Deloitte’s analytics
Symposium 2010
Using advanced analytics
to drive significant
financial impact
Mike Simonetto
Deloitte...
Agenda


•Trends bringing analytics to the
 forefront of business decision making
•Case Studies
•So how can YOU do this?

...
There are two trends bringing analytics to the forefront of business decision making;
first, data availability has explode...
Additionally, computing power has increased exponentially over the past 30 years
and this trend is expected to continue


...
This explosion of data availability and the advances in computing
power have paved the way for advanced analytics

Before ...
As a result, the use of advanced analytics has become a powerful tool to
drive decision making and increase value across s...
These analytics, when focused on the customer, are at the core of
modern pricing and profitability management

           ...
Case studies
Case 1 — Determining optimal incentive program by customer
segment: $10MM in margin lift

Client case: Finding the optimal...
Sample analysis: Optimization of customer-specific incentives

      Profitability by Customer leads to determining the ―r...
Case 2 — Getting granular with segmentation and deal management:
$50MM in annual benefits

Client case: Revisiting segment...
Sample analysis: Micro-segmentation using self-organizing maps


Self-organizing maps of transactional data
              ...
Case 3 — Using advanced analytics to set prices: $50MM in annual
profit improvement potential

Client case: Demand models ...
Sample analysis: Price optimization — multiple strategies and value
ptimization impacts an efficient frontier that allowed...
5. thurs 230 315 simonetto - financial impact of analytics
5. thurs 230 315 simonetto - financial impact of analytics
5. thurs 230 315 simonetto - financial impact of analytics
5. thurs 230 315 simonetto - financial impact of analytics
5. thurs 230 315 simonetto - financial impact of analytics
5. thurs 230 315 simonetto - financial impact of analytics
5. thurs 230 315 simonetto - financial impact of analytics
5. thurs 230 315 simonetto - financial impact of analytics
5. thurs 230 315 simonetto - financial impact of analytics
5. thurs 230 315 simonetto - financial impact of analytics
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5. thurs 230 315 simonetto - financial impact of analytics

