Category: Explorazor

Category Management: A Key Guide for CPG Companies

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

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

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

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

What is Category Management?

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

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

Why is Category Management Important in the CPG Industry?

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

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

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

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

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

Benefits of Category Management

Some of the key benefits of Category Management include:

Increased Sales and Profitability

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

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

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

Improved Operational Efficiency

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

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

Better Understanding of Market Trends and Competition

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


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

Understanding the customer and their needs

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

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

Assessing the competition and market trends 

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

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

Developing and executing a Category Management plan

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

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

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

Steps/Guide to Implement a Category Management Strategy

Conducting a Category Assessment: 

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

Defining Category Roles and Strategies: 

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

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

Implementing Category Tactics: 

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

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

Evaluating and Adjusting Category Performance: 

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

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

Challenges of Category Management

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

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

Siloed organizational structures:

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

Lack of resources: 

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

Resistance to change: 

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

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

How Explorazor helps Fortune 500 Companies with Category Management.

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

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

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

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

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

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

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

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

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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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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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Positioning Your Brand to Drive Preference ft. Gartner

by adminin brand, brand advocacy, brand commitment, brand preference, Explorazor, functional benefits, Gartner, personal benefits, Positioning, societal benefits

Driving brand commitment is a major goal for organizations. Gartner defines brand commitment as ‘the degree to which audiences prefer the brand to alternatives (brand preference), feel a personal connection to it (brand connection) and advocate on its behalf (brand advocacy)’. Thus brand preference, brand connection, and brand advocacy are all subsets of brand commitment. 

Now, once solidified, brand commitment is more than just a regular revenue growth strategy. Brand commitment can drive customers to purchase your products at a premium. It drives customer loyalty and advocacy, where the customer promotes your brand on your behalf. Internally, employees already working with the brand seek to be retained, while human talent scouring for opportunities are attracted to your brand. Thus the need for creating brand preference and commitment is clear. 

Creating A Strong Positioning 

Now, there are 3 kinds of benefits that a brand can provide: functional, societal, and personal. An organization can choose to position itself using any one or more than one type of the 3 benefits, to initiate brand commitment. To drive preference, brands need to create strong positioning, which can be done by

  • Avoiding negative advocacy – this is done by branding through functional benefits
  • Communicating a personal benefit that a consumer can derive by being associated with your brand, and demonstrating simultaneously, how the fulfillment of that personal benefit leads to a ‘greater good’, i.e. societal benefit

Personal benefit is understood as a psychological need that a customer fulfills via brand association, while societal benefits range from ethical production, like zero or negative carbon emission, to any other sustainability initiative.

Positioning Through Personal Benefits, Or Using a Combination of Benefits?

Gartner estimates, from a 2022 research conducted among 1,999 consumers, employees, and B2B buyers, that while providing a personal benefit, like a sense of belonging or a sense of growth, is almost thrice as impactful as the other 2 types of benefits, the type of industry matters too. Positioning through personal benefits yielded the best rewards in the manufacturing, healthcare, and natural resources industries. Brand commitment in the technology industry is boosted through functional benefits, while the same connection works between retail and societal benefits.

Brands can use all the 3 benefits at once or combine personal and functional benefits for best results. The latter is because lack of functional benefits drives negative advocacy, so avoiding that, and inserting the impact of personal benefits for positive outcomes, is the best recipe. 

Societal benefits combined with personal benefits as your brand positioning to drive brand commitment is also good, but excluding personal benefits to combine functional and societal benefits yields the least favorable results, comparatively speaking. 

How Will You Position Your Brand?

Even if you miss out on customers actively advocating for your brand, or feeling a core connection with it, you can still focus on making your brand preferable over the others by choosing the right set of benefits as per your industry and other relevant factors. Just make sure that you include personal benefits in your brand messaging. 

