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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>.

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