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CPG Jargon Buster Master Article

January 20, 2023August 8, 2023 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

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

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?

%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?

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:

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:

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:

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

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:

Household Penetration

How many households are buying my product?

Buying Rate

How much is each household buying?

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

Purchase Frequency (Trips per Buyer)

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

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

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

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

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

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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ALL About Velocity / Sales Rate in CPG

January 9, 2023August 8, 2023 by adminin CPG, FMCG, Nielsen, Nielsen Database, VelocityLeave a Comment

Blog snapshot:

What is velocity, and why is it important to focus on this measure? After learning about ACV and %ACV in our CPG Jargon Buster Series, let’s have a look at what velocity is, its relation to sales and distribution, how to calculate it, and what the two major velocity measures are:

WHAT IS VELOCITY?

While distribution tells you how well your product is distributed in the market, or how widely available it is, velocity tells you well it sells once it is on the shelf. Velocity is the measure you want to look at when judging which product is the best-selling or most preferred by consumers, not distribution.

VELOCITY’S RELATION TO SALES AND DISTRIBUTION

When velocity and distribution are combined, one arrives at retail sales. Thus,

Sales = Velocity x Distribution.

CALCULATING VELOCITY

The formula to calculate velocity is derived as:

Velocity = Sales ÷ Distribution.

TAKING CHARGE OF SALES THROUGH VELOCITY

It is generally considered that distribution is in the hands of the distributor, and the manufacturer can always follow up with the distributor for better product availability across geographical areas. However, if the product is not moving off the shelf, meaning that velocity is low, then the manufacturer has greater control over being able to change that.

Let’s understand this through an example, for greater clarity. Suppose 2 products, A and B, are sold equally in a market of 100 stores. Product A has good distribution but low velocity while product B is vice versa.

The table is as follows:

Market of 100 stores	Sales =	Distribution (x)	Velocity
	(units)	(stores)	(units/store)
Product A	6000	60	100
Product B	6000	100	60

We see that although distribution for product A is not very impressive, the velocity, or the speed at which the product is selling in these stores, equalizes the sales of Product B, which, although present in all 100 stores, only manages to sell as much as Product A.

In the case of Product B, the manufacturer must have a closer look at his pricing and promotional strategies. Why are people not preferring the product even when it’s available to them in the outlet? Are my competitors outdoing me in those areas, or is their product quality better, or better suited to the audience I am trying to capture? Questions like these need to be raised and answered asap.

Tools like Explorazor and its root-cause analysis function can help a lot here.

TWO MAJOR VELOCITY MEASURES:

The example we described above was one of ‘Sales per Store’. This, however, is not and should not be used in real-world scenarios as store sizes differ, which leads to biases when estimating velocity.

When looking at sales for a single retailer or within a single market, we go with the first velocity measure – Sales Per Point of Distribution, or SPPD.

SPPD = Sales ÷ %ACV Distribution

SPPD is great for understanding where the root cause of a problem lies – is it in the distribution, or the velocity? Let’s understand this further with an example:

Mumbai Market
		Distribution	Velocity
Brand	Sales (in Rupees)	%ACV Distribution	SPPD
Product 1	65000	80	812
Product 2	95000	75	1267
Product 3	70000	15	4667
Product 4	80000	20	4000

Above is an item level report for an individual market. We see that Products 1 and 2, although impressively distributed, but have poor velocity. The opposite holds true for Products 3 and 4 – %ACV is poor, while velocity is great.

Note that SPPD works only for one market, be it at the retailer level, the channel, market, or the national level. When comparing across markets, SPPD doesn’t work. Also note that a 100% or close to 100% market distribution will mean that velocity and sales will almost be the same, so managers can overlook velocity in favour of focusing on sales only.

Sales per Million

In a cross-market comparison, certain markets are naturally bigger than others. In other words, the ACV of a Large Market, call it Market L, is bigger than the ACV of a smaller market, Market S.

This is where Sales per million comes in, because it accounts for the ACV of each individual market in the denominator.