  1. 1. Deloitte’s analytics Symposium 2010 Using advanced analytics to drive significant financial impact Mike Simonetto Deloitte Consulting LLP October 2010
  2. 2. Agenda •Trends bringing analytics to the forefront of business decision making •Case Studies •So how can YOU do this? 1 Using advanced analytics to drive significant financial impact Copyright © 2010 Deloitte Development LLC. All rights reserved.
  3. 3. There are two trends bringing analytics to the forefront of business decision making; first, data availability has exploded as a result of ERP, CRM and eCommerce Data explosion Data storage shortage • The amount of digital information continues increasing Global information created vs. available storage tenfold every five years. Exabytes – This year alone, mankind will create 1,200 exabytes of digital data (one exabyte is a billion gigabytes). 2000 – By 2014, researchers predict that mobile data traffic will 1750 increase 39-fold (a compound annual growth rate of 108 percent). 1500 – ―More data will be created in the next four years than in the history of the planet‖ — Mark Hurd (co-president, 1250 Oracle). Information created • Scientists are eyeing the path from bits to huge units: 1000 yottabytes, xonabytes, wekabytes and vundabytes — terms that did not exist 10 years ago. 750 • Wal-Mart handles over 1MM customer transactions 500 every hour, feeding databases estimated at more than Available storage 2.5 petabytes — the equivalent of 167 times the books 250 in the Library of Congress. 0 • The industry of data management is estimated to be worth more than $100 billion, growing at almost twice as 2005 2006 2007 2008 2009 2010 2011 fast as the software business. Source: IDC Sources: The Economist — Special report on managing information. February, 2010. Intel (http://www.intel.com/technology/mooreslaw/) Deloitte Review. The rise of asset intelligence: moving business analytics from reactive to predictive — and beyond. 2010 Etengoff, Aharon. IBM launches world fastest computer chip. TG Daily. September 6, 2010 MacManus, Richard. The coming data explosion. May 31, 2010 http://www.readwriteweb.com. Reardon, Marguerita. Cisco Predicts wireless data explosion. February 9, 2010. http://news.cnet.com/ 2 Using advanced analytics to drive significant financial impact Copyright © 2010 Deloitte Development LLC. All rights reserved.
  4. 4. Additionally, computing power has increased exponentially over the past 30 years and this trend is expected to continue Advances in computer power Moore’s law • In 1969 men landed on the moon with a 32-kilobyte CPU transistors count 1971–2010 and Moore’s law memory computer. Today, the average personal computer has more computing power than the entire # of transistors U.S. space program at that time. • Intel’s co-founder Gordon Moore predicted computer 1,000,000,000 power could double every two years by doubling the number of transistors on a chip (Moore’s law). Intel has 100,000,000 Moore’s law: transistor count kept that pace for over 30 years. doubles every two years – In 1982, the Intel 286 was able to process 2.66 million IPS. In recent months, the newest chips can process 10,000,000 50 Billion IPS. • Decoding the human genome took 10 years the first 1,000,000 time it was done in 2003; now it can be achieved in a week or less. 100,000 • A large consumer credit card issuer, in a recent trial with Hadoop* crunched two years of data (73 billion 10,000 transactions) in 13 minutes, which in the past took over one month (using traditional methods). 2,300 *Hadoop is a software framework that supports data-intensive distributed applications. 1971 1980 1090 00 08 10 Sources: The Economist — Special report on managing information. February, 2010. Intel (http://www.intel.com/technology/mooreslaw/) Source: Intel Deloitte Review. The rise of asset intelligence: moving business analytics from reactive to predictive — and beyond. 2010 Etengoff, Aharon. IBM launches world fastest computer chip. TG Daily. September 6, 2010 Advances in Computer Technology, http://www.lotsofessays.com/viewpaper/1706579.html 3 Using advanced analytics to drive significant financial impact Copyright © 2010 Deloitte Development LLC. All rights reserved.
