Social Media Research: Applying Marketing Research Techniques to Social Data

Have a question you’d like to ask regarding today’s presentation? We welcome you to typeyour questions in the ‘Question & Answer’ window at any time during today’s Webinar. We will answer as many questions as time allows during the Q & A session following this presentation. Got Tweet? #PLData

Bye, Bye Research. Hello Data Mining! Hosted by Sean Case, SVP, Peanut Labs Wednesday, March 10, 2010 Peanut Labs, Inc. · 114 Sansome Street, Suite 920 · San Francisco, CA 94104 www.peanutlabs.com

Presentations by: ,[object Object]

Catherine van Zuylen, VP, Product Marketing, Attensity

Jim Schwab, VP, Business Development – Social Media, Alterian,[object Object]

Predictive modeling and analytics

Emerging technologies in data mining

A presentation format in which one presenter shows 20 slides for 20 seconds each, for a total of six minutes and 40 seconds

Devised in Tokyo in February 2003 by Astrid Klein and Mark Dytham of Tokyo’s Klein-Dytham Architecture

Has since turned into a massive celebration, with events happening in hundreds of cities around the world,[object Object]

Formerly the President of Ipsos Online, North America

25+ years of experience in global marketing research

Known for her story telling, Jean authored The Little Church that Could, a fun and inspirational review of the signs posted outside one church for an entire year

Follow Jean on Twitter @JeanMarie50,[object Object]

Not Bye, Bye Research. It’s welcome Social Media Research. In the Social Network arena there is the opportunity to add social media data to the Marketing Research field.

Evolution of Research Science ,[object Object]

Process and methods need to be developed to make social media data be another source for Marketing Research.,[object Object],[object Object]

Creating the Process Create Search Clean Crawl Clean Sample Weight Score Content Analysis Specify what client wants to measure Identify relevant conversations Identify conversations that do not meet basic quality control requirements Tiered system reflecting unique needs of different data sources Content analysis is applied to every conversation Sampling is used to identify which sources are appropriate for a client Weighting is applied to the sampling matrix to ensure that the included sources are reflected in a consistent proportion over time

Social media presence of Client Brand B is extremely low and may not be suited for quantitative research at this time. ,[object Object]

People talking about this brand are more likely tobe women be aged 35 to 64 have a college degree earn $25k to $75k

Content Analysis ,[object Object]

Retailers: Parking, check-out lines, categories (electronics, apparel)

CPG: taste, feel, product, price

Determine which sets of conversations are similar to each other based on tone of voice and content of the conversation.

Sentiment: positive/negative,[object Object]

Can be weighted to redistribute sample so that overrepresented categories are less likely to skew the data,[object Object]

Data can bring results in new data reports.

Psychographics,[object Object]

Brands with the most positive sentiment include Brand A, Brand G, Brand H, and Brand N.

Brands with the most chatter include Brand B, Brand J, and Brand LPast 30 days n = 378 to 92,000

Retailer Attribute Comparison Employees Crowding Parking Lot Average Scores 5.0 = Positive 3.0 = Neutral 1.0 = Negative Norms 4.0 = High 3.3 = Normal 3.0 = Low Website Hours Washrooms ,[object Object]

Scores are most positive in relation to crowding, the parking lot, and the hours. On the other hands, scores are much lower for opinions of employees and the website.

While Brand Green outperforms Brand Grey on nearly every construct. However, Brand Green and Brand Grey generate very similar opinions related to their websites.,[object Object]

Popular words indicate:- The interests of people talking about the brand, and therefore the contents of marketing materials - Co-branding and co-sponsorship opportunities that are relevant to your consumers - Appropriate language to use in marketing materials, whether slang or formal Use tennis or football metaphors Show basketball or football in marketing materials Obtain tennis or football celebrity endorsements

Psychographics ,[object Object]

The first three constructs are revealing in that each word relates to the exact same idea. However, the words used among Brand A consumers are more intellectual.

This trend follows through in the discussions of technology where Brand A consumers use more technical words.

Income and schooling also reflect a higher socio-economic status for Brand A consumers

Brand A consumers reflect a higher socio-economic status than Brand B consumers,[object Object]

March 10, 2010 Thank you to Peanut Labs for inviting Conversition to share in their webinar! Jean Davis, jean@conversition.com March 10, 2010

Any questions for Jean? We welcome you to type your questions in the ‘Question & Answer’ window at any time during today’s Webinar. We will answer as many questions as time allows during the Q & A session following this presentation.

Catherine H van Zuylen, VP, Product Marketing, Attensity ,[object Object]

Formerly Vice President of Marketing at Block Shield

20 years of experience in product management, product marketing and marketing communications

A Silicon Valley native who grew up across from an apricot orchard and won several blue ribbons at the country fair for her fruits and vegetables

Follow Catherine on Twitter @catevz,[object Object]

Attensity: Over 20 years experience understanding customer conversations in text; 6 patents in natural language processing Suite of applications for social media monitoring, Voice of the Customer Analysis, and Self-Service/Agent Service Over 500 customers worldwide Me: 15 years in marketing; 10+ years in text analytics and internet media A Few Words About Me and Attensity

“Customer Information” is changing and growing exponentially Twitter hit the 10 billion tweet mark last week : over 20% are about products and services Over 247 billion emails are sent every day Millions of customer interaction records in a typical large company.