Sales per Million is calculated as:

Sales

%ACV distribution X (Market’s ACV ÷ 10,00,000)

Note that ‘Sales ÷ %ACV Distribution’ is nothing but the formula for SPPD. Market ACV, as explained above, has to be taken in the denominator to account for the size difference in ACV.

Regarding the ‘in millions’, Market ACVs are large numbers, and we simply ease our calculations by denoting them in millions.

Let’s compare Mumbai, a bigger market, to Pune, which is 3 times smaller:

Mumbai vs Pune market comparison

Clearly, Pune’s numbers are lesser than Mumbai’s because of the size discrepancy. In comes Sales per Million to level that out.

Example of how we calculated Sales per Million (in the below table) using information from the above table:

For Product 1, Mumbai –

Sales = 65,000

%ACV Distribution = 80

Market ACV Size = 120 million

Sales per Million

= 65000 ÷ [(80/100) x (120 million / 1 million)

= 65000 ÷ [0.80 x (120)]

= 677

Similarly for all.

Notice that Pune’s sales compared to Mumbai

For Product 1, is almost equal
For Product 2, not far off
For Products 3 and 4, is miserably low

Without the Sales per Million calculation, Pune as a whole would have been swept under the rug under the guise of ‘It’s a small city, hence our products don’t do well there’. But conducting the above analysis clearly demonstrates that Products 3 and 4 need a lot of attention if they are to sell in Pune.

Some Notes:

Sales per Million can be used within 1 market as well, if you want to keep your velocity measures uniform throughout. SPPD is easier to use than Sales per Million, hence people prefer that too
Velocity is uber-important. Hope we didn’t fail to convey that!

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

January 6, 2023August 8, 2023 by adminin AIC, Average Items CarriedLeave a Comment

Welcome to another blog in the CPG Jargon Buster Series. Today we’ll be gaining clarity on what ‘Average Items Carried’ is and how it is related to %ACV. We’ll also learn how to calculate it in Excel, in case the measure is not present directly in your database.

WHAT IS AVERAGE ITEMS CARRIED/SELLING?

As the name suggests, it is the average number of items that a retailer carries, whether of a brand, category, segment, etc. A brand may carry 7 items or SKUs under its name, and on average, a retailer may carry 2, or 3.5, or 5.8 items of that brand.

How we arrive at this number is through 2 ways – either it is readily available in your Nielsen data as ‘Average Items Carried’ or in your IRI data as ‘Average Items Selling’. We can also calculate it in Excel, as we will see later in this blog.

AIC is one of the 2 components of Total Distribution Points (TDP), with the other being %ACV Distribution.

HOW IS AVERAGE ITEMS CARRIED RELATED TO %ACV?

Just like %ACV, Average Items Carried is related to the quality of your distribution efforts. While %ACV tells you about the breadth of your distribution efforts, AIC/AIS focuses on the depth of your distribution efforts.

Consider this simplest of examples that illustrates how perspectives can shift based on whether you are looking at %ACV or AIC. Suppose there’s a category containing 3 brands, with brand distribution as follows:

Brands	%ACV Distribution
A	95
B	92
C	90

We observe that Brand A had the best %ACV Distribution. However, this is the conclusion without consideration of Average Items Carried within each brand.

Let’s look at the AIC:

Brands	Average Items Carried
A	10.5
B	12.5
C	13.5

Here we see that Brand C has the largest number of items carried by outlets/retailers.

Simply looking at %ACV without considering AIC is not where you want to be as a Brand Manager looking to uncover new growth avenues. To reinforce what was mentioned earlier, %ACV and AIC are two components of TDP, and optimal data analysis assigns importance to both.

EXAMPLE – HOW WIDTH AND DEPTH MATTER IN DECISION-MAKING

Assume that only two Brands, LG and Samsung, are present in a market.

LG offers 4 items/SKUs and is present in 60 stores.

Samsung offers 8 items/SKUs and is present in 70 stores.

