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

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

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

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

Let’s look at January’s updates:

  1. Dynamic KPI creation

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

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

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

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

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

  1. Filter Grouping

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

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

  1. Updates to Root Cause Analysis

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

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

This will be better understood with an example:

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

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

Some Other Updates

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

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


Explorazor is a product of vPhrase Analytics.

Take an Interactive Product Tour of Explorazor Today!

CPG Jargon Buster Master Article

Hello, and welcome to the knowledge hub that is the CPG Jargon Buster Master Article!

Here you will find direct links to many relevant jargon/concepts in the CPG Industry. Each term is explained in brief below, with a link to the detailed blog at the end of it. 

We keep adding more jargon as we write about them, so be sure to bookmark this page and keep learning! We’re also creating a FANTASTIC CPG-specific product for optimal and super-easy data exploration – you might want to check Explorazor out!

Till now, we have covered 

  1. ACV

ACV stands for All Commodity Volume. It is used in the calculation of %ACV (obviously, but the term ‘ACV’ is often used interchangeably with %ACV, so one needs to be mindful of that). 

ACV is nothing but the total monetary sales of a store. Assessing the ACV of a retailer helps suppliers know which outlet presents the best sales potential based on its business health. 

Learn how to calculate ACV using Nielsen data and how ACV relates to %ACV 

Read more: What is ACV in CPG?


  1. %ACV 

A more comprehensive blog than the ACV blog above, %ACV, or %ACV Distribution, helps managers understand the quality of their distribution networks. You might wonder why a product is not selling well in a region despite being apparently well-distributed there. A deep analysis of metrics such as %ACV will help you resolve that. 

Read the blog to understand how to calculate %ACV, and the 5 points to consider when performing the calculations:

Read more: What is %ACV?


  1. Velocity

Velocity is another metric to study distribution. Velocity factors the rate at which products move off the store shelves once they are placed there. 

Managers can take charge of sales by utilizing velocity fully, and understanding the two major velocity measures – Sales per Point of Distribution (SPPD) and Sales per Million. Refer to the blog to learn what these measures are, with examples to help. As Sales per Million is a complex concept we’ve also explained it separately in another blog:

Read more: ALL About Velocity / Sales Rate in CPG


  1. Average Items Carried

This is the average number of items that a retailer carries – be it of a segment, brand, category, etc. For example, suppose that Brand X has 5 products/items under its name. Average items Carried would be from a retailer’s perspective – he could be carrying 2 products, or 2.5 products, or 4 products of Brand X, on average. 

AIC is one of the 2 components of Total Distribution Points (TDP), the other being %ACV Distribution. The blog explains the relationship between AIC and %ACV with respect to TDP (Total Distribution Points), using examples to simplify. 

Learn why AIC and %ACV are called the width and depth in distribution, and how to calculate AIC in Excel:

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


  1. Total Distribution Points – Basics

Total Distribution Points, or Total Points of Distribution, is again a distribution measure, considering both %ACV and Average items Carried to produce a TDP score that helps Brand Managers understand things like product distribution and store health, and base their future strategies accordingly. 

There’s also a method for managers to know whether their brand is being represented in a fair manner on the retailer’s shelf, using TDP. Learn how to calculate TDP and the special case of TDP if %ACV is 95 or above:

Read More: Basics of Total Distribution Points (TDP) in CPG


  1. Sales per Million

How do you compare two markets where one is many times larger than the other? Does a manager simply say “It’s a smaller market, thus sales are less” and be done with it? Shouldn’t s/he investigate if the products in the smaller market are moving as fast as they are in the larger market? 

Sales per million helps compare across markets, while controlling for distribution. It accounts for the varying Market ACVs and stabilizes them, so managers can find how each product is doing in each market, regardless of market size.

Learn how to calculate Sales per Million with a cross-market comparison example following it:

Read More: Sales per Million 


  1. Panel Data Measures

Nielsen and IRI provide the numbers for these 4 measures, and even those who do not use Nielsen/IRI need to have an understanding of household-level analysis using these 4 measures.

