Pricing Analytics Case Study

1. Pricing Analytics Successful Case Study

2. Case Situation & Results: DIY Brand Pricing Analytics • A major brand marketing in the home-improvement retail sector found itself struggling to grow demand. BLA was commissioned to evaluate and test their in-market retail pricing to determine what degree this has been a drag on their performance. We constructed models to measure price elasticities across 18 SKUs. • Our findings revealed that, overall, pricing for their products was price “inelastic”. However, we found that their premium-line of products had very high price elasticities, such that, price increases for these SKUs were not determined to be profitable. We concluded that an actual price rollback was in order. Such a move would actually be slightly profitable and would grow overall sales by an estimated +7%. • After nearly flat sales, the following year witnessed a sales turnaround with actual growth exceeding 8 percent!

3. Sales Model Architecture 3 Brand SKU Price Comptv. Price Home Improvmt Spend Season- ality Weekly Retail Sales by Customer The models determine the impact of these key drivers on weekly retail unit sales by retailer over 3 years, 2010-2012 Weekly Retail Sales are driven by Paslode’s Price Plus Competitor’s Price Plus Home Improvement Spending Plus Seasonality

4. Sales Model Validation 0 5,000 10,000 15,000 20,000 25,000 30,000 35,000 1/9/2010 3/9/2010 5/9/2010 7/9/2010 9/9/2010 11/9/2010 1/9/2011 3/9/2011 5/9/2011 7/9/2011 9/9/2011 11/9/2011 1/9/2012 3/9/2012 5/9/2012 7/9/2012 9/9/2012 11/9/2012 1/9/2013 3/9/2013 5/9/2013 7/9/2013 Actual Model 4 Models show an excellent predictive fit to actual sales R2 =95.3, Holdout R2 =93.2, MAP = +/- 2.9%

5. Retail Sales Variance Drivers: Annual Unit Sales Trend Due To: +0.9% +1.9% -13.6% +0.1% -20.0% -15.0% -10.0% -5.0% 0.0% 5.0% Retailer 1 Retailer 2 Retailer 3 Total 2012 Sales % Impact Competitor Pricing Brand's Pricing Remodeling Spending Base Momentum 5 More favorable Competitor Pricing trends has lifted Retailer Two’s sales trends positive. Retailer Three’s weak sales is affected by both adverse pricing and weak prior sales momentum.

6. Price Elasticity with and without Full Competitor Price Reciprocation -0.2% -0.7% -1.8% -0.6% -0.1% -0.3% -1.1% -0.3% -2.0% -1.8% -1.6% -1.4% -1.2% -1.0% -0.8% -0.6% -0.4% -0.2% 0.0% Retailer 1 Retailer 2 Retailer 3 Total Price Elasticity: Change in Retail Unit Sales Due to a 1% Increase in Retail Price WO Reciprocation Full Reciprocation 6 Overall, brand is “price inelastic” indicating that retail price increases, except in Retailer Three, will tend to be profitable

7. Price Elasticity with and without Full Competitor Price Reciprocation -0.30% -1.20% -0.40% -0.60% -1.40% -1.20% -1.00% -0.80% -0.60% -0.40% -0.20% 0.00% Basic Premium Accessories Total Price Elasticity: Change in Retail Unit Sales Due to a 1% Increase in Retail Price WO Reciprocation 7 The Basic product continues to be “inelastic”. However, the premium product shows significantly higher price sensitivity.

8. Pricing Impact by Product Line 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% Share of Sales Sales Impact Net Profit Impact Premium Line Accessories Standard Line 8 The higher price-point premium line has the highest price sensitivity and therefore accounts for significantly higher share of the pricing impact due to price changes.