  5. 5. This explosion of data availability and the advances in computing power have paved the way for advanced analytics Before Today Traditional analytic methods and tools were New analytical methods and tools can now limited by data and computing power tackle problems not previously solvable • Small datasets with few variables (10-20) and • Handling massive datasets with potentially limited cases (<100) millions of variables, billions of cases • Unrealistic assumptions about the data were • Analyzing unstructured data such as text, made (linear, normal, independent) networks (Web, social), images • Static data sets came from pulling information • Real-time data updates using fiber option and from isolated computers, tapes, and disks high-speed networks • Typical tasks including creating descriptive • Performing exploratory analysis to discover profiles of the data, hypothesis testing, and relevant patterns, trends, and anomalies in the developing small models for scoring data without having an explicit goal in mind • SQL and SAS were used as the main tools to • Executing real-time decision making where an analyze data automated response is needed in milliseconds • Additional computer languages to analyze data: PMML, DMQL, SPSS, DMX, etc. 4 Using advanced analytics to drive significant financial impact Copyright © 2010 Deloitte Development LLC. All rights reserved.
  6. 6. As a result, the use of advanced analytics has become a powerful tool to drive decision making and increase value across sectors and industries Applications of advanced analytics • Netflix analyzes movie ratings of millions of subscribers to recommend movies. Nearly two-thirds of the film selections by Netflix’s customer come from the referrals made by computer. • Best Buy found that 7% of its customers accounted for 43% of its sales and reorganized its stores to concentrate on those customers’ needs. • Centers for Disease Control (CDC) combine information from a wide variety of data feeds for public health surveillance. • Farecast, a part of a large software company’s search engine Bing, can advise customers whether to buy an airline ticket now or wait for the price to come down by examining 225B flight and price records. • Amex found that people who run up large bills on their card and register a new forwarding address in Florida have a greater likelihood to declare bankruptcy (FL has one of the most liberal bankruptcy laws). • eBay analyzes millions of auction pages, bidding history, and feedback to detect fraud. • The IRS utilizes advanced analytics to walk through its gigabytes of data on taxpayers in a new effort to find and ferret out the cheaters. • Google studies the timing and location of search-engine queries to predict flu outbreaks and unemployment trends before official government statistics come out. • The NBA analyzes the movements of players to help coaches orchestrate plays and strategies. Sources: Usama Fayyad, Gregory Piatetsky-Shapiro, and Padhraic Smyth. From Data Mining to Knowledge Discovery in Databases. AI Magazine, 1996 An introduction to data mining. Discovering hidden value in your data warehouse (http://www.thearling.com/text/dmwhite/dmwhite.htm) A brief history of data mining. Data mining software (http://www.data-mining-software.com/data_mining_history.htm) Hays, Constance. What Wal-Mart knows about customer’s habits. The New York Times. November 14, 2004 Judi Hasson, IRS plans a little data mining to catch tax cheats. September 1, 2009 UCLA, Data Mining: What is data mining (http://www.anderson.ucla.edu/faculty/jason.frand/teacher/technologies/palace/datamining.htm) 5 Using advanced analytics to drive significant financial impact Copyright © 2010 Deloitte Development LLC. All rights reserved.
  7. 7. These analytics, when focused on the customer, are at the core of modern pricing and profitability management • Identify demand elasticity of products within segments • Model competitive and marketplace response Price optimization to actions • Conduct what-if analyses for price gaps to competition and private label • Model effects of potential net consumer prices Profitability Trade spend • Build granular pricing strategies by channel, brand management effectiveness product line, and promoted group • Test potential pricing scenarios to see their impact on sales and volume • Isolate trade investments to customers and products • Detailed view of costs-to-serve by customer, channel, • Calculate ROI for historic trade spend by channel, market, etc. customer, product, and event • Holistic view of customer profitability and how it is • Determine volume lift from past promotions impacted by pricing decisions across the entire value chain • Model scenarios of increased and decreased trade spend to optimize future investments • Identify margin leakage for each customer as identified through use of the profitability waterfall • Redistribute trade dollars across channels and customers based on profit impacts We believe the right business analytics create a competitive advantage that drives material benefits to the bottom line. 6 Using advanced analytics to drive significant financial impact Copyright © 2010 Deloitte Development LLC. All rights reserved.
