Richard Pugh – Commercial Director [email protected] Using R to Optimise a Sales Team...
49
Richard Pugh – Commercial Director [email protected]Using R to Optimise a Sales Team Richard Pugh Managing Director, [email protected]Aimee Gott, Richard Weeks, Nick Burgoyne
Richard Pugh Commercial Director [email protected]
Using R to Optimise a Sales Team Richard Pugh Managing Director,
[email protected] Aimee Gott, Richard Weeks, Nick
Burgoyne
Slide 2
Richard Pugh Commercial Director [email protected]
Disclaimer: Cant say much Data and project is of a sensitive nature
Im not able to speak about the customer nor show the data
Simulating the data was fun, though!
Slide 3
Richard Pugh Commercial Director [email protected]
Agenda Project Background The Data What we did Reporting
Forecasting Modelling Extensions Summary
Slide 4
Richard Pugh Commercial Director [email protected]
Project Background
Slide 5
Richard Pugh Commercial Director [email protected]
Project Background Delivered many R-based projects for a company
Invited to present to upper management Strategic use of analytics
to solve meet business challenges Looking across business units
Agile analytics
Slide 6
Richard Pugh Commercial Director [email protected]
Project Background Introduced to the Sales Director who could use
some help with reporting Cant mention the specifics of the customer
so need to simulate a sales team and their behaviours I have
simulated data between January 2011 and December 2014 Lets meet the
sales team
Slide 7
Richard Pugh Commercial Director [email protected]
Sales Person Given sales opportunity by an internal lead generation
team Responsible for taking opportunities to a (hopefully
successful) conclusion John
Slide 8
Richard Pugh Commercial Director [email protected]
Sales Person Quarterly target Communication performed via forecast
stored in a clunky nice CRM tool Actions performed against
opportunity as logged John
Slide 9
Richard Pugh Commercial Director [email protected]
Sales Person John An opportunity is passed to John John thinks:
Opportunity size = 10,000 Likelihood = 60% John communicates:
Opportunity size = 8,000 Likelihood = 30%
Slide 10
Richard Pugh Commercial Director [email protected]
Sales Person John John works on the opportunity Later in the
quarter, John thinks: Opportunity size = 50,000 Likelihood = 90%
Time to close = 2 months John communicates: Opportunity size =
20,000 Likelihood = 50%
Slide 11
Richard Pugh Commercial Director [email protected]
Sales Person Johns behaviours driven by: Habit Situation Time
(within quarter) Target John may have upwards of 50 opportunities
at any time John
Slide 12
Richard Pugh Commercial Director [email protected]
Updates Opportunity JohnPaulGeorgeRingo Central CRM System Johns
Forecast Johns Actions Pauls Forecast Pauls Actions Georges
Forecast Georges Actions Ringos Forecast Ringos Actions
Slide 13
Richard Pugh Commercial Director [email protected]
JohnPaulGeorgeRingo Central CRM System Johns Forecast Johns Actions
Pauls Forecast Pauls Actions Georges Forecast Georges Actions
Ringos Forecast Ringos Actions Brian also has a quarterly target
Brian communicates via another forecast His forecast is an
amalgamation of the forecasts from his team with some tweaks Brian
Brians Forecast
Richard Pugh Commercial Director [email protected] What
we did? 1.Reporting 2.Forecasting 3.Modelling
Slide 22
Richard Pugh Commercial Director [email protected] What
we did: Reporting
Slide 23
Richard Pugh Commercial Director [email protected]
Reporting Reporting from the bad fantastic CRM system was poor
Manual tasks Poor report of forecast change No integration of
forecast with actions performed The Sales Directors view was of
sales as a funnel
Slide 24
Richard Pugh Commercial Director [email protected]
Sales Reports Data extracted from CRM database (SQL Server) using
RODBC Base graphics used to create sales reports The sendmailR
package used to automatically send out the reports each
morning
Richard Pugh Commercial Director [email protected]
Sales Reports Sales Team Individual report each day Sales Managers
Reports for their team Sales Director Amalgamated reports across
teams
Slide 28
Richard Pugh Commercial Director [email protected] What
we did: Forecasting
Slide 29
Richard Pugh Commercial Director [email protected]
Forecasting Typically, the forecast is reported as a point estimate
within a specific time window
Slide 30
Richard Pugh Commercial Director [email protected]
Forecasting For one of the reports, we simulated expected revenue
over a time period
Richard Pugh Commercial Director [email protected]
Forecasting So far, these simulations include only the reported
forecasts No variation on likelihood, size or close date However,
we can look at variation in these characteristics and
re-simulate
Richard Pugh Commercial Director [email protected] What
we did: Modelling
Slide 36
Richard Pugh Commercial Director [email protected]
Forecasting We achieved this forecast by varying opportunity
parameters But... What are the right parameters? Do they vary by
sales person?
Slide 37
Richard Pugh Commercial Director [email protected]
Modelling The assumption is that there are, and any point, 3 sets
of opportunity characteristics: The real (unobservable) likelihood
(e.g. 65%) The likelihood guessed by the sales person (e.g. 75%)
The likelihood reported by the sales person (e.g. 40%) The same
holds true for other opportunity characteristics (size, close date)
Some correlations are apparent (size likelihood)
Slide 38
Richard Pugh Commercial Director [email protected]
Modelling Each salesperson will report their forecast differently
based on a number of factors As the forecast is passed up to the
Sales Director, these biases are compounded How can we produce a
more accurate forecast? OR Can we extract the sales behaviours from
the data?
Slide 39
Richard Pugh Commercial Director [email protected]
Modelling So far, simple approaches proving successful ActualSize =
f(fcSize, activity) ActualClose = f(fcClose, fcSize, activity)
ActualProb = f(fcProb, time, fcSize, activity) Key parameters Size
(bias, error, error ~ activity) Close (bias, error, bias ~ size,
error ~ activity) Prob (bias, error, bias ~ time, bias ~ size,
)
Richard Pugh Commercial Director [email protected]
Extensions Better allocation of opportunities Target Setting
Within-Quarter Tracking Looking at other data sources Interactive
reports Using Google Charts to track the progress of an
opportunity
Slide 46
Richard Pugh Commercial Director [email protected]
Caveat To model behaviours you need many examples of the behaviour
We are lucky that this is a high(ish) volume business Better
applied to teams with high transaction levels (e.g. telesales with
a standard offering)
Richard Pugh Commercial Director [email protected]
Summary Be an Analytic Advocate! Modelling behaviours is great R
perfect tool: Data, modelling, simulation and graphics Watch for
next instalment
![The Pugh Method[1]](https://img.pdfslide.us/doc/110x75/546b3d8bb4af9fb5148b505e/the-pugh-method1.jpg)
