Tag Archives: text analytics

Social Media Research: from Listening to Understanding

USA Today recently published a piece on text analysis.  Yawn!  I know.  Really?  Again?  It suggested that… drum roll please… as a culture we are becoming more self-centered and less “we” or “team” focused.  Masters of the obvious.  I think reality television and social media have provided a loud narcissist’s playground where everybody is talking so loud that nobody can hear well enough to listen.  And more importantly, who cares?

Market Researchers care.  So much so that TMRE’s track on Social Media is not called “social media.”  It’s called Social Media Listening.  This shift fascinates me because it suggests we’ve reached a plateau in our initial understanding of social media – its role and mechanics.  And now we have tools in place to listen and learn.  That’s cool, but what’s next?

Now is the time to plan the next crescendo in social media learning, lifting from the plateau and into higher levels of understanding.  Looks like Jeff Henning from Affinnova will present another inventive thought in a session about listening then asking and understanding, iterating with social media to innovate.  This is a smart first step toward more “why?”

Here’s the next dilemma as I see it.  The analysis in USA Today didn’t actually consider social media at all.  They measured the text in BOOKS!  A full 50 years worth of books not a 15 minute slice of social media.  It was a Global analysis noting that words like “I” and references to the self far outweigh “we” and references to the collective.   This could just be a stylistic shift to first person narrative – but maybe that’s the point.  If we are seeing the trend in books over a 50 year period certainly the trend for self absorption existed before social media.

So the next challenge in social media will be the quality of listening.  For example, could the analysis in USA Today even be performed when 140 characters entices writers to eliminate pronouns?  “I went to the movies” becomes “Went to the movies.”  This could be “I,” “we,” or “the dog,” and no one would know.  Maybe its more essential versus the content.  This is a difficult next step but probably imperative for us as we grow more dependent on social media as a source of insight.  Improving the quality of listening so we really know who is talking and the “why?” behind what they are saying would bring truly new insight AND understanding to social media.

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Today’s guest post is from Kelley Styring. Styring is principal of InsightFarm Inc. a market research and consumer strategy consulting firm. She has led insights for Procter & Gamble, Pepsico, Black & Decker and NASA prior to founding her own firm in 2003. Kelley is a published author and has been featured in USA Today, ABC News, Good Morning America, Brandweek, Fortune, Quirk’s Marketing Research and The Market Research Daily Report from RFL Online.  She will be live blogging from The Market Research Event 2012 this November 12-14 in Boca Raton, Florida.  If you’d like to join her, register today and mention code TMRE12BLOG to save 15% off the standard rate!

Text Analytics for (Really Smart) Dummies, Part 3

Kodak Gallery Gets the Big Picture Via Text Analytics

By Marc Dresner, IIR USA

To paraphrase a colleague who monitors our site traffic: ‘I think text analytics is a hot topic.’

This turned out to be a mild understatement: The chart that accompanied her message showed a shocking spike in traffic on the day we posted Part 1 of our “Text Analytics for (Really Smart) Dummies” series.

As I suspected when I originally started exploring the subject, it appears many market researchers feel like they have just enough information about text analytics to be dangerous, and there’s a clearly a widespread desire to learn more.

To recap our series thus far:

Part 1) Featured a specialist provider with a new DIY product and who has an unusually savvy take on text analytics in market research;

Part 2) Interviewed a corporate tech giant’s researcher who’s using the company’s proprietary text analytics product to study its legion of employees.

For Part 3 of our series, we’ve homed in on an internal research dept from a forward thinking vertical of a classic company with an outward, traditional consumer perspective.


This case is particularly special because as director of research at KODAK Gallery, Lori Tarabek is making a big difference under less than ideal circumstances.

It’s no secret that Eastman-Kodak (OTCQB:EKDKQ) has filed for bankruptcy, and it appears they may sell Kodak Gallery’the 131-yr-old film pioneer’s online imaging biz’for roughly $23.8 million to competitor Shutterfly.

To thrive as a researcher in this uncertain environment requires ingenuity and a reasonable tolerance for risk in order to move the business forward.

And Tarabek has leveraged these traits to great advantage, evinced in particular by a smart gamble on text analytics that’s paid off.

So this third installment in our text analytics Q/A series is, in my opinion, not only the most inspiring of the bunch, but also may be of the most practical value to our audience.

