Category: Data Exploration

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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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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Dynamic KPIs, Saving Filters as Groups, Updates to Root Cause Analysis and More – Explorazor Product Updates January 2023

by adminin Brand Managers, CPG, Data Exploration, Explorazor product updates, root cause analysis

We’re rapidly developing Explorazor to help Brand Managers conduct fast and efficient data exploration. Having already launched seamless root cause analysis, conditional formatting, dual and triple-axis charts in November’s release, we have made some other improvements this time around.

If you are yet to be acquainted with Explorazor, it is a CPG and pharma-specific data exploration tool laying the groundwork for skilled professionals to focus on solving real market problems instead of grappling with unstandardized data and slow laptops all the time. 

The Explorazor proposal for Brand Managers is to work on a harmonized dataset, accessible to all, facilitating instant data pivot extraction (via simple querying) and root cause analysis (via simple clicks) – saving time and effort while accelerating hypothesis testing rates. We do model and engineer your data for you as well.

Let’s look at January’s updates:

  1. Dynamic KPI creation

Users can get custom KPIs created as per their requirement, which are dynamically calculated for every query 

Let’s take an example of ‘Rate of Sales’ as a dynamically created KPI:

Simply insert ‘Rate of Sales’ as a keyword in your query as shown below:

The above image is an example of a dynamically created keyword. Users can get custom KPIs such as Rate of Sales, Market Share, etc. created as per their requirements.

It’s dynamic, so the query will be relevant all the time. As per your query, your numbers of the KPI will be calculated and updated in real-time. For example, you can get KPIs like ‘market share’ which can be calculated dynamically for brands, geography, or distribution, using the keyword, you can use the resulting table to create all kinds of visuals for presentation purposes, and/or perform root cause analysis on it.

  1. Filter Grouping

The rationale is simple – managers use a particular set of filters frequently. Typing in these set of filters repeatedly for every query is undesirable. 

Filter Grouping, as you’re smart enough to figure out by now, allows you to save a group of filters under a common header, and use it to apply the group of filters with ease in the future. Simply recall the header the next time you want to use that set of filters.

  1. Updates to Root Cause Analysis

Explore the root cause analysis/drill-down in detail in the linked blog. 

We have introduced more interactive elements to root cause analysis this time. To show important metrics for a data field, directly click on that field to display all its corresponding values in the left panel. 

This will be better understood with an example:

For any field you click, the numbers on the left panel change dynamically to reflect metrics for our area of interest.

An additional convenience here is the ability to sort the information on an ascending or descending basis. 

Some Other Updates

  1. Recently used keywords will now be prompted as suggestions as you type for quick access. A Google-like feature, and when it comes to a search interface, there’s no reason not to have it Google-like
  2. Min & Max query support is live
  3. There’s an option to edit live data connection options
  4. Updates to conditional formatting

That’s it for this time, and we’ll be back with more updates next month. Our goal remains the same: to help Brand Managers in CPG and Pharma focus only and only on data exploration, and create real impacts through it, with the ultimate objective of improving brand and company revenue. 


Explorazor is a product of vPhrase Analytics.

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10 Data Exploration Tools To Explore in 2023

by adminin data exploration tool, Explorazor, Uncategorized

All of us are in need of some assistance when trying to conjure up magic from data. While the skills lie primarily with the human, choosing the right technology stack is arguably equally as important. Let’s look at some of the top data exploration tools in brief that you can investigate further: 

  1. Explorazor

Explorazor is a data exploration tool that unifies all the datasets of a company into a single, consolidated dataset. The aim is to provide Brand Managers with a single source of truth that they have ready access to at all times, which helps them identify red flags and explore revenue growth opportunities faster than ever, via point-and-click root cause analysis, instant data pivot extraction, a simple search interface, and many other highly relevant features.

Brand & Sales Teams find it extremely easy to adapt to and use this data exploration tool as a complement to Excel and take their hypothesis testing speed to the next level.

  1. Microsoft Power BI

One of the most renowned Business Intelligence platforms in the world, Power BI supports dozens of data sources, allowing users to create and share reports and dashboards. Power BI comes with strong visualization capabilities, also giving users the option to merge reports and dashboard groups for straightforward distribution. 

Data exploration tool comparison: Power BI vs Explorazor

  1. Tableau

Tableau is again a very popular data visualization tool, competing with the likes of Google Charts, Grafana, Power BI, Qlikview, and others. 

Tableau dashboards provide users with advanced visualizations like motion charts, bullet charts, treemaps, box-plots as well as basic pie charts and histogram views.

  1. Looker Studio / Google Data Studio 

Looker Studio, formerly known as Google Data Studio, is a data visualization and dashboarding tool that helps create interactive reports and dashboards quickly. It is typically used to create ‘stories out of numbers’. One of the biggest advantages of Google Data Studio is the automatic integration it offers with many other Google applications like Google Ads, Google Analytics, and Google BigQuery. And it’s free.

