- Paper is on AI solution used to optimize workload in multiple sales support centers of IBM across the globe. Presented in IIM Bangalore's prestigious supply chain conference. - Overall 3000+ sales support professionals work in these sales support centers. AI solution was able to make plan and schedule daily work more effective for these 3000+ professionals. - What do you think can be dollar benefits from this AI solution which can drive more effective daily workforce scheduling for 3000+ sales support employees?

1. SHAPING WORKLOAD IN SALES SUPPORT AND CUSTOMER
FULFILLMENT USING BUSINESS ANALYTICS
Jyotishko Biswas1
and Pitipong Lin2
1
Smarter Supply Chain Analytics, Integrated Supply Chain, IBM Corporation
Embassy Golf Links, Bangalore KA 560071 India
Phone: (91) 9620559998 E-mail: jybiswas@in.ibm.com
2
Senior Technical Staff Member (STSM), Integrated Supply Chain, IBM Corporation
One Rogers Street, Cambridge, MA 02142 USA
Phone: (617) 510-7777 E-mail: pitipong@us.ibm.com
Abstract—This is an IBM Integrated Supply Chain (ISC)
business analytics project to enable a better distribution of
workload across a fiscal quarter for the Global Sales Support
organization. The objective is to improve quality of Sales
Support, reduce overtime costs, and achieve higher service levels.
This allows us to identify sales opportunities having higher
chance of getting progressed to higher stages. To achieve that,
we applied CHAID and Markov Chain models to predict
probability of sales opportunities to be in different stages of
maturity in the coming future. This allows the sales team to
initiate and progress prospective opportunities, thus workload to
Sales Support, that would otherwise peak towards month-end or
quarter-end periods.
Keywords: Sales Support, Customer Fulfillment, Workforce
Analytics, Markov Chain, CHAID
I. INTRODUCTION
Sales Support and Customer Fulfillment (CF) play an
essential role in supporting the many pre-sales and post sales
needs of IBM sellers, Business Partners and clients around
the world. Whether helping to develop a proposal or qualify
a reference during the pre-sales process, or managing the
post-sales order and invoice process, these teams touch a high
volume of transactions every day.
With a merger of Sales Support and CF teams to build an
end-to-end sales transaction support in the ISC, we began
applying analytics to enable a process for “single seller touch
points” through the life of a transaction. For sellers and
business partners, this means spending less time with sales
support activity, reduced cycle time for sales transactions and
fewer hand-offs. For clients, this means a reduction in overall
cycle time from opportunity through order, and consistency
across quotes, contracts and invoices.
The Sales Support and CF personnel consistently experience
a high level of workload at end-of-month or end-of-quarter,
when majority of the deals progress through sales stages.
This can cause the organization to incur high overtime
expenses. In addition, workloads that are highly skewed to
the end of the quarter can be over-baring for the employees,
finally culminating into higher staff turnover.
We have devised a methodology to shape the workload by
allowing sellers to initiate sales opportunity (i.e. create
proposal) with a client earlier in the quarter.
The model identifies deals that have higher likelihood to
progress to higher stages of maturity in the quarter. Sales
Support work typically comes at higher stages. Using these
predictions Sellers can drive progression of opportunities to
higher stages, and hence initiate workload to Sales Support,
before month and quarter end.
We analyze attributes of an opportunity like dollar value,
country of origin, sales channel and its progression across
sales stages till now to predict future stage of the opportunity.
This work at same time provides sales pipeline visibility.
Sales organization understands where the opportunities can be
in near future, using which they can take necessary steps to
improve sales.
Workload shaping to better manage hardware workload has
been happening. Kim and Mvulla [7] proposed an energy
consumption optimization of the cloud environment. Using
task buffers and schedulers they implemented a workload
shaping technique in cloud infrastructure. Mountrouidou et al.
[9] proposed workload shaping techniques for power saving
on disk drives. However, workload shaping where we are
trying to modify volume of work coming into multiple global
centers, to enable better staffing of resources is relatively
new.
