3. 3
About Us
Bottom-Line Analytics is a full-service consulting group
focused on marketing effectiveness and brand performance analytics
We are dedicated to the principles of innovation, excellence, and
uncompromising customer service
Everything we do is geared toward improving the commercial performance
of our clients
Our experts have a total of more than 100 years of direct experience in
research, insights, and ROI measurement
4. 4
Measuring the customer experience is imperative
“You’ve got to start
with the customer experience
and work back toward the technology
– not the other way around.”
~ Steve Jobs
5. 5
Customer Experience Leaders Outperform the Market
CX Leaders
[VALUE]
[CATEGORY
NAME]
[VALUE]
CX Laggards
[VALUE]
0%
20%
40%
60%
80%
100%
120%
CumulativeTotalReturn
Eight-year Stock Performance of Customer
Experience Leaders vs. Laggards vs. S&P 500
(2007-2014)
CX Laggards
In addition to posting a total return
that was 74 points lower than CX
leaders, laggards also had higher
customer frustration, increased
attrition, more negative word-of
mouth, and higher operating
expenses
CX Leaders
Over 8 years, the leaders of
Forrester’s CX Index enjoyed a higher
total return, higher revenues from
better retention, less price sensitivity,
greater wallet share and positive word-
of-mouth) and lower expenses from
reduced acquisition costs, and fewer
complaints,
6. 6
4%
27.6%
11.8%
23.7%
31.6%
What does your company's executive leadership
think about the ROI of CX?
Doesn't believe there's an ROI of CX
Unsure there's an ROI of CX
Believes there's a small ROI of CX
Believes there's a moderate ROI of CX
Believes there's a large ROI of CX
Most Companies Understand that There Is a Sizable
ROI in Customer Experience
7. 7
18.9%
28.4%
35.1%
12.2%
5.4%
How effective is your company at measuring
the business impact of CX?
Very ineffective
Ineffective
Somewhat effective
Mostly effective
Very effective
But TheyAren’t Sure How To Measure It
8. 8
TheAnswer Is with Social Media
United Airlines is
never on-time, and
their service sucks.
Your brand is what people say
about you when you’re not in the room.
~ Jeff Bezos
9. 9But Most Social Media Sentiment Ratings
Are Not VeryAccurate
"Sentiment analysis is a very complex task for a machine because
of the multiple and often unpredictable soft and hard variables that
come into play when interpreting it. The main problem being that
the sentiment of a sentence only rarely lies in the sentence itself
and is instead rooted in the cultural context around that sentence.”
~ Francesco D'Orazio, CIO at FACE Group
"Companies are making decisions based on data
that is just 6% accurate."
~ Carol Haney, SVP at Toluna
10. 10
And They Fall Short In Measuring ROI
21.2%
11.2%
8.8%
8.2%
3.1%
-2.3%
-10% 0% 10% 20% 30% 40% 50% 60% 70% 80% 90%
Correlation to sales of $6B client with the sentiment metrics
of the six leading social data vendors
Sentiment Metric 1
Sentiment Metric 2
Sentiment Metric 3
Sentiment Metric 4
Sentiment Metric 5
Sentiment Metric 6
SEI Pos/Neg Ratio
11. 11
One Exception Is Ours:
The Social Engagement Index, a.k.a., The SEITM
83.1%
21.2%
11.2%
8.8%
8.2%
3.1%
-2.3%
-10% 0% 10% 20% 30% 40% 50% 60% 70% 80% 90%
Correlation to sales of $6B client with the sentiment metrics
of the six leading social data vendors
Sentiment Metric 1
Sentiment Metric 2
Sentiment Metric 3
Sentiment Metric 4
Sentiment Metric 5
Sentiment Metric 6
SEI Pos/Neg Ratio
12. 12The BLASocial Insights,Analytics,
And ROI Framework
Fuse SEITM with
advanced analytics to
understand brand
positioning, content
drivers, reputation and
critical elements of
customer experience
Leverage known tools
to listen and monitor
high-level brand-
experience
conversations
Measure language
based on
engagement and
importance via the
Social Engagement
Index, or SEITM
Listening,
monitoring and
basic sentiment
Measuring
language for
brand insights
Social media
advanced analytics
Social
monetization
Apply a trended SEITM
within media mix
modelling to monetize
customer experience
(earned social media)
alongside all other
media and quantify any
synergistic effects
BLA extends the value of social media insights
We will focus on this
specific application
of SEI today
13. 13
SEITM
SEITMStance
Shift
Syntax &
Structure
Tonality &
Sentiment
Context
Custom
Dictionary
AMore Accurate Way
To Mine Social Conversations
• Measures value of
customer experience
• Links closely to sales
• Indicates brand health
• Uncovers “why’s” and
underlying drivers, both
positive and negative
Experiential
Statement
on Social
Media
E
x
R
x
P
r
Customer
Experience &
Engagement
Rational
14. 14
The Difference Is Stance-shiftAnalysis,
AMethod That Measures What Really Matters In Language
• Stance-shiftanalysis,publishedandpeer-reviewed,revealswhatreallymatterstotheconsumer:
– Stance-shiftmeasuresconsumers’verbalshiftsinpositioning astheytalk.