To effectively harness these “customer conversations”, you need a program to comprehensively Listen across customer conversation channels Analyze accurately and efficiently Relate this information to other information Act on the information We call this the LARA methodology

LARA Methodology: Listen, Analyze, Relate, Act Are you listening where your customers are talking? Are your “social media” listening efforts isolated from your “CRM” listening efforts and separate from your “survey” listening? Are you monitoring your internal customer communities? Text Analysis can help bridge these gaps.

Text Analysis is not Search“Search” is for finding relevant or recent documents that contain a term of interest

But it’s hard with search to get the “big picture” What do people think about my company? What problems are they having? What do they like about me vs. the competition? What new ideas do they have? Who is thinking of switching? 34

“Search” starts with you feeding a system words to look for. “Text Analysis” starts with the data itself and lets it tell a story Dynamic Text Profiling Documents Entities, sentiments, events and relationships, intent, etc ? XML or other “tags”

Text Analysis starts the same way some search engines do… Automatic Language and Character Encoding Identification Identify paragraphs and sentences within text Word Segmentation (Tokenization) and De-Compounding Part-of-Speech Tagging Stemming Noun-Phrase Identification

Then continues with Entity Extraction… Who: People, Person Position, Social Security Numbers What: Companies, Organizations, Financial Indexes, Products (software, weapons, vehicles, etc…) When: Dates, Days, Holidays, Months, Years, Times, Time Periods Where: Addresses, Cities, States, Countries, Facilities (stadiums, plants), Internet Addresses, Phone Numbers How Much: Currencies, Measures Concepts (i.e. Global piracy, unstructured data…) Can be pattern-based – tell the system that a “Prop-Noun followed by Smith” is probably a person Or machine learning – feed it a million proper names and let it deduce names from those examples…

Practical Text Analysis in Action Let’s say that I am a major retailer, and someone posted a review that starts out I bought this Gucciscarffor my mom in your Santana Row store last week. Entities (brands, people, locations, times, products…)

To “connect the dots” in data, you also need to extract noun-verb relationships, sentiment… I bought this Gucci scarf for my mom in your Santana Row store last week. I really like the pattern, but I don’t like how it itches. Entities (brands, people, locations, times, products…) Events and relationships: action and purchasing reason Sentiment (extreme positive, positive, negative, extreme negative)

To “connect the dots” in data, you also need to extract suggestions, intent… I bought this Gucci scarf for my mom in your Santana Row store last week. I really like the pattern, but I don’t like how it itches. I wish this scarf came in cotton. If Gucci made more cotton scarves, I would buy them all. Entities (brands, people, locations, times, products…) Events and relationships (I : buy : this Gucci scarf | I : buy : for mom) Sentiment (extreme positive, positive, negative, extreme negative) Suggestions (I : wish : this scarf came in cotton) Intent (to purchase, to leave) (If Gucci made more cotton scarves, I would buy them.)

How do you do this? You parse sentences like a human…and extract triples…

…and voices (intent, recurrence, etc) Question [?] voice: How can I get free shipping with future orders? Condition [if/then] voice:. I would shop more frequently if you offered free shipping. Intent [intent] voice: I plan to place an order today. Negation [not] negates the meaning of the verb: You did not have the size I was looking for in stock

…and voices (intent, recurrence, etc) Question [?] voice: How can I get free shipping with future orders? Condition [if/then] voice:. I would shop more frequently if you offered free shipping. Intent [intent] voice: I plan to place an order today. Negation [not] negates the meaning of the verb: You did not have the size I was looking for in stock Augment [more] voice: The staff were incredibly professional Recurrence [again] voice: I had to enter my information several times for the order to process Indefinite voice representing suggestions or requests. You should sell wedding dresses, too!

LARA Methodology: Listen, Analyze, Relate, Act Once you’ve done text analysis, you can relate the text to structured information… 01/24/2010 By errodd from San Jose, CA I bought this Gucci scarf for my mom in your Santana Row store last week. I really like the pattern, but I don’t like how it itches. I wish this scarf came in cotton. If Gucci made more cotton scarves, I would buy them all. Can help you answer questions like What were the top concerns of people who rated this product a “4”?

LARA Methodology: Listen, Analyze, Relate, Act: What Can You Do with Text Analysis? The output from text analysis can be exported as XML… It can also be used directly in applications that Seek out and deliver information to those who need it Route and respond to communications Mine and report on information

“Seek Out” information for a self-service knowledgebase Problem Solution Manufacturer: Apple Product: Macbook, Projector, Monitor Component: Adapter cord, Mini-DVI, VGA Action: Do a presentation, connect

Route and respond to all customer communications Responses can be reviewed by agent before sending “refund policy” email response auto-generated Read text and extract knowledge about what the document is saying People Places Events Topics Sentiment … Refund policy? Email Routed to Customer Service for Follow-up and Resolution intent to leave tweet Automatically routed as a mobile alert to legal for review Threatening to sue posting

Mine and report on sentiments, complaints, compliments, and “intentional” behavior across all customer conversations Better understanding their customers Better understanding their customers and gain early warning on product issues

Thank You.Leveraging Customer Conversations Through LARA Catherine H van Zuylen VP, Product Marketing cvanzuylen@attensity.com www.attensity.com Twitter: @attensity

Any questions for Catherine? We welcome you to type your questions in the ‘Question & Answer’ window at any time during today’s Webinar. We will answer as many questions as time allows during the Q & A session following this presentation.

Social Media Research: Applying Marketing Research Techniques to Social Data

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