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Category Management: A Key Guide for CPG Companies

May 8, 2023May 8, 2023 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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Point of Sale Data: How CPG Companies Use It to Improve Decision-Making

April 11, 2023April 11, 2023 by Krina Rathodin CPG, CPG Jargon Buster, Data Exploration

The consumer packaged goods (CPG) industry is a highly competitive market, and companies need to make informed decisions to stay ahead. One tool that CPG companies use to make data-driven decisions is Point of Sale (POS) data.

What does POS mean?

Point of sale (POS) data is a term frequently used by consumer packaged goods (CPG) companies to refer to the data collected at the time and place of purchase. This data includes information about sales, inventory, and promotions, and it’s a critical component of market research and decision-making for CPG companies.

In this post, we’ll explore what POS data is, how CPG companies use it, and the challenges and best practices associated with collecting and analyzing it.

What is Point of Sale (POS) Data?

POS data is the information collected at the time and place of purchase, typically using electronic scanners or manual data entry. This data includes details such as the item purchased, the quantity sold, the price paid, and the time and date of the transaction.

Types of data included in POS data vary by industry and the needs of the company, but they generally include sales data, inventory data, and promotional data.

How CPG Companies Use POS Data ?

CPG companies use POS data to make informed decisions that can help them optimize their sales strategies. The following are some examples of how CPG companies use POS data:

Market Research: POS data helps CPG companies to monitor market trends, understand consumer behavior, and identify opportunities to improve their products and services. For example, a company could use POS data to identify which products are selling well and which ones are not, and then use that information to adjust their product lineup or marketing strategy.

Inventory Management: POS data can help CPG companies optimize their inventory levels, reducing the risk of stockouts and overstocking. This can help reduce costs and increase sales. For example, a company could use POS data to identify which products are selling quickly and adjust their inventory accordingly.

Pricing Strategy: POS data can help CPG companies determine the most effective pricing strategies for their products, based on market demand and competition. For example, a company could use POS data to analyze the sales performance of a product at different price points and then adjust the pricing accordingly.

What are the challenges which companies face while collecting and analyzing POS data?

While POS data can be highly valuable, it’s not without its challenges. Some common challenges include data accuracy, data timeliness, and data completeness.

Data accuracy can be an issue if there are errors in the data collection process, such as incorrect product codes or pricing information. To address this challenge, CPG companies may use data cleaning techniques to identify and correct errors in the data.

Data timeliness is another challenge, as POS data may not always be available in real-time. For example, if a retailer only reports their sales data once a week, a CPG company may not have access to the latest sales information until that report is available.

Data completeness can also be a challenge, as not all retailers may provide the same level of detail in their POS data. To address this challenge, CPG companies may need to work with retailers to ensure that they are collecting and reporting the data that is most relevant to their needs.

Best Practices for Working with POS Data

To make the most of POS data, CPG companies should focus on data visualization and Exploration tools and optimization strategies.

Data exploration tools can help make sense of the data and identify trends, allowing companies to make more informed decisions. For example, a CPG company could use a graph or chart to visualize sales trends over time or compare sales performance across different products or regions.

This is where Explorazor comes in handy for the enterprises. Explorazor is a data exploration tool that can help CPG enterprises get insights quickly and easily.

With Explorazor, you can ask a query in seconds and get insights on your data, without the need for extensive data science knowledge.

Try Explorazor today and discover how it can help you gain valuable insights into your data.

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

January 30, 2023August 8, 2023 by adminin Brand Managers, CPG, Data Exploration, Explorazor product updates, root cause analysis

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

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

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

Let’s look at January’s updates:

Dynamic KPI creation

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

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

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

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

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

Filter Grouping

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

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

Updates to Root Cause Analysis

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

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

This will be better understood with an example:

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

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

Some Other Updates

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

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

Explorazor is a product of vPhrase Analytics.

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

January 18, 2023August 8, 2023 by adminin CPG, Kantar data, NielsenLeave a Comment

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:

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. 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:

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

Product
Brand, or
Category

Nielsen calls it Item Penetration.

For

Channels
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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Sales Per Million

January 16, 2023August 8, 2023 by adminin Brand Managers, CPG, Nielsen Database, Retail, sales, Sales per Million, VelocityLeave a Comment

Sales per million is the great equalizer. It is used to measure how fast your products are moving off the shelves in stores where they are in distribution, while controlling for distribution.

What this means is, suppose there are two markets where one is bigger than the other. Now how do you know if the smaller market sells at the same rate as the bigger market? Is the smaller market selling less because of market size, or is consumer demand weak in that area? Or, on the contrary, do products move faster in the smaller market?

