Measuring customer experience with social media.jan15

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<ul><li><p>Measuring the Customer Experience with Social Media</p><p>New Developments in Measurement and Analytics</p></li><li><p>Measuring the Customer Experience is Essential</p><p>1. It has been found the be the largest single business driver for manybrands</p><p>2. Measurement of this experience is considered to be essential for firmswho aspire to be customer centric.</p></li><li><p>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.</p><p>United Airlines is never on-time and their service sucks</p><p>I love drinkingCoke with pizza!</p><p>My iPhone is an essentialpart of my life!</p><p>Progressive has thecheapest insurance</p><p>but their claims serviceIs terrible!</p><p>I bought a Maytag at Lowes and it</p><p>cleans like no other</p><p>My Honda CRV isgreat on gaseconomy!</p><p>For health reasonsI have cut backon Diet Coke</p><p>The Porsche 911Is the sexiest</p><p>car on the planet!</p><p>I keep getting dropped calls on the </p><p>Sprint network!</p><p>I love how my son plays with his Lego</p><p>blocks</p></li><li><p>A Unique Way to Mine Social Conversations</p><p>4</p><p>StanceShift</p><p>Syntax &amp;Structure</p><p>Tonality &amp;Sentiment</p><p>ExperientialStatement</p><p>CustomDictionary Context</p><p>Personal</p><p>Emotional</p><p>Customer Experience</p><p> Leverages 30+ rules of language through a scoring algorithm that turns textual data into a scaled metric called the Semantic Engagement Index (SEITM)</p><p> Is built upon a validated Linguistics approach known as Stance Shift Analysis Takes into account several critical components of conversations usually ignored</p><p> Captures and measures the value of the customer experience</p><p> Links closely to sales -represents brand health</p><p> Uncovers the Whys and the underlying drivers both positive and negative</p></li><li><p>I just got my cool new iPhone from BestBuy, however, I keep getting dropped calls on the Brand X 4G network</p><p>Positive</p><p>Negative</p><p>Flag Brands &amp; Relative Importance </p><p>Custom coding</p><p>Engagement</p><p>5</p><p>UNIQUE BLA VALUE1. Evaluate the Entire Conversation2. Account for Context3. Adapt to Industry Language, Terms4. Adjust to Channel Communication (Facebook, Twitter, specialist forums, blogs)</p><p>Leveraging social media is about building a metric based on linguistics principlesTeasing out the nuances of language</p><p>Transitional word (Shift in </p><p>Stance)</p></li><li><p>From Millions of Cleanedsocial media </p><p>Conversations</p><p>Powerful social insights on Themes and topics that are most important </p><p>to consumers.</p><p>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</p><p>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</p><p>We Detect Thousands of interesting nodes of Consumer information</p><p>Clear Themes and Topics of Importance </p><p>Emerge</p><p>Advanced Analytics to help drive content strategy and measure social </p><p>ROI.</p><p>Our Supervised Learning Pattern Detection organizes the nodes</p><p>Adding Structure to Unstructured Data: The Solution Path For Consumer Chaos</p></li><li><p>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.</p><p>Using known tools to listen and monitor high level consumer brand-experience conversations.</p><p>Measure language based on engagement and importance through the Semantic Engagement Index (SEITM).</p><p>Listening,Monitoring and basic Sentiment</p><p>MeasuringLanguage for </p><p>brand insights</p><p>SocialMedia Advanced</p><p>Analytics</p><p>Social Monetization</p><p>Applying a trended SEITMwithin Media Mix Modelling to monetise customer-brand experience (earned social media) alongside all other media and quantify any synergistic effects.