The document discusses sales optimization strategies used by an ACPG major company that sells over 200 SKUs to small retail stores. It implemented analytics to (1) reduce order time by prioritizing high probability SKUs, (2) increase average SKUs sold per visit, and (3) increase invoice values. The strategies included store segmentation, logistic regression models to predict focus SKU purchases, and a "heat map" showing past SKU purchases. An algorithm combining these strategies customized SKU lists for each store. This led to a 20% reduction in order time, 8% higher sales values, and 20% more SKUs sold on average per visit, along with increased margins from focus product sales.

1. 1
Sales Optimization
How to get more saleswith the samesalesforce
New Developments in Measurement and Analytics

2. 2
Background
 ACPGmajorused a hand-held device to takesales orders fromsmall independent
retail stores
 The problem was that as the company had over 200 SKUsand the timetaken to
complete an order was inordinately long as the salesperson had to scroll up and down
the list, even though the average SKUs purchased per visit was only 10
 The SKUsthat cameat the top of the list werethose that the store had already bought,
but to sell SKU’snot sold earlier was an issue
 The opportunity toupsell and cross sell existed while taking lesser timein each store

3. 3
Objectives
 Reduce the time taken foran order by bringing the high probability SKUsto
the top ofthe list
 Also bring key focus SKUsto the top of the list – which the management
would like todrive
 Increase the average number of SKUssold per visit
 Increase the value of the invoice foreach trip

4. 4
Analytics Strategy 1
 Given the wide array ofchoices available tostores of differing profile, asegmentation
analysis was done dividing the stores into 6 clusters
 The most popular to least popular products listed foreach cluster
Average
Price
Store Size
Value
sales
Frequency
of
purchase
Product
type
Area Pin
code
Cluster 1 Cluster 2
Cluster 3 Cluster 4
Cluster 5

5. 5
Analytics Strategy 2
• Built Logistic Regression
Models forall the focusSKUs
• Use purchase patterns at the
SKUlevel, Frequency, Size and
other parameterstaken fromthe
invoice level data
• Computed the probability of
purchasing focusSKU foreach
store
• Validated on existing data

6. 6
Analytics Strategy 3
 Pulled the list ofSKUs
bought in the past 6
months ata store level
 Computed the proportion to
total sales asa percentage
 This is designated the
“Heat Map” by store
Heat Map ( SKU bought Intensity)
Cluster 1 Cluster 2 Cluster 3 Cluster 4 Cluster 5
SKU 1
SKU 2
SKU 3
SKU 4
SKU 5
SKU 6
SKU 7
SKU 8
SKU 9
SKU 10
SKU 11
SKU 12
SKU 13
SKU n
Darker color indicates higher proportion of SKU bought by store

7. 7
Algorithm andAutomation
 algorithm wascreated as a
combination of strategies 1, 2
and 3.This is based on
iterations
 Validated on existing data, with
the highest validating Algorithm
used in market
 The existing IT system was
tweakedto deliver customized
lists by store with highlights on
focusSKUs
Datamart
Algorithm

8. 8
Results
 20% reductionin time taken for the order
 8% improvementin valuesalesfor the same sales team
 20% increasein the averageSKUs bought– from10 to 12 per visit
 Improvedmargin from 35% increasein sales fromfocusproducts

9. 9
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