Posts in Get The Job Done!
Predicting Your KPI’s Has Never Been Easier & 29 Other Case Studies

As you may recall this year OdinText was honored to be voted Most Innovative in North America, and Third Most Innovative Globally in the Greenbook Research Industry Trends (GRIT) Report. This year Greenbook offered something special, each of the Top-50 Marketing Research Firms were invited to submit a case study for inclusion in an e-book showcasing the best of the best in consumer insights.

The e-book was released yesterday with submissions from 29 of the top 50 market research firms contributing a brief case study. We’d be happy to share the entire e-book with you, you can request it here.

The case we submitted is one of my favorites where Shell Oil was able to use OdinText to predict three business performance indicators;  Stated Satisfaction/WOM (i.e. NPS),  Actual Return Behavior and Sales. Something they had never been able to do before.

While that case was run using very big data, several of our users have conducted the same powerful analysis with much smaller survey data.

Again, we’d be happy to send you the entire booklet with all the companies listed, and if you’re interested in something similar we’d be happy to schedule a brief informational call to discuss, and/or a demo with your own data.

Again Thank You to Greenbook and everyone who selected us for the honor, and congratulations to all the other great companies listed in the report!

Tim Lynch VP OdinText

Of Tears and Text Analytics

An OdinText User Story - Text Analytics Tips Guest Post (AI Meets VOC)

Today on the blog we have another first in a soon to be ongoing series. We’re inviting OdinText users to participate more on the Text Analytics Tips blog. Today we have Kelsy Saulsbury guest blogging. Kelsy is a relatively new user of OdinText though she’s jumped right in and is doing some very interesting work.

In her post she ponders the apropos topic, whether automation via artificial intelligence may make some tasks too easy, and what if anything might be lost by not having to read every customer comment verbatim.

 

Of Tears and Text Analytics By Kelsy Saulsbury Manager, Consumer Insights & Analytics

“Are you ok?” the woman sitting next to me on the plane asked.  “Yes, I’m fine,” I answered while wiping the tears from my eyes with my fingers.  “I’m just working,” I said.  She looked at me quizzically and went back to reading her book.

I had just spent the past eight hours in two airports and on two long flights, which might make anyone cry.  Yet the real reason for my tears was that I had been reading hundreds of open-end comments about why customers had decided to buy less from us or stop buying from us altogether.  Granted eight hours hand-coding open ends wasn’t the most accurate way to quantify the comments, but it did allow me to feel our customers’ pain from the death of a spouse to financial hardship with a lost job.  Other reasons for buying less food weren’t quite as sad — children off to college or eating out more after retirement and a lifetime of cooking.

I could also hear the frustration in their voices on the occasions when we let them down.  We failed to deliver when we said we would, leaving the dessert missing from a party.  They took off work to meet us, and we never showed.  Anger at time wasted.

Reading their stories allowed me to feel their pain and better share it with our marketing and operations teams.  However, I couldn’t accurately quantify the issues or easily tie them to other questions in the attrition study.  So this year when our attrition study came around, I utilized a text analytics tool (OdinText) for the text analysis of our open ends around why customers were buying less.

It took 1/10th of the time to see more accurately how many people talked about each issue.  It allowed me to better see how the issues clustered together and how they differed based on levels of overall satisfaction.  It was fast, relatively easy to do, and directly tied to other questions in our study.

I’ve seen the benefits of automation, yet I’m left wondering how we best take advantage of text analytics tools without losing the power of the emotion in the words behind the data.  I missed hearing and internalizing the pain in their voices.  I missed the tears and the urgency they created to improve our customers’ experience.

 

Kelsy Saulsbury Manager, Consumer Insights & Analytics Schwan's Company

 

A big thanks to Kelsy for sharing her thoughts on OdinText's Text Analytics Tips blog. We welcome your thoughts and questions in comment section below.

If you’re an OdinText user and have a story to share please reach out. In the near future we’ll be sharing more user blog posts and case studies.

@OdinText

Marketing Research Blooper Reveals Lots of Surprises and Two Important Lessons

April Foolishness: What Happens When You Survey People in the Wrong Language?

