Adventures in Business Analytics – Optimization and the Organization Garry, steve from HP

© Copyright 2012 Hewlett-Packard Development Company, L.P. The information contained herein is subject to change without notice.
Adventures in Business
Analytics – Optimization
and the Organization
Steve Garry
Marketing Optimization and the Organization
November 2014
Generating Better Business
Results Through Analytics

© Copyright 2012 Hewlett-Packard Development Company, L.P. The information contained herein is subject to change without notice.2
Agenda
• Some terms of art
• Optimization - An Overview
• The MDO model and volume decomposition
• Efficiency and response curves
• Optimization process and tools
• Defining the problem
• Objective function
• Scenario building
• Constraints (business rules)
• Optimization Examples
• Optimizing ATL, BTL and discount
• Optimizing base price
• Challenges of organizational engagement
HP Advanced Analytics Team

Terms of art you may encounter
• ATL – “Above the line” Think advertising.
• BTL – “Below the line” Think in-store merchandising activity not dependent on discount.
• Instant Rebate – Price discount offered to potential consumers to spur sales.
• GRP – “Gross Rating Points”. A standard measure of advertising energy. Reach x
frequency - the product of how many people were exposed to the ad in a week and how
many times they were exposed in that week.
• Contribution – Sales volume (retail revenue) created by a business driver tactic.
• Efficiency – Revenue Contribution/Spend. Efficiency of 3.5 implies $3.50 in revenue
contribution for every dollar spent.
• Optimization – Using linear programing analysis to maximize an objective function.
• Objective function – Some business metric that you choose to maximize, minimize or
reach a specific target. Metrics include unit volume, revenue, profit, efficiency, etc.
• Constraints – Business rules that define limits within which the optimization must operate.
For example, minimum and maximum spend for a tactic, date specific spend levels, tactic-
campaign specific spend levels, or some combination of these.
HP Advanced Analytics Team

Optimization – An Overview
• The model and volume decomposition
• Efficiency and response curves

These comprehensive models account for combined contribution of all market drivers to total
sales. Statistical modeling shows us the individual affects of these business drivers on sales.
Marketing Demand Optimization (MDO) Model
• Decomposition of
Revenue by Business
Driver
• Return on Investment
Metrics by
Marketing/Sales Vehicle
• Price Elasticity by
Product Type
• Optimization Tools
to Drive Future
Spend Allocation
• Business Reporting
Tools to Understand
Future Performance
• Improved forecasting.
MDOMultipleRegressionModel
Sales
TV
Radio
Print
Online
Display
Paid
Search
Mobile
Instant
Rebate
Flyers
End
CapDemo
Days
InfoLab
SMB
Catalog
Economy
Seasonality
Holidays
Category
Trend
Distribution
Price
Advertising
Trade Promotion
Structural/
Contextual
Retail POS
5
Granular data:
Store/SKU/Week
retail POS data for
units, revenue &
inventory. 130
wks. 5M+ records.

The Job of the MDO Model is Volume Decomposition
2,254
3,111
2,254
3,111
IPG Sales
by Driver
Category
$10,235
$9,164
$657
$117
$296
Core
Price
Discount
BTL
ATL
Total IPG
Sales
FY2011
$10,235
Sales Contributions(1)
($M)
3.1
1.8
0.5
0.3
3.4
2.4
1.2
2.8
1.3
2.4
5.9
Efficiency(1) =
Contribution $ / Cost
(1) Adjustments factor included to account for non-modeled US Sales for all ATL and SMB Catalog
ATL
BTL
Price
Discount
Price
Discount
$657$476
$63
$94
SMB Catalog $46
Infolab Training $21
Demo Days $40
Audit/
Merchandising
$10
Radio $2
Paid Search $4
Newspaper $58
Magazine $35
Online Display $61
TV $136
Toner
Ink
Laser HW
Ink HW
Sales Contributions
($M)