  8. 8. Case studies
  9. 9. Case 1 — Determining optimal incentive program by customer segment: $10MM in margin lift Client case: Finding the optimal incentive program for each customer leads to revenue and margin lift Client case example Analytics in action What was found? • Three distinct types of incentive programs were used with customers. • No analysis had been done to determine how each program incented specific customer segments. What was achieved? • Customers were segmented according to traditional segmentation, size, and competitive intensity. • Marginal profitability and volume metrics were analyzed for each customer/incentive combination. • Optimal commercial conditions types and levels were recommended for each group. • Reassignment of customers is in progress, with $10MM in margin lift to date. 8 Using advanced analytics to drive significant financial impact Copyright © 2010 Deloitte Development LLC. All rights reserved.
  10. 10. Sample analysis: Optimization of customer-specific incentives Profitability by Customer leads to determining the ―right‖ incentives Step One: Group customers, Step Two: Determine ―what Step Three: Reallocate create baseline works‖ for each group incentives to best return Marginal Volume and Profit of Each Incentive Ranking of Incentive Groups for Determination Potential Groups of Incentives Group and Customer Segment of Preferred Commercial Conditions Groups Micro- Dis- Rebates Customer Group 4 Group 1 Group 2 Group 3 Group 5 Group 6 Group 7 Group 8 Customer Group 1 Group 2 Group 3 Group 5 Group 6 Group 7 Group 8 Segment Segment finance counts A € 0,00 -€ 0,35 -€ 0,58 -€ 0,08 -€ 0,19 -€ 0,76 -€ 0,21 -€ 0,33 B € 0,00 -€ 0,37 -€ 0,39 € 0,04 -€ 0,12 € 0,17 -€ 0,12 -€ 0,20 A 35 30 4 6 7 18 8 Customer -€ 0,12 B 36 28 4 9 5 28 5 Group 1    C D € 0,00 € 0,00 € 0,13 Group 4 € 0,13 1 -€ Group 2 -€ Group 3 -€ Group 5 -€ Group 6 Segment -€ 0,17 € 0,26 Group € 0,44 0,04 € 0,32 0,06 € 1,39 0,05 € 0,46 0,07 € 0,19 Group 7 Group 8 A € 0,00 € 0,13 -€ 0,87 -€ 0,07 -€ 0,74 € 0,13 -€ 0,13 € 0,01 -€ 0,44 -€ 0,13 -€ 1,12 -€ 0,25 -€ 0,85 C 7 21 4 12 25 24 6 E € 0,00 -€ 0,08 -€ 0,31 D 30 35 12 12 1 14 12 Group 2    F € 0,00 B -€ 0,49 C € 0,00 € 0,44 -€ 0,12 -€ 0,89 -€ 0,05 € 0,00 € 0,13 -€ 0,55 € 0,25 -€ 0,39 -€ 0,08 -€ 0,01 € 0,25 -€ 0,28 -€ 0,12 -€ 0,37 € 0,12 € 0,08 -€ 0,12 -€ 0,19 -€ 0,29 € 0,13 -€ 0,42 -€ 0,16 -€ 0,09 -€ 0,72 -€ 0,59 G € 0,00 -€ 0,24 € 0,42 E 30 28 14 12 1 15 12 H € 0,00 D € 0,57 € 0,00 € 0,18 € 1,06 -€ 0,26 € 0,71 -€ 0,33 € 1,94 € 0,39 € 1,95 € 0,07 € 0,74 € 1,03 € 0,42 -€ 0,33 F 42 20 10 8 1 42 9 Group 3    I € 0,00 E € 1,16 € 0,00 € 2,54 € 1,62 -€ 0,60 € 1,20 -€ 0,47 € 4,71 € 0,08 € 1,37 -€ 0,32 € 1,83 -€ 0,23 -€ 0,03 -€ 0,65 J € 0,00 F -€ 0,13 € 0,00 € 0,07 -€ 0,11 -€ 1,01 -€ 0,13 -€ 0,28 € 0,12 € 0,39 € 0,16 -€ 0,30 -€ 0,16 -€ 0,11 € 0,21 -€ 0,40 G 35 21 2 12 20 36 2 G € 0,00 € 0,44 -€ 0,76 -€ 0,08 € 0,26 € 0,02 € 0,08 € 0,52 -€ 0,33 € 0,11 -€ 0,48 -€ 0,16 -€ 0,39 Group 4 K € 0,00 € 0,11 € 0,03 H 18 14 18 20 1 14 20 (Baseline)    L € 0,00 H € 0,06 I € 0,00 € 0,36 € 0,18 € 0,05 € 0,18 € 0,00 € 0,88 € 0,90 € 0,31 € 0,64 € 0,22 € 0,13 € 0,10 € 1,46 € 0,35 € 0,46 € 0,61 € 2,49 € 0,10 € 1,58 € 0,95 € 0,20 € 4,35 € 1,91 € 1,33 € 0,22 € 1,31 I 21 28 4 36 1 25 12 M € 0,00 € 0,25 € 0,15 J € 0,00 -€ 0,65 -€ 0,27 € 0,02 -€ 0,38 -€ 0,24 € 0,12 -€ 0,68 J 30 24 6 15 8 7 7 Group 5    K L € 0,00 € 0,00 -€ 0,41 -€ 0,46 -€ 0,13 € 0,02 € 0,39 € 0,31 -€ 0,33 -€ 0,07 -€ 0,34 -€ 0,05 € 0,48 € 0,06 -€ 0,41 € 0,09 K 28 20 6 7 30 6 6 M € 0,00 -€ 0,27 -€ 0,01 € 0,83 -€ 0,03 -€ 0,01 € 0,06 -€ 0,32 L 21 20 10 8 18 42 1 Group 6    M 21 18 1 20 12 28 10 Group 7    Customers Group 8    that did not receive incentives Define groups based on incentives Groups are compared based on impact Incentives are not reduced, only received to volume and profit redistributed  Customers belong to a distinct group  Weighting margin improvement by  Promotes granting incentives with each month, based on incentives volume change maximizes total dollar highest return on investment received return  Aligns incentives with what generates  Customer performance is compared to  Accepts that some incentives will the most incremental volume for a baseline group that received no reduce profit percentage, but generate customer as well incentives more than enough sales to compensate Implementing the commercial conditions suggested by this analysis will improve a $5B CPG company’s bottom line by $12MM this year -9- Copyright © 2010 Deloitte Consulting. All rights reserved. 9 Using advanced analytics to drive significant financial impact Copyright © 2010 Deloitte Development LLC. All rights reserved.