In table format with additional information:

# of items in stores
	LG (present in 60 stores)	SAMSUNG (present in 70 stores)
Item 1	60	30
Item 2	65	35
Item 3	70	30
Item 4	55	30
Item 5	–	35
Item 6	–	35
Item 7	–	45
Item 8	–	40
Total	250	280

Now, for LG:

Average number of items of LG in stores:

= 250 / 60

= 4.16 items

LG’s efficiency rate:

= Average number of items of LG in stores / Total items that LG offers

= 4.16 / 4

= 1.04

Similarly for Samsung:

Average number of items of Samsung in stores:

= 280 / 70

= 4 items

Samsung’s efficiency rate:

= Average number of items of Samsung in stores / Total items that Samsung offers

= 4 / 8

= 0.50

Conclusion: While Samsung had greater distribution width by being present in more stores than LG (70 to 60) and more items listed to be sold (280 to 250), LG had greater distribution depth as is evidenced by its higher efficiency rate. This means that while Samsung is more widely distributed in the market, it is not as successful as LG when it comes to securing distribution depth.

HOW TO CALCULATE ‘AVERAGE ITEMS CARRIED’ IN EXCEL

Very straightforward: you will have a %ACV of, say, a Brand, and the %ACV of all the items (or SKUs) within that brand. Now,

Add up the %ACVs of all the items/SKUs
Divide by the %ACV of the Brand

Cooking up an example:

	%ACV Distribution
Total Brand	90
Item 1	80
Item 2	45
Item 3	25
Item 4	30

Adding up all the items:

80 + 45 + 25 + 30 = 180

Dividing by 90, we get the AIC as 2.0. We infer that retailers, on average, carry/sell 2.0 of the 4 items offered by the Brand.

Hope you found this blog helpful, and do not forget to refer to our CPG Jargon Buster Master Article for knowledge on the various CPG concepts. We’re building a product centred around Managers in CPG and Pharma cos only, so if you’re interested in exploring the niche Explorazor, you’re most welcome to!

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What is %ACV?

December 25, 2022August 8, 2023 by adminin ACVLeave a Comment

In this blog, we’ll understand precisely what %ACV distribution is, and why you as a manager should be paying maximum attention to it. The contents of the blog are as follows:

What is %ACV
Why managers should care about it
How to calculate %ACV, and
Some points to consider when using it in your data analysis

The total sales made is the bottom line of all your efforts, but for a commodity to sell, it needs to be present in stores, and the right ones at that. Distribution, therefore, is widely considered as the most important sales driver, and %ACV helps you get your distribution right.

WHY SHOULD MANAGERS CARE ABOUT %ACV?

Here are some reasons:

%ACV helps managers understand the quality of their distribution networks, so they are not deceived into feeling cozy because their products are seemingly well-distributed, when, in fact, they might be well-distributed only at the surface level. %ACV can answer why certain products are not selling in an area despite widespread distribution in that area.

On the other end of things, CPG managers get to know which retailers are the fastest at moving products off their shelves, and categorize them as such. Managers can then focus on specifically targeting these stores and ensuring distribution’s on point there. Knowing which stores are the best performing also provides a blueprint which can be referred to and possibly replicated.

If managers care about their distribution goals, and what’s really going on at the store-level, they should care about %ACV.

WHAT IS %ACV DISTRIBUTION?

%ACV Distribution, simply known as %ACV, stands for All Commodity Volume. It is a metric that can be understood as the ‘percentage of stores selling, where each store is prioritized based on its size’. This figure is then compared to the sales of other (rival) retailers, territory-wise.

Now, size here means the total annual sales of the store, called All Commodity Volume (ACV). This means that the larger the store you are present in, by (ACV) size, the more weight is assigned to it.

However,

IT’S ALL ABOUT SCANNING

Being present in a large store means nothing if your product is not getting scanned. Your brand may have a dedicated shelf or shelf tag in a store, but if

The product’s out of stock, or
Is in stock, but is not moving out (customers aren’t purchasing it)

it won’t be captured under %ACV distribution in the Nielsen and the IRI data.

%ACV distribution helps managers understand the quality of their distribution networks. The golden word to gauge quality is ‘scanning’.

HOW IS %ACV CALCULATED?

The formula to get Retailer %ACV is this:

(ACV of that retailer/ ACV of all the retailers) * 100

The City of Mumbai has 3 retailers (oh, the oversimplification) selling your brand.