I’m going to break with convention today and, in lieu of an April Fool’s gag, I’m going to tell you about an actual goof we recently made that yielded some unexpected but interesting results for researchers.

As you know, last week on the blog we highlighted findings from an international, multilingual Text Analytics Poll™ we conducted around culture. This particular poll spanned 10 countries and eight languages, and when we went to field it we accidentally sent the question to our U.S. sample in Portuguese!

Shockingly, in many cases, people STILL took the time to answer our question! How?

First, bear in mind that these Text Analytics Polls™ consist of only one question and it’s open-ended, not multiple choice. The methodology we use intercepts respondents online and requires them to type an answer to our question before they can proceed to content they’re interested in.

Under the circumstances, you might expect someone to simply type “n/a” or “don’t understand” or even some gibberish in order to move on quickly, and indeed we saw plenty of that. But in many cases, people took the time to thoughtfully point out the error, and even with wit.

Verbatim examples [sic]:

“Are you kidding me, an old american who can say ¡adios!”

“Tuesday they serve grilled cheese sandwiches.” “What the heck is that language?”

“No habla espanol”

“i have no idea what that means”

“2 years of Spanish class and I still don't understand”

Others expressed themselves more…colorfully…

“No, I don't speak illegal immigrant.”

“Speak English! I'm switching to News 13 Orlando. They have better coverage than FT.”

Author’s note: I suspect that last quote was from someone who was intercepted while trying to access a Financial Times article. ;-)

While a lot of people clearly assumed our question was written in Spanish, still others took the time to figure out what the language was and even to translate the question!

“I had to use google translate to understand the question.”

“what the heck does this mean i don't speak Portuguese”

But what surprised me most was that a lot of Americans actually answered our question—i.e., they complied with what we had asked—even though it was written in Portuguese. And many of those replies were in Spanish!!!

We caught our mistake quickly enough when we went to machine-translate the responses and we were told that replies to a question in Portuguese were now being translated from English to English, but two important lessons were learned here:

Takeaway One: Had we made this mistake with a multiple-choice instrument, we either might not have caught it until after the analysis or perhaps not at all. Not only would respondents not have been able to tell us that we had made a mistake, but they would’ve had the easy option of just clicking a response at random. And unless those random clicks amounted to a conspicuous pattern in the data, we could’ve potentially taken the data as valid!

Takeaway Two: The notion that people will not take the time to thoughtfully respond to an open-ended question is total bunk. People not only took the time to answer our question in detail when it was correctly served to them in their own language, but they even spared a thought for us when they didn’t understand the language!

I want to emphasize here that if you’re one of those researchers (and I used to be among this group, by the way) who thinks you can’t include an open-ended question in a quantitative instrument, compel the respondent to answer it, and get a meaningful answer to your question, you are not only mistaken but you’re doing yourself and your client a huge disservice.

Take it from this April fool, open-ended questions not only tell you what you didn’t know; they tell you what you didn’t know you didn’t know.

Thanks for reading. I’d love to hear what you think!

@TomHCAnderson

P.S. Find out how much more value an open-ended question can add to your survey using OdinText. Contact us to talk about it.

About Tom H. C. Anderson

Tom H. C. Anderson is the founder and managing partner of OdinText, a venture-backed firm based in Stamford, CT whose patented SAS platform is used by Fortune 500 companies like Disney, Coca-Cola and Shell Oil to mine insights from complex, unstructured and mixed data. A recognized authority and pioneer in the field of text analytics with more than two decades of experience in market research, Anderson is the recipient of numerous awards for innovation from industry associations such as CASRO, ESOMAR and the ARF. He was named one of the "Four under 40" market research leaders by the American Marketing Association in 2010. He tweets under the handle @tomhcanderson.

Text Analytics: It's Not Just for BIG Data

In a world focused on the value of Big Data, it's important to realize that Small Data is meaningful, too, and worth analyzing to gain understanding. Let me show you with a personal example. If you're a regular reader of the OdinText blog, you probably know that our company President, Tom Anderson, writes about performing text analytics on large data sets.  And yes, OdinText is ideal for understanding data after launching a rapid survey then collecting thousands of responses.

However for this blog post, I'm going to focus on the use of Text Analytics for smaller, nontraditional data set:  emails.