</p><p>Extend the Value of Social Media Insights</p><p>BLA Social Insights, Analytics and ROI Framework</p><p>We will focus on this specific application of SEI today</p></li><li><p>Available Social Media Sentiment Metrics fall short as a tool for measuring ROI, but the SEITM shows great promise</p><p>-20% 0% 20% 40% 60% 80% 100%</p><p>Sentiment Metric 1</p><p>Sentiment Metric 2</p><p>Sentiment Metric 3</p><p>Sentiment Metric 4</p><p>Sentiment Metric 5</p><p>Sentiment Metric 6</p><p>SEI POS/NEG RATIO</p><p>11.2%</p><p>3.1%</p><p>-2.3%</p><p>8.8%</p><p>21.2%</p><p>8.2%</p><p>83.1%</p><p>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.</p><p>Sentiment Metric 1</p><p>Sentiment Metric 2</p><p>Sentiment Metric 3</p><p>Sentiment Metric 4</p><p>Sentiment Metric 5</p><p>Sentiment Metric 6</p><p>SEI POS/NEG RATIO</p></li><li><p>The correlation* to sales over time shows the SEI has Predictive Power</p><p>9</p><p>SEI validation: four categories</p><p>Correlation = 86%</p><p>Correlation = 84%</p><p>Correlation = 81%</p><p>Correlation = 83%Correlation = 83%</p><p>*Lead lag analysis has confirmed that causation is only one way the SEI to a large degree is able to drive hard commercial metrics.</p></li><li><p>SEI validation across ~ 20 diverse brands, both US and international.Validated more than any other social metric</p><p>52%53%</p><p>56%57%</p><p>59%68%</p><p>73%74%</p><p>77%79%79%79%79%</p><p>81%81%</p><p>84%86%86%</p><p>88%</p><p>0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%</p><p>Haircare BrandPersonal Care Brand 4Personal Care Brand 3Personal Care Brand 2Personal Care Brand 1</p><p>DIY Retailer Brand 2AVERAGE</p><p>Business ServicesHospitality Brand 2Restaurant Brand 3</p><p>Cosmetic BrandHospitality Brand 1</p><p>SoftdrinkRestaurant Brand 2</p><p>DIY Retailer Brand 1Restaurant Brand 1</p><p>Telecom BrandMovie 1Movie 2</p><p>SEI/Sales Correlations</p><p>10</p></li><li><p>Recent Marketing-Mix Model casesCustomer experience (SEI earned media) as the largest sales driver</p><p>44.9%</p><p>9.1%4.0%2.2%1.8%</p><p>2.3%5.9%</p><p>29.8%</p><p>Brand Gamma Decomposition of SalesBaseline</p><p>TV</p><p>Print</p><p>Radio</p><p>Owned Digital SEO</p><p>Paid Digital Mobile</p><p>Paid Digital Search</p><p>Customer Experience EarnedDigital Social SEI</p><p>54.1%</p><p>5.5%1.5%4.4%</p><p>2.1%2.8%5.9%</p><p>23.7%</p><p>Brand Beta Decomposition of SalesBaseline</p><p>TV</p><p>Print</p><p>Radio</p><p>Owned Digital SEO</p><p>Paid Digital Mobile</p><p>Paid Digital Search</p><p>Customer Experience EarnedDigital Social SEI</p><p>60.4%</p><p>4.5%2.1%</p><p>3.3%1.1%2.1%5.3%</p><p>21.2%</p><p>Brand Alpha Decomposition of SalesBaseline</p><p>TV</p><p>Print</p><p>Radio</p><p>Owned Digital SEO</p><p>Paid Digital Mobile</p><p>Paid Digital Search</p><p>Customer Experience EarnedDigital Social SEI</p><p>64.8%1.5%0.2%0.3%3.6%0.1%</p><p>1.8%</p><p>27.8%</p><p>Brand Omega Decomposition of SalesBaseline</p><p>TV</p><p>Print</p><p>Radio</p><p>Owned Digital SEO</p><p>Paid Digital Mobile</p><p>Paid Digital Search</p><p>Customer Experience EarnedDigital Social SEI</p><p>6Copyright 2015</p></li><li><p>Case 1: Defining the Coffee Retailer Brand and Position</p></li><li><p>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.</p><p>Insight &amp; Outcomes</p><p>The key drivers to Positive SEI were:</p><p>1. A place to hang out2. To meet people3. Atmosphere4. Beverage Products</p><p>The client developed a 2 for 1 promotion to drive store level sales.</p><p>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.</p><p>13</p><p>Case 2: Key Content Drivers of Retail Sales</p><p>Positive Social Engagement </p><p>100</p><p>Place to Hang Out 211</p><p>Place to Hang Out 83</p><p>To Meet People 325</p><p>To Meet People188</p><p>Atmosphere466</p><p>Atmosphere288</p><p>To Meet People229</p><p>To Meet People85</p><p>Beverage A271</p><p>Beverage A 74</p><p>Note: Separate analysis - Classification &amp; Regression Trees (CART)</p></li><li><p>Brand Positioning Using Socially Engaged Chatter</p><p>Meeting Friends</p><p>Hanging out</p></li><li><p>Case 2: Social Content Drivers for Brand Positioning</p></li><li><p>Case 1: SEITM &amp; Marketing Contributions for Zip</p><p>78.6%</p><p>2.1%</p><p>6.8%</p><p>3.3%3.0%2.5%2.4%1.9%1.1%0.4%</p><p>23.5%</p><p>Zip Modeled Incremental Contributions</p><p>Baseline</p><p>SEI/Mktg Synergy</p><p>SEI-Social Media</p><p>Radio</p><p>POS Signage</p><p>TV</p><p>Digital Display</p><p>Sampling</p><p>Pub.Reltns</p><p>OOH</p><p> 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.