Thanks for reading and I encourage you to share your own stories and questions about text analytics with our community! Keep the conversation going!

Yours faithfully,
Marc

Q. What attracted you to text analytics?

I needed to find a way to analyze 1.5-2K weekly open-end responses from two NPS/satisfaction tracking studies, without sufficient time or resources for traditional coding. The goal was to find a way to categorize these verbatim responses into logical topic areas that could serve as a diagnostic indicator for key metric trend changes and guide further investigation.

Q. Have you found that certain types of data better lend themselves to text analytics?

I’ve used Anderson Analytics’ OdinText text analytics software in two ways: for the tracking surveys I mentioned above, which I think it’s ideal for; and for an ad-hoc project where I wanted to include all of the responses collected (4K+) and get a sense for the relative frequency of the potential barriers to action.

Currently we’re at the criteria development phase and it looks promising, but I’d anticipate making a case by case judgment on the value of text analytics for standalone projects.

The essential question is this: Will the investment in time required to set up the categorization criteria be paid off in terms of better analytical quality and efficencies (from high response volume or when expedited turnaround time is required)?

In the case of tracking satisfaction or recommendation/NPS, I believe all of these criteria are met. The consistency of criteria being applied removes the human interpretation bias, and large data sets can be analyzed very quickly (after the criteria/model is in place).

Q. Can the same tools be used for social media data as for, say, survey data?

Social media data may benefit from text analytics tools if there is some consistency in the type of subjects likely to be discussed or the primary goal is to track trends in sentiment. The need for better social media exchange analysis will drive enhancements in the linguistic analysis capabilities and variety of tools available.

Q. What thoughts or advice do you have for other researchers who may be considering text analytics solutions?

Text analytics tools require an investment in upfront time to deliver real value, so advance planning and some runway room to improve the quality of the criteria model need to be factored in. Learning and testing the logic construct is important so that you understand how to set up the criteria to maximize the accuracy of what is returned.

The best test for me is still to compare how a subset of responses is coded, verbatim by verbatim. The ongoing maintenance work required to sustain the value will depend on the pace of change in the key metric or subject matters that influence it, but failure to spend that time will quickly denigrate the quality returned.

Relative assessments represent the strongest use case ‘ levels changing over time, correlated with other metrics, etc. As with any type of large data set analysis, the goal should be to spend the majority of your time thinking about the conclusions and recommendations from the data, and if you are not there, you need to re-evaluate your process, tools or both.

Thanks for the great insight and sound advice Lori!

Editor’s note: To learn more about text analytics, don’t miss The Market Research Technology Event ‘ a unique forum dedicated to the exploration and promotion of technological innovations in consumer and market research and business intelligence’taking place April 30 thru May 2 in Las Vegas.  As a reader of this blog, when you register to join us, mention code MRTECH12BLOG and save 10% off the standard rate!

ABOUT THE AUTHOR/INTERVIEWER
Marc Dresner is an IIR USA communication lead specializing in audience engagement. He is the former executive editor of Research Business Report, a confidential newsletter for the market research industry. He may be reached at mdresner@iirusa.com. Follow him @mdrezz.

“Text Analytics for (Really Smart) Dummies” Part 2: Big Blue’s Unique Use Case

IBM Turns Text Analytics Inward to Learn from Employees
By Marc Dresner, IIR USA

Part I of our text analytics series featured a specialist provider’something of a pioneer in the field’whose firm has recently developed a DIY text analytics tool. I think you’ll agree that Tom Anderson did a nice job of helping us parse fad from fact.

So for Part II of ‘Text Analytics for (Really Smart) Dummies,’ we’ve turned to an intriguing text analytics use case, courtesy of another pioneer in the space: Big Blue (NYSE:IBM).

What I appreciate about this one is that IBM is both a text analytics user and a provider, which may or may not inform your thinking when you read this interview.

I can’t speak to the software, because that wasn’t covered in this session, but we have here a great example of text analytics in action.

They call them ‘IBMers”an almost cult-like bunch of some of the best and brightest technologists on the planet, driven by a culture of innovation embodied in three letters: I-B-M.

And it’s up to Senior Research Manager Dr. James Newswanger to help IBM maintain a competitive edge by understanding and learning from them’

Q: Jim, please tell us a bit about your role and if/how you need to analyze qualitative data and/or text.