Sales per million takes into account the varying Total ACVs of different markets and stabilizes them in the denominator in its formula. Let’s look at the formula and then an example:

HOW TO CALCULATE SALES PER MILLION

Sales per Million is calculated as:

Sales

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

‘Sales ÷ %ACV Distribution’ is the formula for ‘Sales per Point of Distribution (SPPD)’ which is used for checking velocity within a single market, or a single retailer.

Also, ‘Sales’ here can be expressed in terms of units as well as in terms of rupees/dollars.

Market ACV has to be taken in the denominator to account for the size difference in ACV. Market ACVs are very large numbers, so we denote them in millions.

EXAMPLE – SALES PER MILLION

With the theory cleared, let’s understand the concept in practicality through an example:

Let’s suppose that the Mumbai market is 3x larger than Pune. The numbers below point to the same:

Observe that Pune’s Market ACV is significantly lesser than that of Mumbai.

Now, let’s calculate Sales per Million 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.

Pune’s sales velocity compared to Mumbai

For Product 1, is essentially the same
For Product 2, has some discrepancy, but not too much
For Products 3 and 4, is very low

What’s the benefit here?

With the stakes equalized, we note that Product 3 and Product 4 are actually not doing well in Pune, and that cannot be attributed to Pune being a smaller market. The actual reason may lie in a weaker consumer demand, or lack of a suitable strategy for the city, or any other reason.

It was calculation using Sales per Million that helped us identify that Pune needs more attention if products are to do well there.

Note that one can use Sales per Million instead of SPPD (Sales per Point of Distribution) for single market/retailer calculation as well. While SPPD is easier to perform, managers who prefer uniformity in calculations do opt for Sales per million as against SPPD.
Refer to the blog on velocity for more detail on SPPD and Sales per million. Also invest 10 minutes each day to learn about ACV, %ACV, Average Items Carried, and the basics of TDP.

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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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Basics of Total Distribution Points (TDP) in CPG

January 3, 2023August 8, 2023 by adminin CPG, Nielsen, Nielsen Database, TDP, Total Distribution PointsLeave a Comment

We previously discussed ACV and %ACV as part of our CPG Jargon Buster Series. Let’s focus on TDP, Total Distribution Points, also sometimes known as TPD or Total Points of Distribution.

TDP numbers reflect the overall health of your brand from a distribution perspective. The higher the TDP numbers rise, the better your brand’s overall health.

CALCULATING TDP

TDP is closely related to %ACV – for distribution width, and Average Items Carried – for distribution depth. In fact, Nielsen states the method for calculating TDP as follows:

“You can find it by calculating the number of retailers your products are in (breadth) and the number of products you’re selling in those stores (depth).”

TDP is generally part of your Nielsen database, so you will have it ready at your table. However, if you have to calculate it yourself, here’s how it goes:

Suppose a Brand has 5 items/SKUs in its portfolio. TDP would be applicable at the item level of the Brand, meaning the 5 items/SKUs, and is calculated simply as the sum of the %ACV distribution of all these items. It is not necessary that these items be part of a brand; they can be clubbed under a category, segment, or any other similar product aggregations as well.

Example:

	%ACV Distribution
Total Brand A	80
Item A	50
Item B	60
Item C	65
Item D	75

TDP = 250 (50 + 60 + 65 + 75).

The maximum TDP score one can achieve here is 400, where %ACV distribution for all items is 100. One cannot set a partition and say that a certain TDP score and above is good, and below it is concerning. It all depends on the unique set of circumstances that surround your company, brand, category, etc.

Note: Avoid double-counting by excluding Total Brand from the calculation.

IF %ACV DISTRIBUTION IS 95% OR ABOVE

If we were to calculate the TDP of Brands with %ACV Distribution of 95% or more, the TDP score and the Average Items Carried would be almost the same, provided that we shift the decimal point two places to the left.

Look at the table below:

	%ACV Distribution
Total Brand X	98
Item 1	66
Item 1	64
Item 1	78
Item 1	82
Item 1	80

TDP = 370

Average Items Carried = 370 ÷ 98 = 3.77

Notice how if we would have moved the decimal two places in the TDP, we would have arrived at 3.70 of Brand X’s items carried by a retailer, on average.

A MASTER MEASURE – TDP

By allowing data analysts to look at both how widely the product items are being distributed and how well they are performing once they are in the store, TDP provides a solid base for managers to base their next strategies and objectives on.

TDP further helps Managers in CPG understand Volume vs Brand Distribution. The item-level scrutiny ensures that managers know when their product is off the retailer’s shelf, as would be reflected in the total volume reduction.

TDP also lets managers know whether their brand is being represented in a fair manner on the retailer’s shelf. The method to do that is to find out your TDP percentage as against competition ÷ the in-store volume percentage. The volume percentage should not be higher than the TDP percentage.

Finally, TDP also allows you to gain intel on whether the product category has expanded, and find ways to bypass competition in securing shelf space to increase velocity.

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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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