Net Revenue Breakdown - CFA Society Brazilcfasociety.org.br/pdf/rc/UDESC_Apresentacao.pdf · Lojas...
Transcript of Net Revenue Breakdown - CFA Society Brazilcfasociety.org.br/pdf/rc/UDESC_Apresentacao.pdf · Lojas...
Net Revenue Breakdown
OmnichannelBusiness Model
FavourableConditions
Superior Returns
AlreadyPriced
NotableRisks
68%
Brick & Mortar
Source: Company’s data, team’s estimates.
Net Revenue Breakdown
68%
25%
Brick & Mortar
E-commerce
OmnichannelBusiness Model
FavourableConditions
Superior Returns
AlreadyPriced
NotableRisks
Source: Company’s data, team’s estimates.
68%
25%
7% Brick and Mortar
E-Commerce
Financial Services
Net Revenue Breakdown
OmnichannelBusiness Model
FavourableConditions
Superior Returns
AlreadyPriced
NotableRisks
Source: Company’s data, team’s estimates.
68%
25%
7%Brick and Mortar
E-Commerce
Financial Services
Net Revenue Breakdown
Physical Retail Financial Services
E-commerce
705 conventional stores
125 virtual stores
10 distribution centers
571 cities
3.3 MM Luizacred cards
R$ 4.8 BI Luizacred portoflio
21.8 MM visits per month
+550K SKUsMarketplace
+80K SKUs B2C
OmnichannelBusiness Model
FavourableConditions
Superior Returns
AlreadyPriced
NotableRisks
Source: Company’s data, team’s estimates.
MGLU’s Stores Distribution
Source: Company’s data.
OmnichannelBusiness Model
FavourableConditions
Superior Returns
AlreadyPriced
NotableRisks
MGLU’s Stores Distribution
OmnichannelBusiness Model
FavourableConditions
Superior Returns
AlreadyPriced
NotableRisks
Source: Company’s data.
OmnichannelBusiness Model
FavourableConditions
Superior Returns
AlreadyPriced
NotableRisks
-7%
5%
27%
Employee Productivity* (% yoy)
MGLUVVAR
Máquina de Vendas
Source: NoVarejo. *Revenue/Employee
Employee Productivity
-7%
5%
27%
Employee Productivity* (% yoy)
Source: NoVarejo. *Revenue/Employee
MGLUVVAR
Máquina de Vendas
Employee Productivity
OmnichannelBusiness Model
FavourableConditions
Superior Returns
AlreadyPriced
NotableRisks
6%3%
-5%
Revenue per store (% yoy)
Revenue per Store
MGLU
VVARMáquina de Vendas
Source: NoVarejo.
F&A Top 20 Players Market Distribution
Source: NoVarejo.
OmnichannelBusiness Model
FavourableConditions
Superior Returns
AlreadyPriced
NotableRisks
0.5
-2.0
-0.7
yoy
MGLU
Via Varejo
Máquina de Vendas
15.6
3.3
6.3
9.0
23.2
2016
Via Varejo
MGLU
Máquina deVendas
Lojas CEM
Others
Source: NoVarejo.
Marketshare Change (2016) (p.p.)
100116
134
113 114129
100121
161177
234
364
0%
2%
4%
6%
8%
10%
12%
0
50
100
150
200
250
300
350
400
2012 2013 2014 2015 2016 1H17
MGLU Ebitda Margins VVAR Ebitda Margins
B2W Ebitda Margins
Source: Company’s data, team’s estimates.
OmnichannelBusiness Model
FavourableConditions
Superior Returns
AlreadyPriced
NotableRisks
8.5%
100116
134
113 114129
100121
161177
234
364
0%
2%
4%
6%
8%
10%
12%
0
50
100
150
200
250
300
350
400
2012 2013 2014 2015 2016 1H17
SSS Stores Growth Index MGLU Ebitda Margins
VVAR Ebitda Margins B2W Ebitda Margins
8.5%
Source: Company’s data, team’s estimates.
OmnichannelBusiness Model
FavourableConditions
Superior Returns
AlreadyPriced
NotableRisks
100116
134
113 114129
100121
161177
234
364
0%
2%
4%
6%
8%
10%
12%
0
50
100
150
200
250
300
350
400
2012 2013 2014 2015 2016 1H17
SSS Stores Growth Index Ecommerce Growth IndexMGLU Ebitda Margins VVAR Ebitda MarginsB2W Ebitda Margins
8.5%
OmnichannelBusiness Model
FavourableConditions
Superior Returns
AlreadyPriced
NotableRisks
Source: Company’s data, team’s estimates.
E-commerce Revenue Growth
2010 2016
Source: Company’s data.