SMALL Data (from email) Text Analytics

I recently joined OdinText as Vice President, working closely with Tom on all our corporate initiatives. I live in a small town in Connecticut with an approximate population of 60,000.  Last year I was elected to serve our town government as an RTM member along with 40 other individuals.  Presently, our town's budget is $290M and the RTM is designing the budget for the next year.

Many citizens email elected members to let them know how they feel about the budget.  To date, I have received 280 emails. (Before you go down a different path with this, please know that I respond personally to each one -- people who take the time to write me deserve a personal response.  I did not and will not include in this blog post how I intend to vote on the upcoming budget, nor will I include anything about party affiliations. And I certainly will not share names.)

As the emails were coming in, I started to wonder … what if I ran this the data I was receiving through OdinText?  Would I be able to use the tool to identify, understand and quantify the themes in the people’s thoughts on how I should vote on the budget?

The Resulting Themes from Small Data Analytics

A note about the methodology:  Each email that I received contained the citizen's name, their email address and content in open text format.  Without a key driver metric like OSAT, CSAT or NPS to analyze the text against, I chose to use overall sentiment. Here is what I learned

Emails about the town budget show that our citizens feel Joy but RTM members need to recognize their Sadness, Fear and Anger

Joy:

“I have been a homeowner in Fairfield for 37 years, raised 4 kids here and love the community.”

Sadness:

“I am writing you to tell you that I am so unhappy with the way you have managed our town.”

Fear:

“My greatest concern seems to be the inability of our elected members to cut spending and run the town like a business”

Anger:

“We live in a very small house and still have to pay an absurd amount of money in taxes.”

Understanding the resulting themes in their own words

Reduce Taxes (90.16%)

“Fairfield taxes are much higher than surrounding communities.”

“Fairfield taxes are out of line with similar communities”

“The town has to stop raising taxes at such a feverish rate.”

“High taxes are slowly eroding the town of Fairfield.”

Moving if Taxes are Increased (25.13%)

“I am on a fixed income at 64, and cannot afford Fairfield’s taxes now. Please recognize that I cannot easily sell my house, due to the economy & the amount of homes on the market here”

“regret to say most of our colleagues and friends have an "exit strategy" to leave Fairfield”

“Our town is losing residents who are fed up and have moved or are moving to Westport and other towns with lower mil rates”

Reduce Spending (33.33%)

“... bring spending under control”

“Stop the spending please”

“... needs to trim fat at the local level, cut services, stop spending money”

“We need to keep taxes down as much as possible - even if it means spending cuts.”

Education ‘don’t cut’ (8.74%)

“… takes great pride in its education system”

“… promise of an excellent public education”

“… fiscal responsibility; however, not at the expense of the children and their right to an excellent education.”

Education ‘please cut’ (9.83%)

“Let's shave funding from all programs including education”

“... deeply questioning our education budget”

“... reduce the Education budget”

“I have a cherished budgetary item that I want protected--the library. Cut that last, after you cut education, police, official salaries”

Big Value from Small Data in Little Time

I performed this text analysis in 30 minutes. Ironically, it has taken me longer to write this blog post than it did to quantify the text from all those emails. Yet the information and understanding I have gleaned will empower me as I make decisions on this important topic. A small investment in small data has paid off in a BIG way.

Tim Lynch - @OdinText

The Hidden Cost of Big-Ticket Text Analytics: Time

How Messy Multi Departmental Procurement and Lengthy Implementation Stall Text Analytics Insights The inspiration for this week’s clip in our “Get the Job Done!” series is the big-ticket procurement and implementation process—and all of those folks whose opinions you don’t need.

Take a look:

 

We hear all the time from prospective clients who’ve found themselves bogged down in the painful, protracted process of getting buy-in for enterprise text analytics platforms that offer something for everyone and come with a six-figure price tag.

Oftentimes, this procurement process involves people in the organization who have lots of opinions but no research expertise and who, in cases, won’t even be using the purchase in question.

Worse yet, after everyone has had his/her say and the purchase has finally gone through, the original intended user finds the whole initiative mired in a lengthy, complicated implementation!