Here are the one-line introductions:

  1. Household Penetration

How many households are buying my product?

  1. Buying Rate

How much is each household buying?

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

  1. Purchase Frequency (Trips per Buyer)

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

  1. Purchase Size (Sales per Trip)

(For each household) How much do they buy at one time?

These 4 measures in table format can be used by managers to understand the consumer dynamics that drive the total sales for their product.

Understand these 4 measures in detail, and how they relate to sales:

Read More: Panel Data Measures


  1. Market Basket Analysis

Market Basket Analysis (MBA) is a powerful data mining technique used in the CPG industry to analyze customer purchase behavior and identify relationships between products.

Learn how Market Basket Analysis can help you gain valuable insights into consumer behavior in the CPG industry.

Read more on: Market Basket Analysis


  1. Point of Sale

The consumer packaged goods (CPG) industry is a highly competitive market, and companies need to make informed decisions to stay ahead.

One tool that CPG companies use to make data-driven decisions is Point of Sale (POS) data.

Learn how CPG and Pharma companies optimize their performance using Point of Sale


  1. Customer Segmentation

Customer segmentation, is a technique that helps you divide your audience into distinct groups based on their characteristics, behavior, or preferences.

By doing so, enterprises can tailor your strategies to each segment’s specific needs, improving your chances of success.

Read more on: Customer Segmentation


  1. Price Elasticity of Demand

Price elasticity of demand is calculated by dividing the percentage change in the quantity demanded of a product by the percentage change in the price of that product. 

The resulting number is a measure of how sensitive the quantity of the product demanded is to changes in its price. 

The formula for calculation Price of Elasticity is:

Price Elasticity of Demand = (% Change in Quantity Demanded) / (% Change in Price)

Check out our blog on how CPG companies take decision on the basis of Price Elasticity.

Take an Interactive Product Tour of Explorazor Today!

ALL About Velocity / Sales Rate in CPG

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 storesSales =Distribution (x)Velocity
(units)(stores)(units/store)
Product A 600060100
Product B600010060

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.

  1. 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
DistributionVelocity
Brand Sales (in Rupees)%ACV DistributionSPPD
Product 16500080812
Product 295000751267
Product 370000154667
Product 480000204000

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.

  1. 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 with respect to Sales per Million
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.

Mumbai vs Pune Velocity comparison in perspective of Sales per Million

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: 

  1. 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
  2. Velocity is uber-important. Hope we didn’t fail to convey that!

Take an Interactive Product Tour of Explorazor today!

10 Data Exploration Tools To Explore in 2023

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

  1. Explorazor

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

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

  1. Microsoft Power BI

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

Data exploration tool comparison: Power BI vs Explorazor

  1. Tableau

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

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

  1. Looker Studio / Google Data Studio 

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

  1. Looker

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

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

  1. Datapine

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

  1. Jupyter Notebook

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

  1. ThoughtSpot

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

  1. Domo

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

The Ultimate Data Exploration Tool?

Dare we explain what Excel does? 

Not in a hundred years.

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

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

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

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

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

Take an Interactive Product Tour of Explorazor!

Retaining Sales Talent is Becoming a Challenge. Here’s What You Can Do About It

In a November 2022 report, Gartner’s Chief of Research Craig Riley raised a pressing issue – sales talent attrition is on the rise, and retaining them will be harder than ever in 2023. His comments were based on an August 2022 survey conducted amongst 900+ B2B buyers, which concluded that a staggering 89% felt ‘burned out from work’. It’s not just the USA. The Great Resignation debate is live and raging across the world.

Forbes echoed the same sentiment as Gartner, recognizing the growing talent crisis in sales and cautioning managers of the various harms that come along with high attrition rates, one of them being damaged customer relationships. Every sales manager reading this bit will resonate with the word ‘catastrophic.’ Forbes research also indicated that more than half of employees feel overworked by their employers.