OmnichannelBusiness Model
FavourableConditions
Superior Returns
AlreadyPriced
NotableRisks
1.24 1.10.78
-1.8 -1.9
2016
Máquina de Vendas MGLU
Privalia B2W Digital
Via Varejo
E-commerce Revenue Growth
2010 2016
Source: Company’s data.
OmnichannelBusiness Model
FavourableConditions
Superior Returns
AlreadyPriced
NotableRisks
Source: SBVC.
Marketshare Change (2016) (p.p.)
1.24 1.10.78
-1.8 -1.9
2016
Máquina de Vendas MGLU
Privalia B2W Digital
Via Varejo
E-commerce Revenues Market Share
OmnichannelBusiness Model
FavourableConditions
Superior Returns
AlreadyPriced
NotableRisks
Source: SBVC.Source: SBVC.
Marketshare Change (2016) (p.p.)
53.0%
5.1%5.7%6.0%6.5%
23.7%
2016
B2W Digital Via Varejo
MGLU Privalia
Máquina de Vendas Others
10.9
12.7
16.6
16.7
21.8
21.9
21.9
24.7
28.8
56.2
228.5
B2W (Shoptime)
Amazon
CNova (pontofrio.com)
Dafiti
Magazine Luiza
Walmart.com
Netshoes
B2W (Submarino.com)
CNova (casasbahia.com)
B2W (Americanas.com)
Mercado Livre
E-commerce Websites’ Visits Per Month(millions of users in august/2017)
Source: SimilarWeb.
OmnichannelBusiness Model
FavourableConditions
Superior Returns
AlreadyPriced
NotableRisks
OmnichannelBusiness Model
FavourableConditions
Superior Returns
AlreadyPriced
NotableRisks
Consumer’s Evaluation of Website’s Quality– LTM (as of october/2017)
Source: Reclameaqui.com.br and Euromonitor.
7.5
7.0
6.8
5.8
3.6
B2W
Magazine Luiza
Netshoes
Walmart
Cnova
OmnichannelBusiness Model
FavourableConditions
Superior Returns
AlreadyPriced
NotableRisks
Profitability Service
Source: Reclameaqui.com.br and Euromonitor.
Consumer’s Evaluation of Website’s Quality– LTM (as of 10/13/17)
7.5
7.0
6.8
5.8
3.6
B2W
Magazine Luiza
Netshoes
Walmart
Cnova
OmnichannelBusiness Model
FavourableConditions
Superior Returns
AlreadyPriced
NotableRisks
5% of Online GMV
+250 Sellers
5% of Online GMV
+250 Sellers
No COGS
Lower SG&A
OmnichannelBusiness Model
FavourableConditions
Superior Returns
AlreadyPriced
NotableRisks
830 Stores
ShoppableDistribution
Centers (SDCs)
+ 1k 3P LogisticsOperators
10 DistributionCenters (DCs)
Source: Company’s data.
OmnichannelBusiness Model
FavourableConditions
Superior Returns
AlreadyPriced
NotableRisks
830 Stores
ShoppableDistribution
Centers (SDCs)
+ 1k 3P LogisticsOperators
10 DistributionCenters (DCs)
million sf of totalstorage area
1.9
million sf of totalstorage area
3.5
Source: Company’s data.
5.4
OmnichannelBusiness Model
FavourableConditions
Superior Returns
AlreadyPriced
NotableRisks
830 Stores
ShoppableDistribution
Centers (SDCs)
+ 1k 3P LogisticsOperators
10 DistributionCenters (DCs)
million sf of totalstorage area
1.9
million sf of totalstorage area
3.5
5.4
Source: Company’s data.