Organizations can use these 9 categories as frameworks to develop their own positioning through personal benefits:

  1. A sense of belonging – Making customers feel like they are a part of a certain community
  2. Life purpose – Making customers feel like they can achieve their ambitions through your brand
  3. Growth – Self-explanatory; making customers feel like they can achieve personal development through your product/service
  4. Self-consistency – Basically telling the customer ‘You live a certain life; adopt our product/service to be consistent with the way you live your life’
  5. Autonomy – Helping the customer take charge of their life, or be independent
  6. Competence – Related to autonomy; helping people feel competent, or experts, in something
  7. Security – In other words, offering peace of mind
  8. Esteem – Telling the customer ‘Associate with our product/service and feel confident’
  9. Energy – Providing adventure, or entertainment, or the strength needed to go through life, as an offering

Regardless of How You Position It..

You will need all the intel on market, competition, forecasts, opportunities, threats, and a clear understanding of your own internal budgets, allocations, performance, etc. Right now, if you are doing it in Excel, we have a better proposal for you. Explorazor is refreshing the way users explore data by consolidating all datasets that an organization possesses and bringing it under a common Explorazor roof. There, they can extract data pivots instantly and conduct actual root-cause analysis on the consolidated data. 

Users work faster on Explorazor, because they have ready access to all the data they need, pivot extraction is instant, and their laptops operate faster than before due to data being held in server as against the browser.

Explorazor users, typically Brand or Sales Managers, depend less on the Insights Team for their analysis and test out far more hypotheses than before. 

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We credit Gartner with all observations taken from their survey report

Data Consolidation – The Need of the Hour for Brand & Sales Teams

by adminin data consolidation, Excel, Explorazor, root cause analysis

In this blog, we’ll be highlighting some issues that Brand & Sales teams face with data on a daily basis, and making a case for how data consolidation can remedy these core issues:

So Many Data Sources, So Little Time

Brand & Sales teams deal with so many different datasets at a time: there’s primary sales, secondary sales, MS Value, Media Spends, Research Data from Kantar, Nielsen, or IQVIA, depending on the industry; and more. 

To manage the plethora of data, professionals mainly use Excel. The research we conducted at vPhrase, interviewing 100+ experienced industry professionals, validated many of our hypotheses when we initially started out developing Explorazor, the data exploration tool designed especially for frictionless data exploration. Some of these hypotheses were:

  1. Managers use Excel by default – without any additional support

Excel has become one of the constants of life – all operations are conducted on Excel, without any other tool to even support or augment it. Power BI does extend some value, but it’s meant for dashboards and not exploration.  

To think of replacing Excel was not even in the minds of the professionals we interviewed.

  1. Excel is great – but it does pose some problems

For what man, walking the face of this earth, can deny Excel’s greatness? Shakespearean passions aside, Excel is THE standard for a reason – it facilitates data analysis, holds enormous datasets, enables pivot extraction, conditional formatting and n other functions.

However, we believe that there is an easier way for Brand Managers to conduct data exploration and analysis, without leaving Excel entirely.

As such, we spoke about it with Senior Managers – they agreed that while Excel is the go-to for all number crunching, insight extraction and strategy formation, having to work on multiple datasets means more time consumption and manual work. Laptops process data slowly as compared to a cloud server, which also contributes to time consumption. Furthermore, due to fragmented data storage, managers often have to rely on Insights teams for ad-hoc analysis and crucial insights, something they would rather prefer to live without.

After validating both our hypotheses, we presented Explorazor to our audience – and asked them to gauge one central benefit that Explorazor provides:

Data Consolidation – The Answer To Many of Excel’s Drawbacks

Explorazor is a data exploration tool that lets users conduct queries, obtain data pivots, and conduct root cause analysis via point-and-click, on an INTEGRATED, CONSOLIDATED dataset. There are various industry terminologies going around, like data stitching, data consolidation, unifying datasets, combining datasets, etc., all refer to the same thing. The Explorazor team cleans, standardizes, and combines the datasets for a single-platform usage through Explorazor.

A clarification here: Explorazor complements Excel, and does not replace it.

