2.11.14
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Are you swimming or drowning in the sea of big data? Whether you’re doing the backstroke or sinking in it, the rate of data collection is growing. So how do you get from the tumultuous ocean of big data to a calm, quiet bay? We will chart how to take the sea of data that organizations are collecting on individuals and transform it into meaningful drops of information. Take social media data, for instance. Businesses use Facebook, Twitter, and other social sites to measure opinions. A community manager, lets say, can use this data to track reactions to a new website and optimize a marketing campaign based on fans’ and followers’ comments. Join our panel to learn how to: -Utilize the information you already have. -Leverage the technology. -Fill the data scientist role in your organization. -Organize big data. -Make big data actionable.
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2.11.14
- 1. How to Turn Big Data Into Little Data #SMTLive
- 2. Join the Conversation… long and Follow a thoughts 157,34 your share t Twitter a 5e on #SMTliv Subm quest it your ions i n the GotoW Prese ebinar ntatio windo n w #SMTLive
- 4. Thanks to Our Sponsor The platform to gain deeper insight into customer segments, markets and competitors.
- 5. Powering The Social Economy Turning Big Social Data Into Actionable Social Data Katie Van Domelen datasift.com @datasift hello@datasift.com Product Marketing @ktvan Manager
- 6. Messy & unstructured Size is no longer the issue Need structured actionable data
- 7. How? With Relevance & Context “Most companies spend 80% of their time on data engineering rather than actually analyzing the data.” – NVP Report Step 1: Separate the signal from the noise Step 2: Add meaningful classifications Journalist Churn Tier-1 Customer Profile Content CRM
- 8. Dell Makes Social Data Actionable Identify specific drivers of sentiment changes across business lines, products, : and features Challenge Solution: Custom app with advanced filtering and custom taxonomies to find relevant posts and provide the context that makes them actionable Result: Real-time insight enabled fast global changes in under 24 hours that increased customer loyalty by 39%.
- 9. Unstructured Data: The Fourth Listening Post The Fourth Listening Post JD Pow e S NP r Rating Ni n so el A method for breaking Big Unstructured Data such as Social Posts into valuable bits of “Little Data,” is to assess how it enhances, explains, validates and redirects traditional sources of information. Ac c tS wi tc hin g Market Share DM Res TCS Confidential ults C Call e S ta nt e r ts 9
- 10. “Little” Data is Specifically Actionable Data: Insight by Each Channel in Retail Banking TCS Confidential 10
- 11. Constant “Little Data” vs Big Bang Data: Continuous CX Insight NPS Survey after transactions Customer Service Call after account issue NPS Survey after new service enrollment Telephone Survey by 3rd party Telco Customer Experience 2+ year customer social feedback “Little” data can better trace the full lifecycle of customer experience… From individual transactions to broader experiences. TCS Confidential 11
- 12. Insight to Outcome Approach to Breaking Big Data into Actionable Bits of Little Data Data and Method Data and Method Actionable Insight Enabled Actions Business Outcomes Enabled Action Actionable Insight • Brand/product spending> • Profile of churn segments Channel/Decision tree including What criteria do they • Call center transcripts>Text analysis use to choose banks? What triggers churn? • Social posts>Text analysis What’s the timing of • Churn Stats+Voice of Choice>Regression analysis those triggers? What offers do they respond to? What channels do they use? What other brands are they attached to for partnership ideas? • Segment at risk/churn targets based on behavioral differences • Define churn triggers/value props/CX criteria in for each segment • Define loyalty drivers and best rewards for each segment • Identify key segments and provide special loyalty offers to key segments company will have to make operational changes to make it so...) TCS Confidential Business Outcome ■ Retain 5% of “at risk” customer 12
- 13. Upcoming Webinars 2/18 Social Selling: It’s About the Listening, Not The Talking http://socialmediatoday.com/social-selling-2014-webinar
Notes de l'éditeur
- http://www.marketingprofs.com/charts/2013/11340/digital-marketers-on-twitter-share-retweet?adref=nl080613 Quote on lower person reading article. Social Media Today’s number’s are growing exponentially lately, which is great for Jive because as we develop new content we ccan WE are unique in our reach
- My name is Katie Van Domelen, product marketing manager here at DataSift where our mission is power any business decision made with social data. We work with world-class social media applications like Simply Measured and Sysomos, and corporations pioneering social data use in the enterprise like Dell, MasterCard and CBSinteractive. Our platform aggregates social data across multiple sources, processes it and delivers it to our customers in a single API to a variety of data destinations.