OmnichannelBusiness Model
FavourableConditions
Superior Returns
AlreadyPriced
NotableRisks
Large freight cost reduction
Free in store pick-up mostly up to 48hrs
Faster and cheaper last mile delivery
OmnichannelBusiness Model
FavourableConditions
Superior Returns
AlreadyPriced
NotableRisks
Large freight cost reduction
Free in store pick-up mostly up to 48hrs
Faster and cheaper last mile delivery
CONSUMER CONTROL
OmnichannelBusiness Model
FavourableConditions
Superior Returns
AlreadyPriced
NotableRisks
FRED TRAJANO - CEO 2016 - Present
LUIZA TRAJANOChairman
OmnichannelBusiness Model
FavourableConditions
Superior Returns
AlreadyPriced
NotableRisks
FRED TRAJANO - CEO 2016 - Present
OmnichannelBusiness Model
FavourableConditions
Superior Returns
AlreadyPriced
NotableRisks
Years of Experience
LUIZA TRAJANOChairman
0
1
2
3
4
5
Threat of Substitute Products
Competitive Rivalry Within Industry
Bargaining Power of Suppliers
Bargaining Power of Customers
Threat of New Entrants
60
65
70
75
80
85
90
95
100
105
-20%
-15%
-10%
-5%
0%
5%
10%
15%
20%
2013 2014 2015 2016 2017*
Retail Revenue** yoyGDP yoyCredit*** - Total yoyEmployed population - yoyConsumer confidence index yoy (Right axis)
Source: Brazilian Central Bank, IBGE. *YTD
OmnichannelBusiness Model
FavourableConditions
Superior Returns
AlreadyPriced
NotableRisks
60
65
70
75
80
85
90
95
100
105
-20%
-15%
-10%
-5%
0%
5%
10%
15%
20%
2013 2014 2015 2016 2017*
Retail Revenue** yoyGDP yoyCredit*** - Total yoyEmployed population - yoyConsumer confidence index yoy (Right axis)
OmnichannelBusiness Model
FavourableConditions
Superior Returns
AlreadyPriced
NotableRisks
13.8%
7.3%
6.5%
9.0% 9.0% 9.0% 9.0%
-3.6%
0.6%
2.5%
2.0% 2.0%2.0% 2.0%
-4.0%
-3.0%
-2.0%
-1.0%
0.0%
1.0%
2.0%
3.0%
0.0%
2.0%
4.0%
6.0%
8.0%
10.0%
12.0%
14.0%
16.0%
2016A 2017E 2018E 2019E 2020E 2021E 2022E
Selic (%)
Brazilian GDP (%) YoY
Source: Brazilian Central Bank, team’s estimates.Source: Brazilian Central Bank, IBGE. *YTD
OmnichannelBusiness Model
FavourableConditions
Superior Returns
AlreadyPriced
NotableRisks
Source: NoVarejo.
OmnichannelBusiness Model
FavourableConditions
Superior Returns
AlreadyPriced
NotableRisks
Source: NoVarejo.
OmnichannelBusiness Model
FavourableConditions
Superior Returns
AlreadyPriced
NotableRisks
MGLU’s Stores Distribution
Source: Company’s data.
OmnichannelBusiness Model
FavourableConditions
Superior Returns
AlreadyPriced
NotableRisks
MGLU’s Stores Distribution MGLU’s Stores Distribution Potential
Source: Company’s data, team’s estimates.Source: Company’s data.
799
859 904
945 977
1,012 1,039
7,232
7,949
8,641
9,362
10,010 10,553
11,115
300
400
500
600
700
800
900
1,000
1,100
1,200
1,300
-
2,000
4,000
6,000
8,000
10,000
12,000
2016A 2017E 2018E 2019E 2020E 2021E 2022E
Stores Revenue B&M
Source: Company’s data, team’s estimates.
OmnichannelBusiness Model
FavourableConditions
Superior Returns
AlreadyPriced
NotableRisks
799
859 904
945 977
1,012 1,039
7,232
7,949
8,641
9,362
10,010 10,553
11,115
300
400
500
600
700
800
900
1,000
1,100
1,200
1,300
-
2,000
4,000
6,000
8,000
10,000
12,000
2016A 2017E 2018E 2019E 2020E 2021E 2022E
Stores Revenue B&M
Source: Company’s data, team’s estimates.
OmnichannelBusiness Model
FavourableConditions
Superior Returns
AlreadyPriced
NotableRisks
240 New Stores
Net Revenue CAGR of 7.1%
In The Next 5 Years:
Smartphone’s and Broadband Penetration in Brazil
Source: IDC and Internet Live Stats.
OmnichannelBusiness Model
FavourableConditions
Superior Returns
AlreadyPriced
NotableRisks
Growth in Online Spending per User
Rapid Penetration ofSmartphones &
Broadband
Online Experience Enhancement
46% 49%
51%
58%
64%
17%
26%
46%
70%
81%
2011 2012 2013 2014 2015
Broadband Penetration in Brazil (as% of total population)
Smartphone Penetration
20.1
16.015.3 15.1 15.1
0.7
Brazil WesternEurope
NorthAmerica
USA UK France
E-commerce Market𝑪𝑨𝑮𝑹𝟐𝟎𝟏𝟎−𝟐𝟎𝟏𝟔 (%)
Source: E-bit.
OmnichannelBusiness Model
FavourableConditions
Superior Returns
AlreadyPriced
NotableRisks
20.1
16.015.3 15.1 15.1
0.7
Brazil WesternEurope
NorthAmerica
USA UK France
E-commerce Market𝑪𝑨𝑮𝑹𝟐𝟎𝟏𝟎−𝟐𝟎𝟏𝟔 (%)
Source: E-bit.
OmnichannelBusiness Model
FavourableConditions
Superior Returns
AlreadyPriced
NotableRisks
Market Size:
E-commerce as % of retail sales, 2016
2.6
4.7
7.5
8.0
12.1
15.7
19.6
India
Brazil
Japan
Germany
USA
UK
China
Source: Euromonitor.
