Category: Kantar data

Panel Data Measures – Household Penetration, Buying Rate, Purchase Frequency and Purchase Size

by adminin CPG, Kantar data, NielsenLeave a Comment on Panel Data Measures – Household Penetration, Buying Rate, Purchase Frequency and Purchase Size

Let’s continue with our CPG Jargon Buster Series. Having already covered ACV, %ACV, Velocity, Sales per Million, Average Items Carried and the basics of TDP, we shall now look at the 4 Panel Data measures mentioned in the title, namely Household Penetration, Buying Rate, Purchase Frequency, and Purchase Size.

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. Some terminologies used for this approach are: Sales Driver Analysis, Key Measures Report, Market Summary, and Purchase Summary.

Let’s understand our 4 measures in detail:

  1. Penetration

The percentage of households purchasing your products through a retailer or any channel is penetration. Take an example of Market Y containing 100 households. If 48 households buy Product X at least once during the year, the penetration of Product X in Market Y was 48%.

For a specific 

  1. Product
  2. Brand, or
  3. Category

Nielsen calls it Item Penetration. 

For 

  1. Channels
  2. Retailers

Nielsen uses the term Shopper Penetration.

You can easily derive your sales through this formula:

Sales = (Total number of households x Penetration) x Buying Rate

Let’s move on to Buying Rate

2. Buying Rate

We explained it above as ‘How much is each household buying?’. Termed by Nielsen as ‘Item Sales per Item. Buying Rate refers to the average amount of a product purchased during the entire year (or any time period; it’s usually a year) by one household. Households are buying households only.

For example, if the annual Buying Rate of Product X is noted to be Rs. 50, this means that every household that purchased Product X spent an average of Rs. 50 over Product X during the year.

We saw the formula above, where 

Sales = (Total number of households x Penetration) x Buying Rate

Now, Buying Rate itself is dependent on 2 things:

Buying Rate = Purchase Frequency x Purchase Size 

3. Purchase Frequency (Trips per Buyer)

Nielsen calls it ‘Item Trips per Item Buyer’. How frequently your product is purchased by an average buying household over a year is purchase frequency – straightforward.

Example: Suppose the annual purchase frequency for Product X is 3.8. This means that Product X was purchased by every buying household, on average, 3.8 times over the course of the year.

4. Purchase Size (Sales per Trip)

For every buying household, what was the average amount purchased in a single trip, is purchase size. Note that the condition of ‘a single trip’ is a must. 

Nielsen calls it ‘Item Sales per Item Trip’.

For example, if the annual purchase size of Product X is 1.3, this means that every household that purchased Product X purchased 1.3 units of it in one go, each time they purchased it during the entire year. 

The Calculation Part

Assume these given set of variables:

In the last year (52 weeks) a store had 2,00,000 shoppers, out of whom 20,000 purchased your products.

Each of these 20,000 purchasers purchased your products 5 times over the year’s course, and for every purchase, they spent Rs. 30 for two 3L packets.

So now, Penetration = 20000 / 200000 = 10%

Purchase Frequency = 5

Purchase Size (Rupees) = 2 x 30(rs) = Rs. 60

Purchase Size (Units) = 2

Purchase Size (Litres)  = 2 x  3 = 6 litres

Buying Rate (Rupees) = 5 x 30(rs) = Rs. 150

Total Sales (Rupees) = 20,000 x 150(rs) = Rs. 3000000

Your total sales amounts to Rs. 30 Lacs.

Hope you were able to grasp all the concepts, and do check out our custom-made for CPG data exploration tool Explorazor. Until next time!

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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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How Kantar Data Helps Brand Managers in the CPG Industry

by adminin Brand Managers, CPG, CPG, Kantar dataLeave a Comment on How Kantar Data Helps Brand Managers in the CPG Industry

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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