9. The NLP Initiative amounted to a -10% price rollback for the Premium Line. Estimated Impact of NLP Initiative: Generates +6.8% Gain in Sales - 5,000 10,000 15,000 20,000 25,000 30,000 3/16/2013 4/16/2013 5/16/2013 6/16/2013 7/16/2013 8/16/2013 Total and Estimated Brand Retail Sales with and without NLP Initiative on Premium product line Model Est Reg Price Actual at Discounted Price 9

10. Brand Price Sensitivity Curves Shifting Slightly in 2013 0.94 0.99 1.04 1.09 1.14 1.19 1.24 1.29 1.34 1.39 -15% -10% -5% 0% 5% 10% 15% UnitSalesMillions Price Change 2012 Elasticity 2013 Current Elasticity 10 Overall price sensitivity has shifted “slightly” to more elastic, but the shift is ever-so small. DIY Brand Price Elasticity

11. Brand Price Sensitivity Curves 9.00 10.00 11.00 12.00 13.00 14.00 15.00 16.00 1.00 1.02 1.04 1.06 1.08 1.10 1.12 -15% -10% -5% 0% 5% 10% 15% NetProfit$Mil UnitSalesMil. Price Change Unit Sales Mil wo Reciprication Unit Sales Mil w Reciprication Profit Mil wo Reciprocation 11 When competitors match the brand’s price increase, the overall sales impact is significantly less when competitors match the brand’s price increase Price Elasticity for Sales & Net Profit

12. Forecast of Home Improvement Spending 6.2% 4.2% 4.5% 4.3% 3.0% 3.3% 7.0% 7.5% -20.0% -15.0% -10.0% -5.0% 0.0% 5.0% 10.0% 15.0% 20.0% $0.0 $20.0 $40.0 $60.0 $80.0 $100.0 $120.0 $140.0 $160.0 Four-Quarter Moving Total in Billions Four-Quarter Moving Rate of Change 12 Total Home Improvement Spending & Forecast The most recent official LIRA forecast of Home Improvement Spending calls for a significant +14.7% growth in the next year. We see a more modest improvement from +4.8 to +6.4% gain over the next 12 months

13. Impact of Home Improvement Spending on Brand Sales 1,040,000 1,045,000 1,050,000 1,055,000 1,060,000 1,065,000 1,070,000 1,075,000 1,080,000 1,085,000 -15% -10% -5% 0% 5% 10% 15% AnnualRetailSales Change in Home Improvement Spending Home Improvement Spending & Unit Sales Unit Sales 13 Overall Home Improvement Spending is Expected to Increase 6.4% in the next year should have a +1.2% impact on Brand’s Retail Sales.

14. Pricing Impact on retail unit sales by SKU 0.2% 0.2% 0.2% 0.2% 0.3% 0.3% 0.3% 0.3% 0.8% 1.0% 1.1% 2.3% 3.4% 7.6% 7.6% 14.7% 22.5% 37.1% 0.0% 5.0% 10.0% 15.0% 20.0% 25.0% 30.0% 35.0% 40.0% 650604 - 2-3/8 X .113 SM BR RD 2M 650387 - 3 X .131 RS HDG PLUS RD 650238 - 2-3/8 X .113 RSRD 650535 - 3-1/4X131 SMBT 1M FNP 30D 650237 - 2-3/8 X .113 RD 650523 - 2-3/8X 113 RSBT 1M FNP 650526 - 2-3/8X113 RSHDG 1M FNP 30 650239 - 3-1/4 X .131 RD 650524 - 3 X.120 SM BT FNP 30D 650603 - 2-3/8 X .113 RS BR RD 2M 650564 - 2 x.113 RSHDG PLUS RDFNP 650527 - 3 X120 RSHDG+ 1M FNP 30D 650236 - 3 X .120 RD 650388 - 3-1/4 X .131 HDG RD 650210 - 3 X .131 RD 650381 - 2 X .113 RS HDG RD 650383 - 2-3/8 X.113RS HDG RD 650385 - 3 X .120 RS HDG RD Retail Unit Sales Impact % Due to Pricing Retail Sales Impact % 14 5 of the 18 SKUs listed here account for about 90% of the impact due to a Brand pricing change