An Integrated or Consolidated Dataset Means 

  1. Better correlation between data points

Your primary sales is doing good, but your call average has actually been declining since COVID. Such data correlation is easily obtained on platforms like Explorazor. 

Similarly, Dolo made huge sales during the pandemic, but it actually sold more due to HCP recommendations and ad campaigns, rather than calling and field sales efforts. Now that the pandemic has receded, it comes to light that the rural areas have largely been ignored and a sizable chunk of sales comes only from select urban areas.  

Such data exploration and correlation is much easier on an integrated dataset.

  1. Better root cause analysis

We’ve written a separate blog just covering this point. Explorazor helps users arrive at the ACTUAL root cause of events, because users can conduct drill-down and drill-across on the entire data at a time. 

It’s all about better decision-making.

  1. Time and Effort Efficiency 

Managers spend more time testing hypotheses and conducting ad-hoc analysis independently, without having to revert to Insights Teams. Explorazor lightens the laptop burden of processing huge datasets by storing data on server, accessible via browser. 

An Integrated or Consolidated Dataset Also Means 

  • Faster analysis, faster laptops
  • Better, and easier analysis
  • Greater Independence for Brand Managers
  • Greater space for Insights Teams to focus on long-term strategies
  • A space for users have ready access to required datasets
  • A space for users to collaborate on projects
  • A data-driven work culture
  • Greater revenue 

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How Data Analysis Helps Marketers

by adminin data analysis, Explorazor, marketers, Marketing

Previously we discussed the not-so-subtle relationship between branding and sales. Now we’ll see 3 ways how data analysis serves marketers, namely plugging their spendings, achieving precise segmentation and targeting, and assisting in creating personalized offerings for customers.

We also propose integrating your datasets into one standardized, consolidated dataset that helps you conduct data analysis much faster and better than before. We’ll discuss ‘how’ later in this blog, under the heading ‘How to consolidate the Data Analysis process’.

Let’s begin:

How Data Analysis Helps Marketers: 

  1. Plugging Spendings

Marketers previously had little idea where they spent their budget. They just did so on the basis of their experience and intuition, with little data to back it up. If the campaign failed, the corrective course of action too was based on assumptions, experience and intuition, since the manager of the past 

  1. Did not have access to the data points he needed
  2. Could not / would not utilize technology to analyze the right data points

Hold on, are we describing the marketers of today?

Unfortunately, we are partially doing so. This is because while access to the data points is no longer as big an issue, the inability or unwillingness to analyze the data to make data-backed decisions is still something prevalent amongst marketers. One of the primary reasons found is along the lines of ‘Oh, marketing is not an exact science, so we cannot treat it as such. Marketing is equally an art as it is a science’. And while the creative element will always remain, the fact is that marketing is increasingly turning into a science. The best marketers no longer rely on just themselves; they let the data influence their decisions. 

Data analysis helps marketers know where they are spending their dollar, so they can avoid ineffective spending. The need to spend budgets to test hypotheses that may prove to be baseless is eliminated. Additionally, marketers can, within the same budget, conduct A/B Testing and undertake more campaigns than before to increase their reach, awareness and revenue. 

Mapping out and optimizing the logistical costs of making products available in stores is done by modeling KPIs in Excel. Similarly, data availability and analysis drive budget allocation, for example, allocation for LinkedIn advertising

  1. Helping Achieve Precise Segmentation & Targeting

The rise of omnichannel marketing is a clear indication that customers are more distributed than ever. The opportunities for reaching the target customer have increased, but so has the difficulty. Not to mention the competition. 

The fundamental questions of ‘who are we selling to’ and ‘how are we going to approach them’ are answerable only when the right data is present at the right time. Marketers have to use data to answer questions such as

  1. Who is our target audience?
  2. Where does our target audience lie / what channels do they consistently use/are exposed to? How many channels do we want to be present on?
  3. Based on our product, budget, and other influential factors such as logistical reach and size of workforce, what type of targeting should we adopt? Mass targeting, differentiated, niche, or microtargeting being some options
  4. What is the competition doing in the market? What parameters does he work on?