What it does:
Provides modeled returns by tactic, timing or
geography based on historical performance. Estimates
results of changes in spend, tactic mix or timing
based on goals (Objective Function) and business
rules (Constraints).
What it answers:
1. Fixed Spend: How do I optimize advertising, trade
and promotion spend and timing to maximize
revenue based on a budget?
2. Increased Spend: How do I maximize unit sales
results in event of budget increases?
3. Reduced Spend: How do I minimize profit loses
due to reductions in my discount budget?
4. Forecast: How will an existing media plan perform?
5. Find Limit: How much will I need to spend to reach
my business goals in the most efficient way?
6. Forecast: What would the results of doubling my
advertising spend be?
Optimizing to determine results of different spend scenarios
0%
2%
4%
6%
8%
10%
12%
%LiftOffofBase
Average Weekly Spend ($)
Modeled Response Curves for PDIGITA: Digital
IITO: OJ Pro on Printers
0%
10%
20%
30%
40%
50%
60%
%LiftOffofBase
Modeled Response Curves for Insant Rebate
Spend on Printers
0%
5%
10%
15%
20%
25%
30%
35%
%LiftOffofBase
Modeled Response Curves for TV: TV IITO: OJ
Pro on Printers
0%
1%
1%
2%
2%
3%
3%
4%
%LiftOffofBase
Modeled Response Curves for MAGZN:
Magazine IITO: OJ Pro on Printers

% Lift Off of Base
Activity Level (GRPS, Hours, $s Off)
6 7 8 9 10 11
H I J K L
Tactic and/or Campaign Type # Parm A Parm B Parm C Parm D Parm E
PDIGITA: Digital IITO: OJ Pro 1 0.10667 -0.1076353 0.36169021 -4.692617 3.76
Annual Avg. Wkly Avg Wkly GRPs
Avg.
Wkly Lift Inc Rev Efficiency
Avg Wkly
Spend
Core Unit Vol 3,678,805 70,746 37.00 9.9% $1,041,176 3.96 $262,799
ASP 3,678,805 $121.65 20.60 7.05% $740,164 5.06 $146,336
-100,000
0
100,000
200,000
300,000
400,000
500,000
600,000
700,000
800,000
900,000
0%
2%
4%
6%
8%
10%
12%
%LiftOffofBase
Modeled Response Curves for PDIGITA: Digital IITO: OJ Pro on Printers
Lift (Curve) Green Blue Revenue @ Optimal Spend Incremental Revenue Less Cost
Each driver in the model has a response curve
• Some type of response function is necessary
for proper optimization.
• Response curve (red line) describes changes in
LIFT associated with different levels of support.
• Diminishing Returns: As a general rule, as
support increases lift per unit of support
declines.
• The optimization routine uses response curves
to determine what combination of spends
across ALL response curves yield the best
results.
• The ROI curve (blue) maps out the change in
revenue or profit as support changes.
• In this case spend for maximum efficiency
occurs long before maximum revenue is
reached. Typically revenue or profit would be
the objective function to be maximized.
Response curve
Spend for maximum efficiency
Spend for maximum revenue
Incremental Revenue Curve
Support level

Optimization Process and Tools
• Defining the problem
• Objective function
• Scenario building
• Constraints

