measuring customer-experience roi with social media
Post on 25-Jan-2017
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1
Measuring Customer Experience ROIwith Social Media
New Developments in Measurement and Analytics
2
Let’s Get Started
3About 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
4Measuring 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
5Customer Experience Leaders Outperform the Market
CX Leaders[VALUE]
[CATEGORY NAME]
[VALUE]
CX Laggards[VALUE]
0%
20%
40%
60%
80%
100%
120%
Cum
ulat
ive
Tota
l Ret
urn
Eight-year Stock Performance of Customer Experience Leaders vs. Laggards vs. S&P 500
(2007-2014)
CX LaggardsIn 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 LeadersOver 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
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
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 They Aren’t Sure How To Measure It
8The Answer 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
9But Most Social Media Sentiment Ratings Are Not Very Accurate
"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
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
11One 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
12The BLA Social 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
Measuringlanguage for
brand insightsSocial media
advanced analyticsSocial
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
SEITMSEITMStance Shift
Syntax & Structure
Tonality & Sentiment Context
Custom Dictionary
A More 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
Ex
Rx
Pr
Customer Experience &Engagement Rational
14The Difference Is Stance-shift Analysis, A Method That Measures What Really Matters In Language• Stance-shift analysis, published and peer-reviewed, reveals what really matters to the consumer:
– Stance-shift measures consumers’ verbal shifts in positioning as they talk.– We capture the emotion, intensity, appraisal, and commitment in the context of the conversations to uncover the deep subtleties
and what is said.
• It enables us to solve what others miss: Size, Trend and New Concepts– Focusing only on what matters: We filter and sizerelative importance through engagement–far superior to simple words/comment
frequency.– Consumertrends: We capture the shifts and prioritize getting the trendright, validated through the independent measure of our
metrics vssales.– Stance is tuned to detect topicsand concepts, which we link to quantified opinions, evaluations and endorsements through adaptive
tonality, allowing us to map strengths, weaknesses, opportunities & threats.
• Semantic Engagement Index: SEITM integrates our stance-shift measurement to power our consumer insights.
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
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-Stance Analysis Accounts For Context, Industry Terminology And Channel-Specific Language
17Our Process Brings Structure To Consumer Data Chaos
From millions of cleanedsocial media conversations
We detect thousands of interesting “nodes” of consumer information
Our supervised learning pattern detection organizes the nodes
Small Pepermint Afternoon Snack 12 PackGreat Deal Breakfast yum LargeMiss it Get me one Orange on saleMorning Half Priced got coupon Drive HomeVanilla Mocha 8 Oz need a hit
Clear themes and topics of importance emerge
Powerful social insights on themes and topics that are
most important to consumers
Advanced analytics to help drive content strategy
and measure social ROI
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 & BevBrand
83%
Hospitality Brand
19The SEITM Has Been Validated Across 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 BrandsValidated more than any other social metric
20The SEITM Has Broad-based Application
$Monitor and manage
consumer conversationsthat 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 andthe customer experience with our media mix models
21
Case 1: Defining the Coffee Retailer Brand and Position
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 people188
Atmosphere288
Atmosphere466
Note: Separate analysis - Classification & Regression Trees (CART)
Positive Social Engagement
100To meet people
229Place to hang out
83Beverage A
271
Beverage A74
To meet people85
To meet people325
Insight & Outcomes
Key drivers to positive SEI™:1. A place to hang out
2. To meet people3. 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 out211
23
Case 2: Social Content Drivers for Brand Positioning
24SEITM 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 ContributionsBaselineSEI/Mktg SynergySEI-Social MediaRadioPOS SignageTVDigital DisplaySamplingPub.ReltnsOOH
Zip’s Marketing ContributionsBy 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
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
/200
93/
23/2
009
4/23
/200
95/
23/2
009
6/23
/200
97/
23/2
009
8/23
/200
99/
23/2
009
10/2
3/20
0911
/23/
2009
12/2
3/20
091/
23/2
010
2/23
/201
03/
23/2
010
4/23
/201
05/
23/2
010
6/23
/201
07/
23/2
010
8/23
/201
09/
23/2
010
10/2
3/20
10
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.
26
188
3,516
103 128 300301
350491
724
930
-500
1,000 1,500 2,000 2,500 3,000 3,500 4,000
Bas
elin
e N
et P
ositi
ve
SE
I
Gre
at A
rom
a
Yum
my
Flav
ors
Gre
at G
ift Id
ea
Con
veni
ent
Tast
es G
reat
Col
d or
H
ot
Tast
es G
reat
Gre
at fo
r Tak
ing
to th
e O
ffice
Tast
es L
ike
the
Rea
l Th
Ing
Tota
l Net
Pos
itive
SE
I
Zip Powder All Social Channels Engagement Content Drivers
Content Motivation Drivers of Sales Conversion for Zip Powder
Further analytics of Zip’s “content drivers” of SEITM consumer
engagement revealed key drivers to be “tasted like the real thing” and was great for “taking to the office” and enjoying that original taste of
the parent brand.
Current Positioning
OptimizedPositioning
By focusing its communications toward these benefits, Zip managed to continue
a strong 11% growth in Year Two.
27
Case 3: Scoring and Evaluating Sports Sponsorships
28
Assessing the ROI of Sport SponsorshipsThis 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 NFLcould 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
Social Media ROIMarketing Mix Modelling
Pricing Optimization
Radial Landscape MappingKey Drivers AnalysisDemand Forecasting
Customer Satisfaction ModellingDigital Performance Analytics
Dashboards
Segmentation Analysis
BLA Is a Trusted Advisor to a Wide Array of ClientsWe believe in the continuous innovative application of analytics to advance
customer-centric decision making for improved business performance.
30
Our Leadership Team
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 WolfeDavid 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 WeinbergerMasood is the Bottom-Line Analytics
partner in the UK and heads the company efforts across EMEA. Before
joining Bottom-Line Analytics, Masoodwas 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-Line Analytics Leadership
32
EMEA Office:5th Floor, 39 Deansgate,Manchester, M3 2BA, United Kingdom
Contact Us US Office:Suite 100, 1780 Chadds Lake Dr, NEMarietta, Georgia, 30068-1608Atlanta, USA
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