The review of literature is in section II. Section III discusses
how CHAID segmentation technique is used to identify
factors which impact time of an opportunity to close. For
example, factors like country in which opportunity originated,
whether opportunity managed by IBM Sales Team or

2. Business Partners impact time an opportunity takes to
progress through the sales pipeline. In section IV, we address
an approach to build a Markov Chain model on IBM closed
opportunities. Section V focuses on multiple uses of the
analytics solution to the sales and sales support organizations.
Finally, section VI offers concluding remarks and suggestions
for future research.
II. REVIEW OF LITERATURE
This section reviews literature in workforce predictive
analytics.
Heching and Lin [1] developed a workforce analytics
solution that enabled IBM to efficiently manage and plan
resources for its global customer fulfillment transaction
centers. The paper described use cases that focus on demand
forecasting, which applied predictive analytics to support
decisions on workload change and the number of resources
required to support the workload.
Gresh et al. [2} developed Resource Capacity Planning
Optimizer which applies supply chain management
techniques to better plan IBM’s need for skilled labor for
consulting, application development and other areas.
Asuink et al. [3] used a linear programming model for
personnel staffing strategies, estimating anticipated changes
in the transformation of the army as the IT systems and
infrastructure expand using trends in the shadow workforce.
Aldore-Noiman et al. [4] introduced an arrival count model
which is based on a mixed Poisson process, applying data
from a call center. A call center, requiring high customer
service levels, could use the quality-efficiency driven
technique's square-root staffing rule to balance the workload
per staff.
Gmach et al. [5] looked at the ability to pool resources in an
enterprise data center environment due to advances in
virtualization technology. The capacity planning model used
demand inputs such as types of workload demand patterns
and generation of synthetic workloads that predict future
demands based on patterns.
Eiselt and Marianov [6] studied a methodology to assign
tasks to employees using a workload distribution to generate
results.
III. SEGMENTATION USING CHAID
CHAID is a classification technique used to build decision
trees. We used CHAID to identify the key factors which
impact time taken by an opportunity to mature. This is
important, since in our solution we want to treat opportunities
which move fast differently than opportunities which move
slowly.
Closed in =< "X" days
Closed in > "X" days
Closed in =< "X" days
Closed in > "X" days
Closed in =< "X" days
Closed in > "X" days Closed in =< "X" days
Closed in > "X" days
Closed in =< "X" days
Closed in > "X" days
Closed in =< "X" days
Closed in > "X" days
All Opportunities
1452
1442
Country A and B
340
452
Country C and D
199
380
Country J
218
63
Country E,F and G
504
363
Country H and I
191
184
Fig. 1. First split with Country
Closed in =< "X" days
Closed in > "X" days
Closed in =< "X" days
Closed in > "X" days
Closed in =< "X" days
Closed in > "X" days
Online
118
75
Country A and B
340
452
Direct and BP
222
377
Fig. 2. Country A and B split by Channel
There are different factors which impact time taken for an
opportunity to progress. For example opportunity of
countries E, F, G, H, I and J progress across stages quickly,
than time taken by opportunities of other countries. We used
CHAID to identify these factors.
An opportunity closes in average “X” days; it is constant for
all decision trees built. We use X to represent a constant due
to the sensitivity of proprietary information.
Below are details of the decision tree model:
• SPSS Statistics used to build decision tree.
• Model successfully validated on test data.
• Pearson Chi Square test used to identify most significant
factor to split dependent variable.
• Pearson Chi Square test used to identify similar classes
within factor which need to be merged.
• Bonferroni adjustment of the significance values done.

3. IBM sells its products and services through different sales
channels like Direct, Business Partners (BP) and Online.
Sales Channel is impacting time taken by opportunity to
mature (Fig. 2).
In our problem we wanted to find out factors that impact
time taken by an opportunity to progress to higher sales
stages. Factors that have higher impact are:
• Country of Origin: Some countries take more time to
mature and opportunities of some countries mature
considerably quickly.