3. Helping Provide Personalized Offerings

Precise targeting also helps marketers create a database over time that they can use as a reference for providing personalized products, services and experiences to the customer. 

With consistent maintenance and upgradation, this database can serve as a pivotal point where the customer prefers your brand and your brand only. 

There has been ample research showing that customers are more satisfied, more likely to complete a purchase, and even willing to pay more when offered a personalized product/service as compared to a non-personalized one. The trend is only growing with Generation Z, who are more willing to share their personal details to receive personalized recommendations in return.

Leveraging both big and small data to achieve this is the way to create a lasting and authentic bond with customers.

How To Consolidate The Data Analysis Process

Our proposal is very simple: gather all of your present and incoming data under a single platform – Explorazor. Conduct queries on the integrated, standardized dataset, and you will receive instant data pivots. It’s a no hassle process that accelerates data analysis, empowering marketers with high-quality information and leaving them with so much more time on their hands to do stuff more productive than extracting insights from multiple datasets stored in different files. There’s so much more to it, like downloading of required data pivots as CSV files if needed, ability to drill-through and drill-across any metric of interest, customization options, and other features built especially for Brand & Sales Teams and Managers.

We encourage marketers to do more with their marketing strategies and campaigns by leaning more on data analysis than intuition and experience, and we encourage using Explorazor to expedite the process of extracting data pivots from data. 

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In Retail, Building the Right Omnichannel Strategy is Everything

by adminin Customers, Explorazor, Omnichannel, Retail, sales

Let’s look at the retail landscape today and how important an omnichannel strategy can prove to be in efficiently marketing a brand and driving revenues.

THE NEED FOR AN OMNICHANNEL STRATEGY:

As many times as you have read it, the truth stands that the pandemic initially forced customers to shop online, and in time, customers adopted digital services big-time. This is evidenced by the fact that a whopping 20 million people in Southeast Asia alone converted to being digital customers in the first half of the year 2021. Online searches for terms like ‘instant delivery’ have shot up by 215% in India, just 2021 to 2022. 

The conclusion is that customers today are digitally present, and experience content and brands across so many touchpoints, that it is mandatory to have an omnichannel strategy in place for your brand.

An omnichannel strategy is a cohesive sales and marketing approach that seeks to use every customer touchpoint to provide a consistent and effective customer experience. It involves offering a company’s products & services at all customer touchpoints. Such touchpoints include digital channels like web and app, physical, brick-and-mortar experiences, and any other platform or device that a customer accesses.

‘Effective customer experience’ here means that a customer is satisfied with the product and/or while the company simultaneously generates revenue from that activity. 

2 WAYS AN OMNICHANNEL STRATEGY HELPS COMPANIES:

  1. Increased customer satisfaction – and company revenue

Let’s understand the power of omnichannel strategy in achieving customer satisfaction while simultaneously increasing revenue. 

Consider the example of Petco Health and Wellness, an American pet retailer selling pet food, products, and providing related services. During the pandemic, it understood that its competition and other third-party online retailers were not able to fulfill orders within time. Petco introduced ship-from-store options where customers could browse and purchase online, and pick up the same from physical stores nearest to them. 

Petco met the customers where they were, and provided a key micro-service that made customers love them – and made them some money. Petco’s acquisition costs were cut down by two-thirds, and they recorded a 100% year-on-year increase in sales.

  1. High CLV and retention rates 

Once sold to, they need to be retained. If you’re wondering who ‘they’ are, you’re probably working too late and need some sleep.

Back to the point. Once sold to, customers need to be retained. Creating an omnichannel presence, or being ‘omnipresent’ lets customers interact with your brand wherever and whenever they choose to. It lets the customers dictate what they want the brand to do, and brands can use this opportunity to foster real-time customer engagement and create lifetime value for the customers. 

We’ve covered the ‘how to do it’ in a related blog ‘Getting Omnichannel Right in Retail‘, but just factor in what you’ve read till now and the fact that the global e-commerce share of retail sales is expected to increase to a staggering 24% by 2026, and you’ll see that there’s no doubt that the companies absolutely need an omnichannel retail strategy.