OF SELLER DRAGS AND ‘COGS IN THE MACHINE’

One of the key causes that leads sales talent to this level of exhaustion is termed ‘seller drag’, a phenomenon that causes employees to procrastinate on work and deliver lower output. One of the roots of this ‘seller drag’ lies in employees having to undertake non-value-adding administrative tasks, and a consistent emotion of being just a ‘cog in the machine.’ 

To tackle this problem, many organizations opt for tools and/or platforms that help departments achieve their objectives. We find advocacy for this approach from Stephen Diorio (Executive Director of the Revenue Enablement Institute and an author, among other things) who discussed the need to reconfigure the daily workflows of sales professionals by simplifying their technology stack. 

However, there’s just a slight problem with this strategy:

No one is particularly interested in using these technology stacks

SIMPLICITY – THE ROOT OF TECHNOLOGY STACK ADOPTION

Greg Munster, Global Sales Operations Director of Canonical, quips 

“After years of supporting sellers with sales enablement systems at IBM, Red Hat and Lenovo, I’ve learned the key differentiator – and driver of value – always comes down to simplicity, intuitiveness, and user adoption in the eyes of the sales user of process or tool.”

Technology stacks carefully curated by Insights Teams, for example, for their sales managers, are perceived as ‘complex’ and capable of only compounding the daily data-related struggles of these sales champions. As such, they steer clear of such tools right from the get-go – our own research at vPhrase Analytics found that out, when we interviewed senior Brand Managers from globally-renowned firms such as HUL, Marico, Godrej, and others. While tools were available in abundance, their usage was next to none.

RETAINING SALES TALENT – WHAT YOU CAN DO ABOUT IT

If you’ve kept up till now, you would’ve noticed we 

  • Recognized a very pressing issue in the form of higher sales attrition in 2023 
  • Pinpointed the tiring daily workflows of sales professionals as a core contributing cause to the attrition, and 
  • Spoke about the well-intended approach of concerned departments like Insights Teams in building technology stacks, but the approach being ill-received due to the stacks being too complex to adopt and use

It’s a focused discussion we’re having. Let’s continue:

A SIMPLE AND EFFECTIVE TOOL FOR YOUR TECHNOLOGY STACK

We dove into the heart of the matter and found the ideal solution: consolidating data from multiple Excel files. Sales Managers have multiple datasets at their disposal and have to constantly shift between and examine multiple Excel files to extract a data pivot or test out a hypothesis. They face multiple challenges en route; access to some files is missing or delayed, standardizing and updating these files is a consistent, time-consuming process.

Sales Managers revert to Insights Teams to help them with ad-hoc analysis. Help does arrive, but often late, rendering the insights to a great extent, valueless. Can’t blame the Insights Team, either. They’ve got tasks other than conducting ad-hoc analysis for Sales.

We’ve developed Explorazor, a simple data exploration tool that integrates multiple datasets into 1 standardized dataset and provides unified data access to users. Users query the consolidated dataset via a simple search interface and extract instant data pivots. Explorazor is powered with double-click, or point-and-click drill-down for instant root cause analysis. The idea is to ensure on-time and independent data analysis for managers, and these features prove pivotal in helping them identify market opportunities and internal and external issues. Explorazor contributes to revenue growth and employee satisfaction simultaneously.

There are many features we haven’t talked about here, like the ability to download desired data pivots as CSV files and take them to Excel, or the super-clean user interface that makes managers want to work on Explorazor. You can also read the blog we’ve written on how Explorazor differs from Power BI.

CONCLUSION

Retain sales talent by easing their day-to-day challenges. Explorazor can help simplify the daily data exploration activities of Sales Managers, and it’s a very simple tool to understand and adopt, and effective to boot. While it’s not the only tool you may ever need, it certainly is the perfect complement to Excel, and therefore a must-have tool in your tech stack.

Why not take a quick look at Explorazor? Here’s an introductory video to get you started, and you can schedule a call with our solutions consultant for the full demo. 

Take an Interactive Product Tour of Explorazor!

Positioning Your Brand to Drive Preference ft. Gartner

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

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

Creating A Strong Positioning 

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

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

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

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

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

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

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

How Will You Position Your Brand?