Defining the optimization question and approach
Sponsor Date Requested Requested Due Date
Optimization Overview & Objectives
Campaigns Evaluated
Tactics Evaluated
Timing (week start date (Saturday),week stop date (Saturday), particular constraints, CY v FY)
Description
Allocation (K) -$ 0% -$ 0% -$ 0% -$ 0% -$ 0%
Opex (K)
Partner (K)
ATL Tactic (K) -$ 0% -$ 0% -$ 0% 0% 0%
TV (K)
Paid Digital (K)
Search (K)
Mobile (K)
Social (K)
Newspaper (K)
OOH (K)
Start Date
Stop Date
Optimize other spend Y/N Other Notes
BTL Locked Y
Contra Locked Y
Q1-$694K; Q2-$1416; Q3-
$0; Q4- $1416
Optimized across weeks.
Optimized w/i Qtrs.
Optimized across weeks.
Optimized w/i Qtrs.
Allow up to 20% of each
Qtr. spend to go to mobile
Scenario 0: Spend spread evenly across weeks within quarters using current IITO OJ Pro digital campaign as proxy, No optimization. Scenario 1: Optimize
scenario 0 and maintain quarterly spend boundaries. Scenario 2: Optimize scenario 0 and maintain quarterly spend boundaries and allow up to 20% of quarterly
spend to go to mobile.
Shivaun Korfanta October 7, 2014 10/10/2014 @ 1:00PM
Determine optimal quarterly paid digital/mobile spend for OJ Pro Family advertising . A total of $3.5M has been allocated. Money cannot move across
quarters.
OJ Pro Family advertising only. OJ Pro X will spend more money on lower funnel activities.
Maximize Profit (50%) and Revenue (50%)
Quarterly allocations are: Q1-$694K; Q2-$1,416; Q3-$0; Q4- $1,416. Money cannot move across quarterly allocation. Digital proxy is ITTO OJ Pro digital.
Mobile proxy is Mobile - Mobile Printing with lift reduced by 0.5
Scenario 0 Scenario 1 Scenario 2 Scenario 3 Scenario 4
Base Case - 0 OJP Base
Qtrly Spend allocated
evenly across weeks.
$3.5M Optimized Qtrly on
Digitial
$3.5M Optimized Qtrly on
Digitial & Mobile with 20%
Cap
Q1-$694K; Q2-$1416; Q3-
$0; Q4- $1416
Spread evenly across
weeks. No optimization
Q1-$694K; Q2-$1416; Q3-
$0; Q4- $1416
• First & most important step - Optimization request
form. Confirm in writing what you are agreeing to
do and make sure everyone is OK with it.
• Describe the objective or purpose of the analysis.
In this case “optimize allocation of spend on digital
advertising across time”. There may be several
questions compressed into one statement –
clarification, disambiguation is required.
• Establish the scenarios to be created. In this case:
• base scenario (un-optimized)
• scenario 1 – optimize quarterly spends across the
weeks in each quarter
• Scenario 2 – same as #1 but allow 20% of each
quarter spend to go to Mobile.
• Establish objective function. In this case balance
between profit (50%) and revenue (50%).
• Establish constraints. Here,
• Quarterly spend allocations
• Spend only on paid digital tactic or mobile in
scenario 2.
• Spend cannot move across quarters

© Copyright 2012 Hewlett-Packard Development Company, L.P. The information contained herein is subject to change without notice. This slide presentation shell be his final grand work @HP.11
Setting up tactic support
• This tactics page allows the user to set up an ATL, BTL and discount marketing plan for the year and then
optimize it. New un-modeled campaigns are given proxy response curves from existing campaigns.
• Changes in support data for scenarios can be entered by hand or copied and pasted from a spreadsheet.
• Optimizations maximize the objective function by changing the allocation of support across tactics,
campaigns, time and product line.
• Adjusting the “effect index” can raise or lower the lift of the curves to adjust for new campaigns that you
expect to perform better or worse than their proxies.

Defining constraints – the Business Rules
• Constraints set limits on how much can be moved to or from any tactic-campaign and time period
• Here the amount of spend for the proxy digital and mobile campaigns in each quarter is laid out.
• The slider bar near the top lets the user adjust how we weight the objective function. Here we are balancing
revenue and profit 50%:50%.
• Iterative adjustment of constraints by business teams, yield results that are reasonable, executable and
politically palatable. The results of one scenario often suggest several new scenarios.
• Constraints are very flexible and can create scenarios that are complex and realistic.

Optimization Examples
• Optimizing ATL, BTL and discount to support Demo Days
• Optimizing base price