• Sales Channel: Opportunities managed online can close
faster than opportunities owned by Business Partners or
IBM sales.
• Opportunity Won or Lost: Time taken for an
opportunity to close is less for won opportunities.
• Opportunity Value: Opportunity takes more time to
close if its value is high.
IV. TRANSITION PROBABILITIES OF SECOND ORDER
MARKOV CHAIN
We use second order Markov Chain to predict movement
of opportunities across various stages over time.
We have seen in Section III that country significantly
impacts time taken by an opportunity to mature. Hence we
have separate Markov chain models for opportunities of
countries which taken more time to mature and countries
where opportunities move fast. Country group A has countries
whose opportunities move slowly and country group B has
countries whose opportunities move fast.
In Table 2 Second order Markov chain transition
probabilities tell us probability of opportunity to be in
“Accomplished” stage or won in next 28 days. These
transition probabilities have been built on data of opportunity
progression across stages of all opportunities which got
closed in 2011. In “Accomplished” stage proposal creation,
generating price quotes and other activities are done by Sales
Support organization.
Markov Chains have been used in diverse areas when it is
believed that the current condition is dependent on previous
conditions. Shamshad et al. [10] had predicted future rainfall
using First and Second order Markov Chains. Saibeni [8]
predicted account receivable collections using Markov Chain.
Lu [11] used discrete and continuous time Markov Chain
models to predict credit risk of Chiao Tung Bank in Taiwan.
Let us illustrate the different stages that an opportunity
goes through before they are won or lost (Fig. 3).
Won
Acknowledged
Accomplished
Confirmed
Recognized
Fig. 3. Example of Sales Stages
Table 2. Probability of Opportunities to Move to Higher
Stages
Movement across Stages
Country
Group A
Country
Group B
Recognized to Acknowledged to Won 24% 46%
Confirmed to Acknowledged to Won 25% 42%
Accomplished to Acknowledged to Won 26% 27%
Acknowledged to Acknowledged to Won 27% 22%
Accomplished to Accomplished to Won 13% 9%
Confirmed to Accomplished to Won 21% 20%
Recognized to Accomplished to Won 19% 22%
Confirmed to Confirmed to Won 5% 5%
Recognized to Confirmed to Won 7% 9%
Recognized to Recognized to Won 1% 2%
Recognized to Recognized to Accomplished 8% 9%
Confirmed to Confirmed to Accomplished 32% 38%
Recognized to Confirmed to Accomplished 24% 29%
Transition Probabilities used to get Probability to Reach Higher
Stages - 28 days between 2 time periods
We see that an opportunity after reaching higher stage if
doesn’t progress further for some time, then it’s chance for
further progression comes down. In comparison an
opportunity which just reached high stage from lower stage
will have more chance to progress further. This is observed
from transition probabilities when each time period is 28
days. An opportunity in “Acknowledged” stage in present and
previous time period will be won in next period with 22%
probability. However opportunity which moved from
“Recognized” stage in previous period to “Acknowledged”
stage in current period will have 42% probability to be won in
next period (Table 2).
Similarly we have transition probabilities which tell us
probability of opportunity being accomplished or won in next
14 days.
V. PREDICTIONS USING MARKOV CHAIN, HOW ARE
PREDICTIONS USED AND THEIR ACCURACY
A. Probability of an opportunity being accomplished in
coming 14 and 28 days.

4. Transition probabilities of Markov Chain used to predict the
probability of opportunities to be in higher stages in the
coming 14 days and 28 days. We use stage in which
opportunity is in present and previous time period to predict
stage in which opportunity will be in next period.
For example, suppose today is 29th Feb 2012. An
opportunity from Country A has been in the “Confirmed”
stage from 29th Feb till 16th Feb (the current time period)
and was in the “Recognized” stage from 15th Feb till 2nd Feb
(previous time period). Country A's opportunities take time to
close. Hence we have to use transition probabilities of
countries whose opportunities mature at slower pace.
