Using Salesforce, ERP, Tableau & R in Sales Forecasting

SALES FORECASTING: USING
SALESFORCE, ERP, TABLEAU
& R

• Introduction
• Predictive Modeling Methodology
• Data Preparation
• Accuracy vs Complexity
• Choosing a Model
• Evaluating and Deploying the Model
• Senturus Overview
• Additional Resources
Agenda
Copyright 2016 Senturus, Inc. All Rights Reserved.

Presenters
Asa Levi
Senior Consultant
Senturus, Inc.
Greg Herrera
President and Co-Founder
Senturus, Inc.

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This slide deck is from the webinar: A Pragmatic
Approach to Sales Forecasting, Using Salesforce, ERPs,
Tableau & R
To view the FREE video recording of the presentation or
download this deck, go to:
http://www.senturus.com/resources/a-pragmatic-
approach-to-sales-forecasting/
Hear the Recording

A PRAGMATIC APPROACH TO
SALES FORECASTING
USING SALESFORCE, ERPS, TABLEAU & R

Predictive Modeling Methodology
• Define business goals
• Specify model objective function
• Design/build modeling database
• Partition modeling data
• Derive potential predictors
• Analyze predictor strength
• Perform sub-population analysis
• Build model algorithms
• Evaluate model performance
• Deploy model

Sales Forecast Algorithms Require
Historical Snapshots
Opportunity Amount Stage Probability Close Date
Northwind Traders $45k Early 20% 2016-08-25
Rugged Bikes $50k Middle 40% 2016-08-31
Grand Cycle Store $65k Favored 60% 2016-07-25
Six months ago, the pipeline said this:
Account Revenue Last 12 Months Order Frequency
Northwind Traders $450k 4 in last 6 months
Rugged Bikes $0 Unknown
Grand Cycle Store $650k 12 in last 6 months
Six months ago, the ERP System said this:

Sales Pipelines are Current Snapshots
Opportunity Amount Stage Probability Close Date
Bike Universe $50k Early 20% 2016-12-25
The Cracker Box $40k Middle 40% 2017-02-25
Racing Bike Outlet $35k Favored 60% 2016-09-01
Northwind Traders $65k Verbal 70% 2016-11-25
Rugged Bikes $60k Closing 90% 2016-08-31
Grand Cycle Store $55k Closed Won 100% 2016-07-25
As of now, the pipeline says this:
In Salesforce and most other CRM systems, the sales
pipeline is tracked in an opportunity table, which
maintains only the current values.

• Configure the CRM system to capture each pipeline
update and organize these records in the data
warehouse, or
• Use the data warehouse for everything
– Store periodic snapshots of the entire pipeline (e.g.,
Daily, Weekly, Monthly)
– Capture each pipeline update by comparing new data
warehouse records with the existing records from the
prior warehouse load
Techniques for Maintaining Historical Pipeline

Salesforce has built-in functionality that tracks changes
to a specific group of five pipeline fields:
1. Amount
2. Close Date
3. Forecast Category
4. Probability
5. Stage
Salesforce Opportunity History Table

Salesforce Tracks History Functionality
• Salesforce can be configured to track changes to
any/all fields in the opportunity table
• The records will appear in opportunity field history

Goal: Merge Predictions for Actionable Insights
Opportunity Amount Stage
Bike Universe $50k Early
The Cracker Box $40k Middle
Racing Bike Outlet $35k Favored
Northwind Traders $65k Verbal
Rugged Bikes $60k Closing
Grand Cycle Store $55k Closed Won
Add prediction information
• Prediction data should be stored in another table,
along with the date of the prediction
• Certain data points are identified for prediction
Opportunity Prediction
Date
Amount Probability
Bike Universe 5/1/16 $30k Low
Bike Universe 7/1/16 $60k High
Bike Universe 8/1/16 $50k Medium

Quantitative vs. Qualitative
• Quantitative
- How much (do we expect to sell)?
• Qualitative
- Which (probability group is this lead in)?
• Different methods
• Different measurement
Opportunity Prediction
Date
Amount Probability
Bike Universe 7/1/16 $60k High
Bike Universe 8/1/16 $50k Medium

How to Measure Accuracy
• Separate data into training and test data sets
• Models built on training data
• Performance is measured on the test data
• Measure by a core set of KPIs
• Results fed back for further model iteration

Regression KPIs
• Absolute Error (AE)
• Mean Absolute Percentage Error (MAPE)
• Bias
• Accuracy = 1 - MAPE

 
Actual
ActualForecast
Opportunity Prediction Actual AE MAPE Bias
Bike Universe $30k $50k $20k 40% -40%
Rugged Bikes $40k $30k $10k 33% 33%
Northwind
Traders
$60k $55k $5k 9% 9%
Total $130k $135k $35k 26% 4%

Custom KPIs
• Standard KPIs assign equal penalty to over and
under forecasting – this is usually unrealistic
• Custom KPIs can come to the rescue
• For example, MAPE is calculated like this:
• If a bike costs $40 to
manufacture and sells
for $400 there may be a
higher cost for missed sales than over production