OmnichannelBusiness Model
FavourableConditions
Superior Returns
AlreadyPriced
NotableRisks
E-commerce as % of retail sales, 2016
2.6
4.7
7.5
8.0
12.1
15.7
19.6
India
Brazil
Japan
Germany
USA
UK
China
Source: Euromonitor.
OmnichannelBusiness Model
FavourableConditions
Superior Returns
AlreadyPriced
NotableRisks
$294$322
$354
$389
$426
$460$485
$13 $15 $17 $19 $21 $24 $27
2015 2016 2017E 2018E 2019E 2020E 2021E
USA BR
𝑪𝑨𝑮𝑹𝟐𝟎𝟏𝟓−𝟐𝟎𝟐𝟏𝑬𝑩𝑹 = 𝟏𝟐. 𝟒%
𝑪𝑨𝑮𝑹𝟐𝟎𝟏𝟓−𝟐𝟎𝟐𝟏𝑬𝑼𝑺𝑨 = 𝟖. 𝟕%
E-commerce Sales (USD billions)
Source: Forrester, SBVC.
OmnichannelBusiness Model
FavourableConditions
Superior Returns
AlreadyPriced
NotableRisks
Customer Experience
Growing User Base
Integrated Logistics
Online GMV Growth
2,724
4,053 4,657
5,220 5,389 5,871 6,767
196
461
921 1,796
3,162
4,900 30%31%
33%36%
38%
42%
47%
0%
5%
10%
15%
20%
25%
30%
35%
40%
45%
50%
-
2,000
4,000
6,000
8,000
10,000
12,000
14,000
2016A 2017E 2018E 2019E 2020E 2021E 2022E
B2C GMV
Market Place GMV
Online/Total GMV
Source: Company’s data, team’s estimates.
OmnichannelBusiness Model
FavourableConditions
Superior Returns
AlreadyPriced
NotableRisks
Online GMV Growth
2,724
4,053 4,657
5,220 5,389 5,871 6,767
196
461
921 1,796
3,162
4,900 30%31%
33%36%
38%
42%
47%
0%
5%
10%
15%
20%
25%
30%
35%
40%
45%
50%
-
2,000
4,000
6,000
8,000
10,000
12,000
14,000
2016A 2017E 2018E 2019E 2020E 2021E 2022E
B2C GMV
Market Place GMV
Online/Total GMV
Source: Company’s data, team’s estimates.
OmnichannelBusiness Model
FavourableConditions
Superior Returns
AlreadyPriced
NotableRisks
2017E
Market Place % of Online GMV
Market Place EBITDA Margin
Take Rate
B2C EBITDA Margins
B2C % of Online GMV
Online % of Total GMV
2022E
31% 47%
95% 58%
13% 17%
5% 42%
22% 41%
12% 11%
Marketplace Growth
Consumption Recovery
OmnichannelBusiness Model
FavourableConditions
Superior Returns
AlreadyPriced
NotableRisks
Marketplace Growth
COGS Dilution and Working Capital Improvement
33%
22%
35%
29%
B&M Gross Margin Online Gross Margin
COGS Dilution2016
2022
Inventory Days
87
80
2016
2022
Consumption Recovery
Source: Company’s data, team’s estimates.
OmnichannelBusiness Model
FavourableConditions
Superior Returns
AlreadyPriced
NotableRisks
Marketplace Growth
SG&A DilutionConsumption Recovery
Online Growth
Stores Digitalization
In-Store Pick Up
Integrated Logistics
3.1%
9.1%
27.5%
2.3%
6.5%
24.6%
% Rent Expenses % People Expenses % SG&A
2016
2022
Source: Company’s data, team’s estimates.
OmnichannelBusiness Model
FavourableConditions
Superior Returns
AlreadyPriced
NotableRisks
Marketplace Growth
Interest Rate Expenses ReductionsConsumption Recovery
Online Growth
Stores Digitalization
In-Store Pick Up
Integrated Logistics
Leverage Reduction
SELIC Reduction0.59
2.492.58
3.66
MGLU LAME VVAR B2W
Main Player’s Net Debt / EBITDA
Source: Companies’ data.
OmnichannelBusiness Model
FavourableConditions
Superior Returns
AlreadyPriced
NotableRisks
Marketplace Growth
Interest Rate Expenses ReductionsConsumption Recovery
Online Growth
Stores Digitalization
In-Store Pick Up
Integrated Logistics
Leverage Reduction
SELIC Reduction
-1.0
-1.4
-2.4
2017E 2022E 2027E
Net Debt / EBITDA
Source: Company’s data, team’s estimates.
OmnichannelBusiness Model
FavourableConditions
Superior Returns
AlreadyPriced
NotableRisks
Marketplace Growth
Interest Rate Expenses ReductionsConsumption Recovery
Online Growth
Stores Digitalization
In-Store Pick Up
Integrated Logistics
Leverage Reduction
SELIC Reduction
6.1%
2.7% 2.6%
2016 2022 2027
% Interest Rate Expenses*
Source: Company’s data, team’s estimates. *As a percentage of net revenue.