Transition probability of an opportunity to move to the
“Accomplished” stage between 1st March and 14th March
(next time period), when presently it is in “Confirmed” stage,
and has been in “Recognized” stage in the previous time
period is 21%. Hence, probability of this opportunity to be
“Accomplished” between 1st March and 14th March is 21%.
Here we have considered each time period’s duration as two
weeks (Table 3).
In the same way we have predicted the probability of this
opportunity to progress to accomplished stage between1st
March and 28th March (Table 4). The difference would be
the duration of each time period would be four weeks. Hence,
we will look into the stage this opportunity is in between 2nd
Feb till 29th Feb (present time period) and also the stage in
which the opportunity was between 5th Jan and 1st Feb
(previous time period).
Opportunities are generally put in three probability buckets,
high, medium and low. Group “high” comprises top 33
percent opportunities having highest probability. 33 percent
of opportunities having lowest probability fall in group “low”.
Remaining opportunities fall in “medium” group.
B. Probability of opportunities to be won stage in the coming
14 and 28 days
Similarly using transition probabilities, we predicted the
probability of the opportunity to be won in next 14 days when
it is other sales stages in current and previous period (Table
5).
Table 3. Probability of Opportunity being “Accomplished” in coming 14 days
Opportunity
Number
Country Channel
Probability of being Won b/w 1st
and 14th March
Probability
Bucket
Stage from 16th Feb
till 29th Feb
Stage from 2nd Feb
till 15th Feb
Opportunity a Country A BP 21% High Confirmed Recognized
Opportunity b Country A Direct 21% High Confirmed Recognized
Opportunity c Country A Direct 21% High Confirmed Recognized
Opportunity d Country A Direct 21% High Confirmed Recognized
Table 4. Probability of Opportunity being “Accomplished” in coming 28 days
Opportunity
Number
Country Channel
Being Accomplished b/w 1st and
28th March
Probability
Bucket
Stage from 2nd Feb
till 29th Feb
Stage from 5th Jan
till 1st Feb
Opportunity 5 Country A Direct 8% Low Recognized Recognized
Opportunity 6 Country A Direct 8% Low Recognized Recognized
Opportunity 7 Country A Direct 8% Low Recognized Recognized
Opportunity 8 Country A ibm.com 8% Low Recognized Recognized
Table 5. Probability of Opportunity being won in coming 28 days
Opportunity
Number
Country Channel
Probability of being Won b/w 1st
and 28th March
Probability
Bucket
Stage from 2nd Feb
till 29th Feb
Stage from 5th Jan
till 1st Feb
Opportunity 9 Country A Direct 26% High Acknowledged Accomplished
Opportunity 10 Country A Direct 26% High Acknowledged Accomplished
Opportunity 11 Country A Direct 26% High Acknowledged Accomplished
Opportunity 12 Country A Direct 26% High Acknowledged Accomplished

5. Table 6. Accuracy of Results of Country A
Initial Stage Actual Predicted Error
Confirmed 54 72 33%
Number of Opportunities Accomplished in 14 days
Table 7. Accuracy of Results of Country A
Initial Stage Actual Predicted Error
Recognized 4 5 25%
Confirmed 30 19 37%
Accomplished 23 24 4%
Acknowledged 58 63 9%
Number of Opportunities Won in 14 days
Table 8. Accuracy of Results of Country B
Initial Stage Actual Predicted Error
Confirmed 24 24 0.0%
Number of Opportunities Accomplished in 14 days
Table 9. Accuracy of Results of Country B
Initial Stage Actual Predicted Error
Recognized 9 6 33%
Confirmed 4 5 25%
Accomplished 26 13 50%
Acknowledged 43 22 49%
Number of Opportunities Won in 14 days
C. Accuracy and Use of the Predictions
To measure accuracy of our predictions, we compare
number of opportunities which actually arrived in a specific
stage with what we predicted. For example in Country A, we
predicted 63 “Acknowledged” opportunities were won in 14
days. In reality, 58 “Acknowledged” opportunities were won.