SOME PREREQUISITES FOR BUILDING AN OMNICHANNEL STRATEGY:

Before you go about building an omnichannel presence, here are some of the things you need to have in place. Keep in mind that the list is more exhaustive, and below points are indicative of the nature of preparation you need to undertake. 

We can also help you with one of the rather important points..stick till the end.

  1. Creating/Mapping Customer Journeys 

When managers and teams work to create a framework for understanding customer journeys and how they react in certain recurring situations, for example, a festival that comes along every year, they are able to understand what the customer wants, and provide it to them. 

  1. Knowing who you are targeting

Let’s get this point through with an example. Think With Google shared crucial information for marketers wanting to reach audiences in Indonesia in the month of Ramadan. The data divided customers into 5 segments:

  1. The devoted prayer
  2. The homemaker 
  3. The Ramadan groomer 
  4. The tech followers, and 
  5. The home-comer

With almost the entirety of Indonesia following the religion of Islam, access to such data proves invaluable when slicing the total audience according to the right kind of demographics.

  1. Knowing what you what to communicate

Once you have the right audience figured out, taking the right message to them is equally important. Create a crystal clear overarching brand positioning that you want to reach to reach your audience with

  1. Conducting quick data analysis 

Driving real-time sales and delivering personalized CX in an industry where customers display volatile, or easily influenced, behavioral patterns requires lightning-fast data analysis. And this is where we believe we can help companies.

KICKSTART YOUR OMNICHANNEL STRATEGY WITH …

In the quest to deliver a standard, unified experience to customers, we’re proposing that you work on a standardized, unified dataset as the starting point of your omnichannel strategy. Our data exploration tool Explorazor is built specifically to help brand teams arrive at high-quality insights in an easier and faster manner than their current mode of working, which is primarily on Excel. The usage is very simple – query the integrated dataset using standard keywords such as ‘MS Value’ for Market Share Value and get instant data pivots. 

Explorazor is also infused with seamless root cause analysis, where managers can identify areas/events of concern via simple double-clicks. Other features such as pivots being downloadable as CSV files, various customizable options and chart style settings, time-period recognition (which is not present in BI Tools such as Power BI and Tableau) make data analysis so much easier, faster, and better for managers.

There’s no reinventing the wheel – one doesn’t have to completely leave Excel to use Explorazor either. Explorazor simply simplifies work done on Excel, to frame it as such. 

Multiple Brand Managers from Fortune 500 love Explorazor. As one of them shared his opinion with us “Explorazor is a more intelligent Excel to me”.  

Start with Explorazor, and end with more effective omnichannel strategies, optimized media spends, and higher revenues. Contact us at support@vphrase.com for a free trial and/or connect with our solutions consultant for a free demo.

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Interested in Becoming a Brand Manager? Know your IQVIA Data!

by adminin Brand Managers, data pivots, Explorazor, IQVIA, pharmaceutical, prescription, sales data

This blog aims to introduce budding Brand Managers to IQVIA and some of its data columns, to help them understand how IQVIA data helps Brand Managers in the pharmaceutical industry achieve their objectives. 

IQVIA, as its official website introduces, ‘is a leading global provider of advanced analytics, technology solutions and clinical research services to the life sciences industry dedicated to creating intelligent connections that deliver unique innovations and actionable insights’. We have also written similar articles on Kantar and Nielsen data, which you can find in our blog section.

Brand Managers in the pharmaceutical industry use IQVIA data to develop innovative strategies and drive brand sales and adoption. As we mentioned in a similar blog ‘Know Your Nielsen Data’ becoming a Brand Manager requires superior data handling skills and a pragmatic approach to data, where one can arrive at high-quality, real-life conclusions looking at hard numbers.

Just a word: Explorazor is supporting Brand Managers big-time with respect to analyzing their data. More on that later. And yes, Explorazor differs from Power BI.

