driving digital strategy
TRANSCRIPT
DRIVING DIGITAL STRATEGY
A GUIDE TO REIMAGINING
YOUR BUSINESS
BY SUNIL GUPTA
Contents
Figure I-1 3
Figure 1-1 4
Figure 1-2 5
Figure 1-3 6
Figure 2-1 7
Figure 2-2 8
Figure 2-3 9
Figure 2-4 10
Figure 2-5 11
Figure 3-1 12
Figure 3-2 13
Figure 5-1 14
Figure 5-2 15
Table 6-1 16
Figure 7-1 17
Figure 7-2 18
Figure 7-3 19
Figure 7-4 20
Figure 8-1 21
Figure 8-2 22
Figure 8-3 23
Figure 9-1 24
Figure 9-2 25
Figure 9-3 26
Figure 10-1 27
Figure 10-2 28
Figure 10-3 29
Introduction Page 6 31/05/18 8:09 PM
DIGITALLEADERSHIP
REI
MAG
INE
YOUR BUSINESS
R&D • Operations • Om
nichannel
REEVALUATE YOUR VA
LUE
CH
AINR
EB
UILD
YO
UR
ORGANIZATION
Transition • O
rganization design • Skills
RECONNECT WITH YOUR CU
STO
ME
RS
Acquiring • Engaging • M
easu
ring
BUILD FOR THE FUTURE
STRENGTHEN THE CORE
Sco
pe •
Busin
ess m
odel • Platform
FIGURE I-1
A framework for digital leadership
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Chapter_01 Page 18 31/05/18 2:23 AM
eBay
Marketplace
App storePhone
Studio
VOD
AWS
Books andelectronics
Kindle
Adnetwork
Amazon
IBM
Microsoft
Hulu
Netflix
ABCNBC
HTC
Samsung AppleApple
Apple
HP
Barnes & NobleWalmartBest Buy
Figure 1-1
Amazon’s business and its competitors
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Farmequipment
Farmmanagement
Seedoptimization
Irrigation
Weatherdata
• Weather maps• Weather forecasts• Rain, humidity, temperature sensors
• Seed databases• Farm-performance
databases• Seed-optimization
applications
• Field sensors• Irrigation nodes• Irrigation application
• Tractors• Tillers• Planters
John Deere’s transformation
Source: Adapted from Michael E. Porter and James E. Heppelman, “How Smart, Connected Products Are Transforming Competition,” Harvard Business Review, November 2014.
Figure 1-2
5
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Multiplecustomers
WhatsAppFacebookAmazon MarketplaceUber
WeChatAmazonPaytmPeloton ridersNike+ Run Club Multiple
productsSingleproducts
Singlecustomer
AutomobilesConsumer productsFinancial servicesPeloton bikesNike shoes
Echo and e-commerceKindle and ebooksiPod and iTunesPeloton videosNike+ FuelBand
Network effects:Create virtuous circle
Traditional strategy:Make it cheaper or better
Complements:Razor-blade strategy
Network effectsand complements
Figure 1-3
New ways to provide value to customers and create competitive advantage
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$0
$2,000
$4,000
$6,000
$8,000
$10,000
$12,000
$14,000
$16,000
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
2016
2017
CD Vinyl Video
Digital single downloads Digital album downloads Streaming and subscription
Mobile and other digital
Figure 2-1
revenue from the sale of recorded music in the united States, 2000–2017 ($ millions)
Source: Recording Industry Association of America.
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$18,000
$16,000
$14,000
$12,000
$10,000
$8,000
$6,000
$4,000
$2,000
$0
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
2016
2017
CD Vinyl Video
Digital single downloads
Concert revenue
Digital album downloads Streaming and subscription
Mobile and other digital
Figure 2-2
revenue from concerts and from the sale of recorded music in the united States, 2000–2017 ($ millions)
Source: Recording Industry Association of America and Pollstar.
8
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Dig
ital
Sub
scri
ber
s ('0
00)
500
1,000
1,500
2,000
2,500
00
100
324390
454
509566
640 676 699 727760 799 831
875910
957 9901,041
1,0941,161
1,212
1,332
1,608
1,9162,009
2,114
2,213
224
Mar Apr Jun
2011
Sep Dec Mar Jun
2012 2013 2014 2015 2016 2017
Sep Dec Mar Jun Sep Dec Mar Jun Sep Dec Mar Jun Sep Dec Mar Jun Sep Dec Mar Jun Sep Dec
Figure 2-3
Digital subscribers to the New York Times
Source: Compiled from NYT press releases and financial reports.Note: The numbers show digital news subscribers only and do not include subscribers to other digital products such as cooking and crosswords, and print subscribers who have digital access to the NYT.
9
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30,000
25,000
20,000
15,000
10,000
5,000
0
1Q10
2Q10
3Q10
4Q10
1Q11
2Q11
3Q11
4Q11
1Q12
2Q12
3Q12
4Q12
1Q13
2Q13
3Q13
4Q13
1Q14
2Q14
3Q14
4Q14
1Q15
2Q15
3Q15
4Q15
1Q16
2Q16
3Q16
4Q16
1Q17
2Q17
3Q17
4Q17
Video Internet Voice
Figure 2-4
Number and type of Comcast subscribers, 2010–2017
Source: Compiled from company reports.