 
Actual
ActualForecast
Opportunity Prediction Actual MAPE Bias
Bike Universe $30k $50k 40% -40%
Rugged Bikes $40k $30k 33% 33%
Northwind
Traders
$60k $55k 9% 9%
Total $130k $135k 26% 4%

The Common Refrain
Why can I predict that our net sales will be within 5%
of last year, but we are 30% off at the product level?!?
• The answer is hidden in the total bias calculation
• A simple analogy is predicting the sum of dice rolls
• Suppose you have 100 dice
• Try to predict a single roll – what is the best guess
• If you guess 3 or 4 your expected error is 50%
• Now try to predict the sum of 100 rolls
• Guess 350 rolls and your expected error would be around
5%

The Central Limit Theorem

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and download this deck, go to:
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approach-to-sales-forecasting/
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Granularity , Accuracy
• The more granular a prediction, the less accurate it is
• A prediction is like a random choice in the area under the
curve, the area is more centralized as the data gets
more accurate

Accuracy By Industry
• No singular goal works for all industries
• Important: understand the business model when setting
accuracy goals

Choosing a Model
• With a measurement framework, begin model
selection
• Choose the predictors
- Data elements fed into our model
• This step is not prescriptive
- Use business knowledge and analysis to choose the
right predictors
• From our ERP and CRM data we create a set of
possible predictors:
YTD RevenueRemaining Revenue
PPY Remaining Revenue
PY Revenue
PYTD Revenue
PY Remaining Revenue
PPY Revenue
PPYTD Revenue
Low Likelihood Revenue
Med. Likelihood Revenue
High Likelihood Revenue

Choosing a Model
YTD RevenueRemaining
Revenue
PY Revenue
PYTD Revenue
PPY Revenue
PPYTD Revenue
• Figure out which predictors to use in our modeling
• Look at the relationship between the predictors

Investigating Predictor Relationships
YTD Revenue
Remaining Revenue
PY Revenue
PYTD Revenue
PPY Revenue
PPYTD Revenue

Removing Outliers
• Unusual data can skew results during model
creation
• Outliers can be automatically cleaned using the
mvoutlier package in R
• Remove outliers in a consistent and documented
manner

Setting Up A Regression
Use best subset selection to find the best set of
predictors given a number of predictors
Separate models need to be compared by plotting RSS and
adjusted R2

Occam’s Razor Applies!
• Improve accuracy on the training data by choosing a
more complex method
• More flexible methods produce better results on
training – not necessarily on test data though

Results
• Using best subset selection we were able to generate a
linear model with these coefficients:
• Model attained 24.4% MAPE at the customer level,
predicting 2015 sales on the test data set
• Model can be stored and applied over and over in the
future
Error Histogram

Learn and Iterate
Each time predictions are ran, the MAPE is stored, and it
should be periodically evaluated for accuracy and
possible predictor changes
Error Histogram

Insights
• Use the predictions to take action
• Some of the predictions:
YTD
Revenue
Low
Likelihood
Medium
Likelihood
High
Likelihood Pipeline Prediction
Last week’s
Prediction
Bike Universe $1,419,255 $732,754 $375,930 $26,350 $1,135,035 $1,988,451 $1,987,455
Racing Bike Outlet $1,109,188 $1,075,916 $487,885 $247,269 $1,811,071 $1,731,399 $1,730,443
Northwind Traders $1,446,140 $112,982 $243,254 $104,520 $460,756 $1,560,967 $1,560,565
Rugged Bikes $1,140,681 $1,136,466 $253,616 $770,128 $2,160,210 $1,917,966 $1,714,933
Grand Cycle Store $1,606,350 $1,685,855 $552,993 $296,978 $2,535,825 $2,820,317 $2,820,317
• Rugged Bikes is down year over year, and far below
pipeline

Clustering to Find Insight
Look at customer ordering patters and pipeline to see
similar behaviors among groups of customers
Opportunity Avg Order
Size Orders Pipeline PY Sales
Bike Universe
110 10 $30,000 $40,000
The Cracker Box
140 5 $20,000 $30,000
Racing Bike
Outlet 150 6 $10,000 $15,000
Northwind
Traders 140 7 $15,000 $15,000
Rugged Bikes
150 3 $12,000 $10,000

Clustering to Find Insight
• Pattern may be unknown – look to see if there are
“similar” customers
• Clustering algorithms can help
• Predictor selection is key
• Need to normalize your data before processing

Conclusion
Predictive methods can help:
• Give insight into future values
- “How much sales can we expect for the rest of the
year at Bike Universe?”
• Classify data
- “Which of our customers behave similarly, and how
do we target our actions to serve each group best?”
• Quickly identify changes in large data sets
- “Why are sales predictions down so much at Rugged
Bikes?”

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Copyright 2016 by Senturus, Inc.
This entire presentation is copyrighted and may not be
reused or distributed without the written consent of
Senturus, Inc.

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