JN: I lead a group known as Corporate Workplace Analytics within the IBM CIO division. We study IBMers’ use of our major enterprise IT tools, including the intranet, Connections, Lotus Notes, social media, etc. Our group specializes in primary and secondary studies, as well as quantitative and qualitative methods. We have been increasingly focused on text analytics and ways to make meaning of content in unstructured data sets.

Q: For clarification, what exactly do you mean when you refer to “text analytics”?

JN: To me, text analytics is the process of taking in content, using software to mine the content for meaning, and offering a presentation interface that allows researchers to find insight.

Q: Is text analytics really new?

JN: Some parts of text analytics are old. For example, human beings acting as coders have been mining open-end comments for meaning for a long time.

Q: How have text analytics evolved over the past few years?

JN: What’s new is the expansion of online commentary offered by blogs, microblogs, news feeds, etc., and the software tools available to analyze text, often in real, or near-real, time.

Q: Is all text analytics the same or are there different types?

JN: There are different types of text analysis, driven by particular project requirements. Some researchers need to find basic themes in text, some need to identify “sentiment” associated with themes, some need to link themes to authors and networks, etc. And the time frames and database sizes can vary widely.

Q: How has market research as a use case evolved in terms of text analytics, both in general and for your company specifically?

JN: Some market research professionals make claims that text mining can substitute for other forms of traditional primary research. Some say, instead of launching a scientific survey to determine opinions on a particular topic, we might just mine the blogosphere or Facebook postings, etc., for the end result.

Q: What are some of the most important use cases?

JN: Examples involving challenges to traditional market research techniques’especially sampling rules’are the most important use cases in my opinion.

Q: How much of text analytics involves Big Data, and do you think market researchers should/do have the skills and tools needed to leverage it?

JN: Text analytics may involve Big Data, it depends on particular project requirements. If I want to mine the Web for public opinions about a topic as broad as a popular movie, television show, or government issue, then Big Data sets will clearly be in play. Data set requirements are driven by the number and scope of content sources, the length of time under review, and any real-time demands on delivery.

Q: What criteria should an organization use to base a decision to (a) develop an in-house text analytics capability, b) outsource text analytics or c) adopt a hybrid model?

JN: To develop an in-house text analytics capability, an organization must have analysts capable of using one or more software tools developed for text mining. The organization must own or license software deemed effective for this analysis.

Q: What questions need to be asked in order to determine an appropriate capabilities provider?

JN: Some of the issues to consider include cost, user interface, scope of content coverage and source of content, presentation and reporting capabilities, specific linguistic analysis features, and customization.

Thanks Jim! That concludes Part II. Next up, Kodak Gallery weighs text analytics’ potential fit within its market research efforts.

Editor’s note: To learn more about text analytics, don’t miss The Market Research Technology Event ‘a unique forum dedicated to the exploration and promotion of technological innovations in consumer and market research and business intelligence’taking place April 30 thru May 2 in Las Vegas.  As a reader of this blog, when you register to join us, mention code MRTECH12BLOG and save 10% off the standard rate!

ABOUT THE AUTHOR/INTERVIEWER
Marc Dresner is an IIR USA communication lead specializing in audience engagement. He is the former executive editor of Research Business Report, a confidential newsletter for the market research industry. He may be reached at mdresner@iirusa.com. Follow him @mdrezz.

Text Analytics for (Very Smart) Dummies, Part I

Today’s Most Popular and Least Understood Research Tool Explained’Sort Of
By Marc Dresner, IIR USA

So you think you know text analytics? Maybe. Or not. We hear about this ‘revolutionary’ methodology all the time now, but it clearly means different things to different marketers, let alone researchers.

Ill-defined is oft ill-conceived in my book, but this seems hardly the case.

Not too long ago, after a series of conversations on not necessarily related topics, it occurred to me that there’s a somewhat alarmingly nebulous aspect to this seemingly straightforward concept.

So I ask you: What is text analytics’

Don’t look to me for answers, because like most people I’ve spoken with in research circles, I thought we had this figured out. I daresay we may have been wrong.

To illustrate, I present the first of three simple text Q-&-A interviews.