10
Chapter_02 Page 57 31/05/18 2:32 AM
Unwillingness to takeownership of the asset
2%
More flexibleproduct usage
14%
Staggeredpayments over a
period of time39%Desire for quick and
reliable service18%
Less upfront(or total) costs
27%
Figure 2-5
Why consumers accept service-based payment models
Source: Vivek Agarwal, Vinay Arora, and Kris Renker, Evolving Service Centric Business Models:
Quest for Profitability and Predictability, Accenture Report, 2013.
11
Chapter_03 Page 75 31/05/18 5:38 PM
Q2 FY 20
13
Q3 FY 20
13
Q4 FY 20
13
Q1 FY 20
14
Q2 FY 20
14
Q3 FY 20
14
Q4 FY 20
14
Q1 FY 20
15
Q2 FY 20
15
Q3 FY 20
15
Q4 FY 20
15
Q1 FY 20
16
Q2 FY 20
16
Q3 FY 20
16
Q4 FY 20
16*
Q1 FY 20
17
Q2 FY 20
17
Q3 FY 20
17
Q4 FY 20
17
Q1 FY 20
180%
8 8
1112
1415 15 16
1820 20 21
2425 25
2729
30 30 31
5%
10%
15%
20%
25%
30%
35%
FIGURE 3-1
Starbucks’s mobile transactions as a percentage of total transactions, 2013–2018
Source: Company reports and BI Intelligence.
Note: Q4 FY 2016 excludes week 53. Starbucks’s fiscal year ends on the Sunday closest to September 30.
12
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GM, Ford, BMW
Verizon, Vodafone,Telefónica
Tesla, Google
Google, Apple,app developers
Automanufacturers
New players
Telecomoperators
Technologyplayers Consumer
FIGURE 3-2
New ecosystem of automotive industry
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Chapter_05 Page 102 31/05/18 5:36 PM
FIGURE 5-1
The power of 1%
Source: Karim R. Lakhani, Marco Iansiti, and Kerry Herman, “GE and the Industrial Internet,” Case 614–032 (Boston: Harvard Business School, 2014).
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Delivery speed
Delivery to safe place
Appearance of the delivery personnel
Scheduled delivery in time window
Delivery at announced time
Low cost
Enviromentally friendly delivery
0% 10% 20% 30% 40% 50% 60% 70% 80% 90%
FIGURE 5-2
Amazon customers’ most important factors for delivery
Source: Amazon customer survey, 2017, courtesy of Rohit Sodha, Amazon’s country lead for transportation in Middle Europe.
15
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TABLE 6-1
Kiehl’s sales and profitability by channel
Channel % of sales Operating profit as % of sales
Company stores 48% 17.9%
Department stores 42% 17.6%
Kiehls.com 10% 40.0%
Source: Adapted from Robert J. Dolan and Leslie K. John, “Kiehl’s Since 1851: Pathway to Profitable Growth,” Case 514-044 (Boston: Harvard Business School, 2013, revised 2015).
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500
400
300
200
100
0
–100
–2001 2 5 10 20 40 60 80 100 120 140 160 180 190 195 198 199 200
Customer number
Pro
fit (S
EK
)Figure 7-1
Profitability of Kanthal’s customers ranked from most to least profitable
Source: Robert S. Kaplan, “Kanthal (A),” Case 190-002 (Boston: Harvard Business School, 1989, revised 2001).
17
00
50
100
150
200
250
1 5 10 15 20 30 40 50 60 70 80 90 95 99 100
Pro
fits
(% o
f to
tal)
Cumulative % of customers
Figure 7-2
Cumulative profitability of Kanthal’s customers
Source: Robert S. Kaplan, “Kanthal (A),” Case 190–002 (Boston: Harvard Business School, 1989, revised 2001).
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Advocate
Consume
Purchase
Search andevaluate
FMOT
SMOT
TMOT
ZMOT
Figure 7-3
Moments of truth
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Chapter_07 Page 149 31/05/18 5:36 PM
Ad $$
Impressions
Clicks
Start an app
Finish app
Approved
Active
Remainactive
CPM
CTR
10%
24%
80%
67%
55%
Display = $2.06
Search = $44.75
Display = 0.045%
Search = 4.1%
Display = 13.3%
Search = 8.6%
Display = 38.9%
Search = 17.5%
Figure 7-4
Consumer journey for BBVA Compass Bank’s online customer-acquisition campaign
Source: Sunil Gupta and Joseph Davies-Gavin, “BBVA Compass: Marketing Resource Allocation,” Case 511-096 (Boston: Harvard Business School, 2011), and Teaching Note 512-051 (Boston: Harvard Business School, 2012).
