Posts tagged Word Clouds
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.

 

What Does the Co-Occurence Graph Tell You?

Text Analytics Tips - Branding What does the co-occurrence graph tell you?Text Analytics Tips by Gosia

The co-occurrence graph in OdinText may look simple at first sight but it is in fact a very complex visualization. Based on an example we are going to show you how to read and interpret this graph. See the attached screenshots of a single co-occurrence graph based on a satisfaction survey of 500 car dealership customers (Fig. 1-4).

The co-occurrence graph is based on multidimensional scaling techniques that allow you to view the similarity between individual cases of data (e.g., automatic terms) taking into account various aspects of the data (i.e., frequency of occurrence, co-occurrence, relationship with the key metric). This graph plots the co-occurrence of words represented by the spatial distance between them, i.e., it plots as well as it can terms which are often mentioned together right next to each other (aka approximate overlap/concurrence).

Figure 1. Co-occurrence graph (all nodes and lines visible).

The attached graph (Fig. 1 above) is based on 50 most frequently occurring automatic terms (words) mentioned by the car dealership customers. Each node represents one term. The node’s size corresponds to the number of occurrences, i.e., in how many customer comments a given word was found (the greater node’s size, the greater the number of occurrences). In this example, green nodes correspond to higher overall satisfaction and red nodes to lower overall satisfaction given by customers who mentioned a given term, whereas brown nodes reflect satisfaction scores close to the metric midpoint. Finally, the thickness of the line connecting two nodes highlights how often the two terms are mentioned together (aka actual overlap/concurrence); the thicker the line, the more often they are mentioned together in a comment.

Figure 2. Co-occurrence graph (“unprofessional” node and lines highlighted).

So what are the most interesting insights based on a quick look at the co-occurrence graph of the car dealership customer satisfaction survey?

  • “Unprofessional” is the most negative term (red node) and it is most often mentioned together with “manager” or “employees” (Fig. 2 above).
  • “Waiting” is a relatively frequently occurring (medium-sized node) and a neutral term (brown node). It is often mentioned together with “room” (another neutral term) as well as “luxurious”, “coffee”, and “best”, which are corresponding to high overall satisfaction (light green node). Thus, it seems that the luxurious waiting room with available coffee is highly appreciated by customers and makes the waiting experience less negative (Fig. 3 below).
  • The dealership “staff” is often mentioned together with such positive terms as “always”, “caring”, “nice”, “trained”, and “quick” (Fig. 4 below). However, staff is also mentioned with more negative terms including “unprofessional”, “trust”, “helpful” suggesting a few negative customer evaluations related to these terms which may need attention and improvement.

    Figure 3. Co-occurrence graph (“waiting” node and lines highlighted).

    Figure 4. Co-occurrence graph (“staff” node and lines highlighted).

    Hopefully, this quick example can help you extract quick and valuable insights based on your own data!

Gosia

Text Analytics Tips with Gosi

[NOTE: Gosia is a Data Scientist at OdinText Inc. Experienced in text mining and predictive analytics, she is a Ph.D. with extensive research experience in mass media’s influence on cognition, emotions, and behavior.  Please feel free to request additional information or an OdinText demo here.]