Reflection- Learning Launch to test the POS Systems in Small Grocery Stores in India 

 Challenge 

 In India , there are approx . 12 million small grocery retail shops what we call them as “ Mom and Pop ”   stores also , they constitute approx . 98 % of grocery retail market in India in terms of market size but   almost 95 % of them do not use any kind of digital tool or Point of Sales ( POS ) Machine for their business   operations . This results in various challenges to these retailers like slow speed of operations with slower   check outs , virtually no control on their inventories or purchase , no customer data or analysis to fall   back upon for selection of their product assortments , no control on promotions offered by large brands   and FMCG companies at these stores and last but not the least all this culminates in to no shopping   experience to their consumers despite them controlling such huge pie of the market . 

 An absence of a structured Point of Sales ( POS ) system with order taking terminals , QR code scanner and   receipt printer etc . acts as a steep barrier in their business growth compared to super markets and   convenience stores of the organized chains which are now eating in to their market share even in rural   and semi - rural Indian market . 

 My company in to Franchise Retail Network of small Grocers have completed a detailed assimilation of   Retailers paint point through interviews , market research , specific requirements of Indian grocers for   the POS machine , established hypothesis and key assumptions to create a POS system as per Indian   market needs and conditions as most of these stores have very limited space . We had completed our   first prototype , prioritize our key assumptions for customers , execution and dependability along with   scalability and were ready to experiment with our POS machine in real market place . 

 Selection and Application 

 Since we have already completed our core hypothesis , had prioritized our key assumptions and finalized   the design test for our POS , we had decided to use the Learning launch tool to quickly experiment with   select 10 stores in different geographies with different consumer base and SEC income groups . 

 Our primary objective was to give a very seamless POS solution to these Retailers as most of them are   family run stores and often managed by senior citizens or people with basic understanding of   technology , Our first two primary targets were to solve their huge problems of taking orders by scanning   the bar codes so as to accurately record sales data , brand wise data in to the data base tables and also   to enable very simple feature for them to take in to account their purchases as in absence of purchase   and sales entries , they often do not know when to replenish their 20 % stocks that gives them 80 % sales   and how to avoid unnecessary stock up of slow moving sku ’s to eliminate their small capital flow . 

 Clearly our objective was to design an India centric product and some hardware that could even stand   the heat and dust at these stores spread across this vast country , so the common feedback which we   had gathered during the data immersion and collection process , we had created very bold font for   favorite sku ’s which these retailers can tag and move to the right hand of their POS screens , ideally 

 there are 200 - 250 items that generates 85 % of sales in these stores and we put all those items in to   favorite category so that the retailers can easily find them on right hand side on their POS with easy   search options . We used bright Red and Green colors for products in good quantity in stocks or products   with low quantity in stocks . 

 We had also put one of our Designers at each of these 10 stores for full 16 hours of operations to   carefully monitor the key assumptions and how they are faring in real time operations as people who   have written the code can easily understand the dynamics of any exclusion or inclusion of features . 

 We gave some basic features that were not available in established POS companies and they were   purely based on the retailer ’s feedback only : 

 1 . Generally these stores are manned by 1 person only and sometime there are 2 - 3 customers at 

 the counter , so we had given them the options to open new sales entry window and even to   hold the window of any existing customer in case he or she needs to select more items , at one   point of time , retailer can open 10 windows . 

 2 . In India , there is a norm of offering monthly credit to the customers by these retailers and by 

 now they have recorded them in their manual diaries , where there was a potential risk of   forgetting any entry and thus a significant loss is highly probable , so we have given them a   feature in which they can check out a kart as paid in cash or card or “ By Credit “ . This feature   enables them to record and receipt of their credit amounts with 100 % accuracy . 

 3 . Perhaps globally , only India is a country where there is a culture of Maximum Retail Price ( MRP ) 

 that comes as printed price on the product pack and Sales price ( The price offered by the   retailer after some discount , often it comes out from their own margin ) , so here we had   introduced a feature where they can adjust their Sales price whenever they scan any product   and save it in their local data base to analyze and maintain price parity with their competitors . 

