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Telling a Compelling Story Using SAS® Visual Analytics

Parri GignacDirector, Data ServicesKellogg Company

Telling a Compelling Story using

SAS Visual Analytics

Parri Gignac

Kellogg Company

TELLIN G A COMPEL L ING STORY USING SA S V ISUA L A NA LY TICS

TELLIN G A COMPEL L ING STORY USING SA S V ISUA L A NA LY TICS

Abstract

Effective use of Visual Analytics allows for crafting compelling stories that

create a dialog with the audience. This demonstration will show how

moving beyond a static Power Point presentation into a dynamic

visualization tool such as Visual Analytics enables a richer and more

meaningful communication between the presenter and audience.

More than just presenting data, it is crafting a compelling story that will

drive home the overall message. Watch a live presentation of a case

study which leverages VA to tell the story.

TELLIN G A COMPEL L ING STORY USING SA S V ISUA L A NA LY TICS

Set Your Story Up for Success!

Know “The Ask”

Understand Your Audience

Anticipate Questions

Frame Your Story

Get Feedback and Fine Tune the Storyline

TELLIN G A COMPEL L ING STORY USING SA S V ISUA L A NA LY TICS

Case Study: Don’t Override the Models!• The Ask:

• Demonstrate to the Sales Planning Analysts (SPA’s) why they should trust

modeled sales base and reduce the amount of overrides (model vs plan)

• Understand Your Audience:

• Sales Planning Team (low tech!)

• Anticipate Questions:

• Additional VA briefs were added

• Frame Your Story:

• Background, VA screenshots, and Fast Facts help to set up the story but the real

engagement takes place using SAS Visual Analytics to drive the conversation

• Get Feedback and Fine Tune:

• Reviewing with key business partners helped to fine tune the message to be more

meaningful for the audience

TELLIN G A COMPEL L ING STORY USING SA S V ISUA L A NA LY TICS

Case Study: Helpful Info

• Base

• Represents sales at the register absent of promotion support (ads, price reduction,

display).

• Model

• Base modeled using 3 years of historical sales by the Business Analytics team

• Plan

• Base that incorporates overrides on the Model by Sales Planners

• Category

• Ready to Eat Cereal (RTEC), Toaster Pastries, Bars, and Crackers, to name a few!

• Type

• Subset of Category: examples are Frosted Flakes medium size or Pop Tarts 8 count

• Consumption

• Actual sales scanned at retailer registers and collected by Nielsen

Visual Analytics Reports - Top 10 Modeled Accounts2015 Actual Consumption Through 2/21/2015

8

Background

• To further simplify the planning process, 2015 marked the start of consumption based planning in TPM at a new level. Modeling was adapted to align with the new process.

• The following charts provide highlights of the first round evaluating 2015 Plan Base vs Modeled Base, using Nielsen consumption as the benchmark for the Top 10 accounts and modeled categories.

• 2015 Plan Base and Modeled Base as of November 2014. Actual Nielsen reported Base Consumption weeks Jan 4 – Feb 21, 2015.

• This study includes 60+ national and regional retailers such as Walmart, Kroger, Target, Publix, Wegman’s, Harris Teeter, SpartanNash, Demoulas, and Roundy’s

9

Modeled vs Planned Base for Top 10 Accounts

• The top 10 accounts would have been more accurate using modeled base vs planned base by 2% Error difference: a 10% improvement of Model Base over Plan Base.

Current Planning in TPM

Plan Base – Top 10(as of 3/30/2015 - in millions)

2% Plan Base – Top 10(as of 3/30/2015 - in millions)

2015 Weeks 1-7 $650 $13

2015 Full Year $5,496 $110

What is the current dollar value of Plan Base in TPM for the top 10 accounts?

10

Category Error Comparison – Model vs Plan Base

• 9 out of 11 categories would have been more accurate by using the modeled base

11

Top 10 Accounts Error Comparison – Model vs Plan Base

• 8 out of 10 accounts would have had higher accuracy across the listed categories by using the modeled base

12

Top 10 Types Error Comparison – Model vs Plan Base

• 8 of the 10 top RTEC types would have been more accurate by using the modeled base

13

Category #10 at Top 10 Accounts

• For Category #10, modeled base is closer to Nielsen base units than planned base

14

How Does Type Level Modeling Compare to Material Level?

• Year over Year comparison of Type level modeling vs Material level modeling evaluated against Nielsen reported base:

• The move to Type level modeling has improved model error overall from 22% to 17%, which for this first round of evaluation, is a 20% improvement over Material level modeling (2014 vs 2015).

Top 10 Accounts

2014Material

Level

2015Type Level

Accuracy Gain

Model Error 22% 17% 5%

15

How Much Does the Model Change Over Time?

• As more Nielsen actuals are included for evaluation, analysis shows overall, modeled base is consistent over successive versions.

Modeled Base versions from July 2014 through February 2015

Total Consumption

Base Consumption

Model Versions

RTEC

16

Fast Facts

• The top 10 accounts would have been more accurate using modeled base vs planned base by 2% Error difference: a 10% improvement of Model Base over Plan Base.

• Modeled Base is more accurate than Planned Base in:

– 9 out of 11 top categories

– 8 out of 10 top accounts

– 8 of the 10 top RTEC types

– Toaster Pastries for top 10 accounts

• For the first 7 weeks of 2015, the move to type level modeling is a 20% improvement

TELLIN G A COMPEL L ING STORY USING SA S V ISUA L A NA LY TICS

Demo: SAS Visual Analytics

TELLIN G A COMPEL L ING STORY USING SA S V ISUA L A NA LY TICS

Thank You!

Huge thank you to the team for enabling the power of analytics and SAS Visual Analytics at Kellogg’s for the case study:

Joshua Bredeweg

Rick Dimon

David Kestner

Katherine Sanborn

Mary Woodward

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