</p><p> 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.</p><p>16</p></li><li><p>Case 2: Zip Brand Sales &amp; SEITM Time Correlations</p><p> -</p><p> 1.0</p><p> 2.0</p><p> 3.0</p><p> 4.0</p><p> 5.0</p><p> 6.0</p><p> 7.0</p><p> 8.0</p><p> 9.0</p><p> -</p><p> 2,000,000</p><p> 4,000,000</p><p> 6,000,000</p><p> 8,000,000</p><p> 10,000,000</p><p> 12,000,000</p><p> 14,000,000</p><p>2/23</p><p>/</p><p>3/23</p><p>/</p><p>4/23</p><p>/</p><p>5/23</p><p>/</p><p>6/23</p><p>/</p><p>7/23</p><p>/</p><p>8/23</p><p>/</p><p>9/23</p><p>/</p><p>10/2</p><p>3</p><p>11/2</p><p>3</p><p>12/2</p><p>3</p><p>1/23</p><p>/</p><p>2/23</p><p>/</p><p>3/23</p><p>/</p><p>4/23</p><p>/</p><p>5/23</p><p>/</p><p>6/23</p><p>/</p><p>7/23</p><p>/</p><p>8/23</p><p>/</p><p>9/23</p><p>/</p><p>10/2</p><p>3</p><p>Zip Sales</p><p>Zip.SEI.Ratio</p><p>SEI Ratio Norm</p><p>17</p><p>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.</p><p>Note plotted metric is ratio of Positive to Negative SEITM</p></li><li><p>Case 2: Content Motivation Drivers of Sales Conversion for Zip Powder</p><p>18</p><p>188</p><p>3,516</p><p>103 128300</p><p>301350</p><p>491</p><p>724</p><p>930</p><p> - 500</p><p> 1,000 1,500 2,000 2,500 3,000 3,500 4,000</p><p>Base</p><p>line </p><p> Net</p><p> Pos</p><p>itive</p><p> SEI</p><p>Grea</p><p>t Aro</p><p>ma</p><p>Yum</p><p>my </p><p>Flav</p><p>ors</p><p>Grea</p><p>t Gift</p><p> Idea</p><p>Conv</p><p>enie</p><p>nt</p><p>Tast</p><p>es G</p><p>reat</p><p> Col</p><p>d or</p><p> Hot</p><p>Tast</p><p>es G</p><p>reat</p><p>Grea</p><p>t for</p><p> Tak</p><p>ing </p><p>to th</p><p>e O</p><p>ffice</p><p>Tast</p><p>es Li</p><p>ke th</p><p>e Re</p><p>al T</p><p>hIng</p><p>Tota</p><p>l Net</p><p> Pos</p><p>itive</p><p> SEI</p><p>Zip Powder All Social Channels Engagement Content Drivers</p><p>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.</p><p>Current Positioning</p><p>DesiredPositioning</p></li><li><p>Case 3: Scoring and Evaluating Sports Sponsorships</p></li><li><p>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</p><p>Example: Assessing Sport Marketing ROI (65% of marketing budget)</p><p>9</p></li><li><p> Bottom-Line Analytics LLC is a consulting group focusing on a broad portfolio of marketing analytics, including marketing optimization modeling</p><p> 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.</p><p> We are dedicated to the principles of innovation, excellence and uncompromising customer service. </p><p> Most important, however, we are dedicated to getting tangible and positive business results for our clients.</p><p>ABOUT US</p></li><li><p>Full Service Analytics Capability</p><p>Social Media ROI</p><p>Marketing Mix Modelling </p><p>Pricing Optimization</p><p>Radial Landscape Mapping</p><p>Key Drivers Analysis</p><p>Demand Forecasting </p><p>Customer Satisfaction Modelling</p><p>Digital Performance Analytics Dashboards</p><p>Segmentation Analysis</p><p>22</p><p>BLA is a trusted advisor to a wide array of clients</p><p>We believe in the continuous innovative application of analytics to advance customer centric decision </p><p>making for improved business performance.</p></li><li><p>23</p><p>BLA leadership bios</p><p>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&amp;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. </p><p>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.</p><p>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.</p><p>23</p></li></ul>

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