 So we had lot of confirming data and appreciations coming both at our end from these retailers about   our POS after 45 days of trials , we have successfully applied the learning launch tool and establish the   fact that the product by us is better than the alternatives available in the market and it clearly addresses   the specific needs of the consumers or the Indian grocers . All the key assumptions and design criteria   were tested in the real time market conditions where a select group of 10 retailers run their stores on   these POS machines and had created substantial data on their sales , consumer , product movement ,   purchase trends etc . and for the first time they had been able to create their provisional P&L ’s . 

 Insight 

 During this pilot at 10 Stores , we had gained more specific insights to our design process and one thing   that came up as a surprise and a revolution both was the sheer fact that despite FMCG brands spending   millions of dollars in marketing their offers at these stores , there was no clear visibility to both retailer   and the consumers about various price offers and buy 1 get 1 scheme etc . etc . , We saw with our own   eyes that retailers were not offering the right promotions to the customers unintentionally . 

 We immediately jotted this core insight and started working on our visualization process for a new   feature which got launched few months that enabled the retailer to discover the related offer on any   product once they scan their bar codes and the system also tells them the inventory of the offer on go at 

 the same time on the POS screen , this had resulted in high penetration and awareness of these offers   and acted as win win for both customer and retailers . 

 Approach 

 The successful trial and learning launch of our POS system at these 10 stores have opened various other   dimensions for our entire team and we have got a huge pile of data that was previously not visible in the   absence of any digitization at these retailers , the data about customers , brands , products , price   mapping , geographic dimensions and demographic dimensions was so robust that we could do wonders   in further product enhancements , this paved way to use “ Mind Mapping “ Tool in our next set of   developments specially in the “ MIS ” system that we are designing now to look for the specific patterns   and trends per store , city , state , zone , brand , sku and market wise . 


EMAIL: 
ID_NUM: 
NAME_STUDENT: 
PHONE_NUM: 
STREET_ADDRESS: 
URL_PERSONAL: 
USERNAME: 

### Task: You are a PII (Personal Identity Information) Detection. Given a essays written by students, you have to identify ONLY the following:
NAME_STUDENT - The full or partial name of a student that is not necessarily the author of the essay. This excludes instructors, authors, and other person names.
EMAIL - A student’s email address.
USERNAME - A student's username on any platform.
ID_NUM - A number or sequence of characters that could be used to identify a student, such as a student ID or a social security number.
PHONE_NUM - A phone number associated with a student.
URL_PERSONAL - A URL that might be used to identify a student.
STREET_ADDRESS - A full or partial street address that is associated with the student, such as their home address.
### Input: Reflection- Learning Launch to test the POS Systems in Small Grocery Stores in India 

 Challenge 

 In India , there are approx . 12 million small grocery retail shops what we call them as “ Mom and Pop ”   stores also , they constitute approx . 98 % of grocery retail market in India in terms of market size but   almost 95 % of them do not use any kind of digital tool or Point of Sales ( POS ) Machine for their business   operations . This results in various challenges to these retailers like slow speed of operations with slower   check outs , virtually no control on their inventories or purchase , no customer data or analysis to fall   back upon for selection of their product assortments , no control on promotions offered by large brands   and FMCG companies at these stores and last but not the least all this culminates in to no shopping   experience to their consumers despite them controlling such huge pie of the market . 

 An absence of a structured Point of Sales ( POS ) system with order taking terminals , QR code scanner and   receipt printer etc . acts as a steep barrier in their business growth compared to super markets and   convenience stores of the organized chains which are now eating in to their market share even in rural   and semi - rural Indian market . 

 My company in to Franchise Retail Network of small Grocers have completed a detailed assimilation of   Retailers paint point through interviews , market research , specific requirements of Indian grocers for   the POS machine , established hypothesis and key assumptions to create a POS system as per Indian   market needs and conditions as most of these stores have very limited space . We had completed our   first prototype , prioritize our key assumptions for customers , execution and dependability along with   scalability and were ready to experiment with our POS machine in real market place . 

 Selection and Application 

 Since we have already completed our core hypothesis , had prioritized our key assumptions and finalized   the design test for our POS , we had decided to use the Learning launch tool to quickly experiment with   select 10 stores in different geographies with different consumer base and SEC income groups . 

