Measuring customer experience with social media.jan15

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  • 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 youre 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 Lowes 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

    4

    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 Whys 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

    5

    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 SEITMwithin 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

    9

    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

    10

  • 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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    2/23

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    Zip Sales

    Zip.SEI.Ratio

    SEI Ratio Norm

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    Tracking the SEITM showed a high correlation to Zips 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

    18

    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

    ing

    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, Kelloggs and Fisher-Price. He has also consulted with such blue-chip firms as AT&T, McDonalds, Coca-Cola, Hyatt Corp., LOreal, 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. Davids career has taken him to such blue-chip firms as Coca-Cola, Kraft Foods, Georgia Pacific and the Home Depot. Davids consulting experience has focused on such verticals as retailing, financial services, apparel, consumer products and insurance. Davids 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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