Apidays Singapore 2024 - Building Digital Trust in a Digital Economy by Veron...
Sales optimization
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
Bangalore, IN Office:
No. 141, 2nd Cross, 2nd Main,
Domlur, 2nd Stage, Bangalore 560071
Phone: +91 80 40917572,+91 80 40916116
info@therainman.com
Contact Us US Office:
Suite 100, 1780 Chadds Lake Dr, NE
Marietta, Georgia, 30068-1608
Atlanta, USA
info@bottomlineanalytics.com