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

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

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

Regardless of How You Position It..

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

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

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

Take an Interactive Product tour of Explorazor!


We credit Gartner with all observations taken from their survey report

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

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

So Many Data Sources, So Little Time

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

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

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

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

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

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

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

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

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

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

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

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

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

An Integrated or Consolidated Dataset Means 

  1. Better correlation between data points

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

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

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

  1. Better root cause analysis

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

It’s all about better decision-making.

  1. Time and Effort Efficiency 

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

An Integrated or Consolidated Dataset Also Means 

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

Take an Interactive Product tour of Explorazor!

Getting Omnichannel Marketing Right in Retail

In a previous blog, we saw how in retail, building the right omnichannel strategy is everything. Today we’ll discuss some omnichannel strategies that retailers of today can/must use to be successful.   

Google’s e-Conomy SEA 2022 report speaks about how SouthEast Asia is three years ahead of the projected time in reaching $200 million in gross merchandise value in 2022 itself. GMV is defined by Investopedia as “the total value of merchandise sold over a given period of time through a customer-to-customer (C2C) exchange site.” GMV is a strong growth signal of how well a company is performing w.r.t revenue, and is also an indicator of the rising digital adoption in consumers all across the world.

Google’s report emphasizes the undertaking of laser-focused marketing strategies in order to take advantage of the plethora of opportunities the region’s digital economy offers.

Sticking to our topic, what are some of the omnichannel marketing strategies that retailers can utilize to elevate their performance and grab market share in huge markets such as SouthEast Asia? Let’s explore:

Omnichannel Strategies in Retail

  1. Ensure Performance Across Channels

Being present on all channels is not enough; performance consistency and focus on delivering the best possible experience to customers, regardless of the channel they choose to interact with your brand, is an important yet very overlooked aspect of an optimal customer experience. 

Just fathom this, how many retail brands do you know that have a super-fast mobile shopping experience going on, just like they provide on desktop? Very few, in my own experience. Those that do, like Myntra, are far more conducive to natural mobile search and purchase than other prominent brands in the Indian market. 

Between two apparel websites that offer almost the same products, how much of a differentiator is mobile & app speed to you? (image format)

  1. Ensure Consistency Across Channels

Again, criminally underrated, because most brands are present just for the sake of being present, without care for ‘how’ they appear to the customer. It’s a very myopic outlook, which can be tackled by building a very strong brand identity system. A brand identity system is a set of brand guidelines that include logos, symbols, characters, and more, basically designed to keep the brand experience consistent, literally everywhere. Think of brands like Pepsi or Coca-Cola. They have been delivering a universally consistent brand experience for decades now. 

Consistency is not limited to branding. Retailers need to ensure that their in-store offers are consistent with their online claims. Similarly, the customer should feel a sense of service efficiency when interacting with your brand across all online channels, and in-store. 

It’s easy for consumers to spot which brands are genuine in building a top-notch omnichannel experience for their customers, and which are just ‘ticking the boxes’. And once you lose the attention of the customer, he’s as good as gone.

  1. Use Technology

By 2030, 125 billion devices are estimated to be connected using IoT, putting the number at 15 connected devices per user to handle. Your omnichannel strategy must consider the new wearables such as smart watches, to deliver increasingly personalized services, as customers are demanding. The data on personalization is there for all to see. A staggering 8+ people out of 10 are willing to share their personal details if they are treated to more personalized (read ‘better’) deals from brands. 

If you are behind on providing fully functional and maintained payment gateways on all fronts and automated chatbots, we take it you haven’t begun to use technology, but most chances are that you have done that. Those are staples today, but remember that the companies who adopted them first got the first-mover advantage. Updating to the next steps such as integrated shopping experience on new wearables will one day be as important as payment gateways.

  1. Learn From The Best

One of the best ways to stay ahead of the competition is to benchmark the best and replicate contextually. The consistency, performance, and technological advancement that Amazon has displayed through Amazon Prime is exemplary. Bringing customer data out of siloes from across devices and channels and connecting them, offering benefits and membership discounts, excellent product service at costs customers are willing to pay, are all elements of a revenue-generating omnichannel experience. 

