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WIFI SSID:Spark+AISummit | Password: UnifiedDataAnalytics
Harish Kumar, RB
A Scalable Data Science
Solution for Sales
Execution in Traditional
Trade Markets
#UnifiedDataAnalytics #...
About RB
3#UnifiedDataAnalytics #SparkAISummit
To make a difference by giving people innovative solutions
for healthier an...
Argentina
11,369
Brazil
100,000
South Africa
4,608
Sri Lanka
52,151
Pakistan
141,487
India
400,403
Bangladesh
71,539
MENA
...
Instore
Knowledge
Good Margin
proposition
Instore execution challenges
5
Customers
Wholesaler
20+ Reps each day
from diffe...
602
1,460
629
312 312
716
211 128 208 190 167
750
98 174
1,351
3,486
1,422
2,141
390 272
1,292
2017/01
2017/02
2017/03
201...
Solution Objectives
7
Retain instore knowledge and experience
Ensure Sales strategy is built into orders
Fragmented situat...
Smart Order
8Smart Order has become the medium for executing ALL field Sales actions
Seasonality
Central
Strategy
Phasing
...
Solution Landscape
9
On-premise
Local Data
SaaS Data
INGEST STORE
PREP &
TRANSFORM
MODEL &
SERVE
INSIGHTS
“2x performance ...
Process Flow
10
Data
Preparation
§ Channel – Class
Data Preparation
§ Top Categories
selection
§ Data Cleansing
Clustering...
Solution outcome
11
Post Smart Order
Daily targets at Store level
Store level MSL
Only 2 KPIs:
Smart Order compliance for ...
DON’T FORGET TO RATE
AND REVIEW THE SESSIONS
SEARCH SPARK + AI SUMMIT
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Building a Scalable Data Science Solution to Outperform Sales Execution in Traditional Trade Markets Slide 1 Building a Scalable Data Science Solution to Outperform Sales Execution in Traditional Trade Markets Slide 2 Building a Scalable Data Science Solution to Outperform Sales Execution in Traditional Trade Markets Slide 3 Building a Scalable Data Science Solution to Outperform Sales Execution in Traditional Trade Markets Slide 4 Building a Scalable Data Science Solution to Outperform Sales Execution in Traditional Trade Markets Slide 5 Building a Scalable Data Science Solution to Outperform Sales Execution in Traditional Trade Markets Slide 6 Building a Scalable Data Science Solution to Outperform Sales Execution in Traditional Trade Markets Slide 7 Building a Scalable Data Science Solution to Outperform Sales Execution in Traditional Trade Markets Slide 8 Building a Scalable Data Science Solution to Outperform Sales Execution in Traditional Trade Markets Slide 9 Building a Scalable Data Science Solution to Outperform Sales Execution in Traditional Trade Markets Slide 10 Building a Scalable Data Science Solution to Outperform Sales Execution in Traditional Trade Markets Slide 11 Building a Scalable Data Science Solution to Outperform Sales Execution in Traditional Trade Markets Slide 12
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Building a Scalable Data Science Solution to Outperform Sales Execution in Traditional Trade Markets

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RB is a multinational consumer goods company with more than 40,000 employees operating in 60+ locations and a portfolio of leading brands such as Airborne, Air Wick, Clearasil and Lysol. RB serves the ‘traditional trade’ markets globally which are a complex network of more than 1.2 million small retailers, corner stores, open markets and street vendors. This makes it difficult to drive effective sales strategies in a competitive market due to limited range, disparate data, and high attrition. To overcome these business challenges, RB developed a solution that analyzes years of buying patterns and market specific data, ties them to sales strategy and generates a weekly sales order at the individual store level. Using the scale-out compute power on Azure Databricks enabled us to quickly deploy the solution across multiple markets where we are able to process orders for up to 50,000 stores per hour. In this session we will share our approach to building this solution.

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Building a Scalable Data Science Solution to Outperform Sales Execution in Traditional Trade Markets