  1. Looker

Is this data exploration tool the reason Google changed its name from ‘Data Studio’ to ‘Looker Studio’? We wonder…

Looker is a data visualization tool that is in direct competition with Power BI and Tableau. Instead of describing Looker’s capabilities here, we’ll leave you with a link that compares all the 3 tools with respect to certain parameters. Click here

  1. Datapine

Datapine offers dashboards according to function, industry, and platform for users to make data-driven decisions. It is suitable for beginners as well as advanced users, providing suitable features for both. Datapine’s advanced SQL mode lets users build their own queries. Overall, Datapine is focused on providing an interactive + fast BI experience.

  1. Jupyter Notebook

This web application is for developers to use live code for report creation based on data and visualizations. Jupyter Notebook is free and open-source, and is compatible with a browser or on desktop platforms.  However, Python’s package manager, pip, or the Anaconda platform have to be installed. Jupyter supports 40+ programming languages as well.

  1. ThoughtSpot

An analytics platform where users can explore data from multiple source types via natural language searches. ThoughtSpot is hugely successful with SpotIQ, its AI system, which located deep insights on its own, uncovering hidden data patterns and trends

  1. Domo

The Domo website describes it as ‘a low-code data app platform that takes the power of BI to the next level to combine all your data and put it to work across any business process or workflow.’ Providing +1,000 built-in integrations/connectors for data transfer, Domo also supports custom app creation to integrate with the platform, also allowing easy access to visualization tools and connectors. If you are a business that does not have your own ETL software and data warehouse, Domo could prove useful for you.

The Ultimate Data Exploration Tool?

Dare we explain what Excel does? 

Not in a hundred years.

We have, however, dared to identify some of Excel’s shortcomings when it comes to seamless data exploration. Scouring multiple files in Excel and extracting pivots from each proves to be tedious. 

To prevent productivity from being hampered, we developed Explorazor, a data unification platform that integrates all of a Brand Manager’s data into one single dataset. This includes Nielsen, Kantar, IQVIA (for pharma), and the common primary sales, secondary sales, media spends, etc. 

On Explorazor, users extract data pivots on the integrated dataset instantly, are able to conduct root-cause analysis on multiple datasets at a time, and query the data using an extremely simple search interface. 

This results in managers wanting to test out more hypotheses, conduct ad-hoc analyses themselves, and pry higher quality decisions from the same data that they previously worked on, on Excel.

Explorazor is a great fit for Excel wizards to work their magic better. Have a look at the website.

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The Not-So-Subtle Relationship Between Branding & Sales

by adminin Brand Recognition, Brand Trust, Branding, Data Exploration, sales

Today we’ll be talking about branding’s impact on sales using some examples from the FMCG industry. The purpose of this article is to convey, in no uncertain terms, that companies need to pay attention to and hammer down their branding strategies right now. We’ll also be exploring how ease of data analysis can help make better branding and sales decisions – and a very simple and effective method of easing data analysis. Let’s begin:

Function of A Brand – Seth Godin

You might have heard of various definitions of ‘brand’, but one of the most complete definitions that I have come across is from Seth Godin. I quote “A brand is the set of expectations, memories, stories and relationships that, taken together, account for a consumer’s decision to choose one product or service over another.” 

He further goes on to say “If the consumer (whether it’s a business, a buyer, a voter or a donor) doesn’t pay a premium, make a selection or spread the word, then no brand value exists for that customer”.

Why this quote is complete is because it outlines the benefits that companies get when they get their branding right –

  1. The consumer pays a premium to get your brand, simply by virtue of it being your brand
  2. The consumer at the very least chooses your brand over others, in the event of other factors, such as price, being the same
  3. The consumer herself begins actively engaging in promoting your brand via word-of-mouth

The right branding should get you sales and free promotion, per Seth Godin.

To get the branding right, one has to focus on branding in the first place.

Using Branding For Sales – Recognition & Trust! 

Now that the need for branding is established, let’s skim over the very first ingredients needed to get the branding underway. A foolproof method is to start off by building greater brand recognition and fostering brand trust. 

  1. Attention Grabber: Brand Recognition

The competition for grabbing the mental space of a consumer is always ON. Round-the-year branding, even though it may not seem to be the most impactful at times, readies the consumer for the moment-of-truth, when she is looking to make a purchase. Hardly a consumer knows the difference between Tide & Surf Excel, but almost every consumer buys on the basis of the perceived value they derive from the advertising campaigns of each brand. 

In other words, if they give first mental recognition to your brand when opting for a solution to their need, they are more likely to prefer your brand to others.

The right branding can even trump core value offered to consumer!

  1. Care & Nurture: Brand Trust

Brand trust is one of the biggest drivers of brand loyalty, repeat customer purchase decisions, and long-term customer satisfaction.

Case Study: HUL Star-Sellers

In around 1997, HUL wanted to set up distribution of basic necessities like oils, detergents, and soaps across all villages in India. Distribution was one thing; store acceptance was another. HUL identified local influencers in villages even with populations of less than 2000 people and used them as ‘faces’ of the brand to persuade retailers to stock their products and sell in the local markets. 

The branding was unconventional, but it hit the mark because HUL used the concept of brand trust as its base. 