  11. 11. Case 2 — Getting granular with segmentation and deal management: $50MM in annual benefits Client case: Revisiting segments to include performance metrics improves customer performance Client case example Analytics in action What was found? • Clients existing segmentation was based purely on demographic variables. • Profitability and other performance We the examine the waterfall at a percent of sales and per hL level to understand measures did not factor into segmentation. differences in profitability between groups Cost Breakdown • Customer segment management occurred Large/Positive Large/Negative Small/Positive Small/Negative Expense Type %Sales (Dollars/hl) %Sales (Dollars/hl) %Sales (Dollars/hl) %Sales (Dollars/hl) ad hoc within each selling unit. Cost of good sold Customer specific -57.4% -0.6% ($48.1) ($0.5) -55.4% -6.6% ($47.3) ($5.7) -56.3% 0.0% ($45.8) ($0.0) -55.0% -1.1% $26.2 ($45.9) Agency handling (Fixed) -13.7% ($11.5) -37.8% ($32.3) -12.4% ($10.1) -30.1% ($1.0) Marketing/merchandising -6.0% ($5.0) -7.6% ($6.5) -6.1% ($5.0) -6.6% ($25.1) Distribution (route) Charges -4.4% ($3.7) -18.0% ($15.3) -10.1% ($8.2) -33.9% ($5.5) What was achieved? Warehouse (COGS) Other Rebates -1.4% -3.4% ($1.1) ($2.9) -2.7% -5.8% ($2.3) ($5.0) -1.2% -1.8% ($1.0) ($1.5) -2.4% -5.0% ($28.3) ($2.0) Agency handling (Variable) -0.8% ($0.7) -1.9% ($1.6) -0.7% ($0.5) -1.1% ($4.2) • Segmentation revisited using transactional Damaged product – Agency level -0.3% ($0.2) August Performance -0.5% ($0.5) -0.2% ($0.2) Project Performance -0.7% ($1.0) Customer equipment Route Baseline -0.8% Target ($0.6) -0.8% ($0.7) -3.5% ($2.8) -11.7% ($0.6) Actual, Returnable bottle charges -0.1% Actual ($0.1) -0.2% Chart ($0.2) Volume Clients -0.1% ($0.1) -0.5%Chart ($9.8) Volume Clients to-date data to understand behavioral attributes. Discount expenses Enterprise Margin -0.4% 34.7% ($0.3) $29.1 -3.2% -23.9% ($2.7) ($20.4) -0.2% 32.2% ($0.2) $26.2 -17.9% -43.5% ($0.4) ($14.9) 201 13% 12% 12% 0% -3% 12% -1% -4% • Profitability and volume were used as Profitability 120% 202 11% 14% 10% -1% -4% 12% -1% -5%  segmentation variables. 100% 203 30% 16% 29% Small/Negative Negative customer account for a 6.5% loss in profit – -4% Large/Positive 28% -2% $349MM (101%) -5% -2% Small/Positive % by segment 80% – Large/Negative ($18.9)MM (-5.4%) • Dashboards were designed to allow for 60% 204 17% 10% 12% Large/Negative Large/Positive – -1% Small/Positive 12% -3% $20.4MM (5.9%) -1% -5% – Small/Negative ($3.9)MM (1.1%) segment management at a selling-unit level, 40% 205 13% 12% 12% +1% -1% 14% 0% -3% including discounting guidelines, pricing, 20% 206 27% 14% 26% 0% -2% 27% 0% -2% 0% and supply chain costs. -20% Customer Count Revenue Profit 5% 10% 15% 20% 25% 30% 5% 10% 15% 20% 25% 30% • New tools are fully deployed, run-rate - 64 - Total 12% 21% -2% -3% 22% -3% -4% benefits for field customer management Legend: total $50MM in margin annually. Target Actual Baseline 10 Using advanced analytics to drive significant financial impact Copyright © 2010 Deloitte Development LLC. All rights reserved.
  12. 12. Sample analysis: Micro-segmentation using self-organizing maps Self-organizing maps of transactional data Self-Organizing Maps Of Transactional Data Unprofitable loans Unprofitable loans have amounts>$25K rate=5% Unprofitable loans rate Unprofitable loans buy- Unprofitable loans A lmost all rate Unprofitable loans sheet rates mostly <6% rates < 5% have no dealer markup exceptions caused FICO range 720-770 negative profits Unprofitable loans are mostly for new cars - 18 - 11 Using advanced analytics to drive significant financial impact Copyright © 2010 Deloitte Development LLC. All rights reserved.
  13. 13. Case 3 — Using advanced analytics to set prices: $50MM in annual profit improvement potential Client case: Demand models can be tools that value the trade-off between competing objectives Client case example Analytics in action What was found? Price Optimization through Advanced Analytics • Price setting was done by rule-of-thumb, profitzbc (Pzbc * Qzbc costzbc), with little analytical rigor. where z zone, b brand, c container Profit = Revenue - Cost • Certain constituents had volume targets, 1. Calculate price sensitivity for all products, controlling for related factors others were focused on profits. log(Qijk ) ijk 1A Price optimization (log( yijk ))an efficient frontier that allowed for multiple strategy ijk (log(xijk )) ijk generated ijk ( zijk ) ijk , setting and quantification of volume-profit tradeoffs for the entire supply chain where xijk own price, yijk set of cross prices, zijk set of other factors Efficient Frontier of