– Wecapturetheemotion,intensity,appraisal,andcommitmentinthecontextoftheconversationstouncoverthedeepsubtleties
andwhatissaid.
• Itenablesustosolvewhatothersmiss:Size,TrendandNewConcepts
– Focusingonlyonwhatmatters:Wefilterandsizerelativeimportancethroughengagement–farsuperiortosimplewords/comment
frequency.
– Consumertrends:Wecapturetheshiftsandprioritizegettingthetrendright,validatedthroughtheindependentmeasureofour
metricsvssales.
– Stanceistunedtodetecttopicsandconcepts,whichwelinktoquantifiedopinions,evaluationsandendorsementsthroughadaptive
tonality,allowingustomapstrengths,weaknesses,opportunities&threats.
• SemanticEngagementIndex:SEITM integrates ourstance-shiftmeasurementtopowerourconsumerinsights.
15. 15
I just got my cool new iPhone from BestBuy;
however, I keep getting dropped calls on the
Brand X 4G network.
Most social sentiment tools would bungle
the analysis of this statement.
Positive
Negative
16. 16
I just got my cool new iPhone from BestBuy;
however, I keep getting dropped calls on the
Brand X 4G network.
Positive
Negative
Flag Brands & Relative
Importance
Custom Coding
Engagement
Transitional
word (Shift in
Stance)
Shift-StanceAnalysisAccounts For Context, Industry
TerminologyAnd Channel-Specific Language
17. 17
Our Process Brings Structure
To Consumer Data Chaos
From millions of cleaned
social media conversations
We detect thousands of
interesting “nodes” of
consumer information
Our supervised learning pattern
detection organizes the nodes
Small Pepermint Afternoon Snack 12Pack
Great Deal Breakfast yum Large
Miss it Get me one Orange on sale
Morning Half Priced got coupon Drive Home
Vanilla Mocha 8Oz need a hit
Clear themes and topics
of importanceemerge
Powerful social insights on
themes and topics that are
most important to consumers
Advanced analytics to help
drive content strategy
and measure social ROI
18. 18The Correlation to Sales Over Time
Shows the SEI™ Has Strong Predictive Power
18
Correlation = 84%
Note: Lead-lag analysis has confirmed that causation is only one way. The SEI™ to a large degree is capable of driving hard commercial metrics.
86%
Telecom Brand
81%
Soft Drink Brand
84%
Food & Bev
Brand
83%
Hospitality
Brand
19. 19The SEITM
Has Been ValidatedAcross a Diverse
Set of Brands in the US and Internationally
52%
53%
56%
57%
59%
68%
73%
74%
77%
79%
79%
79%
79%
81%
81%
84%
86%
86%
88%
0% 20% 40% 60% 80% 100%
Haircare Brand
Personal Care…
Personal Care…
AVERAGE
Hospitality Brand 2
Cosmetic Brand
Softdrink Brand
DIY Retailer…
Telecom Brand
Movie 2
SEI Correlation To Sales for 18 Brands
Validated more than any other social metric
20. 20The SEITM
Has Broad-based Application
$
Monitor and manage
consumer conversations
that are impacting your
brand reputation
Apply deep understanding to
consumer conversations to
develop Content and
Marketing Strategy
Enhance the in-market
execution of promotions,
sports sponsorships, and
events based on real
consumer conversations
Monetize your social
media campaigns and
the customer experience
with our media mix models
22. 22
For a coffee retailer, we uncovered 26 “content drivers,” which are topical themes and components of the SEI.
We conducted CART regression analytics, which arrays these themes in order of importance for prediction of SEI.
Of these 26 drivers, 18 were beverage or food product-related, while 8 were topics related to the store experience.
Store experience was found to be a more important than the products
in terms of driving sales and defining the brand.
Key Content Drivers of Retail Sales
To meet people
188
Atmosphere
288
Atmosphere
466
Note: Separate analysis - Classification & Regression Trees (CART)
Positive Social
Engagement
100
To meet people
229
Place to hang out
83
Beverage A
271
Beverage A
74
To meet people
85
To meet people
325
Insight & Outcomes
Key drivers to positive SEI™:
1. A place to hang out
2. To meet people
3. Atmosphere
4. Beverage products
Based on these findings, the client
developed a “2 for 1” promotion
to drive store-level sales.
This was the most effective promotion
run on any product over the previous
three years, generating a lift in three
weeks equal to approximately 4% of
total sales.