Assume the details are as such:

	No. of stores	ACV (Rupees)	% of stores	%ACV
Retail Store 1	50	80 Mn	50%	40
Retail Store 2	30	100 Mn	30%	50
Retail Store 3	20	20 Mn	20%	10
Total	100	200 Mn	100%	100

Now, if your brand is present in Retail Store 1 and Retail Store 2, then the distribution by % of stores is 80%, but the distribution by %ACV is 90

How we arrived at 90 for %ACV is thus:

[(ACV of Store 1 + ACV of Store 2) divided by Total ACV]

(80 + 100) divided by 200 = 90.

The entire column of ‘%ACV’ is similarly calculated.

Similarly, if your brand is present in Store 1 and Store 3, then the distribution by % of stores would be 70%, but the distribution by %ACV would only be 50%.

Studying these two scenarios in light of the %ACV distribution metric helped us understand the classical ‘I am present in many stores, therefore I should be selling more’ mistake that a manager may make. Store 2 is clearly the most valuable store, even with a lesser number of outlets (30) than Store 1 (50).

To revise, %ACV is meant to categorize, or value stores based on their ACV size, which is the total annual sales of a store, and target the largest store.

SOME POINTS TO CONSIDER WHEN PERFORMING %ACV DISTRIBUTION

When using %ACV distribution in your data analysis, keep in mind the following points:

Scanning = Quality of distribution

An actual product scan is what counts – and it’s all that counts. Nielsen does not consider your product to be distributed when it is sitting in a store shelf and not moving out, and you should follow the same reasoning. Retail authorization means nothing – our focus is on the quality of distribution

Can’t add distribution up

%ACV distribution is non-additive, meaning that if one UPC (Universal Product Code) has 20% distribution and another has 25%, you can’t just add up and conclude that total distribution is 45%. Neither markets, nor products, nor periods can be added. If you do, that would be incorrect, not to mention you may end up with a distribution of more than 100%.

Use the periods, markets, and products available in your database for analysis, without adding them up

Don’t go weekly for non-perishable items

For non-perishable items, you might want to look at longer distribution periods such as 12 weeks for slow-moving products, or 4 weeks for relatively faster-moving products. Conducting 1-week analysis for slow-moving products, for example, will lead to grossly incorrect conclusions, because these products have longer purchase cycles and do not get scanned on a weekly basis. As such, you might be finding faults with your distribution infrastructure when there are none.

Of course, as you widen the territory of analysis on a weekly basis, you will see units being sold, but micro-analysis at retailer-level or for a specific item is not possible in this manner

Be careful with 52-week analysis as well

Longer periods of distribution-related numbers are often extrapolated from smaller data chunks. Now, if the current distribution is fluctuating i.e. moving up or down rather than being stable, and the small data chunk is relatively stable, the extrapolation will not represent the current fluctuation. The extrapolation may consider the average or the maximum of the week/s within the smaller data chunk, and produce a year-long picture or that basis

Individual item distribution vs Total brand distribution

Total brand distribution will always be higher than individual item distribution, since every store will hold your brand, but not all stores will hold every product variety you produce. Discrepancy is to be expected, except for super-seller products which every store wants to keep

Until next time!

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

October 18, 2022August 8, 2023 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:

Sales Value
Sales Volume
Market Share

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

2. Prescription Data

Prescriptions Count

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

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

Extract the best possible insights from it
In the minimum time possible
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!

October 14, 2022August 8, 2023 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:

How Kantar Data Helps Brand Managers in the CPG Industry’, providing a macro view of how Kantar data helps Brand managers in CPG, and
‘Interested in Becoming a Brand Manager? Know Your Nielsen Data!’, a blog similar to this one, delving into Nielsen instead of Kantar data

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

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

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.

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.

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.

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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How Kantar Data Helps Brand Managers in the CPG Industry

October 11, 2022August 8, 2023 by adminin Brand Managers, CPG, CPG, Kantar dataLeave a Comment

We’ll be exploring how Kantar data helps Brand Managers execute their responsibilities and take their brands to the next level. As the company’s official website introduces, Kantar ‘is the world’s leading data, insights and consulting company, helping clients understand people and inspire growth’. Kantar provides data on about 75 local and global markets, covering industries like CPG, Automotive & Mobility, Life Sciences, Retail, Media, Technology & Telecoms, and more.