Our predictions had an error of 9%.
Driving analytics into the fabric of the business, coupled
with process change management, is key to balancing
workload of Sales Support organization. This is the proposed
process change. First, Sellers are provided list of
opportunities having more chance to progress to higher stages
by end of month/quarter. Next, Sellers are asked to focus on
moving some of these opportunities faster to higher stages
before end of month/quarter, resulting in better distribution of
workload.
VI. CONCLUSION
We predicted future stage of opportunities in coming 14 and
28 days in proof of concept. Using our models we predicted
stages of opportunities during month end and quarter end.
We then start our dialogue to first validate our predictions
with sellers. After validation sellers progress some
opportunities to higher stages before month and quarter end.
It is beneficial for sellers to serve as subject matter experts to
verify our results.
To predict future stage of opportunities, we used Second
Order Markov Chain model. We found Markov Chain
models can predict progression of opportunities across
various stages with an acceptable level of accuracy. We also
built First Order Markov chain models, however Second
Order Markov Chain predictions were more accurate.
In the future research we will explore the dependency of
progression of opportunities on client’s behavior and revenue
targets.
REFERENCES
[1] A. Heching and P. Lin, “Smarter Workforce Analytics for
Customer Fulfillment Transaction Centers”, 2013 Proceedings
to INFORMS Conference on Business Analytics & Operations
Research, April 7-9, San Antonio, Texas, 2013
[2] D. L. Gresh, D. P. Connors, J. P. Fasano and R. J. Wittrock,
“Applying Supply Chain Optimization Techniques to
workforce planning problems”, IBM Journal of Research and
Development vol. 51, no. 3/4, Page 251, 2007
[3] J. Ausink, R. Clemence, R. Howe, S. Murray, C. Horn and J.
D. Winkler, “An Optimization Approach to Workforce
Planning for the IT Field”, RAND Corporation 2002
[4] S. Aldore-Noiman, P. D. Feigin and A. Madelbaum
“Workload Forecasting for a Call Center: Methodology and a
Case Study”, The Annals of Applied Statistics vol. 3, no. 4, pp.
1403–1447, 2009
[5] D. Gmach, J. Rolia, L. Cherkasova and A. Kemper , “Workload
Analysis and Demand Prediction of Enterprise Data Center
Applications”, Proceedings of the 2007 IEEE 10th
International Symposium on Workload Characterization, Sep
27-29, Boston, Massachusetts, pp. 171-180.
[6] H.A. Eiselt and V. Marianov “Employee Positioning and
Workload Allocation”, Computers and Operational Research
vol. 35, no. 2, pp. 513-524, 2008
[7] W. Kim and J. Mvulla, “Reducing resource over-provisioning
using workload shaping for energy efficient cloud computing”,
Applied Mathematics and Information Sciences, vol. 7, no. 5,
pp. 2097-2104, Sep 2013
[8] A.A. Saibeni, “Forecasting Accounts Receivable Collections
with Markov Chains and Microsoft Excel”, The CPA Journal,
vol. 80 no. 4, pp 66, April 2010
[9] X. Mountrouidou, A. Riska and E. Smirni, , “Adaptive
workload shaping for power savings on disk drives”,
Proceedings of 2nd
ACM/SPEC International Conference on
Performance Engineering”, pp 109-120, 2011
[10] A. Shamshad, W.M.A. Wan Hussin, M.A. Bawadi. and S. A.
Mohd. Sanusi, “First and Second Order Markov Chain Models
for Synthetic Generation of Wind Speed Time Series” Elsevier
Energy vol. 30, no. 5, pp 693-708 April 2005
[11]S. Lu, “Comparing the reliability of a discrete-time and a
continuous-time Markov chain model in determining credit
risk”, Applied Economics Letters, vol. 16, no. 11, pp. 1143-
1148, 2009.