Each represents neither an entirely consistent nor contradictory definition, but on the whole they most certainly affirm my argument that many of us misapprehend the meaning of text analytics as a methodology and as a tool on some level or other.

About our select three:
- The first is a niche provider and trusted personal favorite who doesn’t mince words, has the right background to speak to the topic and who has even developed a new DIY text analytics software.

- For Part two, I’ve Q/A’d a Fortune 500 corporate research functionary with a unique inward focus and a firm grip and smart take on text analytics.

- Part three features a very special guest, representing an internal research dept from a more forward thinking vertical of a classic company with an outward, traditional consumer perspective.


Lest I get ahead of myself, for Part One, let’s turn loose Tom H. C. Anderson, Founder and Managing Partner of Anderson Analytics OdinText

Anderson’s award-winning firm was notably among the first in MR’circa 2005′to provide text analytics. Over the past few years, based on tremendous experiential knowledge and continous feedback from its clients the firm has developed text analytics solutions for data from large scale survey and call center comments for such clients as Starwood Hotels and Kodak, to social media data for firms like Unilever and LinkedIn.

Q. Please tell us briefly about your current role and your company.

Anderson Analytics helps clients in various industries leverage their structured and unstructured [text] data. My role and my company’s role have been evolving from a more full-service approach to helping clients take a hands-on approach to unstructured data analytics. It’s probably a 50/50 mix, now, but we’ve tried to make our software’OdinText’as intuitive as possible so that our clients can feel comfortable doing most of their own analysis.

Q. Define “text analytics”Is it all the same?

There are certainly alternatives to the more linguistic approach’which has fallen out of favor a bit now’to statistical and machine learning methods, which seem to be proving more effective.

We’ve also done quite a bit of work related to measuring emotion in text. This technique was first pioneered in the field of psychology.

While you don’t need to be wed to any one tool or approach, we’ve found that for most clients accurately understanding what is being discussed (verbatim concepts) is more important than sentiment or emotion. But it really depends on both the business objectives as well as the data source.

Q. There are different use cases for text analytics and, more recently, text analytics firms are turning their attention to MR. How has market research, specifically as a use case, evolved in importance for the text analytics industry in general and for your company specifically?

Market Research has always been our primary use case, though customer service is obviously very closely linked. MR has reacted slower than expected in my opinion, but the industry is coming around.

Our use case is very different than, say, public relations, which uses [text analytics] mainly to monitor comments broadly on social media and then to engage with specific influencers.

Market researchers need deeper insights, and we also have a lot of valuable data in our organizations from survey open ends’especially trackers’to call center logs, etc. These are very rich insight sources with obvious value.

Admittedly, I think social media data has been hyped, but my job is not to sell a specific data source as being more important than another, but to help clients get an accurate read on un- or undermet needs and to help them move up the text analytic value chain.

Q: What are some of the most important use cases?

Within marketing research, relatively speaking, I think there is a bit too much attention focused on social media monitoring. This is just one single source of text data. Most firms have a wealth of rich unstructured data within their organization already that they need to understand’larger survey data studies, CRM feedback etc. There’s also some confusion surrounding the appropriateness of text analytics for qualitative research.

While this can certainly help smaller samples, the ROI is difficult to justify depending on the circumstances: Some of our clients who’ve been using our software on larger data sets have asked us if they can use it on much smaller studies as well. So our clients are actually changing mymy thinking in this area, and I’m now a bit less concerned with how much data you have.

If you’re comfortable using a specific tool, and it’s designed well, then leveraging it on smaller data sets doesn’t require as much of a time investment, and in such cases, the text volume threshold is much lower.

I would say, though, that you still probably want to have at least a few hundred comments and/or multiple sources of smaller samples before text analytics makes sense. While text analytics can technically offer value to even a single focus group, the ROI here is less promising; you should be able to read and synthesize all the responses of a single focus group yourself.

Q: What are the benefits to an enterprise approach to text analytics versus specific use-case approaches?

There’s even more confusion around this than about what text analytics is and isn’t in general.

Enterprise as in ‘Enterprise Content Management’ is one of the many buzzwords that intersect the market research, text analytics and business intelligence fields. All ECM really means is a formalized means of storing data or documents, usually with a simple search function built in.

