1. No category

Hospitality Marketing Decision Engine
www.absolutdata.com
San Francisco
Chicago
New York
© Absolutdata 2014 Proprietary and Confidential
London
Dubai
New Delhi
Bangalore
Singapore
The Marketing Problem –
Data & Decision Making in Silos
…silos exist within and across departments
If you cant measure it, you cant manage it!
© Absolutdata 2014 Proprietary and Confidential
2
The Evolving Demand Management Ecosystem…
Revenue
Management
Demand Forecasts &
Competitive Set
Marketing
Segments, Demographics,
Insights
Pricing
Demand
Management
© Absolutdata 2014 Proprietary and Confidential
Offers & Messages
3
HMDE focuses on measuring true Marketing ROI and increasing it
Key Benefits
Significantly
increase
Marketing ROI
Higher
Revenues & Margins
Drive Synergies
with Revenue
Management
without incremental investments into IT!!
Associated Benefits
1
2 Realize value 3
4
5
6
Consolidated
Track
marketing
More confident
Optimize spend
on media and
promotions
from new
marketing
channels
“Better
Marketing RoI”
“Higher Digital
RoI”
activities and
their impact on
business
and consistent
information
and rapid
decision making
“Informed
decisions”
“Consolidated
view of business”
“Robust
decisions”
Institutionalized
learning
“Improve
knowledge base”
Delivered through - Cloud Based SaaS Architecture
Run using - Man-Machine Combination
Product Core – Decision Science | State of the Art Analytics
© Absolutdata 2014 Proprietary and Confidential
4
Over 10 Data Sources and 300 Variables feed into HMDE
DATA TYPES
DESCRIPTION
Bookings
Bookings, Revenue, Comparable YoY etc.
Online & Offline Spends
Media
Competition - Online & Offline Spends
Delivered, Clicked etc.
Emails
Competition - # of Campaigns
Loyalty Promotions
Promotions
Other Promotions
Key Competition Promotions
Internal and Competition
Pricing and Occupancy
Occupancy – Internal and Competition
Others
Macro - GDP, CPI, Unemployment etc
© Absolutdata 2014 Proprietary and Confidential
5
Case Study – $10BN Hospitality Giant, Impact in first 3 months
Q4’2013 - With HMDE
Q4’2012 - Without HMDE
 Marketing: Spent over $10 MM on various
 Marketing: Changed the mix to leverage Radio.
Timed TV for higher synergies with digital
campaigns to drive demand
 Synergies with Revenue Management: RM was
 Synergies with Revenue Management: Customized
focused on a rate sale to drive advance purchase for
TV and Email campaigns to better support Rate
summer with a 15% discount & minimal marketing
Sale. RM reduced discount from 15% to 10%,
support
Increase in Promotional Revenue (Incremental): $ 6 MM
Savings from lower discount: $3 MM
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6
© Absolutdata 2014 Proprietary and Confidential
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© Absolutdata 2014 Proprietary and Confidential
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Other available outputs
What are my Brand’s growth drivers for Nights Sold?
How did the change in Brand’s key drivers like ADR, promotions, competitor activity contribute to overall growth?
Leading Hotel Brand
Latest 52 weeks Ending Dec 2011
8%
549
281
11,876
5,652
494
270
Negative drivers
(747)
(228)
(681)
(626)
(120)
(5%)
(6.8%)
(3.8%)
(6.2%)
(33)
(12)
55
3.4%
2.8%
2.9%
2.5%
Per Capita
Income
Corporate
Performance
Index
# of Reward
Points
Exchanged
Other
Promotions
# of Brand’s
Properties in
Loyalty Program
Service Wide
Promotions
Competition
Promotions
Summer
Vacation
2011
# of Rooms
Rate Sale
Promotions
2.7%
2.2%
Unexplained
1.2%
1%
Summer
Vacation
2010
1.2%
1%
126
55
5.2%
3.2%
Competition
ADR
% Change in
driver
Elasticity of
driver
39
0.8%
0.5%
110
275
133
154
Brand 1
ADR
Number of
Nights
2010
(2.6%) (0.3%)
(0.3%)
(5.7%)
55
110
4.3%
5.9%
307
242
(176)
5.9%
4.5%
351
238
+6.2%
-
-
-
-
-
-
-
-
-
-
-
-
-
Number of
Nights
2011
4,567
10,981
6.2%
5.0%
472
270
Competition
drivers
Sample Output
Brand’s key
growth drivers
Drivers of Total Number of Nights Sold (in ‘000s)
-1.09
Controllable
Uncontrollable
Note: Volume Due to represents change in the volume driven by each driver (Current vs. Previous year)
© Absolutdata 2014 Proprietary and Confidential
11
How much of Total Nights Sold is incremental versus Baseline?
