Measuring the Customer Experience with Social Media

New Developments in Measurement and Analytics

Measuring the Customer Experience is Essential

1. It has been found the be the largest single business driver for manybrands

2. Measurement of this experience is considered to be essential for firmswho aspire to be “customer centric”.

Millions of social media comments, all reflecting real brand-customer experiences. As Jeff Bezos said: “Your brand is what people say about you when you’re not in the room.”

United Airlines is never on-time and their service sucks

I love drinkingCoke with pizza!

My iPhone is an essentialpart of my life!

Progressive has thecheapest insurance

but their claims serviceIs terrible!

I bought a Maytag at Lowe’s and it

cleans like no other

My Honda CRV isgreat on gaseconomy!

For health reasonsI have cut backon Diet Coke

The Porsche 911Is the sexiest

car on the planet!

I keep getting dropped calls on the

Sprint network!

I love how my son plays with his Lego

blocks

A Unique Way to Mine Social Conversations

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StanceShift

Syntax &Structure

Tonality &Sentiment

ExperientialStatement

CustomDictionary Context

Personal

Emotional

Customer Experience

• Leverages 30+ rules of language through a ‘scoring algorithm’ that turns textual data into a scaled metric called the Semantic Engagement Index (SEITM)

• Is built upon a validated Linguistics approach known as ‘Stance Shift Analysis’• Takes into account several critical components of conversations usually ignored

• Captures and measures the value of the customer experience

• Links closely to sales -represents brand health

• Uncovers the Why’s and the underlying drivers both positive and negative

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

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UNIQUE BLA VALUE1. Evaluate the Entire Conversation2. Account for Context3. Adapt to Industry Language, Terms4. Adjust to Channel Communication (Facebook, Twitter, specialist forums, blogs)

Leveraging social media is about building a metric based on linguistics principlesTeasing out the nuances of language

Transitional word (Shift in

Stance)

From Millions of Cleanedsocial media

Conversations

Powerful social insights on Themes and topics that are most important

to consumers.

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

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

We Detect Thousands of interesting “nodes” of Consumer information

Clear Themes and Topics of Importance

Emerge

Advanced Analytics to help drive content strategy and measure social

ROI.

Our Supervised Learning Pattern Detection organizes the nodes

Adding Structure to Unstructured Data: The Solution Path For Consumer Chaos

Fusing SEITM based language measurement with advanced analytics to understand competitive brand positioning, content drivers, reputation and essential elements and structure of the customer-brand experience.

Using known tools to listen and monitor high level consumer brand-experience conversations.

Measure language based on engagement and importance through the Semantic Engagement Index (SEITM).

Listening,Monitoring and basic Sentiment

MeasuringLanguage for

brand insights

SocialMedia Advanced

Analytics

Social Monetization

Applying a trended SEITM

within Media Mix Modelling to monetise customer-brand experience (earned social media) alongside all other media and quantify any synergistic effects.

Extend the Value of Social Media Insights

BLA Social Insights, Analytics and ROI Framework

We will focus on this specific application of SEI today

Available Social Media “Sentiment Metrics” fall short as a tool for measuring ROI, but the SEITM shows great promise

-20% 0% 20% 40% 60% 80% 100%

Sentiment Metric 1

Sentiment Metric 2

Sentiment Metric 3

Sentiment Metric 4

Sentiment Metric 5

Sentiment Metric 6

SEI™ POS/NEG RATIO

11.2%

3.1%

-2.3%

8.8%

21.2%

8.2%

83.1%

Figure 1: Compares correlation to sales of $6B client with SEI and sentiment metrics for 6 leading social data vendors, there is a wide gap.

Sentiment Metric 1

Sentiment Metric 2

Sentiment Metric 3

Sentiment Metric 4

Sentiment Metric 5

Sentiment Metric 6

SEI™ POS/NEG RATIO

The correlation* to sales over time shows the SEI™ has Predictive Power

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SEI validation: four categories

Correlation = 86%

Correlation = 84%

Correlation = 81%

Correlation = 83%Correlation = 83%

*Lead lag analysis has confirmed that causation is only one way – the SEI™ to a large degree is able to drive hard commercial metrics.

SEI validation across ~ 20 diverse brands, both US and international.Validated more than any other social metric

52%53%

56%57%

59%68%

73%74%

77%79%79%79%79%

81%81%

84%86%86%

88%

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

Haircare BrandPersonal Care Brand 4Personal Care Brand 3Personal Care Brand 2Personal Care Brand 1

DIY Retailer Brand 2AVERAGE

Business ServicesHospitality Brand 2Restaurant Brand 3

Cosmetic BrandHospitality Brand 1

SoftdrinkRestaurant Brand 2

DIY Retailer Brand 1Restaurant Brand 1

Telecom BrandMovie 1Movie 2

SEI/Sales Correlations

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Recent Marketing-Mix Model casesCustomer experience (SEI™ earned media) as the largest sales driver