OmnichannelBusiness Model
FavourableConditions
Superior Returns
AlreadyPriced
NotableRisks
Leverage Reduction
SELIC Reduction
Online Growth
Stores Digitalization
In-Store Pick Up
Integrated Logistics
Marketplace Growth
Consumption Recovery
COGS Dilution
Working Capital Improvement
SG&A Dilution
Interest Expenses Reduction
OmnichannelBusiness Model
FavourableConditions
Superior Returns
AlreadyPriced
NotableRisks
Source: Bloomberg, companies’ data.
OmnichannelBusiness Model
FavourableConditions
Superior Returns
AlreadyPriced
NotableRisks
31%
11%
5%3%
12% 12%10%
13%
MGLU VVAR LAME B2W
ROIC WACC
OmnichannelBusiness Model
FavourableConditions
Superior Returns
AlreadyPriced
NotableRisks
30.8%
ROIC
105.2%ROIC
2022
Source: Bloomberg, companies’ data.
Source: Team’s estimates.
31%
11%
5%3%
12% 12%10%
13%
MGLU VVAR LAME B2W
ROIC WACC
OmnichannelBusiness Model
FavourableConditions
Superior Returns
AlreadyPriced
NotableRisks
Source: Companies’ data, team’s estimate.
-8.2%
3.0%
3.6%
B2W
Via Varejo
Magazine Luiza
3yrs. Average CFO Margin
555 496
689
874
971
1,152
(248) (221) (209) (195) (210) (228)
(334)
(470)
(71) (58)(8)
-
(27)
(196)
409
621
754
924
2017E 2018E 2019E 2020E 2021E 2022E
Free Cash Flow To Equity
Operating Cash Flow CAPEX Cash Flow Net Borrowing Free Cash Flow to Equity
Source: Company’s data, team’s estimate.
OmnichannelBusiness Model
FavourableConditions
Superior Returns
AlreadyPriced
NotableRisks
Source: Companies’ data, team’s estimate.
-8.2%
3.0%
3.6%
B2W
Via Varejo
Magazine Luiza
3yrs. Average CFO Margin
555 496
689
874
971
1,152
(248) (221) (209) (195) (210) (228)
(334)
(470)
(71) (58)(8)
-
(27)
(196)
409
621
754
924
2017E 2018E 2019E 2020E 2021E 2022E
Free Cash Flow To Equity
Operating Cash Flow CAPEX Cash Flow Net Borrowing Free Cash Flow to Equity
Source: Company’s data, team’s estimate.
OmnichannelBusiness Model
FavourableConditions
Superior Returns
AlreadyPriced
NotableRisks
Source: Company’s data, team’s estimate.
-8.2%
3.0%
3.6%
B2W
Via Varejo
Magazine Luiza
3yrs. Average CFO Margin
25.6
46.2
71.78
MGLU 10 yearsFCFE Value
Perpetuity Target Price
Source: Company’s data, team’s estimate.
OmnichannelBusiness Model
FavourableConditions
Superior Returns
AlreadyPriced
NotableRisks
SegmentUnlevered
Beta
Revenue
Comp. Retail (General) 0.8 68.8%
Retail (Online) 1.13 31.1%
1.36Weighted Re-Levered Beta
Ke and Long-Term Growth
25.6
46.2
71.78
MGLU 10 yearsFCFE Value
Perpetuity Target Price
OmnichannelBusiness Model
FavourableConditions
Superior Returns
AlreadyPriced
NotableRisks
Source: Company’s data, team’s estimate.
SegmentUnlevered
Beta
Revenue
Comp. Retail (General) 0.8 68.8%
Retail (Online) 1.13 31.1%
1.36Weighted Re-Levered Beta
Long-Term Real Growth Rate of 2%
Risk free rate 0.37%
Re-levered beta 1.36
Market risk premium 5.69%
Country risk premium 2.40%
Cost of equity (Ke) 10.49%
25.6
46.2
71.78
MGLU 10 yearsFCFE Value
Perpetuity Target Price
OmnichannelBusiness Model
FavourableConditions
Superior Returns
AlreadyPriced
NotableRisks
Source: Company’s data, team’s estimate.
Ke and Long-Term Growth
Target Price
BRL 71.78Current Price
BRL 72.45
Potential
-0.92%
25.6
46.2
71.78
MGLU 10 yearsFCFE Value
Perpetuity Target Price
OmnichannelBusiness Model
FavourableConditions
Superior Returns
AlreadyPriced
NotableRisks
Source: Company’s data, team’s estimate.
OmnichannelBusiness Model
FavourableConditions
Superior Returns
AlreadyPriced
NotableRisks
Source: Company’s data, team’s estimate, bloomberg.