Incremental Volume & Base Volume
primarily driven by price/distribution and long term media
% Contribution of Key Drivers to Total No. of Nights
Sold
Incremental Nights Sold Due to Promotions
Sample Output
 The ‘incremental’ volume is volume gained due to promotions & short term impact of media activities done for the product
 The ‘base’ volume is the volume in a steady state environment without the promotional & media activities. The base volume is
180
Base Sales
20%
15%
80
60
40
20
Supply/ Distribution
Innovation
Competition
© Absolutdata 2014 Proprietary and Confidential
#REF!
Dec-11
Nov-11
Oct-11
Sep-11
Aug-11
Jul-11
Jun-11
May-11
0
Apr-11
10%
Incremental Sales
ADR
Promotions
Macro economic factors
100
Mar-11
25%
120
Feb-11
10%
140
Jan-11
20%
Incremental Nights Sold
160
Continuous Promotion
12
What is the incremental impact of a particular promotion?
Incremental Volume & Base Volume
300
Jan`09 – Dec’09
Nights sold (‘000)
250
Jan`10 – Dec`10
Jan`11 –Dec’11
Sample Output
 The impact/contribution of Promotions is isolated from the impact of marketing levers on the Nights Sold through an MMM
 It can be used to determine the Incremental Nights sold due to Promotions. Hence, we can calculate the ROI of Promotions
Incremental
Revenue
due to
Promotions
Rate Sale Promotion
200
150
100
50
Jan-09
Feb-09
Mar-09
Apr-09
May-09
Jun-09
Jul-09
Aug-09
Sep-09
Oct-09
Nov-09
Dec-09
Jan-10
Feb-10
Mar-10
Apr-10
May-10
Jun-10
Jul-10
Aug-10
Sep-10
Oct-10
Nov-10
Dec-10
Jan-11
Feb-11
Mar-11
Apr-11
May-11
Jun-11
Jul-11
Aug-11
Sep-11
Oct-11
Nov-11
Dec-11
0
Supply
Continuous Promotions
Per Capita Income
Reward Points Exchange
© Absolutdata 2014 Proprietary and Confidential
Total Revenue from
Promotions
Promotions
13
What is the efficiency and ROI of my promotions?
Efficiency = Nights Sold Contribution
Promotion Spends
ROI = Nights Sold Contribution x ADR
Promotion Spends
2010
2011
Contribution to No. of Nights Sold
Efficiency
Comparison across different promotions
123%
65%
Service Wide
Sample Output
Overall Promotions – YoY Comparison
% ROI
Comparison across Peak season and Off season
Promotions
Total
(Jan – Dec)
Peak Season
Off Season
Total Spends (‘000 $)
1,361
861
500
Efficiency
600
650
400
ROI
80%
120%
70%
80%
50%
Sweepstakes
Rate Sale
New Promotion
Raising questions like:




How much do we get back for Every $ Spent on Promotions?
Is our Promotion strategy working - how change in certain Promotion Strategy has helped?
How do the different promotions compare in terms of efficiency and ROI?
Is efficiency of my promotions different in peak season versus off-season?
© Absolutdata 2014 Proprietary and Confidential
14
Do certain Promotions of my own brand Interact with rest of my
portfolio?
Impact of Brand-specific Promotion on Other Brands
Synergy Effect
Revenue ROI 2011
ROI
Promotion 1
41%
Promotion 2
65%
Joint Promotion: Promotion 1 + 2
79%
Promotion 1 + Holiday Period
93%
Joint Promotion + Holiday Period
102%
© Absolutdata 2014 Proprietary and Confidential
Combining a promotion
with other promotions
and/or aligning them with
events/holidays
improves ROI
significantly
15
Which competition levers are impacting my business?
