Post on 26-Jan-2017
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Text analyticsHow to make senseof unstructured data
Text analytics turns unstructured data into categories of information that can:
BY 2020: This market will reach $6.5 billion and increase at a rate of 25% annually.
• Be analyzed
• Reveal new opportunities
• Determine relationships and trends
2020
$6.5 BILLION
25% ANNUAL INCREASE
Learn more about text analytics
What’s under your
microscope?
The many forms of
unstructured data
70%
of all unstructured
data isgenerated by
customers
80% of business
information is
unstructured data
Videos andvideo transcripts
Emails
Reports,spreadsheets
Contracts, warranties
Telephone / memberlisting books
Advertisements,marketing materials,
annual reports
Employeeevaluations
Orderinformation
Blog posts
Survey comment and review sites
Inbound customercommunications
and customer call logs
Social mediacommunication
More than meets the eye Take a closer look at unstructured data
Find out more about unstructured data
• Not bound by rigid, finite answers
• Rich with emotion
• Holds vital information about the customer journey
It’s increasing
Projected digitaldata growth:
It’s used by the best
Organizations that practice data-driven decision making are:
5%
more productive6%
more profitable
5x more likelyto exceed
project expectations
2013 by
2020by
4.4 trilliongigabytes
44 trilliongigabytes
It’s human
Financial tradingPerform analysis to determine how much to trade and when
Voice of the consumerMonitor social channels to proactively address customer concerns
Warranty claimsIdentify trends for repairs or damage to determine if a recall is necessary
Lead generationScan social media for people interested in a certain product or service
RecruitmentDiscover potential hires and analyze their social media postings for cultural fit and expertise
Review sitesCondense valuable customer reviews to two- or three-word phrases
Bringing your data into focusHow organizations use text analytics
Follow the link for more on how organizations use text analytics
Types of text analytics
Ways to frame your data
Sentiment analysisAnalyze opinions or tones
Topic modelingIdentify dominant themes
Term frequency-inverse document frequencyUncover frequency of a word
Named entity recognitionRecognize people, organizations, places and dates
Event extractionDiscover relationships between people, organizations, places and dates
Take a deeper look at types of text analytics
Vendor selection What tool is best for your organization?
• Business goals• Budget• Internal resources
Before you begin your search, consider:
• How well does it integrate data across systems?• How effectively does it help tell the story of the data?• How successfully does it drive personalization?
When vetting potential solutions, consider:
Follow the link for more vendor selection tips
Make the best decisions about your business by putting unstructured data
under the lens of text analytics.
For more on the importance of unstructured data, follow the link
Sources:
1. Davies, Andrew. “Why Unstructured Data Holds the Key to Understanding the Customer.” Why Unstructured Data Holds the Key to Understanding the Customer. Sift Media, 6 Apr. 2015. Web. 11 Nov. 2015. <http://www.mycustomer.com/feature/data-technology/unstructured-data-key-understanding-customer/169317>.
2. Halper, Fern, Marcia Kaufman, and Daniel Kirsh. “Text Analytics: The Hurwitz Victory Report.” (n.d.): 1-22. SAS. Hurwitz & Associates, 2013. Web. 12 Nov. 2015. <http://www.sas.com/news/ana-lysts/Hurwitz_Victory_Index-TextAnalytics_SAS.PDF>.
3. Keylock, Matt. “The Unstructured Data Challenge.” Dunnhumby. Dunnhumby, 12 Dec. 2012. Web. 12 Nov. 2015. <https://www.dunnhumby.com/insight/the-unstructured-data-challenge>.
4. Lamont, Judith. “Text Analytics: Greater Usability, Less Time to Insight.” KMWorld Magazine. Information Today, 29 Oct. 2015. Web. 11 Nov. 2015. <http://www.kmworld.com/Articles/Editorial/Fea-tures/Text-analytics-greater-usability-less-time-to-insight-107036.aspx>.
5. Merrett, Rebecca. “5 Tools and Techniques for Text Analytics.” CIO. IDG Publications, 18 May 2015. Web. 11 Nov. 2015. <http://www.cio.com.au/article/575209/5-tools-techniques-text-analytics/>.
6. Patterson, Laura. “Why Your Data Scientists Need to Be Storytellers, and How to Get Them There.” MarketingProfs. MarketingProfs LLC, 12 Nov. 2014. Web. 11 Nov. 2015. <http://www.marketing-profs.com/articles/2014/26436/why-your-data-scientists-need-to-be-storytellers-and-how-to-get-them-there>.7.
7. Pickett, Stephen. “How Understanding Unstructured Data Is Useful for Customer Insight.” How Understanding Unstructured Data Is Useful for Customer Insight. Digital Marketing Magazine, 7 July 2015. Web. 11 Nov. 2015. <http://digitalmarketingmagazine.co.uk/digital-marketing-data/how-understanding-unstructured-data-is-useful-for-customer-insight/2198>.
8. “Text Analysis; 10 Business Use Cases You May Not Have Thought Of….” Text Analysis Blog. AYLIEN, 19 Aug. 2014. Web. 12 Nov. 2015. <http://blog.aylien.com/post/95184867153/text-analy-sis-10-business-use-cases-you-may-not>.
9. Turner, Michelle. “Channeling Billy Beane.” Editorial. Marketing Insight Sept.-Oct. 2015: n. pag. American Marketing Association. American Marketing Association, Sept.-Oct. 2015. Web. 11 Nov. 2015. <https://www.ama.org/publications/MarketingInsights/Pages/channeling-billy-beane.aspx>.
10. Turner, Vernon, John F. Gantz, David Reinsel, and Stephen Minton. “The Digital Universe of Opportunities: Rich Data and the Increasing Value of the Internet of Things.” EMC Digital Universe with Research & Analysis by IDC. EMC, Apr. 2014. Web. 11 Nov. 2015. <http://idcdocserv.com/1678>.
Infographic created by www.4imprint.com, based on the Text Analytics Blue Paper. Download Blue Paper at: http://info.4imprint.com/blue-paper/text-analytics
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