Overview of Salesforce.com (SFDC) ExperienceIn the early days of the SalesForce.com (SFDC) API (SOAP-based), I owned the whole ($2B) pipeline of software services for SunGard. I managed all reporting and analytics within the SFDC implementation, becoming popular by (1) discovering underlying business opportunities cross-product; (2) analyzing the sales and customer management strategy within the Miller-Heiman model (turns out that is what as good fit); (3) developing new allocation process for sharing revenue, costs and resources (including offshore) across groups (as part of a strategic sourcing PMO); and (4) handling within a standard framework the weekly CEO pipeline and projections calls.

All this code I developed in C#.NET. Moving into more recent projects and into the world of developerForce, APEX, S-Controls, SOQL etc., I have been lead architect and manager for integrations and migrations among various platforms to/from SDFC.

For a San Francisco-based Asset Manager (RS Investments), a new customer web portal project integrated SFDC with WebSphere, other contact mgt. databases, and other third party APIs (for procurement, content management, etc.), all accomplished in a middle-tier (distributed architecture, both message-based and more tightly coupled as API calls) and data architecture that I led from design through implementation (in both Java and .NET). This included integrating BI and statistical tools.

Various projects at and investment banking firm and broker-dealer (Sandler O’Neill) concerned Enterprise Architecture with SFDC as a key component, integrating data from online auction sites, retail banking PO clients, as well as trading and back office operations for the investment bank. This included data ingestion, ETL and data architecture within the cloud, but with additional focus on front-end applications development interfaced. Here the Apex Data Loader and Boomi (less so) were used, as well as other ETL and cloud tools.

The following is an excerpt from the Executive Summary of an early SFDC pipeline analysis I performed in 2005 for a large software-house client, referred to above:

Overall ResultsThe analysis represents a detailed look at the probabilities (via the six predefined Miller-Heiman stages) assigned historically to opportunities, and their accuracy based on the results of the opportunity (won or lost).  The results were encouraging and can be summarized as:

For {large global banking client}, for example, where there exist 284 total closed opportunities of history (189 won, 95 lost):

Those marked “1 - In Legal” were won with ACTUAL probability of 88.8% (compare to default for that stage of 90%).

The data for the others follow:2 - Verbal Acceptance = 60.19418% (default is 85%)3 - Vendor Finalist = 51.20482% (default is 70%)4 - Solution Proposal = 43.44262% (default is 50%)5 - Needs Analysis = 25.39683% (default is 25%)6 - Opportunity Qualified = 9.89011% (default is 10%)

Note that for stages 2 and 3, the opportunities were overestimated (not surprising for this particular account’s general “MO” and within our new PMO framework), but otherwise the assigned stages, as one can see, were PRETTY DARN CLOSE (to use post-doctorate statistical jargon) - or at least much closer than estimated using my initial Bayesian models.

The report was then run for the universe of ALL opportunities within SFDC, and the results were, of 16319 closed opportunities:

1 - In Legal = 93.86889%  (90%)2 - Verbal Acceptance = 87.38268%  (85%)3 - Vendor Finalist = 59.89095%  (70%)4 - Solution Proposal = 48.95791% (50%)5 - Needs Analysis = 32.91536% (25%)6 - Opportunity Qualified = 41.77731% (10%)

These are fairly close also; though note that Stage 3 opportunities are typically overvalued, with stage 5 opportunities are undervalued.

Stage Six: ‘Opportunity Qualified’The only mathematical / predictive anomaly is in Stage 6.  Why are we winning these 41% of the time?  It is either (1) a bug, (2) bad data, or (3) explained via the phenomenon that most opportunities start as stage 6, so this data is skewed toward a representation of the whole universe of opportunities (wherein 66% of the closed opportunities in SFDC were won).

InterpretationThe Global Account Management group can feel good about projections of “expected value” when looking at open opportunities in SFDC as historically the data “priors” are fairly accurate.  As detailed below, we will be making enhancements to the model and its metrics by “calibrating” new projections using the actuals above to get – in theory – more accuracy and statistical confidence. Future reports of this type will reflect the new model, and its effect on the cost and profit allocations framework proposed (see below).

Notes:The summarized results above (and herein) does not take into account (1) opportunities that overwrote the default probability (this is a negligible %); and (2) whether the deal amounts given at each stage are of similar accuracy.  This is discussed further below, and this analysis will be in the next review.Reports have been developed that look for anomalies in the SFDC raw data, and that analyze the pipeline by stage, and dump the latest ‘events’ (activities and notes) for relevant outliers and other data points of interest in an automated way. These will help automate the weekly CEO pipeline and projection call data and integrate with the data flow with the Business Units (again, as part of the new PMO).