Speaking of retail, Nike offers one of the best in-store + online integrations in the world. 

Credits: LinkedIn Pulse

The omnichannel retail infrastructure that Nike has put together is the very definition of seamlessness. Whether one is at the store, shopping online, or wants the product delivered to their home or the nearest retail store, everything feels automatic. It’s so good, you don’t notice it.

Take an Interactive Product Tour of Explorazor.

Conduct Drill-Down on an Integrated Dataset via Point-and-Click

Brand and sales teams have to drill-down (some call it double-click) on data all the time to conduct root-cause analysis. Let’s explore how to conduct deep exploration, or drill-down and drill-across, on Explorazor.  

Explorazor is a data exploration and analysis platform that mitigates most of Excel’s drawbacks that Brand & Sales Managers have when working on multiple files on Excel. On Explorazor, managers work on a single integrated, standardized dataset that produces data pivots instantly when queried via a simple search interface.

(Hey, we’re improving! In addition to this blog, be sure to check out more updates to the root cause analysis feature)

Arriving at the ACTUAL Root Cause with Explorazor

Why do we say ‘actual’? Because with Explorazor, users conduct drill-down on the entire data at a time. Such exploration power allows managers to start their investigation, say from a Brand, all the way to an SKU, in an almost seamless fashion.

Let’s use screenshots from Explorazor to understand how Explorazor eases data exploration via drill-down and drill-across: 

  1. Here we are on the ‘Ask’ section of the Explorazor screen, where we have the Dimensions and Measures to the left and the space for simple querying at the center-top.

The following query has already been entered: ‘Quarterly Market Share Value of Alpha Supplement (Brand) for All-India’. We did this by entering four keywords: 

  1. MS Value
  2. Quarterly
  3. Alpha Supplement
  4. All India

Filtering by Alpha Supplement in ‘Brand’ and All India in ‘Market’ and opting for a simple line graphical representation shows us that MS Value has been more or less consistent over the past 12 quarters, but displaying gradual degrowth since the spike in Q2-21.

2. Since the Market Share is down, has it impacted Market Sales in the same manner as well?

Let’s find out by viewing the ‘Absolute Growth in Market Sales of brand Alpha Supplement on a quarterly basis’. 

We are now interested in performing a root cause analysis of Quarter 2 – 2022, so we click on it to do the same. Notice the clean interface that allows readers to take in all the information at once.

By the way, when it comes to intuitive query recognition and time period filters, Explorazor is much smarter than Power BI. To quote a couple of lines from the linked blog “If there are certain parameters in Power BI which you want to look at but which are not present on the dashboard, you cannot do so until you edit the entire dashboard. 

With Explorazor, users can drill down into as well as drill across a particular data point”.

3. Back to the steps: Market Sales Value has been highlighted as the area of our interest. From the list of further possible exploration areas, let’s select ‘Geo classification’ and analyze the results:

4. Drilling across by adding ‘Market’ to the set of applied filters, we learn that even in this degrowth, North has performed exceedingly well. East, South, and West are major areas of concern, in that order. 

Simplicity At The Core

Explorazor has simplified the data analysis process of managers through multiple custom-built features such as the drill-down & drill-across we saw above. The intuitive search interface and the ease of display navigation, in addition to the core offering of working on an all-inclusive, integrated dataset make Explorazor the first choice for managers wanting to ease their day-to-day data exploration and decision-making processes.

Take an Interactive Product Tour of Explorazor today!

How Data Analysis Helps Marketers

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

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

Let’s begin:

How Data Analysis Helps Marketers: 

  1. Plugging Spendings

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

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

Hold on, are we describing the marketers of today?

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

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

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

  1. Helping Achieve Precise Segmentation & Targeting

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

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

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

3. Helping Provide Personalized Offerings

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

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

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

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

How To Consolidate The Data Analysis Process

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

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

Take an Interactive Product Tour of Explorazor today!