IQVIA helps Brand Managers in the pharma industry:

  • Take decisions regarding brand expansion and advise ways how they can go about doing it, like analyzing growth potential, evaluating pipelines, understanding risk-opportunity ratio, the investment landscape, and more 
  • Adhere to market requirements, identify regulations, licenses, valuations and any potential market hurdle that may arise
  • Address customer needs better, by sharing information on the market behavior, competition’s performance, customer psychology and behavior, and even mapping a customer’s purchase journey
  • Achieve brand differentiation through timely delivery of evidence-based insights from across the globe. IQVIA’s competitive tracking processes cover and share information from more than 75 markets worldwide
  • Other ways that IQVIA data helps Brand Managers include risk evaluation and mitigation, networking with experts across clinical functions and obtaining intel & tracking of 45,000+ drug profiles and 10,000+ drugs

SOME DATASETS THAT BRAND MANAGERS DEAL WITH – IQVIA DATA

1. Sales Data

As with all other industries, the first thing that a Brand Manager in the pharmaceutical industry will look at is the performance of their Brand in the market, thereby referring to the sales data. There are 3 components to the IQVIA sales data:

  1. Sales Value
  2. Sales Volume
  3. Market Share

This sales data received from IQVIA is already classified geography-wise and product-wise as prescribed by the company.

2. Prescription Data

  1. Prescriptions Count

These are the total number of prescriptions via doctors recorded for a product

  1. Prescriptions per doctor (P/D)

 P/D refers to the avg. numbers of prescriptions for a brand.  It is captured specialty-wise, for example, General Physicians, Diabetologists, Oncologists, etc., and bifurcated on a Zonal level. The P/D ratio lets you know about the key specialties that contribute to the sales of your brand in the market.

3. Supply Chain Manager

From the manufacturer to the wholesaler to the final chemist or the outlet location, this dataset helps Brand Managers track end-to-end product flows. Logistics is a highly lucrative industry in and of itself if done right, and such datasets hold massive monetary implications for the company. It also helps the company be available where customers need them, where the market is thriving, or where there’s a gap to be exploited

4. Longitudinal Patient Data (LPD)

LPD data provides pharmaceutical companies with an understanding of disease treatment and how General Physicians are prescribing cures for them. This helps in new product development, as well as the evolution of current products in the portfolio. Another strong benefit of such a dataset is realized when formulating effective sales strategies for the on-field reps. 

There are many such datasets that Brand Managers work on. But here’s an important point:

DATA IS ONLY AS GOOD AS ITS LEVERAGE

We see from the above points that data literally can potentially impact everything – sales, customer service, supply chain infrastructure, competitive environment, etc.  What’s left is to

  1. Extract the best possible insights from it 
  2. In the minimum time possible 
  3. Another key element is to extract a higher number of high-quality insights from data within the same time frame.

Explorazor by vPhrase helps Brand Managers do all of the above. 

LEVERAGE DATA OPTIMALLY USING EXPLORAZOR 

Instead of multiple files from different data sources, the company’s own data sets, etc. what Brand Managers can do is simply choose to view a single, all-inclusive/integrated dataset on Explorazor, query it via simple keywords, and receive data pivots – at their fingertips.

Let’s just put some of the benefits in pointers, for easy reading:

  • The dataset is standardized, so manual labor is saved there 
  • One can start using Explorazor within the day, so there are no hiccups in adoption
  • Due to the integrated dataset, the extracted insights are high-quality
  • The lightweight design interface is custom-built to deliver speedy responses
  • All of this culminates in a Brand Manager wanting to dive deep and test out more hypotheses than before 
  • Speaking of deep dive, Explorazor also supports drill-down and drill-across into a particular data point. Simple click-and-dig, that’s all. See the image below

Some additional benefits: 

  • Tables can be converted to charts, graphs, and multiple other handsome-looking visuals (did we say handsome instead of beautiful? Oh well!)
  • Any data pivot can be transported to Excel by downloading it as a CSV
  • Any data pivot can be pinned to the ‘Dashboard’ for easy viewing 
  • In-project collaboration with team is possible via tagging/assigning of activities

And most important of all,

Custom made for Brand Managers, and that too primarily in FMCG and pharma. Of all the designations, we chose to dedicate our skills to help Brand Managers ease their daily activities, ironing out many data-related inconveniences they face. Explorazor continues to develop and provide a niche solution for Brand Managers.