Let us explore specifically how Kantar data helps Brand Managers, using the CPG industry as an example:

1. Understanding Markets, and Shoppers

Kantar data helps Brand Managers understand the complex purchase patterns of customers, both physical and virtual, in competing categories. It informs them of who is buying the brand and who isn’t. Kantar data also helps BMs understand the overall shopping trends and how competition operates.

Kantar’s specialty lies in:
– Their massive tracking system which captures the shopping decisions of 4,50,000 consumers all over the world
– Smart segmentation that unveils the best growth opportunities
– Competitor activity benchmarking, and
– Tracking behavioral and other types of trends over long periods

2. Growing the Brand and Extending to Newer Categories

Understanding what kind of buyers to target, the feasibility of entering new categories, based on the ability to satisfy what the consumer wants is another way Kantar data helps BMs. Consider also these points:

– Optimizing in-store ROIs via promotion, merchandising, etc.
– Influencing online shopper behavior by devising the right media and marketing mix components
– Hammering down the brand positioning and using existing insights as well as non-data analysis to model the brand structure, to drive sales
– Delving into category based on evidence that provides a futuristic perspective of shopper, category, and retail behavior

3. Driving Innovation

This is related to the classical 4Ps of marketing – how do you innovate your product? What promotional and pricing strategies do you use to sell it at scale? What kind of launch and distribution strategies are best?
Additionally, Brand Managers can use Kantar data to also delve into
– The impact that this innovation will have on the master brand and the brand architecture
– Ways to create the all-important ‘5th P’ – Packaging, for customer attraction
– Ways to optimize the brand portfolio and architecture, and
– Testing and development of concepts, products, and packs

4. Optimizing Investments

Data under this header relates to marketing and retail investment management for optimal returns. It studies
– The best way to conduct advertising spends
– Different digital contexts, examining them to see what works best
– Various touchpoint analyses, their impact and how to improve going ahead
– Various solutions used to drive sales and enhance field efficiencies

The Possibilities are Many

As we mentioned in the very first sentence, Brand Managers in the CPG industry can use Kantar data to take their brands to the next level. The data is there, and that is one part of two. The second falls upon Brand Managers to embark on an exploration journey where they truly analyze the plethora of information in front of them and carve out exceptional insights that serve as action points for the brand’s growth.

If Only Time was in Abundance

It seems heavy, but breaking it down to the simplest of factors tells us that Brand Managers simply do not have the time to conduct such in-depth exploration. This is due to the fact that such data comes in the form of loads of separate files, which are hard to simultaneously, and speedily, manage. Had Brand Managers the time for data exploration, the resulting insights and the subsequent impact of these insights on the brand would have been positively different.

We’ve Got a Present for You

At the risk of sounding cheesy, it’s the gift of time.

Explorazor gets the basics right – all of it. This data exploration tool combines all datasets, including Kantar, so BMs can query on an integrated dataset and receive instant data pivots.

There’s so much more on offer, as we’ve mentioned in other blogs such as ‘Interested in Becoming a Brand Manager? Know Your Nielsen Data!’.
Just read the conclusion, which starts with the header ‘SEPARATE FILE FOR EACH, OR JUST 1 INTEGRATED DATASET?’

Our pursuit is to help you use Kantar data to the fullest. See how, over a demo call.

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

October 10, 2022August 8, 2023 by adminin Brand ManagerLeave a Comment

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

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

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

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

DATASETS THAT BRAND MANAGERS DEAL WITH – NIELSEN DATA

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

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

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

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

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

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

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

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

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

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

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

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

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

SEPARATE FILE FOR EACH, OR JUST 1 INTEGRATED DATASET?

We’re proposing the second!

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

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

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

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

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

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

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

Explorazor is a product of vPhrase Analytics.

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Why Should an Insights Team Consider Explorazor for Brand Managers?