Marketing Mix Optimization Example – Define the objective
Objective: IPS would like to increase the Demo Days in-store activity. They would like to determine the
most efficient source of funding for the incremental spend from the current budget. They would like to
try several optimized spend scenarios of various sizes and funding sources to determine which is most
practical. We will answer two questions:
• Q1: Which is the most efficient funding donor, ATL or IR discount? Scenarios 1 to 6.
• Q2: What are the limits of efficient spend on Demo Days given increased efficiencies provided by
geo-targeting. Scenarios 8 to 11.
Scenario ATL Spend
IR Discount
Spend
Non-DD
BTL
Demo Days
Spend
Ttl Marketing
Spend
Base $##,###,### $##,###,### $##,###,### $##,###,### $##,###,###
Scenario 1 -24% 0% 0% 52% 0%
Scenario 2 -48% 0% 0% 103% 0%
Scenario 3 -71% 0% 0% 154% 0%
Scenario 4 0% -2% 0% 52% 0%
Scenario 5 0% -5% 0% 103% 0%
Scenario 6 0% -7% 0% 154% 0%
Scenario 8* 0% -2% 0% 52% 0%
Scenario 9* 0% -5% 0% 103% 0%
Scenario 10* 0% -7% 0% 154% 0%
Scenario 11* 0% -6% 0% 123% 0%
Scenario 12* 0% -16% 0% 337% 0%
Q1- $s taken from ATL to
support demo days
Q1- $s taken from IR discount
to support demo days
Q2- What are the upper limits
of spend on demo days
Total marketing spend is
constant for all scenarios

-428
-328
-228
-128
-28
72
172
-50
-40
-30
-20
-10
0
10
20
$5M From ATL $10M From ATL $15M From ATL $5M From IR $10M From IR $15M From IR
UnitsVariancetoBase
Thousands
Revenue&ProfitVariancetoBase
Millions
Volume, Profit and Revenue Variance from Optimized Base by Scenario
Revenue Profit Units
Q1: IR Discount is an Efficient Donor of Demo Days Spend, ATL is Not
Scenarios donating ATL to DD lose
volume, profit and revenue as
donations increase. Optimized ATL
has higher efficiencies than DD.
Scenarios donating IR to DD preform
much better than scenarios 1-3 because
DD has a higher efficiency than IR
Total ATL efficiency = 3.5
Total BTL efficiency = 2.9
Total IR Discount efficiency = 1.4

Q2: There are Limits to the Efficient Increase in Demo Days Spend
21.6
27.7 28.0 28.4
26.5
5.1
6.6 6.7 6.8 6.3
173.6
221.8 223.7 226.8
213.0
0
50
100
150
200
250
0
5
10
15
20
25
30
DD $5 frm IR, Lift ↑
40%
40%
40%
40%
40%
UnitsVariancetoOpt.Base
Thousands
Revenue&ProfitVariancetoOpt.Base
Millions
Volume, Profit and Revenue Variance from Optimized Base by Scenarios with
Demo Days Lift Increased by 40%
Revenue Profit Units
DD Eff.
= 2.41
DD Eff.
= 2.23
DD Eff.
= 2.01
DD Eff.
= 1.25
DD Eff.
= 2.68
Increasing Demo Days spend beyond an incremental $12M ($22M total) risks pushing its efficiency below breakeven point
of 2. This assumes an estimated efficiency error range of 10%.

Average
Activity L
6 7 8 9 10 11
H I J K L
Tactic and/or Campaign Type # Parm A Parm B Parm C Parm D Parm E
Demo Days Instant Ink Hours 4 0 0.05251705 0.000375475 0 0.00
Annual Avg. Wkly Avg Wkly GRPs
Avg. Wkly
Lift Inc Rev Efficiency
Avg Wkly
Spend
Core Unit Vol 3,678,805 70,746 14,329.63 7.28% $877,669 1.83 $480,770
ASP 3,678,805 $121.65 12,610.06 7.16% $862,241 2.04 $423,077
$118.22 Ttl GRPs Ttl Revenue Ttl Spend
# of Weeks of Activity 52 OPT 1 745,141 52 Weeks $45,638,802 OPT 1 $25,000,022
52 OPT 2 655,723 52 Weeks $44,836,523 OPT 2 $22,000,000
$0
$100
$200
$300
$400
$500
$600
0%
1%
2%
3%
4%
5%
6%
7%
8%
9%
IncrementalRevenueLessCost
Thousands
%LiftOffofBase
Modeled Response Curves for Demo Days Instant Ink Hours on
Printers
Lift (Curve) Green Blue Optimal Revenue Spend Incremental Revenue Less Cost
An alternative approach using a response curve
Using the response curve for demo days alone can provide valuable information about the upper limit of efficient
spend. This solution approximates the optimization method, albeit less reliably than the full fledged optimization.
This technique does not include variations in seasonality or tactic.
Efficiency here is 1.8
below the breakeven
point
Efficiency here is 2.0 the
profit breakeven point.
• This method estimates $25M
incremental spend yields an
efficiency of 1.8.
• Like the more rigorous optimization
tool approach, this curve estimates
a demo days spend of $22M yields
an efficiency close to 2. That is the
profit breakeven level for efficiency.
• This approach also shows the
extent of headroom for demo days
spend which is close to $12M.
• Both solutions are above the point
of maximum return in profit because
unit sales is a priority.
• Demo days also has to compete
with other ATL tactics for spend. As
demo days spend increases
efficiency declines making
competition tougher.
Current spend