Explrazor is a product of vPhrase Analytics. If you want to try out Explorazor for yourself, contact us at support@vphrase.com. It’s free, and it’s fun.

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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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4 Ways Explorazor Differs from Power BI

by adminin Brand Managers, data pivots, Explorazor, Power BI

In this blog, we are going to explore 4 ways Explorazor differs from Power BI. Power BI is a data visualization tool that allows users to build interactive dashboards and BI reports. Explorazor is a data exploration and analysis tool that helps Brand Managers obtain data pivots instantly, through simple querying on an integrated dataset that combines Kantar data, IQVIA, Nielsen, primary sales, secondary sales, and more.

The common base between Power BI and Explorazor is that they both intend to help users make better sense of their data, and subsequently improve the quality of decision-making. 

How they do this is very different, and this is what we are going to explore:

4 Ways Explorazor Differs From Power BI

  • Made for Everyone 

One of the primary ways Explorazor differs from Power BI is that Explorazor is build for everyone from Business Users, Business Analysts to Data Analysts. Where Power BI helps Business Intelligence professionals and Data Analysts across industries, the ease of Explorazor, of searching using keywords makes it easier for every persona to perform analysis and take action real time.

Everything in the Explorazor UX/UI is upfront, neat, and conveniently placed. The left panel contains the Dimensions and Measures, the search box is at the top, and the answer covers most of the screen in the center. 

There are customization options available to convert tables into charts and graphs, but Explorazor is focused more on ease of usage and simplified presentation easier to perform ad-hoc analysis in real time and helps them obtain instant data pivots, rather than the more intricate, perhaps complex, dashboard and report building customization options provided in Power BI.

  • Smartness 

In no way are we suggesting that Power BI isn’t smart. Far from it. Users using Explorazor find that it is smarter ‘in context’. Let’s see an example:

Business User from the Marketing Team need to see the monthly/quarterly trends of a metric, say Market Share Value.

Explorazor supports this search query:

Likewise, as you will see in the next point, Explorazor not only supports time-based queries, but does that in an intuitive fashion. Because we have built the tool specially for everyone who wants to analyze data on fly, we understand the power of efficiently furnishing these basic results.

Explorazor’s has a simple purpose, and it’s a powerful one: whatever basics Users need, furnish them instantly.

  • Intuitive 

Explorazor lets you achieve a single result through multiple, natural ways. Look at this image as an example:

Whether you type q2 22, q2 – 2022 or quarter2 2022, you can expect a response to be produced. Explorazor basically understands the intent of your questioning. 

Another example:

Users are also able to compare custom time periods, like 

  • Sept 2022 vs Sept 2021, or 
  • November vs April

On a related note, search functionality is straightforward too: If you have multiple KPIs and cannot exactly determine your search query, just start typing and the system will prompt suggestions. 

Another method is to scroll through the left panel and double-click on the metric to add it to the search query.

  • Deep Exploration 

If there are certain parameters in Power BI which you want to look at but which are not present on the dashboard, you cannot do so until you edit the entire dashboard. 

With Explorazor, users can drill down into as well as drill across a particular data point. Root cause analysis is performed below on the Market Sales Value. We choose to drill further by ‘location’ and are immediately able to identify that Kansas and Texas seem to be areas of bother.

Moreover, one can go from a brand to a sub-brand to an SKU on Explorazor – through simple clicks. 

To Conclude

Explorazor is not here to compete with Power BI. It simply seeks to engage Business Users and Data Analysts in comparing the utility of both tools in their daily activities. Explorazor is designed specifically for Brand Managers – so why not explore it? 

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