  1. 1. WIFI SSID:Spark+AISummit | Password: UnifiedDataAnalytics
  2. 2. Harish Kumar, RB A Scalable Data Science Solution for Sales Execution in Traditional Trade Markets #UnifiedDataAnalytics #SparkAISummit
  3. 3. About RB 3#UnifiedDataAnalytics #SparkAISummit To make a difference by giving people innovative solutions for healthier and happier homes People Consumers Shareholders Communities Environment 40k+ 20m+ 132% 765m 61k Products sold daily Total Shareholder Return since 2012 people informed through health and hygiene initiatives tonnes of CO2e saved from the purchase and generation of renewable electricity
  4. 4. Argentina 11,369 Brazil 100,000 South Africa 4,608 Sri Lanka 52,151 Pakistan 141,487 India 400,403 Bangladesh 71,539 MENA 19,433 Egypt 15,594 Turkey 3,259 Indonesia 80,443 Malaysia 80,403 Thailand 22,634 Japan 21,319 Russia 86,565 Central & Eastern Europe TOTAL STORES ~1 275 949 4 Traditional Trade Markets The volume of stores and complex logistics limits the range to drive central strategies
  5. 5. Instore Knowledge Good Margin proposition Instore execution challenges 5 Customers Wholesaler 20+ Reps each day from different Mfrs 60+ Househ olds No Inventory /Cashflo w system A good proposition and in-store knowledge is key to compete for fair wallet share
  6. 6. 602 1,460 629 312 312 716 211 128 208 190 167 750 98 174 1,351 3,486 1,422 2,141 390 272 1,292 2017/01 2017/02 2017/03 2017/04 2017/05 2017/06 2017/07 2017/08 2017/09 2017/10 2017/11 2017/12 2018/01 2018/02 2018/03 2018/04 2018/05 2018/06 2018/07 2018/08 2018/09 2018/10 2018/11 2018/12 2017/01 2017/02 2017/03 2017/04 2017/05 2017/06 2017/07 2017/08 2017/09 2017/10 2017/11 2017/12 2018/01 2018/02 2018/03 2018/04 2018/05 2018/06 2018/07 2018/08 2018/09 2018/10 2018/11 2018/12 CB Other s CB Pas te Harpic Bathroom Harpic Liquid Mortein Aerosol Mortein Coil Mortein LED Com plete Mortein LED Refill Mortein Others Robin B lue Liquid Robin Liquid B leach Assortment Pattern Disparate Buying Patterns 6 Buying Pattern Sample Store Sporadic buying patterns result in bad inputs for traditional forecasting models
  7. 7. Solution Objectives 7 Retain instore knowledge and experience Ensure Sales strategy is built into orders Fragmented situation managed by AI Smart Order ü Drive Sales growth ü Improve product range ü Reduce Attrition impact ü Give more Control to HQ Augmenting Reps to deal with executional challenges
  8. 8. Smart Order 8Smart Order has become the medium for executing ALL field Sales actions Seasonality Central Strategy Phasing Objectives & Constraints Buying Pattern Store Profiling & Clustering AI Engine Recommend Volume PromoSeasonality Adhoc Incentive Category Contribution Max Assortment SKU Ranking Recommend Assortment Optimize for Highest value Revenue Optimizer Margin Optimizer Optimize for Highest GM Smart Order Initiatives & Plans Sales Rep Hand Held
  9. 9. Solution Landscape 9 On-premise Local Data SaaS Data INGEST STORE PREP & TRANSFORM MODEL & SERVE INSIGHTS “2x performance at ½ x price” Azure Blob ADLS Databricks Azure DWH DATA SCIENCE Azure Data Factory CONSUME
  10. 10. Process Flow 10 Data Preparation § Channel – Class Data Preparation § Top Categories selection § Data Cleansing Clustering § Channel-Class level stores are clustered together based on volume, assortment and price across categories Prep for Optimization § Clusters of stores are further put into grids/sub- clustered based on potential § SKU ranking and SKU volume limits are estimated per category § Assortment limits are estimated per category Optimization and Results § Store SKU list is optimized § SKU volume limits are modified based on Trade Offers/Seasonal ity § Store Volume limits and other constraints are used to optimize and maximize revenue for each store
  11. 11. Solution outcome 11 Post Smart Order Daily targets at Store level Store level MSL Only 2 KPIs: Smart Order compliance for Value and Range Pre Smart Order Monthly targets and Sales Rep level Channel level MSL Many KPIs to track: NPDs, Numeric & Merchandising drives, MSL, MTD target achievements, Phasing ü Drive IMS, Improve product range, Reduce Attrition impact, more Control to HQ
  12. 12. DON’T FORGET TO RATE AND REVIEW THE SESSIONS SEARCH SPARK + AI SUMMIT
  • tushar_kale

    Nov. 11, 2019

RB is a multinational consumer goods company with more than 40,000 employees operating in 60+ locations and a portfolio of leading brands such as Airborne, Air Wick, Clearasil and Lysol. RB serves the ‘traditional trade’ markets globally which are a complex network of more than 1.2 million small retailers, corner stores, open markets and street vendors. This makes it difficult to drive effective sales strategies in a competitive market due to limited range, disparate data, and high attrition. To overcome these business challenges, RB developed a solution that analyzes years of buying patterns and market specific data, ties them to sales strategy and generates a weekly sales order at the individual store level. Using the scale-out compute power on Azure Databricks enabled us to quickly deploy the solution across multiple markets where we are able to process orders for up to 50,000 stores per hour. In this session we will share our approach to building this solution.

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