44.9%

9.1%4.0%2.2%1.8%2.3%5.9%

29.8%

Brand Gamma Decomposition of SalesBaseline

TV

Print

Radio

Owned Digital SEO

Paid Digital Mobile

Paid Digital Search

Customer Experience EarnedDigital Social SEI

54.1%

5.5%1.5%4.4%2.1%2.8%

5.9%

23.7%

Brand Beta Decomposition of SalesBaseline

TV

Print

Radio

Owned Digital SEO

Paid Digital Mobile

Paid Digital Search

Customer Experience EarnedDigital Social SEI

60.4%

4.5%2.1%

3.3%1.1%2.1%5.3%

21.2%

Brand Alpha Decomposition of SalesBaseline

TV

Print

Radio

Owned Digital SEO

Paid Digital Mobile

Paid Digital Search

Customer Experience EarnedDigital Social SEI

64.8%1.5%0.2%0.3%3.6%0.1%1.8%

27.8%

Brand Omega Decomposition of SalesBaseline

TV

Print

Radio

Owned Digital SEO

Paid Digital Mobile

Paid Digital Search

Customer Experience EarnedDigital Social SEI

6Copyright 2015

Case 1: Defining the Coffee Retailer Brand and Position

For a coffee retailer, we uncovered 26 “content drivers”, which are topical themes andcomponents of the SEI. We conducted CART regression analytics which arrays thesethemes in order of importance for prediction of SEI. Of these 26 drivers, 18 werebeverage or food products, while 8 were topics related to the store experience. Ourfindings reveal that the store experience were more important drivers than the productsand were a more important factor in defining the brand.

Insight & Outcomes

The key drivers to Positive SEI™ were:

1. A place to hang out2. To meet people3. Atmosphere4. Beverage Products

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 past 3 years, generating a lift in 3 weeks equal to about 4% of total sales.

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Case 2: Key Content Drivers of Retail Sales

Positive Social Engagement

100

Place to Hang Out 211

Place to Hang Out 83

To Meet People 325

To Meet People188

Atmosphere466

Atmosphere288

To Meet People229

To Meet People85

Beverage A271

Beverage A 74

Note: Separate analysis - Classification & Regression Trees (CART)

Brand Positioning Using Socially Engaged Chatter

Meeting Friends

Hanging out

Case 2: Social Content Drivers for Brand Positioning

Case 1: SEITM & 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 Situation: Zip (masked name) is an “instant” beverage launched by this beverage retailer in 2009; and was a deviation from its natural brewed products. Zip was one of the most successful new product launches in the last dozen years. Prior modeling 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 with an extensive sampling and early price promotions. The challenge in year two is to understand how to position the brand and sustain growth momentum.

• Zip Marketing Contributions :By modeling Zip using SEITM, we found that the “buzz and advocacy” stimulated by its marketing efforts drove almost 7% of its volume and marketing efforts also helped boost a sizable “synergistic dividend”.

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Case 2: Zip Brand Sales & SEITM Time Correlations

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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

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3…

Zip Sales

Zip.SEI.Ratio

SEI Ratio Norm

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Tracking the SEITM showed 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. SEITM also shows a “leading indicator” relationship to sales.

Note plotted metric is ratio of Positive to Negative SEITM

Case 2: Content Motivation Drivers of Sales Conversion for Zip Powder

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188

3,516

103 128300

301350

491

724

930

- 500

1,000 1,500 2,000 2,500 3,000 3,500 4,000

Base

line

Net

Pos

itive

SEI

Grea

t Aro

ma

Yum

my

Flav

ors

Grea

t Gift

Idea

Conv

enie

nt

Tast

es G

reat

Col

d or

Hot

Tast

es G

reat

Grea

t for

Tak

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to th

e O

ffice

Tast

es Li

ke th

e Re

al T

hIng

Tota

l Net

Pos

itive

SEI

Zip Powder All Social Channels Engagement Content Drivers

Further analytics of the “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. By focusing communications towards these benefits in year 2, Zip managed to continue a strong 11% growth.

Current Positioning

DesiredPositioning

Case 3: Scoring and Evaluating Sports Sponsorships

We scored SEI for the sponsorships. By investing more in NFL Football and less on NASCAR and Basketball, this company managed to accelerate YOY growth from 3 to +8% the following year

Example: Assessing Sport Marketing ROI (65% of marketing budget)

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Bottom-Line Analytics LLC is a consulting group focusing on a broad portfolio of marketing analytics, including marketing optimization modeling

Our modeling experts have a total of over 100 years of direct experience with marketing optimization modeling. This includes direct experience in over 35 countries and dozens of product categories.

We are dedicated to the principles of innovation, excellence and uncompromising customer service.

Most important, however, we are dedicated to getting tangible and positive business results for our clients.

ABOUT US

Full Service Analytics Capability

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

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BLA is a trusted advisor to a wide array of clients

We believe in the continuous innovative application of analytics to advance customer centric decision

making for improved business performance.

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BLA leadership bios

Michael Wolfe is CEO of Bottom-Line Analytics LLC in the USA. Michael has 30 years of direct experience in marketing science and analytics both on the client and consulting side. On the former, Michael has worked for Coca-Cola, Kraft Foods, Kellogg’s and Fisher-Price. He has also consulted with such blue-chip firms as AT&T, McDonald’s, Coca-Cola, Hyatt Corp., L’Oreal, FedEx and Starbucks. Michael has broad experience in marketing analytics covering marketing ROI modelling, social media analytics, pricing research and brand strategy.

Masood Akhtar is the Bottom-Line Analytics partner in the UK and heads the companyefforts across EMEA. Masood is former Director of Analytics for McCann-Erickson and also hasworked for Mintel International Group, JWT, Costa Coffee, Coca Cola, Hyatt Corp. He is anaccomplished econometrician with extensive experience in marketing ROI analytics, marketingresearch, market segmentation, social media analytics and marketing KPI dashboards.

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 the Home Depot. David’s consulting experience has focused on such verticals as retailing, financial services, apparel, consumer products and insurance. David’s has considerable expertise in the areas of customer analytics, life-time value, shopper marketing, social media, brand strategy, segmentation and marketing ROI analytics.

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