Six months in, they find themselves stuck in more meetings, investing even more precious time building custom, static ontologies that will require ongoing updates, maintenance and even more time down the road…

It’s 2017 and the one thing no one can afford to waste is time.

If you’re looking for a combination of analytics firepower, scalability, and ease of implementation and use, OdinText is unrivaled:

  • One hour of training
  • No customization or ongoing updates necessary
  • Scales to meet the needs of any organization, regardless of size
  • Handles any data set—Big or Small
  • State-of-the-art analytics sophistication
  • Affordable enough not to need a procurement committee

Ask yourself how you’d like to spend the new year. More meetings?

Don’t let other departments, procurement and a lengthy implementation process hijack your text analytics work. Get the job done now!

Contact us today to arrange a demo using your own data and find out how your organization can be up and running text analytics right away.

@TomHCAnderson

Tom H. C. Anderson

OdinText Inc. www.odintext.com

 

ABOUT ODINTEXT

OdinText is a patented SaaS (software-as-a-service) platform for advanced analytics. Fortune 500 companies such as Disney and Shell Oil use OdinText to mine insights from complex, unstructured text data. The technology is available through the venture-backed Stamford, CT firm of the same name founded by CEO Tom H. C. Anderson, a recognized authority and pioneer in the field of text analytics with more than two decades of experience in market research. Anderson is the recipient of numerous awards for innovation from industry associations such as ESOMAR, CASRO, the ARF and the American Marketing Association. He tweets under the handle @tomhcanderson.

Why You Don’t Need a Lumberjack in a Tutu

How to Get Bogged Down with Clarabridge in 2017  

I don’t know if he’s actually a lumberjack, but the man in the video is definitely not a ballerina.

It’s obvious to most that slapping a tutu on someone big and ungainly does not make him a dancer. Yet I see other text analytics software providers attempting to effect a similar illusion all the time.

Take Clarabridge, for example. (I’m not picking on them, but they are the largest player in the market.)

Clarabridge stuffs all sorts of unnecessary features and fancy terminology—word clouds, dashboards, a litany of linguistics jargon— into what amounts to a bloated, inefficient enterprise platform when most of us just need answers to business questions.

And they’re expensive. You won’t even qualify for a Clarabridge demo unless you have a six-figure budget.

A lot of Time, A Lot of Effort, No Results?

Experts and economists stress that increasing productivity should be a primary concern among companies in 2017.

The only thing potentially more time-consuming than getting buy-in for a six-figure investment in text analytics software like Clarabridge may be implementing it.

These platforms require extensive training and rely on antiquated rules-based approaches and custom dictionaries that require frequent updates.

While you’re spending all of this time and effort in meetings to get the solution in place, the job you bought it for isn’t getting done.

By contrast, OdinText takes only one hour of training to get started, it scales beautifully and your team can be conducting actual text analytics in January!

So…How Will You Spend Your Time and Budget Next Year?

Next year, would you prefer to be talking about text analytics or actually doing them?

The way I see it, you have two choices:

  1. Spend six months to a year or more assessing and debating an investment internally and then trying to implement a behemoth text analytics platform across your company.

Or

  1. Hit the ground running with a no-nonsense tool to quickly and effectively get practical answers to real business questions.

Don’t spend 2017 trying to dance with a lumberjack in a tutu. OdinText was designed by researchers for researchers - Get the job done!

Contact us today to arrange a demo using your own data and find out how your organization can be up and running text analytics in as little as a few weeks.

@TomHCAnderson

tomtextanalyticstips

Tom H. C. Anderson OdinText Inc. www.odintext.com

 

ABOUT ODINTEXT

OdinText is a patented SaaS (software-as-a-service) platform for advanced analytics. Fortune 500 companies such as Disney and Shell Oil use OdinText to mine insights from complex, unstructured text data. The technology is available through the venture-backed Stamford, CT firm of the same name founded by CEO Tom H. C. Anderson, a recognized authority and pioneer in the field of text analytics with more than two decades of experience in market research. Anderson is the recipient of numerous awards for innovation from industry associations such as ESOMAR, CASRO, the ARF and the American Marketing Association. He tweets under the handle @tomhcanderson.