15. Pricing Impact on Brand’s net profit by SKU -2.4% -2.4% -2.2% -1.5% -1.3% -0.8% -0.6% 3.6% 3.7% 5.2% 5.4% 6.0% 6.2% 7.6% 9.2% 11.4% 13.6% 17.0% -5.0% 0.0% 5.0% 10.0% 15.0% 20.0% 650535 - 3-1/4X131 SMBT 1M FNP 30D 650524 - 3 X.120 SM BT FNP 30D 650523 - 2-3/8X 113 RSBT 1M FNP 650526 - 2-3/8X113 RSHDG 1M FNP 30 650564 - 2 x.113 RSHDG PLUS RDFNP 650238 - 2-3/8 X .113 RSRD 650237 - 2-3/8 X .113 RD 650527 - 3 X120 RSHDG+ 1M FNP 30D 650604 - 2-3/8 X .113 SM BR RD 2M 650383 - 2-3/8 X.113RS HDG RD 650388 - 3-1/4 X .131 HDG RD 650387 - 3 X .131 RS HDG PLUS RD 650210 - 3 X .131 RD 650603 - 2-3/8 X .113 RS BR RD 2M 650239 - 3-1/4 X .131 RD 650381 - 2 X .113 RS HDG RD 650236 - 3 X .120 RD 650385 - 3 X .120 RS HDG RD Net Margin Yield Impact % Due to Pricing Retail Margin Impact % 15 The top 5 SKUs account for about 76% of the net profit impact due to a pricing change

16. Price Elasticity Trends 0.34 0.35 0.36 0.37 0.38 0.39 0.40 0.41 0.42 0.43 Total Elasticity (Absolute Value) 16 Customers are more price sensitive during the high-season periods of April- October and less so from December-February. Overall price sensitivity increased during the NLP period from June-August.

17. About Us  Bottom-Line Analytics LLC is a consulting group that focuses on Marketing and Social Media analytics  Our modeling experts have a total of over 100 years of direct experience with marketing optimization modeling. This includes direct experience in over 35 countries and dozens of product categories  We are dedicated to the principles of innovation, excellence and uncompromising customer service  Most important, however, we are dedicated to getting tangible and positive business results for our clients 17 Copyright Bottom Line Analytics, LLC - All Rights Reserved, 2013

18. Our Mission  Letting the data speak. Analytics is a not a means to substantiate what you think you know (although that may occur), but rather a means to uncover truth through disciplined analysis.  Analytics must be used with integrity and care. Liars and charlatans can hide behind data.  We will use our clients data for their benefit and will not abrogate the trust of confidentiality  We will be leading innovators in the analytics space and take analytics to areas not heretofore thought possible  Analytics is about insight and prediction. Doing that well and better than competitors is a significant competitive advantage.  Our expertise resides in analytics and our clients’ expertise resides in their particular business. Marrying the two is essential for commercial success.  We are passionate about analytics because we know and can prove it to be the path to greater profitability for our clients 18 Copyright Bottom Line Analytics, LLC - All Rights Reserved, 2013

19. Michael Wolfe CEO, Bottom Line Analytics E: mjw@bottomlineanalytics.com M: 404.841.1620 www.bottomlineanalytics.com David Weinberger CMO, Bottom Line Analytics E: david@bottomlineanalytics.com M: 770.649.0472 www.bottomlineanalytics.com Masood Akhtar EVP Analytics, EMEA E: ma@bottomlineanalytics.com M: +44 7970 789 663 www.bottomlineanalytics.com John Schroeder EVP Business Development E: john@bottomlineanalytics.com M: 312-543-2694. www.bottomlineanalytics.com Alain Recaborde EVP Business Development E: alain@bottomlineanalytics.com M: 404-734-6615 www.bottomlineanalytics.com Team Leadership Dr. Peyton Mason Head of Linguistic Insights E: peyton@bottomlineanalytics.com •M: 704-814-0886 www.bottomlineanalytics.com

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