Top Hospitality Brand
Latest 52 weeks Ending Dec 2011
549
281
11,876
5,652
494
270
6.2%
5.0%
472
270
Impact of changes in marketing levers of Competition
on Brand’s Nights Sold
(228)
(681)
242
(176)
(626)
(120)
(5%)
(6.8%)
(33)
(12)
(3.8%)
(6.2%)
55
55
55
2.9%
2.5%
Per Capita
Income
Corporate
Performance
Index
# of Reward
Points
Exchanged
Other
Promotions
# of Brand’s
Properties in
Loyalty Program
Service Wide
Promotions
Competition
Promotions
Summer
Vacation 2011
# of Rooms
Rate Sale
Promotions
2.7%
2.2%
Unexplained
1.2%
1%
5.2%
3.2%
3.4%
2.8%
Summer
Vacation 2010
1.2%
1%
110
126
Competition
ADR
39
0.8%
0.5%
110
275
133
154
Brand
ADR
Number of
Nights
2010
(2.6%) (0.3%)
(0.3%)
(5.7%)
% Change in
driver
Elasticity of
driver
4.3%
5.9%
307
(747)
5.9%
4.5%
351
238
+6.2%
-
-
-
-
-
-
-
-
-
-
-
-
-
Number of
Nights
2011
4,567
10,981
Sample Output
8%
Drivers of Total Number of Nights Sold (in ‘000s)
-1.09
Controllable
© Absolutdata 2014 Proprietary and Confidential
Uncontrollable
16
What is the impact of holidays and events on Nights Sold?
Seasonality, Category Trends and other External/Macro economic factors
 The impact/contribution of Seasonality, Category Trend and External Factors is isolated from the impact of marketing levers on
the Nights Sold through MMM
 It helps to leverage upon these using the synergy effect with the marketing levers
2.5
0.3
Jan`09 – Dec’09
Jan`10 – Dec`10
Jan`11 –Dec’11
2
0.2
1.5
0.2
1
0.1
0.5
0.1
0
Summer Vacations
Thanksgiving
© Absolutdata 2014 Proprietary and Confidential
Dec-11
Nov-11
Oct-11
Sep-11
Aug-11
Jul-11
Jun-11
May-11
Apr-11
Mar-11
Feb-11
Jan-11
Dec-10
Nov-10
Oct-10
Sep-10
Aug-10
Jul-10
Jun-10
May-10
Apr-10
Mar-10
Feb-10
Jan-10
Dec-09
Nov-09
Oct-09
Sep-09
Aug-09
Jul-09
Jun-09
May-09
Apr-09
Mar-09
Feb-09
0.0
Jan-09
Nights Sold in ‘000s
0.3
Nights Sold
17
How do we account for the time gaps between promotions being
offered and being availed?
Lag Effect of Promotions
 A promotion being availed might be for a booking for weeks later which needs to be incorporated through a lag
300
50
45
Jan`09 – Dec’09
Jan`10 – Dec`10
Jan`11 –Dec’11
40
30
150
25
Introducing lag in promotions
20
100
Promotions
35
200
15
10
50
5
0
Nights Sold
Lagged Effect of Rate Sale Promotions
© Absolutdata 2014 Proprietary and Confidential
Dec-11
Nov-11
Oct-11
Sep-11
Aug-11
Jul-11
Jun-11
May-11
Apr-11
Feb-11
Mar-11
Jan-11
Dec-10
Nov-10
Oct-10
Sep-10
Aug-10
Jul-10
Jun-10
May-10
Apr-10
Mar-10
Feb-10
Jan-10
Dec-09
Nov-09
Oct-09
Sep-09
Aug-09
Jul-09
Jun-09
May-09
Apr-09
Mar-09
Feb-09
0
Jan-09
Nights sold (‘000)
250
Rate Sale Promotions
18
Volume adjustments via attribution model
Illustration for indirect contribution of various channels: Identifying the actual contribution of digital media while taking into
account the contribution made by TV
Cable Total Impact
Display
Cable Total Impact
Network Prime Time Series
WDYTYA New Episodes
Cable Total Impact Series
WDYTYA New Episodes
Paid/Unpaid Search clicks and display
clicks are prompted by ads on Cable TV to
a large extent
Paid Search
Unpaid Search
Display
WDYTYA Email Campaign
Paid Search
Unpaid Search
Unpaid Search
0.1%
2.2%
1.0%
0.1%
3.8%
11.4%
9.0%
10.0%
8.3%
2.6%
0.3%
2.5%
1.1%
1.0%
-1.0%
-0.3%
-2.6%
-3.8%
-0.1%
-2.2%
-0.1%
Paid Search Clicks Non paid search Cable Total Impact
Final
Attribution
7.5%
5.7%
11.1%
Network Prime
Time Impact
1.3%
Display ImpressionsWDYTYA TV Series WDYTYA Email
New Episodes
Campaigns
6.7%
© Absolutdata 2014 Proprietary and Confidential
10.2%
0.8%
19