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 LeviSenior Consultant

Senturus, Inc.

Greg HerreraPresident and Co-

FounderSenturus, Inc.

Hundreds of Free Resourceshttp://www.senturus.com/resources/

RESOURCE LIBRARYAn extensive, free library of past webinars, demonstrations, whitepapers, presentations, helpful hints, and more.


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


http://www.senturus.com/resources/a-pragmatic-approach-to-sales-forecasting/




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 RequireHistorical 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 UnknownGrand 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. Amount2. Close Date3. Forecast Category4. Probability5. 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

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


Goal: Merge Predictions for Actionable Insights

Opportunity Amount

Stage

Bike Universe $50k EarlyThe Cracker Box $40k MiddleRacing Bike Outlet $35k FavoredNorthwind Traders $65k VerbalRugged Bikes $60k ClosingGrand 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

6/1/16 $40k 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 5/1/16 $30k Low

Bike Universe 6/1/16 $40k Low

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

ActualForecastOpportunity

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 sellsfor $400 there may be a higher cost for missed sales than over production

Actual

ActualForecastOpportunity

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


To view the FREE video recording of the presentation and download this deck, go to:

http://www.senturus.com/resources/a-pragmatic-approach-to-sales-forecasting

/

The Senturus comprehensive library of recorded webinars, demos, white papers, presentations, and case studies is available on our website:

http://www.senturus.com/resources/

Hear the Recording









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 RelationshipsYTD Revenue

Remaining Revenue


PY Revenue

PYTD Revenue


PPY Revenue

PPYTD Revenue




YTD Reve

nue

Remain

ing Revenue

PPY Rem

aining

Revenue

PY Reve

nue

PYTD Reve

nue

PY Rem

aining Reve

nue

PPY Reve

nue

PPYTD

Revenue

Low Lik

elihood

Revenue

Med. Like

lihood R

evenu

e

High Like

lihood Rev

enue


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


Investigating Predictor RelationshipsYTD Revenue

Remaining Revenue


PY Revenue

PYTD Revenue


PPY Revenue

PPYTD Revenue




YTD Reve

nue

Remain

ing Revenue

PPY Rem

aining

Revenue

PY Reve

nue

PYTD Reve

nue

PY Rem

aining Reve

nue

PPY Reve

nue

PPYTD

Revenue

Low Lik

elihood

Revenue

Med. Like

lihood R

evenu

e

High Like

lihood Rev

enue


Setting Up A RegressionUse 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 InsightLook at customer ordering patters and pipeline to see similar behaviors among groups of customers

Opportunity Avg Order Size Orders Pipeline PY Sales

Bike Universe110 10 $30,000 $40,000

The Cracker Box 140 5 $20,000 $30,000Racing Bike Outlet 150 6 $10,000 $15,000Northwind Traders 140 7 $15,000 $15,000Rugged 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


ConclusionPredictive 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?”

To view the FREE video recording of the presentation and download this deck, go to: http://www.senturus.com/resources/a-pragmatic-approach-to-sales-forecasting

/

The Senturus comprehensive library of recorded webinars, demos, white papers, presentations, and case studies is available on our website:


Hear the Recording








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Using Salesforce, ERP, Tableau & R in Sales Forecasting