 Our primary objective was to give a very seamless POS solution to these Retailers as most of them are   family run stores and often managed by senior citizens or people with basic understanding of   technology , Our first two primary targets were to solve their huge problems of taking orders by scanning   the bar codes so as to accurately record sales data , brand wise data in to the data base tables and also   to enable very simple feature for them to take in to account their purchases as in absence of purchase   and sales entries , they often do not know when to replenish their 20 % stocks that gives them 80 % sales   and how to avoid unnecessary stock up of slow moving sku ’s to eliminate their small capital flow . 

 Clearly our objective was to design an India centric product and some hardware that could even stand   the heat and dust at these stores spread across this vast country , so the common feedback which we   had gathered during the data immersion and collection process , we had created very bold font for   favorite sku ’s which these retailers can tag and move to the right hand of their POS screens , ideally 

 there are 200 - 250 items that generates 85 % of sales in these stores and we put all those items in to   favorite category so that the retailers can easily find them on right hand side on their POS with easy   search options . We used bright Red and Green colors for products in good quantity in stocks or products   with low quantity in stocks . 

 We had also put one of our Designers at each of these 10 stores for full 16 hours of operations to   carefully monitor the key assumptions and how they are faring in real time operations as people who   have written the code can easily understand the dynamics of any exclusion or inclusion of features . 

 We gave some basic features that were not available in established POS companies and they were   purely based on the retailer ’s feedback only : 

 1 . Generally these stores are manned by 1 person only and sometime there are 2 - 3 customers at 

 the counter , so we had given them the options to open new sales entry window and even to   hold the window of any existing customer in case he or she needs to select more items , at one   point of time , retailer can open 10 windows . 

 2 . In India , there is a norm of offering monthly credit to the customers by these retailers and by 

 now they have recorded them in their manual diaries , where there was a potential risk of   forgetting any entry and thus a significant loss is highly probable , so we have given them a   feature in which they can check out a kart as paid in cash or card or “ By Credit “ . This feature   enables them to record and receipt of their credit amounts with 100 % accuracy . 

 3 . Perhaps globally , only India is a country where there is a culture of Maximum Retail Price ( MRP ) 

 that comes as printed price on the product pack and Sales price ( The price offered by the   retailer after some discount , often it comes out from their own margin ) , so here we had   introduced a feature where they can adjust their Sales price whenever they scan any product   and save it in their local data base to analyze and maintain price parity with their competitors . 

 So we had lot of confirming data and appreciations coming both at our end from these retailers about   our POS after 45 days of trials , we have successfully applied the learning launch tool and establish the   fact that the product by us is better than the alternatives available in the market and it clearly addresses   the specific needs of the consumers or the Indian grocers . All the key assumptions and design criteria   were tested in the real time market conditions where a select group of 10 retailers run their stores on   these POS machines and had created substantial data on their sales , consumer , product movement ,   purchase trends etc . and for the first time they had been able to create their provisional P&L ’s . 

 Insight 

 During this pilot at 10 Stores , we had gained more specific insights to our design process and one thing   that came up as a surprise and a revolution both was the sheer fact that despite FMCG brands spending   millions of dollars in marketing their offers at these stores , there was no clear visibility to both retailer   and the consumers about various price offers and buy 1 get 1 scheme etc . etc . , We saw with our own   eyes that retailers were not offering the right promotions to the customers unintentionally . 

 We immediately jotted this core insight and started working on our visualization process for a new   feature which got launched few months that enabled the retailer to discover the related offer on any   product once they scan their bar codes and the system also tells them the inventory of the offer on go at 

 the same time on the POS screen , this had resulted in high penetration and awareness of these offers   and acted as win win for both customer and retailers . 

 Approach 

 The successful trial and learning launch of our POS system at these 10 stores have opened various other   dimensions for our entire team and we have got a huge pile of data that was previously not visible in the   absence of any digitization at these retailers , the data about customers , brands , products , price   mapping , geographic dimensions and demographic dimensions was so robust that we could do wonders   in further product enhancements , this paved way to use “ Mind Mapping “ Tool in our next set of   developments specially in the “ MIS ” system that we are designing now to look for the specific patterns   and trends per store , city , state , zone , brand , sku and market wise .
### Output: 