Place to hang out
211
24. 24
SEITM and Marketing Contributions for “Zip”
78.6%
2.1%
6.8%
3.3%
3.0%
2.5%
2.4%
1.9%
1.1%0.4%
23.5%
Zip Modeled Incremental Contributions
Baseline
SEI/Mktg Synergy
SEI-Social Media
Radio
POS Signage
TV
Digital Display
Sampling
Pub.Reltns
OOH
Zip’s Marketing Contributions
By modeling Zip with SEITM, BLA found
that buzz and advocacy stimulated
by its marketing efforts drove almost 7%
of its volume, and marketing efforts helped
boost a sizable synergistic dividend.
Zip’s Situation
In 2009, a beverage retailer launched “Zip”
(masked name), an “instant” beverage,
which was a deviation from its naturally
brewed products. Zip was one of the most
successful product launches in 12 years.
Previous modeling research had shown that
Zip actually generated a +3% lift to total
retail sales. The successful launch strategy
was aimed at getting maximum trial and
exposure, driven by an extensive sampling
period and early-stage price promotions.
The challenge in Year Two was to
understand how to position the brand in
order to sustain growth momentum.
25. 25
Zip Sales and SEITM
Correlations Over Time
-
1.0
2.0
3.0
4.0
5.0
6.0
7.0
8.0
9.0
-
2,000,000
4,000,000
6,000,000
8,000,000
10,000,000
12,000,000
14,000,000
2/23/2009
3/23/2009
4/23/2009
5/23/2009
6/23/2009
7/23/2009
8/23/2009
9/23/2009
10/23/2009
11/23/2009
12/23/2009
1/23/2010
2/23/2010
3/23/2010
4/23/2010
5/23/2010
6/23/2010
7/23/2010
8/23/2010
9/23/2010
10/23/2010
Zip Sales Zip.SEI.Ratio SEI Ratio Norm
Tracking the SEITM over time
revealed a high correlation to
Zip’s first year sales.
This was clear evidence
of a powerful and effective effort to
generate strong buzz and advocacy
toward the brand, with
a strong linkage to sales.
Note: Plotted metric is ratio of Positive to Negative SEITM
The SEITM proved to have
a leading-indicator relationship
with Zip sales.
28. 28
Assessing the ROI of Sport Sponsorships
This client spent 65% of its total
marketing budget on sports marketing
without understanding what they were
getting back they were getting for any
of the sports they sponsored.
We used the SEI for each
sponsorship to determine the ROI,
which showed that the NFL
could provide high returns
and high growth.
By investing more in NFL Football
and less on NASCAR and NCAA
Basketball, this client managed to
accelerate YOY growth from 3% to +8%
the following year.
29. 29
Social Media ROI
Marketing Mix Modelling
Pricing Optimization
Radial Landscape Mapping
Key Drivers Analysis
Demand Forecasting
Customer Satisfaction Modelling
Digital Performance Analytics
Dashboards
Segmentation Analysis
BLAIs a TrustedAdvisor to a WideArray of Clients
We believe in the continuous innovative application of analytics to advance
customer-centric decision making for improved business performance.
31. 31
Michael is CEO of Bottom-Line Analytics
LLC in the US. Michael has 30 years of
direct experience in marketing science
and analytics. On the client-side, he’s
worked for Coca-Cola, Kraft Foods,
Kellogg’s, and Fisher-Price. As a
consultant, he’s worked with such blue-
chip firms as AT&T, McDonald’s, Coca-
Cola, Hyatt Corp., L’Oreal, FedEx, and
Starbucks. He has broad experience in
marketing analytics covering marketing
ROI modeling, social media analytics,
pricing research, and brand strategy.
Michael Wolfe
David Weinberger is CMO of Bottom-
Line Analytics. David’s career has taken
him to such blue-chip firms as Coca-
Cola, Kraft Foods, Georgia Pacific, and
Home Depot. David’s consulting
experience has focused on such verticals
as retailing, financial services, apparel,
consumer products, and insurance.
David has considerable expertise in the
areas of customer analytics, life-time
value, shopper marketing, social media,
brand strategy, segmentation, and
marketing ROI analytics.
David Weinberger
Masood is the Bottom-Line Analytics
partner in the UK and heads the
company efforts across EMEA. Before
joining Bottom-Line Analytics, Masood
was Director of Analytics for McCann-
Erickson and has worked for Mintel
International Group, JWT, Costa
Coffee, Coca Cola, and Hyatt Corp. He
is an accomplished econometrician
with extensive experience in marketing
ROI analytics, marketing research,
market segmentation, social media
analytics, and marketing KPI
dashboards.
Masood Akhtar
Bottom-LineAnalytics Leadership
32. 32
EMEA Office:
5th Floor, 39 Deansgate,
Manchester, M3 2BA, United Kingdom
Contact Us US Office:
Suite 100, 1780 Chadds Lake Dr, NE
Marietta, Georgia, 30068-1608
Atlanta, USA