SegmentMedian
EV/EBITDATarget Price
Revenue Comp.
Furniture, Appliances and Electronics
6.9 39.8 69%
E-commerce 20.8 109.7 31%
Target Price 61.5 0x
2x
4x
6x
8x
10x
12x
14x
2013 2014 2015 2016 2017
+𝜎1
−𝜎1−𝜎2
+𝜎2
0x
2x
4x
6x
8x
10x
12x
14x
2013 2014 2015 2016 2017
OmnichannelBusiness Model
FavourableConditions
Superior Returns
AlreadyPriced
NotableRisks
Source: Company’s data, team’s estimate, bloomberg.
SegmentMedian
EV/EBITDATarget Price
Revenue Comp.
Furniture, Appliances and Electronics
6.9 39.8 69%
E-commerce 20.8 109.7 31%
Target Price 61.5
+𝜎1
−𝜎1−𝜎2
+𝜎2
Target Price
BRL 61.5EV/EBITDA BF
13.4x
OR3 MER1 MR1
OR2 MER2
OR1 LR1
PROBABILITY
Low Medium High
Se
ve
reM
od
erat
eIn
sig
nif
ica
nt
IMP
AC
T
OmnichannelBusiness Model
FavourableConditions
Superior Returns
AlreadyPriced
NotableRisks
MER1 MR1
PROBABILITY
Low Medium High
Seve
reM
od
erat
eIn
sign
ific
ant
IMP
AC
T
OmnichannelBusiness Model
FavourableConditions
Superior Returns
AlreadyPriced
NotableRisks
OmnichannelBusiness Model
FavourableConditions
Superior Returns
AlreadyPriced
NotableRisks
A fiercer competition
Worsening of the economic scenario
MR1
MER1
Market Risk 1:
Macroeconomic Risk 1:
OmnichannelBusiness Model
FavourableConditions
Superior Returns
AlreadyPriced
NotableRisks
MARKETPLACE
B2C
Kindle+
E-books
OmnichannelBusiness Model
FavourableConditions
Superior Returns
AlreadyPriced
NotableRisks
MARKETPLACE
B2C
Kindle+
E-books
2 day shipping + 10x payment
MARKETPLACE
OmnichannelBusiness Model
FavourableConditions
Superior Returns
AlreadyPriced
NotableRisks
MARKETPLACE
B2C
Kindle+
E-books
2 day shipping + 10x payment
MARKETPLACE
Superior Delivery Sistem
FULFILLMENT BY AMAZON
MARKETPLACE
B2C
Kindle+
E-books
2 day shipping + 10x payment
MARKETPLACEFULFILLMENT BY AMAZON
Superior Delivery Sistem
CustomerLoyalty
AMAZON PRIME
OmnichannelBusiness Model
FavourableConditions
Superior Returns
AlreadyPriced
NotableRisks
OmnichannelBusiness Model
FavourableConditions
Superior Returns
AlreadyPriced
NotableRisks
Brazil’s future
OmnichannelBusiness Model
FavourableConditions
Superior Returns
AlreadyPriced
NotableRisks
Brazil’s future
Presidential election
OmnichannelBusiness Model
FavourableConditions
Superior Returns
AlreadyPriced
NotableRisks
Brazil’s future
Presidential election
Fiscal problem
OmnichannelBusiness Model
FavourableConditions
Superior Returns
AlreadyPriced
NotableRisks
Brazil’s future
Presidential election
Fiscal problem
Primary Surplus -2,44% of PIB
-4%
-2%
0%
2%
4%
6%
2003 2006 2010 2013 2017
Source: Brazilian Central Bank.
Base Case
Revenue CAGR
EBITDA Margin
Avg ROIC
Target Price
Upside/Downside
Market Place % of Online GMV
Avg Market Place Take Rate
New Stores
BearCase
226 209
10.4% 9.5%
42% 32%
9.8% 8.4%
10.7% 9.7%
72.8% 65%
BRL 71.78 BRL 59.05
-0.92% -18.4%
-27
-196
409
621
754
924
1,071
1,174
1,2791,352
1,393
-27-196
325
520
609
776
901965
1,0791,151
1,222
2017E 2019E 2021E 2023E 2025E 2027E
Bear Case Free Cash Flow to Equity
Base Case
Bear Case
OmnichannelBusiness Model
FavourableConditions
Superior Returns
AlreadyPriced
NotableRisks
Source: Company’s data, team’s estimates.
MercadoLibre sped up investments/expenditures in free shipping to boost its top line
MercadoLibre likely has the best marketplace operation in the country
But MGLU also offers free shipping (30% of deliveries), leveraging on its store base,which coupled with already decent traffic in its website and focusing on service levels
644 671 679 726 760 792 815 841 859 880 898 911 920 927
111114 120
133144
153162
171 180 185 193 199 203 206
2014 2015 2016 2017E 2018E 2019E 2020E 2021E 2022E 2023E 2024E 2025E 2026E 2027E
Conventional Virtual
Source: Company’s data, team’s estimates.