Optimized Prices Quantity = Own Price + Cross-Prices + Other Factors What was achieved? Conditional on ß1 < 0, ß2 > 0 2. Optimize overall profit to produce a set of optimal prices for all products • Demand models were built for the top 80 Margin (000s) products (accounting for 95% of revenue). • Alternative scenarios were built along an To allow for differentiated pricing strategies, we also ran price optimization for Conditional on [Pimported>Pdomestic], [Qproduced(i,j,k)<Qcapacity(i,j,k)], category A and category B separately • Contribution margin was estimated for the entire ∆Price<- [Penalty: P(zona)neighbor1>P(zona) neighbor2], [Penalty:(Units) efficient frontier to allow a negotiation Quantity /+X%], [∂Q/∂P*P/Q], [Pijk<Pijk], [Qijk<Qijk], and much more… value chain: [client value chain] • Business and operations constraints controlled between margin and volume. Category A optimization results: price gap vs. traditional channel, max volume change by retailer, min Max (Profit) = Revenue - Cost margins by retailer, capacity restrictions and • Category-by-category selections of margin product mix constraints - 47 - • Strategy A: total 12-month potential margin A B increase for the modern channel served by Cleansed to targets allowed for picking the best point for protect client confidentiality bottler only is estimated at $10.4 million (US$ 2.7MM), while increasing sales volume by 0.5% or 284 thousand units. each category along that frontier. • Strategy B: The focus is on increasing volume without losing margin and the results are 854 Strategy A Strategy B thousand more units and $4.0MM more margin • Once fully implemented later this year, the Category B -6 - estimated benefit will be $50MM. A B Strategy A Strategy B - 19 - 12 Using advanced analytics to drive significant financial impact Copyright © 2010 Deloitte Development LLC. All rights reserved.
  14. 14. Sample analysis: Price optimization — multiple strategies and value ptimization impacts an efficient frontier that allowed for multiple strategy chain generated and quantification of volume-profit tradeoffs for the entire supply chain Price optimization generatedefficient frontier that allowed for multiplefor multiple strategy Price optimization generated an an efficient frontier that allowed strategy setting and quantification of Pricesvolume profit tradeoffs for the chain supply chain Efficient Frontier of Optim ized volume-profit tradeoffs for the entire supply entire setting and quantification of Efficient frontierEfficient Frontier of Optim ized Prices of optimized prices Distribution of price changes Margin (000s) • Contribution margin w as estimated for the entire Quantity (Units) value chain • • Contribution margin was estimate for the entire value chain Business and operational constraints controlled • Contribution margin w as estimated for the entire •optimization results: operational constrains controlled optimization Business and price gap vs. traditional Quantity (Units) value chain channel, max volume change by retailer, min results: price gap vs. traditional channel, max volume change Margin improvement margins by retailer, capacity restrictions and Strategy A Strategy B Difference by Business and margins by retailer, capacity restrictions, • retailer, minimum product mix constraints operational constraints controlled and product-mix constraints • Strategy A: total 12-month potential margin Cleansed to 1.0% 0.8% -19% optimization results: price gap vs. traditional protect client Cleansed to •increase for of $10.4 million, w hile increasing by retailer, min Strategy A: Total 12-monthchange margin increase channel, max volume potential of confidentiality 8.9% 3.7% -58% sales volume by 0.5% or 284 thousand units protect client $10.4 million, while increasing sales volume by and or 284 margins by retailer, capacity restrictions 0.5% confidentiality 1.8% 0.9% -51% • Strategy B: Increase focus on volume w ithout thousand units product mix constraints losing margin; 854 thousand incremental more 5.3% 2.3% -57% •units and $4.0MM more margin Increase focus on volume without losing margin; 854 sed to 3.0% 1.4% -54% • Strategy A: total more units and $4 million more thousand incremental 12-month potential margin margin t client 0.2%–0.3% difference in margin improvement calculations are due to the timing of the TPS matching ** increase for of $10.4 million, w hile increasing ntiality -6 - sales volume by 0.5% or 284 thousand units 13 Using advanced analytics to drive significant financial impact • Strategy B: Increase focusDeloitte Development LLC. All rights reserved. Copyright © 2010 on volume w ithout

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