had to embolden the entire paragraph.

Related: If you’ve reached here, we’re sure you’re very interested in becoming a Brand Manager. Why not get a glimpse of how Brand Managers work on Excel? Head over to Modeling Basic FMCG KPIs in Excel.

Continuing, we believe that the value of Explorazor is clear for all to see. Instead of working slowly on slow laptops (large files; slow processing), there’s the option to work fast on fast laptops. Users also avoid repetition; the integrated dataset produces the required data pivot in one go. With a cleaner laptop and fresher mental space, Brand Managers test out hypotheses at accelerated speeds, improving the quality of their decision-making.

Which is really the end goal of all this incessant data crunching, wouldn’t you agree?

Explorazor is a product of vPhrase Analytics.

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3 Data-Related Challenges Brand Managers Face and How to Solve Them

by adminin Brand Manager, data analysis, data cuts, Data Exploration, data-related challenge, Excel, Explorazor, pivot

Tell us a better love story than Brand Managers and data.

Brand Managers possess some of the strongest number-crunching skills in the industry. Everything’s solved and managed in Excel; sales, logistics, marketing; development, execution, evaluation. Operations and decisions are dependent purely on data, and these invite data-related challenges as well.

Let’s look at 3 data-related challenges Brand Managers face, and the possible solution to each:

Data-Related Challenge 1 – Data Fragmentation

The swiftness of strategic decisions suffers the most when data is fragmented across files and sheets. The data currently residing in Excel is stored under different column headers and cannot be combined. Internal and external data reside separately, and pivots have to be repetitively extracted from each individual dataset to move further with the analysis.

Fragmented, unsynchronized datasets also affect the quality of insights derived. One reason we can think of is the sheer (and avoidable, as you will see in the solution) manual effort BMs put in, in bringing the data at one place to perform analysis on it.

Solution

We have a tailored method to organize your data. Explorazor by vPhrase Analytics is a data exploration platform built specifically for Brand Managers to query their data better and extract instant data cuts from it. What Explorazor does is combine all the datasets currently residing in Excel, and provide unified, single-view access for Brand Managers to explore. Examples of such datasets would be primary sales, secondary sales, Kantar, IQVIA, and more. 

Explorazor relieves Brand Managers from having to constantly switch between files and sheets to find relevant data cuts. Correlating reasons for market loss, estimating the right media budget spend, gauging discounting effectiveness, finding best-performing regions, etc. become much easier. We imagine that a seamless experience will encourage Brand Managers to explore further and deeper into event root causes, key focus areas, and other ad-hoc analyses.

Data-Related Challenge 2 – Data Standardization

Metric definition is the first hurdle in the data standardization process. What Nielsen defines as an Urban area and a Rural area and what internal company definitions for the same terms are, are mostly dissimilar. Information capturing done by field sales personnel contains numerous kinds of errors. The spellings are different, the name of a state is mentioned in a shorter form, capitalization issues, etc etc. 

Raw data standardization is a necessary prerequisite for efficient data analysis, and right now it is a task that Brand Managers would love to sweep off their table.

Solution

Our team at Explorazor ensures that all your data is modeled and standardized so data analysis can be conducted without having to worry about missing data points.

Redundant, duplicate, inaccurate, and irrelevant data is expelled, leaving a de-cluttered dataset that serves as a base for higher-quality analysis and insights extraction.

A clean dataset is also helpful when creating routine dashboards and presentations for senior management.     

Data-Related Challenge 3 – Large (and Clumsy) Data Dumps

The data dumps that Brand Managers work on are too large – Excel cannot output results fast on our laptops, as one would like. Loading – and ensuring that the data is saved – takes excessive time. An abundance of formula insertion slows the workbook down. 

Thinking about quick pivots? Think again. Then again, and then again, because your laptop is slow and you have lots of time on your hands…

Solution

Loading huge Excel files is no joke. To create pivots, and to create them now, is one of the prime reasons we believe a solution like Explorazor will go a long way in assisting Brand Managers save time. All data resides on servers and is accessible via a browser, so laptops breathe freely again. Brand Managers, using a simple search interface on Explorazor, can conduct ad-hoc analysis and test out hypotheses at accelerated speeds. 

If you want to take the pivots to Excel – permission granted. All pivots are downloadable as CSV files. Convert pivots into charts using simple customization options and pin them to pinboards. Each project within Explorazor allows its separate pinboard creation.

Explorazor is built for Brand Managers

Explorazor alleviates data-related challenges which Brand Managers face, as well as: 

  • Saves their time by taking the processing load off their laptops
  • Eases their data exploration journey by providing unified access to all their datasets
  • Enhances the quality of their insights by standardizing all current and incoming data
  • Increases their independence by letting them conduct ad-hoc analyses on their own, without over-reliance on BI/Insights teams 

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