classification of sales opportunities for software company · introduction sales operations portal...
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© 2005 Robert H. Smith School of BusinessUniversity of Maryland
Classification of Sales Opportunities for Software
Company
Jeanne M. RussoDaniel Rozas
Pablo MacouzetSriram Gandhi
Vino Babu
© 2005 Robert H. Smith School of BusinessUniversity of Maryland
Introduction
Sales Operations Portal introduced in 2004 to replace SalesForce.com system.Design as a single source for remote access to all sales related activities: Account management, calendar, tasks, opportunitiesUsed by all field employees : Account Executives, Sales Consultants, Sales Managers & DirectorsData related to Sales Opportunities will be the subject of our analysis
© 2005 Robert H. Smith School of BusinessUniversity of Maryland
Introduction- Sales Opportunities
• Life cycle of a Sales OpportunityCustomers
- Partners
- Homepage
- Events
- Industry Seminars
- Customer Referral
- Cold Calls
Lead
Opportunity
Order
Lost DealSales Process
© 2005 Robert H. Smith School of BusinessUniversity of Maryland
Objectives
1. Taking advantage of data gathered by the system to predict the likelihood of open opportunities becoming won or lost deals
2. Improve the process to assign resources to opportunities based on the sales potential and skill of the sales force
3. Better understand the most influential factors that lead to a win
© 2005 Robert H. Smith School of BusinessUniversity of Maryland
Sales Opportunity- Parameters
Opportunity TypeLead SourceRevenue StreamRegionOpportunity Creation DateOpportunity Close DateAge (days since creation)AmountMilestoneCompetitors
New CustomerCustomer - New BusinessCustomer - ExpansionPartners
ReferralsEvents ….
- Lost- Order
22 Regions
LicenseConsultingSupport Maintenance
© 2005 Robert H. Smith School of BusinessUniversity of Maryland
Dataset
Raw data from ROLAP database exported to Excel2080 recordsOpportunity Milestone is response variableNaive rule: 86% of opportunities result in orders
Cleanup & TransformationsConsolidate Regions (from 22 to 8)Consolidate Opportunity streams and create dummy
© 2005 Robert H. Smith School of BusinessUniversity of Maryland
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
DSI Expansion New Business (existingcustomer)
New Customer
Order Rate
Mean Rate
Single Dummy
Opportunity Type
© 2005 Robert H. Smith School of BusinessUniversity of Maryland
0%
20%
40%
60%
80%
100%
<$100K $100K - $250K $250K>
Order RateMean Order Rate
Opportunity Amount
© 2005 Robert H. Smith School of BusinessUniversity of Maryland
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
1 2 3 4 5 6 7 8 9 10
Age Bin
New CustomerDSI ExpansionAge Distribution: New CustomerAge Distribution: DSI Expansion
Bin To Count1 6 2162 14 1923 27 2124 38 1785 56 2086 82 1897 114 2008 169 1979 269 199
10 1329 199270
FromOpportunity Age (Days)
5783
115170
7152839
0Sales Divergence
Uniform Distribution
Non-uniform Distribution
Opportunity Age
© 2005 Robert H. Smith School of BusinessUniversity of Maryland
0%
5%
10%
15%
20%
25%
1 2 3 4 5 20 21 22
Consolidated Region
Single Dummy Variable
Loss Rate by Region
© 2005 Robert H. Smith School of BusinessUniversity of Maryland
0
50
100
150
200
250
1 2 3 4 5 6 7 8 9 10 11 12
Month Created
Volu
me
0%
5%
10%
15%
20%
25%
Loss
Rat
e
Volume
Loss Rate
Mean Loss Rate
Dummy Variable
Creation Period
© 2005 Robert H. Smith School of BusinessUniversity of Maryland
0%
20%
40%
60%
80%
100%
120%
1 2 3 4 5 6 7 8 9 10 11 12
Close Month
Ord
er R
ate
0
100
200
300
400
500
600
700
Volu
me
Order rate
Mean Rate
Volume
Closing Period
© 2005 Robert H. Smith School of BusinessUniversity of Maryland
Regression ModelsLogistic Regression
ComplexityHit R
atio
New Customer
Business Cycle
More Involvement
Product Placement
Validation Set
© 2005 Robert H. Smith School of BusinessUniversity of Maryland
Applications
Opportunity probability can be used in conjunction with other data to determine a master plan for sales efforts
© 2005 Robert H. Smith School of BusinessUniversity of Maryland
RecommendationsIncorporate the predictive model as part of the Sales Force reports already available.Automate the process so that the model improves as more data is gathered.Quantify costs associated with false positives and negatives to be incorporated into the model.Add granularity to competitors data (currently missing values)Investigate why particular regions have lower hit ratio and losing high amount dealsInclude opportunity probability as part of the quota and commissions system.
© 2005 Robert H. Smith School of BusinessUniversity of Maryland