1st: High quality service level instead of starting a price war.
B2C MARKETPLACEFULFILLMENT BY AMAZON
Amazon is in the 2nd step of its Playbook.
The next step requires a large amount of investment in BR.
AMAZON PRIME
Amazon can’t influence pricing in marketplaces
Tax system
Rising costs to build up traffic on website
Logistics Challenges
Brazilian Market’s Challenges:
M&A is an option?
Fantastic Management Team
Innovation that Amazon can learn and/or replicate
Amazon’s last acquisitions more commonly aim these things
MELI is the most probable acquisition
But, MELI’s valuation (USD 11bn) would be Amazon’s greatest acquistion
Base Case
Revenue CAGR
EBITDA Margin
Avg ROIC
Target Price
Upside/Downside
Market Place % of Online GMV
Avg Market Place Take Rate
New Stores
Bull Case
226 242
10.4% 12%
42% 48%
9.8% 10.4%
10.7% 11.2%
72.8% 75.9%
BRL 71.78 BRL 84.24
-0.92% +16.4%
-27
-235
397
633
783
929
1,221
1,474
1,581
1,693 1,657
-27 -196
409
621
754
924
1,0711,174
1,2791,352
1,393
2017E 2019E 2021E 2023E 2025E 2027E
Bull Case Free Cash Flow to Equity
Bull Case
Base Case
Source: Company’s data, team’s estimates.
Base Case
Revenue CAGR
EBITDA Margin
Avg ROIC
Target Price
Upside/Downside
Market Place % of Online GMV
Avg Market Place Take Rate
New Stores
BearCase
226 209
10.4% 9.5%
42% 32%
9.8% 8.4%
10.7% 9.7%
72.8% 65%
BRL 71.78 BRL 59.05
-0.92% -18.4%
-27
-196
409
621
754
924
1,071
1,174
1,2791,352
1,393
-27-196
325
520
609
776
901965
1,0791,151
1,222
2017E 2019E 2021E 2023E 2025E 2027E
Bear Case Free Cash Flow to Equity
Base Case
Bear Case
Source: Company’s data, team’s estimates.
R$68.73 32% 38% 42% 46% 50%
8% 61.90 65.20 67.40 69.60 71.80
10% 63.80 67.40 69.80 72.20 74.70
12% 65.60 69.60 71.78 74.90 77.50
13% 66.60 70.80 73.40 76.20 78.90
14% 67.60 71.80 74.60 77.50 80.30
Tak
e R
ate
Market Place Sensitivity Analysis
GMV Penetration of Market Place 2022
R$68.73 32% 38% 42% 46% 50%
8% -15% -10% -7% -4% -1%
10% -12% -7% -4% 0% 3%
12% -9% -4% -1% 3% 7% BUY
13% -8% -2% 1% 5% 9% HOLD
14% -7% -1% 3% 7% 11% SELL
Market Place Sensitivity Analysis
GMV Penetration of Market Place 2022
Tak
e R
ate
Source: Company’s data, team’s estimates.
R$73.69 1.0% 1.5% 2.0% 2.5% 3.0%
8.9% 84.76 89.83 95.83 103.05 111.90
9.7% 75.45 79.39 83.97 89.37 95.84
10.5% 67.15 70.19 71.78 77.74 82.50
11.3% 60.26 62.66 65.39 68.50 72.10
12.2% 54.04 55.93 58.05 60.45 63.18
Target Price Sensitivity Analysis
Terminal Growth (g)
Sta
rtin
g K
e
R$0.00 1.0% 1.5% 2.0% 2.5% 3.0%
8.9% 16% 23% 32% 42% 54%
9.7% 4% 9% 15% 23% 32%
10.5% -8% -4% -1% 7% 13% BUY
11.3% -17% -14% -10% -6% -1% HOLD
12.2% -26% -23% -20% -17% -13% SELL
Target Price Sensitivity Analysis
Terminal Growth (g)
Sta
rtin
g K
e
Source: Company’s data, team’s estimates.
11286
14250
26014
423
945
2583
0
1000
2000
3000
4000
5000
6000
0
5000
10000
15000
20000
25000
30000
NET REVENUE EBITDA NET INCOME Source: Company’s data, team’s estimate,
2016A 2017E 2022E 2027E
Total EBITDA
Margins6.62% 7.84% 10.69% 11.78%
B&M EBITDA
Margins6.43% 7.58% 10.30% 10.87%
Ecommerce
EBITDA Margins11.17% 11.39% 13.51% 15.53%
Financial Services
EBITDA Margins0.00% 0.00% 0.00% 0.00%
Margins Evolution
Net Revenue 𝑪𝑨𝑮𝑹𝟐𝟎𝟏𝟔−𝟐𝟎𝟐𝟕
7.2%
EBITDA 𝑪𝑨𝑮𝑹𝟐𝟎𝟏𝟔−𝟐𝟎𝟐𝟕
13.0%
Net Income 𝑪𝑨𝑮𝑹𝟐𝟎𝟏𝟔−𝟐𝟎𝟐𝟕
24.3%
Source: Company’s data, team’s estimate.