October 1, 2022August 8, 2023 by adminin ad-hoc query, Brand Managers, dashboarding tool, data pivots, Excel, Explorazor, Insights Team

In this blog, we’ll be making a case for why an Insights team should consider Explorazor for Brand Managers, discussing the problem with current dashboarding tools, and how Explorazor will benefit Insights Teams massively.

Before we begin, just a quick introduction to Explorazor – it is a data exploration tool designed specifically for Brand Managers to obtain instant data pivots on an all-inclusive, integrated dataset, using a simple search functionality, with the ability to pin insights to dashboard and download any data point as a CSV file.

Let’s begin:

Introduction

Now, a part of the job for an Insights team is to introduce new products/tools in the company to help Brand Managers –

Extract the maximum out of the data – as in, extracting the best insights which lead to the best decisions
Doing so in a manner that eases, not complicates, a Brand Manager’s interactions with the data

We interviewed 100+ Brand Managers from Unilever, Nestle, Reckitt, Glenmark, Godrej, and more, asking them about the lives of Brand Managers and the various data-related challenges they face. We then developed Explorazor, the data exploration tool in question, in such a way as to

Help Brand Managers get any data pivots instantly
Ensure that it is so simple, that Brand Managers have no difficulty in adopting it

Regarding the instant data pivots, it is possible on Explorazor because Explorazor hosts all data in an integrated manner. Think of Kantar, Nielsen, IQVIA, primary sales, secondary sales, media, and every other dataset, all combined into one. Regarding the instant adoption, there is no end-user difficulty in usage, nor is there any novel proposal – Explorazor simply seeks to improve their Excel experience, without leaving Excel entirely.

Brand Managers are strikingly similar in their possession of skill sets, job roles, and the right personality traits, so the background they came from didn’t prove to be an adoption hindrance as well.

The Problem with Dashboarding Tools

The major part of a Brand Manager’s job is to ensure that the brand is operating smoothly, across geographies. To do this on a typical BI dashboard, they would have to make, manage, and maintain tens of dashboards at a time. Workload is increased.

Additionally, the data is stored individually, with no communication between them whatsoever. Creating a connection between them to answer ad-hoc queries is a painstaking task, and Brand Managers simply ask the Insights Team to revert with an answer to their ad-hoc queries.

Finally, even if the dashboarding tool is able to do all of this, Brand Managers educating themselves on the ins and outs of the tool and how to use it, is, simply put, not going to happen. As a Growth Analytics Lead of a 35,000+ employee company told us “There are so many tools in a company, but nobody uses them. They just pose questions to us!”

Such is the case of the average company – BI tools that help create dashboards are lying unused because they don’t help people (in our case, Brand Managers) ease their daily lives.

Benefiting Both Parties

The primary reason for Insights Teams to consider Explorazor for their Brand Managers is that Explorazor empowers the Brand Manager to ‘Do It Himself’. No longer is the Brand Manager dependent on anyone to run ad-hoc analysis. The win-win here is that the Insights Team’s time is freed up to concentrate on what they should actually be doing – strategizing for the long-term, running different types of modeling on different datasets to create better forecasts and targets for the business, and so on.

Consider an example: Suppose your company owns an apparel brand present in retail stores across the nation. Now as an Insights Team, you can focus on things like mapping out the loyalty base in each region, and studying why it is shifting, if that’s the case. You can then start benchmarking competition and finding out if a particular campaign they ran during that time period led to a shift in the loyalty base. If sales showed de-growth in stores in a particular city during the year-end, you can dig deep to find the exact answer for it – it may be something as minute as the discount scheme offered by competition being better than yours. You can design loyalty programs to win back your customer base, in addition to ensuring you have a better discount scheme the next time around. In a utopian and very possible scenario, Brand Managers would be thanking you over emails and town halls.

All of this is possible, if only you have the freedom to execute what you are capable of.

Insights team should consider Explorazor for Brand Managers. If you are interested in knowing more about Explorazor, kindly schedule a 30-min demo call with us here.

If you want to understand how Explorazor helps Brand Managers explore data on an integrated dataset, we suggest you skim through the ‘3 Types of Analysis Brand Managers can Perform Super-Easily on Explorazor’ blog.

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

September 28, 2022August 8, 2023 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.

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

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?’

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.

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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