Somehow ‘Enterprise’ has taken on a level of importance that lacks meaning. Even survey companies have started calling themselves Enterprise Feedback Management (EFM) firms now; I suppose mainly to differentiate themselves from the popular tools out there that do pretty much the same thing as they do for free (Survey Monkey etc.). The idea seems to be if we can’t beat them let’s change what we call ourselves’

Anyway, getting back to your question and how it relates to text analytics’ Some text analytics firms have taken the ECM approach, probably because they came from this BI space before they got into text analytics.

‘Enterprise’ by definition has to be simplistic. So if you’re looking for a very simplistic search type of solution across your enterprise, then ECM may be an option. There is quite a bit of debate on how useful it is to look at customers and data holistically across organizations and departments.

The approach we’ve taken is to develop software with specific departmental use case in mind, as a SaaS (Software as a service). This means clients don’t need to invest in their own IT hardware or support.

Of course ECM and, more specifically, SaaS applications are not mutually exclusive. Many clients have integrated survey data, CRM data and social media into our tool. We’re also looking into how we might fit our tool into a client’s ECM from another vendor. Secondly, for clients who want to run software on their own servers and do their own integration and upgrades, licensing the more specific SaaS software is also usually an option.

Q: How does Big Data fit into text analytics, and do you think market researchers have the skills and tools needed to leverage what’s available?

Depends on how you define Big Data. Generally, I would say no. Even the larger traditional market research houses have few if any staff with the experience or tools necessary to handle Big Data. And MR industry statistical packages’the usual ones-typically crash with larger data sets, and sampling becomes necessary. This is one of the other reasons we developed OdinText: The datasets we were working with started getting too big for some of the tools we had been using!

Big Data becomes more important further down the text analytics value chain when predictive analytics and modeling are used. I’d love to see more market researchers get past just monitoring and do more of this really interesting work.

Q: What criteria should an organization use to determine whether to (a) develop an in-house text analytics capability, b) outsource text analytics or c) adopt a hybrid model?

I think initially a hybrid model may be ideal. Select a vendor that has experience with text analytics in your specific use case. Ideally, the vendor should be able to train you in best practices and use of their tool, but also be able to handle more full-service approach assuming an important out-of-the-ordinary analysis need comes up or your staff is spread too thin.

Q: What questions need to be asked in order to identify the right capabilities provider when one is required?

You need to ask, ‘So what’? Don’t fall for a bunch of techno-jargon you don’t understand. If the provider is not able to speak specifically about how text analytics can help your department become more valuable, and make specific contributions and improvements to your decision making and process improvements, then you should be talking to someone else. Simple as that.

Editor’s note: Next up, IBM’s take on text analytics for internal understanding!

For learn more about text analytics, don’t miss The Market Research Technology Event ‘ a unique forum dedicated to the exploration and promotion of technological innovations in consumer and market research and business intelligence’taking place April 30 thru May 2 in Las Vegas.  As a reader of this blog, when you register to join us, mention code MRTECH12BLOG and save 10% off the standard rate!

ABOUT THE AUTHOR/INTERVIEWER
Marc Dresner is an IIR USA communication lead specializing in audience engagement. He is the former executive editor of Research Business Report, a confidential newsletter for the market research industry. He may be reached at mdresner@iirusa.com. Follow him @mdrezz.

Today’s Free Web Seminar Next Thursday – Unlock a Better Business Strategy with Text Analytics

Start Date/Time: Today – Thu, Nov 19, 2009 2:00 PM – 3:00 PM EST

With SPSS text analytics, you can read documents, blogs, wikis, tweets, e-mails, call center notes, surveys and other free form text ‘ and turn the insight you gain into a true strategic asset. We’ll show you how to use text analytics with social media and other Web 2.0 sites to understand trends, and what your customers want, and how they’ll behave. We’ll demonstrate using text analytics in modeling to make your models better, and how to use automatic translation from more than 30 languages to make sense of your global customer base.

Your customers are talking about you, and you can gain a genuine advantage by knowing what they are saying. Attend this webinar and learn how you can ‘listen in’ ‘ and act on the information to gain a competitive edge.

This webinar will show how to:
- Use text analytics to make sense of any text, including Web 2.0 sources such as social networking sites
- Use the information to get a better understanding of your customers, your products, and your competitors.
- Make sense of free-form text resources, and act on the insight you gain
- Unlock predictive secrets from text sources, and combine that information with structured data to build powerful predictive
models that can inform decision making.

