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

1. © 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

2. © 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

3. © Copyright 2012 Hewlett-Packard Development Company, L.P. The information contained herein is subject to change without notice.3 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

4. © Copyright 2012 Hewlett-Packard Development Company, L.P. The information contained herein is subject to change without notice. Optimization – An Overview • The model and volume decomposition • Efficiency and response curves

5. © Copyright 2012 Hewlett-Packard Development Company, L.P. The information contained herein is subject to change without notice.5 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.

6. © Copyright 2012 Hewlett-Packard Development Company, L.P. The information contained herein is subject to change without notice.6 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)

7. © Copyright 2012 Hewlett-Packard Development Company, L.P. The information contained herein is subject to change without notice.7 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 ($) Modeled Response Curves for PDIGITA: Digital IITO: OJ Pro on Printers 0% 10% 20% 30% 40% 50% 60% %LiftOffofBase Average Weekly Spend ($) Modeled Response Curves for Insant Rebate Spend on Printers 0% 5% 10% 15% 20% 25% 30% 35% %LiftOffofBase Average Weekly Spend ($) Modeled Response Curves for TV: TV IITO: OJ Pro on Printers 0% 1% 1% 2% 2% 3% 3% 4% %LiftOffofBase Average Weekly Spend ($) Modeled Response Curves for MAGZN: Magazine IITO: OJ Pro on Printers

8. © Copyright 2012 Hewlett-Packard Development Company, L.P. The information contained herein is subject to change without notice.8 % Lift Off of Base Average Weekly Spend ($) Average Weekly Spend ($) 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 $146,336 -100,000 0 100,000 200,000 300,000 400,000 500,000 600,000 700,000 800,000 900,000 0% 2% 4% 6% 8% 10% 12% %LiftOffofBase Average Weekly Spend ($) 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

9. © Copyright 2012 Hewlett-Packard Development Company, L.P. The information contained herein is subject to change without notice. Optimization Process and Tools • Defining the problem • Objective function • Scenario building • Constraints

10. © Copyright 2012 Hewlett-Packard Development Company, L.P. The information contained herein is subject to change without notice.10 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

11. © 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.

12. © Copyright 2012 Hewlett-Packard Development Company, L.P. The information contained herein is subject to change without notice.12 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.

13. © Copyright 2012 Hewlett-Packard Development Company, L.P. The information contained herein is subject to change without notice. Optimization Examples • Optimizing ATL, BTL and discount to support Demo Days • Optimizing base price

14. © Copyright 2012 Hewlett-Packard Development Company, L.P. The information contained herein is subject to change without notice.14 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

15. © Copyright 2012 Hewlett-Packard Development Company, L.P. The information contained herein is subject to change without notice.15 -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

16. © Copyright 2012 Hewlett-Packard Development Company, L.P. The information contained herein is subject to change without notice.16 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% DD $10 frm IR, Lift ↑ 40% DD $12 frm IR, Lift ↑ 40% DD $15 frm IR, Lift ↑ 40% DD $33 frm IR, Lift ↑ 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%.

17. © Copyright 2012 Hewlett-Packard Development Company, L.P. The information contained herein is subject to change without notice.17 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 $600 0% 1% 2% 3% 4% 5% 6% 7% 8% 9% IncrementalRevenueLessCost Thousands %LiftOffofBase Average Weekly Spend ($) 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

18. © Copyright 2012 Hewlett-Packard Development Company, L.P. The information contained herein is subject to change without notice.18 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.

19. © Copyright 2012 Hewlett-Packard Development Company, L.P. The information contained herein is subject to change without notice.19 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

20. © Copyright 2012 Hewlett-Packard Development Company, L.P. The information contained herein is subject to change without notice.20 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 HP Pricing Scenario Results 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

21. © Copyright 2012 Hewlett-Packard Development Company, L.P. The information contained herein is subject to change without notice.21 Optimizing base prices based on base price elasticities and margins HP Pricing Scenario Results 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

22. © Copyright 2012 Hewlett-Packard Development Company, L.P. The information contained herein is subject to change without notice. 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.

23. © 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.23 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

24. © 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.24 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.”

25. © 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.25 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.”

28. © Copyright 2012 Hewlett-Packard Development Company, L.P. The information contained herein is subject to change without notice.28 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.

29. © Copyright 2012 Hewlett-Packard Development Company, L.P. The information contained herein is subject to change without notice.29 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

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