You will find multiple other examples of HUL paying focused attention on creation of brand trust. Ventures like Project Shakti are another reason why HUL was able to not only create thousands of jobs and revenue for the company, but also forge a lasting impact on the masses that today holds HUL’s name synonymous with ‘trust’. 

From Cadbury to Pepsi…

Cadbury noted that the term ‘Eclairs’ was a commonly used term for a type of candy, and retailers were dishing out other brands in the name of ‘Eclairs’ instead of Cadbury’s well-known Eclairs. It undertook a product realignment campaign and renamed the product to ‘Chocolairs’.

Pepsi keeps changing its logos to keep up with trends, spending millions of dollars each time.

Tropicana’s package rebranding in 2009 for reasons similar to Pepsi’s, failed drastically, resulting in 20% year-on-year sales degrowth. As marketing professor and Ph.D. holder Mark Ritson noted, and we quote Brandstruck, the new design “achieved something Tropicana’s competitors had failed to in 20 years – a degradation of its brand equity and an undermining of its status as market leader.”    

There are hundreds of examples in the FMCG industry itself, of how brands spend time, effort, and money to brand and rebrand their well-established products.

Branding seems to be pretty important for all of these brands.

Is it for you?

An Important Sub-Component – Proper Data Analysis

Just like all the sub-components in a branding strategy pave the way for good branding, a company’s overall choices of people, processes and products combine to produce effective decisions that impact every facet of the company, including branding and sales. 

While we’re sure your choices of people and processes are most apt, we do have a proposal to add Explorazor to your product portfolio. 

Explorazor is a data exploration and analysis tool built to ease the daily tasks of Senior Managers in Brand & Sales Teams, who currently work on Excel. Explorazor does not replace Excel; we are interested in complementing Excel. You can also explore some ways Explorazor differs from Power BI.

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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.

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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. 

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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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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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4 Common Expectations that Brand Managers Have from A Data Exploration Tool

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

Excel is all-prevailing. A 2022 survey by Microbizmag estimates that Microsoft Productivity Services, which includes Excel, are used by 1.1 billion people on the planet. That’s approximately 1 in every 8 people alive. Launched in 1985, Excel is still the ultimate number-crunching tool today, with little competition. Capture data, mix-and-match, collaborate with colleagues on complex issues – Excel does it all. 

But there’s another part to it 

People do face some problems using Excel. It is tedious to delve into the seemingly never-ending maze of rows and columns, day in and day out. Many have to make do with that, but for Brand Managers, things can be simplified. 

There are solutions in the market that can help ease the daily operations of Brand Managers who use Excel to explore data and arrive at decisions. The proposal here is a data exploration platform that allows Brand Managers to mitigate Excel’s shortcomings, without having to completely revolutionize the way they work currently. 

Let’s approach this on an assumption that a Brand Manager is interested in such a tool, and naturally has certain expectations of it. Can the tool fulfill these expectations?

After speaking with many Brand Managers.. 

From top firms like Unilever, Abbott, SC Johnson, Asian Paints, and many others, we found the following 4 common expectations that Brand Managers have from such a data exploration platform if they are to consider using it:

1 – Brand Managers want data exploration on integrated datasets 

The planning and strategizing components are strenuous enough, in addition to the many other responsibilities BMs shoulder. They don’t need another tool to come in and create a mess; if they do go for a tool, it ought to simplify their routine activities.

Solution –  A method of simplifying data exploration that tools like Explorazor offer is the integration of multiple data sources under a single roof. Brand Managers would then no longer have to scour through multiple Excel sheets to find a singular piece of data. Simply hop on a platform that unifies all datasets, and extract the desired responses from it. 

2 – Brand Managers want the data exploration tool to respond quickly

A fair question that BMs would ask now is ‘How long will it take me to extract the right data points to obtain a response?’ 

The basis for such a question is that Excel typically hosts enormous volumes of data, and the whole process from data uploading to insights extraction is very slow. 

Solution – Explorazor’s simple search function yields real-time responses. Ask for any specific data cut simply by posing a question to the system, and a relevant chart/graph/table is readily presented. Since the whole dataset is integrated, one is already at the right place – there is no need to spend time finding the right sheet. 

One can download these data cuts as CSV files too if needed

3 – Brand Managers want pivot tables 

Not much needs to be said regarding pivot’s importance, and it’s perfectly safe to say that no tool would be worth its value if it doesn’t support pivots.

Solution – Create pivots on Explorazor by simply mentioning the column names you want a pivot on. Additionally, Explorazor’s pivot feature empowers Brand Managers to obtain cross-sectional data tables by using metrics from multiple data sources at a time.

4 – Brand Managers want a data exploration tool! (hidden expectation)

Brand Managers are incredibly busy, resourceful individuals who would love to have technology ease their daily tasks. 

Solution – Explorazor does not require a radical shift in the current working method of Brand Managers. It is just a unifying platform that brings together diverse data and analytics that drive value for an organization and seeks to simplify a Brand Manager’s data exploration journey as well.

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Why not take some time to see Explorazor in action yourself? 

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