Case study of how econometric modeling and pricing analytics helped DIY brand gain higher returns and impact from pricing decisions

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 roll-
back 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
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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
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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

20. Our Team’s Experience
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