Featured Speaker
Jane Hendricks, Product Marketing Manager, SPSS, an IBM Company

Mention priority code MWS0026BLOG when registering:
https://www1.gotomeeting.com/register/646359377

Free Web Seminar Next Thursday – Unlock a Better Business Strategy with Text Analytics

Start Date/Time: Thu, Nov 19, 2009 2:00 PM – 3:00 PM EST

With SPSS text analytics, you can read documents, blogs, wikis, tweets, e-mails, call center notes, surveys and other free form text ‘ and turn the insight you gain into a true strategic asset. We’ll show you how to use text analytics with social media and other Web 2.0 sites to understand trends, and what your customers want, and how they’ll behave. We’ll demonstrate using text analytics in modeling to make your models better, and how to use automatic translation from more than 30 languages to make sense of your global customer base.

Your customers are talking about you, and you can gain a genuine advantage by knowing what they are saying. Attend this webinar and learn how you can ‘listen in’ ‘ and act on the information to gain a competitive edge.

This webinar will show how to:
- Use text analytics to make sense of any text, including Web 2.0 sources such as social networking sites
- Use the information to get a better understanding of your customers, your products, and your competitors.
- Make sense of free-form text resources, and act on the insight you gain
- Unlock predictive secrets from text sources, and combine that information with structured data to build powerful predictive
models that can inform decision making.

Featured Speaker
Jane Hendricks, Product Marketing Manager, SPSS, an IBM Company

Mention priority code MWS0026BLOG when registering:
https://www1.gotomeeting.com/register/646359377

Reminder: Free Web Seminar Next Thursday – Unlock a Better Business Strategy with Text Analytics

Start Date/Time: Thu, Nov 19, 2009 2:00 PM – 3:00 PM EST

With SPSS text analytics, you can read documents, blogs, wikis, tweets, e-mails, call center notes, surveys and other free form text ‘ and turn the insight you gain into a true strategic asset. We’ll show you how to use text analytics with social media and other Web 2.0 sites to understand trends, and what your customers want, and how they’ll behave. We’ll demonstrate using text analytics in modeling to make your models better, and how to use automatic translation from more than 30 languages to make sense of your global customer base.

Your customers are talking about you, and you can gain a genuine advantage by knowing what they are saying. Attend this webinar and learn how you can ‘listen in’ ‘ and act on the information to gain a competitive edge.

This webinar will show how to:
- Use text analytics to make sense of any text, including Web 2.0 sources such as social networking sites
- Use the information to get a better understanding of your customers, your products, and your competitors.
- Make sense of free-form text resources, and act on the insight you gain
- Unlock predictive secrets from text sources, and combine that information with structured data to build powerful predictive
models that can inform decision making.

Featured Speaker
Jane Hendricks, Product Marketing Manager, SPSS, an IBM Company

Mention priority code MWS0026BLOG when registering:
https://www1.gotomeeting.com/register/646359377

Free Web Seminar – Unlock a Better Business Strategy with Text Analytics

Start Date/Time: Thu, Nov 19, 2009 2:00 PM – 3:00 PM EST

With SPSS text analytics, you can read documents, blogs, wikis, tweets, e-mails, call center notes, surveys and other free form text ‘ and turn the insight you gain into a true strategic asset. We’ll show you how to use text analytics with social media and other Web 2.0 sites to understand trends, and what your customers want, and how they’ll behave. We’ll demonstrate using text analytics in modeling to make your models better, and how to use automatic translation from more than 30 languages to make sense of your global customer base.

Your customers are talking about you, and you can gain a genuine advantage by knowing what they are saying. Attend this webinar and learn how you can ‘listen in’ ‘ and act on the information to gain a competitive edge.

This webinar will show how to:
- Use text analytics to make sense of any text, including Web 2.0 sources such as social networking sites
- Use the information to get a better understanding of your customers, your products, and your competitors.
- Make sense of free-form text resources, and act on the insight you gain
- Unlock predictive secrets from text sources, and combine that information with structured data to build powerful predictive
models that can inform decision making.

Featured Speaker
Jane Hendricks, Product Marketing Manager, SPSS, an IBM Company

Mention priority code MWS0026BLOG when registering:
https://www1.gotomeeting.com/register/646359377