72 80 73 78 83 11949 53 55 58 59 6165 45 41 30 34
2460 42 39 28 32231 1 1 2 2 2
1290
-53
449
581619
732
2017E 2018E 2019E 2020E 2021E 2022E
OthersLogisticsNew StoresReformsITCash from operations and financing activities
Fresh Capital from Follow-On
Marketplace consolidation, generatinga lot of cash
We compared to the “Class C” geographical distribution throughout Brazil
We analyzed MGLU’s and competitors stores geographic distribution
We estimated the number of stores that could still be opened by state, inorder to equalize stores concentration by the target population in similarlevels of the current explored states
We excluded RJ and ES from the study, because of highrates of thefts of goods.
MGLU’s Stores Distribution MGLU’s Stores Distribution Potential
Source: Company’s data, team’s estimates.Source: Company’s data.
Source: Company’s data, team’s estimate, 10-Year US Treasury Inflation Protected, Damodaran and EMBI +.
Year 2017 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027
D/E 76% 47% 38% 31% 27% 23% 20% 18% 16% 15% 14%
% Ecommerce 31% 33% 36% 38% 42% 47% 51% 53% 54% 54% 54%
Re-levered beta 1.36 1.19 1.15 1.11 1.10 1.10 1.10 1.09 1.08 1.08 1.07
Ke 10.49% 9.56% 9.32% 9.10% 9.06% 9.04% 9.02% 8.96% 8.94% 8.92% 8.88%
Ke
2016A 2017E 2018E 2019E 2020E 2021E 2022E 2023E 2024E 2025E 2026E 2027E
IPCA (%) 6.3% 3.2% 4.1% 3.9% 4.0% 4.0% 4.0% 4.0% 4.0% 4.0% 4.0% 4.0%
Brazilian GDP (%) YoY -3.6% 0.6% 2.5% 2.0% 2.0% 2.0% 2.0% 2.0% 2.0% 2.0% 2.0% 2.0%
Selic (%) 13.8% 7.3% 6.5% 9.0% 9.0% 9.0% 9.0% 9.0% 9.0% 9.0% 9.0% 9.0%Source: Team’s estimate and Brazilian Central Bank.
OR3 MER1 MR1
OR2 MER2
OR1 LR1
PROBABILITY
Low Medium High
Se
ve
reM
od
erat
eIn
sig
nif
ica
nt
IMP
AC
T
• (MR1) Entrance of a new competitor
• (MER1) Worsening of the economic scenario
• (MER2) Class “C”, the master of results
• (OR1) E-commerce security must be up-to-date
• (OR2) Marketplace growth as a greater
challenge
• (OR3) High dependency to Fred’s leadership
• (LR1) Potential changes in tax (PIS and
CONFINS) on Products
The holding LTD Participações (owns 57.5% of MGLU) elects the executive board.
With a legacy of continuous growth (6.8% CAGR net revenue since IPO), the family hasimplemented a great work culture that resulted in many management awards, especially being19 years among the best companies in the Great Place to Work ranking.
Source: Company’s data.
Historically, there has been synergies betweenfamily members who control the company, whichguaranteed the “luiza way” company’s culture anda low turnover rate that failed to ~30%.
According to the “TopExecutive Compensation2011” study, HR consultingfirm HayGroup andMagazine’s results of 2016,the company has a variableand aggressivecompensation executiveprogram, above the retailaverage of ~35%.
Source: Company’s data.
• Elected CEO in 2016, who has been working onthe digital transformation of Magazine Luizasince early 2000’s.
• Elected by Forbes as one of 25th best CEO’s2017 of Brazil, Frederico rely on an executiveboard that average 15 years in companyexperience.
Management has proved resilience and strong capacity ofexecution against important milestones:
(i) Dilma’s impeachment,(ii) Brazil’s biggest recession,(iii) Creation of Luizalabs and(iv) the launch of the marketplace platform.
Source: Company’s data.
• Worked analysing the sectors oftechnology, internet andtelecommunications at Westsphere EquityInvestors.
• Worked as an Analyst of the retail andconsumer goods sectors at Deutsche BankSecurities
Started at Magazine Luiza in 2000, assumingthe e-commerce department. After, hadpassages as Marketing Director, CommercialDirector, Executive Director in Sales andMarketing and, currently, CEO.
Source: Company’s data.