- Looking at the world of big data, one of the largest challenges we face is dealing with unstructured, messy data. In a survey of over fifty executives representing leading Fortune 1000 companies, 40% indicated that their biggest challenge was data variety and complexity vs. only 10% who mentioned volume.* Social data is the perfect embodiment of this exact issue. Different formats across networks, unstructured text content, images, videos, etc. This makes it hard to analyze in it’s own right, but next to impossible to successfully merge with legacy data sources using existing data processes and applications. Considering that only 34% companies say they’re able to associate social media efforts with business metrics**, the importance of making the connection between social data and other business data is reaching critical levels. Ultimately, the key to doing that is translating unstructured social data, into structured information making it easier to manage and analyze. *http://www.crypto-gen.com/pdf/NVP-Big-Data-Survey-2013-Summary-Report.pdf **Altimeter State of Social Business 2013 (page 3)
- A big piece of this, especially in social, is relevance & context. The biggest mistakes companies make in this area is they go out looking for “every mention of my brand on ____ (ex:Twitter.)” First, a straight forward boolean search on keywords is not the most efficient way to filter. A single tweet going through our system has upwards of 75 target fields of metadata and enrichments attached to it. Getting more creative about how and what you search for is the difference between signal and noise. Using location, profile information, source, or content of a link, can make a huge difference in getting a complete and relevant data set. As you narrow down what you’re looking for in terms of brand mentions – expand out what you look for in related content – about your product category or service type for example. Once you’ve got that relevant data set – and you’ve found a tweet like this lets say, the next most critical step is to put it in context. This is where you start to add in that structure. Using custom taxonomies to classify social content based on your objectives. For example, if you’re trying to get actionable customer insights – knowing what type of person is behind the message, what intent is underlying their comment and how they’re connected to your business – really turns this from a simple negative comment, into an actionable piece of information. Think strategically about the taxonomies and structures you’re already using to do analysis across data sets in other areas of the business, applying those as tags here will make it easier to integrate your social data and maximize impact.
- I wanted to share an example of a customer who’s really exemplified the way that driving relevance and context in a social data set turns “big social data” into “actionable social data.” Dell has been an active social media player for some time, monitoring social comments and incredibly invested in engaging with their customer community online. But they were struggling with both the volume of content they were getting – and being able to translate that volume into something meaningful. They might see that “sentiment went down 10% on Tuesday” – that’s a sharp dive so they’d try to dive in to get more detail. They’d see the sentiment breakdown and volumes for the day but they still don’t know why sentiment is dropping. So they do some basic text analysis, maybe a tag cloud which says the biggest “negative” word is Price, everything else is dwarfed. Well typically they see negative sentiment attached to price – so it’s still not clear what the actual driver is for the drop on that day. Using DataSift to drive a more precise filter for relevant mentions and deliver to their custom application, SNA. Dell has created both a social net advocacy score and a custom taxonomy to categorize posts based on their intent as well as the related business unit(s), product(s) and even features across products. So now when they see a negative trend and dig into it, they don’t see a generic tag like “price” – they see that it was tagged as “laptops” and not just laptops, but “XPS” laptops and not just “XPS” but specifically related to the features of “Linux” and “Price.” So now they know that the new XPS Linux notebooks that they just launched were mistakenly priced higher than the Windows version of the same machine. With that precise, real-time insight they can make a global change to fix the mistake and communicate directly with customers about it in days rather than weeks or months.