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Chapter_08 Page 157 31/05/18 8:02 PM
Figure 8-1
Time spent by uS consumers on their smartphones
Source: Simon Khalaf and Lali Kesiraju, “U.S. Consumers Time-Spent on Mobile Crosses 5 Hours a Day,” March 2, 2017, http://flurrymobile.tumblr.com/post/157921590345/us-consumers-time-spent-on-mobile-crosses-5.
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Emotional sparkDrives genuine personal stories
1
2
3
45
6
7
Digitalengine
Data driving real-time content andtransactions
EngagementThrough Facebook andsocial media
OffersFrom merchants andMC assets
OptimizationReal-time testing of offersand themes
AmplificationPaid and organic programmaticbuying
Network effectsSharing and massengagement
Incremental transactionsWin-win for all parties
Figure 8-2
Digital engine at Mastercard
Source: Adapted from company documents.
22
Chapter_08 Page 164 31/05/18 8:02 PM
Spend per active card(‘000 USD)
Transactions per active card Number of active MCCs
50%
33%
Duringyear
Postyear
Preyear
Duringyear
Postyear
Postyear
–10%
24%
7%
2%29%
16%4%
Engaged Look-alike
Preyear
Preyear
Duringyear
Figure 8-3
Transactional impact of uS “Priceless Cities”
Engaged consumers spent 4 percent more in pre-year compared to a matched sample of look-alike con-sumers in the control group, but they spent 50 percent more during the campaign year and 33 percent more after the campaign year.
Source: Company documents.
Note: Engaged denotes cardholders who respond to “Priceless Cities” offers. Look-alike is a matched con-trol sample. MCC stands for “merchant category code.” Pre-year: May 2012–April 2013. During year: May 2013–April 2014. Post-year: May 2014–April 2015.
23
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$405.54$312.01$309.57$306.08
$289.88$182.98$177.29
$169.26$132.88$104.41
$75.97$75.71$75.71$74.93$73.58$72.97$70.16
Zara
Levi’s
Monster Energy
H&M
Victoria’s Secret
McDonald’s
Starbucks
Blackberry
Disney
Skittles
Subway
Oreo
Adidas
Nike
Dr. Pepper
Xbox
Coca-Cola
Avg: $174.17
$1,613.11$834.76$618.53
BMW
Value of a fan(New brands 2013)
WalmartTarget
Figure 9-1
Value of a Facebook fan
Source: Todd Wasserman, “A Facebook Fan Is Worth $174, Researcher Says,” Mashable, April 17, 2013.
24
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Attribution model Description Comments
Lastinteraction
Firstinteraction
Linear
Time decay
Position-based
Regression- ormodel-based
Experiment-based
Last ad or clickaccounts for 100% of thesale
First ad or clickaccounts for 100% of sale
All ads or clicks get a uniform weight
All ads or clicks get some attribution;more recent ads get higher weight
Ads get attributionbased on positionin the path
Attributes weightbased on regression- or other model-based approaches
Attribution based onA/B testing
Difficult or expensive to conduct
Most accurate way to determinead effectiveness but difficult tomanage across many differentad networks and consumers
Ignores that some ads are viewed simply because they are on the relevant site(e.g., contextual ads) even if the ads themselves have no impact
Scientific way to allocate weightsto ads along consumer journeypath
Ad hoc weights
Ad hoc allocation
Overweighs ads that appearfrequently
Overweighs ads that appear frequently
Ignores ads later in the funnel that convert customer
Ad hoc weights (How much decay?)
Ignores long-term effects of ads earlier in the funnel
Overweighs ads that appearfrequently
A B
Overweighs ads that appear frequently
Unclear why ads later in thefunnel should have higherweight
Figure 9-2
Attribution models
Source: Sunil Gupta and Joseph Davin, “Digital Marketing,” Core Curriculum: Readings in Marketing, Harvard Business Publishing, and adapted from Google Analytics Help, “Attribution Modeling Overview,” https://support.google.com/analytics/answer/1662518?hl=en.
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Deg
ree
of
influ
ence
Time from purchase
Fewest people Most people
10
9
8
7
6
5
4
3
2
1
7–12
+ mos
befo
re
4–6 m
os b
efore
2–3 m
os b
efore
1 mon
th be
fore
3 wee
ks b
efore
2 wee
ks b
efore
1 wee
k befo
re
4–6 d
ays b
efore
2–3 d
ays b
efore
Day of
pur
chas
e
Figure 9-3
intensity of consumer search for automobiles
Source: Jim Lecinski, Winning the Zero Moment of Truth (Palo Alto: Think with Google, 2011), 25.
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Mostly print
Profi
t
Time
FutureToday
All digital
FIGURE 10-1
Potential profit path of NYT during its digital transformation
27
0
0.2
0.4
0.6
0.8
1
1.2
1.4
1.6
1.8
2010 2011 2012 2013 2014 2015 2016 2017
Net
inco
me
($ b
illio
ns)
FIGURE 10-2
Fall and rise of Adobe’s net income during its digital transition
Source: Compiled from Adobe’s financial statements.
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AdobeFinance
Product Price
Promo
Distribution
Customermanagement
Salesforce
Onlinecommunication
FIGURE 10-3
Impact of new business model on Adobe’s operations
29