Commission management system (cmsr)
cmsr turns the incentive calculation into a very simple process that, after configuration and association to a target group, includes the incentive plan validation and simulation through what-if scenarios. This is completed, in order to determine the financial and sales potential impact before going to production (like weDo technology)
You can install cmsr from github with:
# install.packages("devtools")
devtools::install_github("jmcimula/cmsr")
data(cmsales)
: A dataset containing the sales reps activitiesdata(cmstelecom)
: Activation tracking of sales forces in telecom industrycms_markdown()
: commission based on the Mark Down valuecms_markup()
: commission based on the Mark Up valuecmscalc()
: calculation of commission of sales repscms_kpi_rate()
: calculation of the bonus based on the performance of the sales reps
library(cmsr) # for functions
cmscalc(cmsales,com.type="Markdown",rmv.na= TRUE)
# sales_rep_id region product total_prod target sales total_price total_disc total_sales bonus markdown
#1 SLREP001 kinshasa Beer 710 533 450 1093.40 644.4900 668.7450 2.3777600 0.4105634
#2 SLREP001 kinshasa Flower 410 308 338 676.50 518.6610 538.1805 2.7864375 0.2333171
#3 SLREP001 kinshasa Fruit 1050 788 824 787.50 574.7400 596.3700 1.7107210 0.2701714
#4 SLREP001 kinshasa Shoe 165 125 149 1153.35 968.6043 1005.0572 18.1739001 0.1601818
#5 SLREP001 kinshasa Soap 125 95 88 100.00 65.4720 67.9360 0.7018182 0.3452800
#6 SLREP002 katanga Beer 635 478 597 977.90 855.0234 887.2017 4.2093300 0.1256535
#7 SLREP002 katanga Flower 420 315 358 693.00 549.3510 570.0255 4.0071625 0.2072857
#8 SLREP002 katanga Fruit 1190 893 1011 892.50 705.1725 731.7113 1.8336331 0.2098908
#9 SLREP002 katanga Shoe 190 144 124 1328.10 806.0868 836.4234 5.6660940 0.3930526
#10 SLREP002 katanga Soap 165 124 136 132.00 101.1840 104.9920 1.4153333 0.2334545
#11 SLREP003 bascongo Beer 650 488 485 1001.00 694.6170 720.7585 2.4565233 0.3060769
#12 SLREP003 bascongo Flower 480 361 394 792.00 604.5930 627.3465 3.8409969 0.2366250
#13 SLREP003 bascongo Fruit 1200 901 1018 900.00 710.0550 736.7775 1.8148580 0.2110500
#14 SLREP003 bascongo Shoe 195 147 136 1363.05 884.0952 917.3676 6.7453500 0.3513846
#15 SLREP003 bascongo Soap 195 147 170 156.00 126.4800 131.2400 1.9968077 0.1892308
#16 SLREP004 bandundu Beer 685 515 623 1054.90 892.2606 925.8403 4.0132865 0.1541752
#17 SLREP004 bandundu Flower 535 402 448 882.75 687.4560 713.3280 2.7866205 0.2212336
#18 SLREP004 bandundu Fruit 1220 916 1106 915.00 771.4350 800.4675 2.0024982 0.1569016
#19 SLREP004 bandundu Shoe 200 152 161 1398.00 1046.6127 1086.0014 11.2872190 0.2513500
#20 SLREP004 bandundu Soap 225 170 182 180.00 135.4080 140.5040 1.4987717 0.2477333
This dashboard could allow the GTM (GoToMarket) teams to track the performance in acquisition (Activations, Gross connection, reconnection, Churn, Net Add) in order to enhance the visibility of activations carried out by the sales forces.
cms_tracker(cmstelecom, region = c("kinshasa","katanga","bandundu"), from = "2016-06-01", to = "2016-06-03")
# agent_id ADJ DISC GCN NN NWR PREACT REC
#1 SLREP001 105 163 194 61 89 91 30
#2 SLREP002 92 22 228 260 136 198 54
#3 SLREP003 71 69 208 189 137 228 50
#4 SLREP004 120 46 276 277 156 276 47
#5 SLREP005 65 103 161 126 96 193 68
It is possible to fix the target in acquisition for tracking the performance of the sales forces in this order (ADJ,DISC,GCN,NN,NWR,PREACT,REC). See ?cmstelecom
for more details.
How to run this function with the target:
cms_tracker(cmstelecom, region = c("kinshasa","katanga","bandundu"),
from = "2016-06-01", to = "2016-06-03",
set.target = c(100,130,90,80,75,120,100)
)
library(cmsr)
library(ggplot2)
library(ggthemes)
#First
ggplot(cmstelecom, aes(date,nb_subs)) + geom_point(color="aquamarine4") + facet_wrap(~agent_id, nrow=2, scales="free")
#Second
ggplot(cmstelecom, aes(date,nb_subs, color=factor(region))) + geom_point()+ggtitle("Tendance des activations par province") + theme_economist() + scale_colour_economist()
#Third
ggplot(cmstelecom, aes(date,nb_subs)) + geom_point(color="darkgoldenrod4") + facet_grid(agent_id~wording)
#Fourth
ggplot(cmstelecom, aes(activation,nb_subs, color=factor(agent_id))) + geom_point() + scale_colour_tableau()
Jean Marie Cimula
GPL (>= 2)