Price Optimization - If both Elasticities and Margins are Large?
Here high margin is paired with high elasticity with predictable results. The elasticity has been
empirically measured for this SKU and the margin is taken from the P&L. In this case
increasing price will lead to decreased volume, profit and revenue even if competition follows.
0
1,000,000
2,000,000
3,000,000
4,000,000
5,000,000
6,000,000
$0
$5,000,000
$10,000,000
$15,000,000
$20,000,000
$25,000,000
$30,000,000
-20.0% -16.0% -12.0% -8.0% -4.0% 0.0% 4.0% 8.0% 12.0% 16.0% 20.0%
AbsoluteVolume(Units)
AbsoluteProfit($000)
% Chg In Price
Profit is a Function of Elasticity of Demand and Contribution Margin
Absolute Profit ($000) Absolute Volume (Units)
HP 02 Color Ink Print Cartridge - Elasticty: -2.210 Marginal Contribution: $6.27 (86.3%)
Competition Follows
Response function based on base
price elasticity (i.e., % change in
volume/% change in price)
Objective function to
be maximized.

Modeled base price elasticities and margins of 40 SKUs
SKU_01
SKU_02
SKU_03
SKU_04
SKU_05
SKU_06
SKU_07
SKU_08
SKU_09
SKU_10
SKU_11
SKU_12
SKU_13
SKU_14
SKU_15
SKU_16 SKU_17SKU_18
SKU_19 SKU_20
SKU_21
SKU_22
SKU_23
SKU_24
SKU_25
SKU_27
SKU_28
SKU_29
SKU_30
SKU_31
SKU_32
SKU_33
SKU_34
SKU_35
SKU_36
SKU_37
SKU_38
SKU_39
SKU_40
0.840
0.860
0.880
0.900
0.920
0.940
0.000 0.500 1.000 1.500 2.000 2.500 3.000 3.500
%MarginalContribution
Absolute Base Price Elasticity
Relative Revenue Size of Business and Position of 40 SKU's on the
Profit Optimization Curve
Lower Your Price
Raise Your Price
Sphere volume = Revenue
Margin = 1/Elasticity (Profit Optimized Price)
High price sensitivityLow price sensitivity

Peanut Butter pricing approach leads to trifecta loses
HP Pricing Scenario Results
Proposed Abs Change % Change
Volume 70,635,258$ (12,823,073)$ -15.4%
Profit 1,386,560,299$ (75,948,938)$ -5.2%
Revenue 1,547,766,102$ (106,876,894)$ -6.5%
-18.0%
-16.0%
-14.0%
-12.0%
-10.0%
-8.0%
-6.0%
-4.0%
-2.0%
0.0%
Volume Profit Revenue
%Change
Taking price up 10% on all SKU’s will not produce good results.
Price Chg # SKU's
-15% 0
-10% 0
-5% 0
0% 0
5% 0
10% 40
15% 0
Volume

Optimizing base prices based on base price elasticities and margins
Proposed Abs Change % Change
Volume 100,312,710$ 16,854,379$ 20.2%
Profit 1,588,571,434$ 126,062,196$ 8.6%
Revenue 1,822,159,688$ 167,516,692$ 10.1%
0.0%
5.0%
10.0%
15.0%
20.0%
25.0%
Volume Profit Revenue
%Change
HP Pricing Scenario ResultsPrice Chg # SKU's
-15% 13
-10% 5
-5% 0
0% 12
5% 4
10% 5
15% 1
The one year profit swing between peanut butter and optimized approach is
$200M. A finer tuned optimized would result in even larger gains.
Portfolios Can Be
Optimized for
Profitability and
Constrained to
Reach Volume
and Revenue
Objectives

Challenges to Organizational
Engagement
Success of analytics is defined by its being successfully
imbedded in everyday business processes of the
organization. This may be more difficult to achieve than
you might think.
• The ambiguous nature of the perceived gifts of analytics
• Some business environments make analytics difficult: Things that
help and hurt.

The perceived benefits of analytics
PROS
• Improves the quality of decisions
• Speeds up decision making
• Provides increased
understanding and certainty
• Can provide a framework for
continuous improvement and
decision support across the value
chain in pricing, tracking,
optimization and forecasting.
• Can provide competitive
advantage that is difficult to
duplicate.
CONS
• Requires change in knowledge,
beliefs, skill set, execution
• Reduces the degrees of freedom
for narrative development.
• May create more complex
internal processes and models
of the market.
• May demonstrate how unwise
we have been in the past.
• Requires data you may not have
and arcane methods that are
hard to understand
The benefits of analytics are often seen as a mixed blessing by some in the organization

Some environments are analytic friendly others not
Characteristic Rationale
Org. Structure • Simpler structures with top down leadership are easier to work in.
Change management is easier but committed support from top
leadership is essential.
• Very complex/siloed orgs that are highly matrixed where group
functions are likely to overlap make it difficult to establish critical
mass and manage change.
Culture • Cultures that embrace change make MOC easier. Less reliance
on and acceptance of untested hypotheses, received wisdom, tribal
knowledge make shift to analytics much easier.
• Beware the power of the “narrative line” and the anecdote that
can trump data. “I don’t think that advertising works in high
tech.” “We didn’t see any sales lift from advertising.” “That
trade event worked very well.”

Some environments are analytic friendly others not
Characteristic Rationale
Data • Marketing and sales data is essential for most analytics methods.
Organizations are often ill-equipped to provide the granular data
required for analytics. Data is foundational for analytics.
• Connecting the need for data to the fruits of analytics can impede
progress.
Business Size • You can be too small to benefit from large scale analytics (MMM).
• Large organizations usually recognize significant benefits from
analytics.
Business
Success
• Difficult situations/poor business results are generally good for
the development and adoption of analytics. Few people ask “Why
were our sales so good?”
• Historical business success can make recognition of the need for
analytics difficult. “We’ve always been successful doing it this way
in the past.”

Thank you

Appendix

Response curves are created using modeled results at various support levels
• Response curves determine how quickly the effect of the tactic moderates and reaches
saturation.
• Response curves are created by using five functions, some concave, some “S” shaped.
• Functions and parameters are selected on a best fit basis.

Optimizing Marketing Spend Can Pay Big Dividends
OptimalNet
Rev.
TV Advertising
Price
Discount
$0
$1,000
$2,000
$3,000
$4,000
$5,000
$6,000
$7,000
$8,000
$9,000
$10,000
$0 $10,000 $20,000
NetRevenueLift(000)
Weekly Investment (000)
Branding TV Response Curve
HW ATL
HW ATL
Opt HW ATL
-$5,000
-$4,000
-$3,000
-$2,000
-$1,000
$0
$1,000
$2,000
$3,000
$4,000
$5,000
$0 $10,000 $20,000
NetRevenueLift(000)
Weekly Investment (000)
Price Discount Response Curve
HW Discount
HW Discount
Opt HW Discount
Response curves from MMM (above) are used by an
optimization program to determine the best allocation of
spending that satisfies explicit business constraints.
Optimized changes in spend allocation …yield these changes in results.
Moving spend from
discounting to
advertising and BTL
substantially
increases volume,
profit and revenue.
10% was the limit on
price discount
donation.
-20% -10% 0% 10% 20% 30% 40% 50% 60% 70%
BTL
Price Discount
ATL
Optimized Change in Spend Across ATL,
BTL and Price Discount
0% 2% 4% 6% 8% 10% 12% 14% 16% 18%
Unit Volume
Revenue
Profit
Optimized Change in Business Results

Adventures in Business Analytics – Optimization and the Organization Garry, steve from HP

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