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Text Analytics: What are They, Why are They Important to CXM?

It's impressive that the internet can generate so much qualitative data through ordinary online conversations. Your Facebook page asks “how are you feeling?” LinkedIn asks for testimonials rather than rankings to endorse colleagues. We use comments as a way to evaluate the success of our blog posts. And while online reviews may show a star rating, it’s only by reading the full review that we can best appreciate the product’s true worth.

What Does All This Qualitative Data Mean?

For companies, it can be a challenge to process it all so that it translates into something more quantitative — which is still the language spoken by the enterprise. This is where text analytics comes in.

How many of us have had an executive ask us “so what does it mean that the blog received a lot of comments?” or “what’s the ROI of Facebook comments?”

A decade ago, I developed surveys and performed statistical analysis for a non profit. Once surveys had been completed, they were scanned and the results were analyzed using SPSS — the tool statisticians loved to hate. 

At the time, I was asked to try out a new tool called Text Analytics for Surveys. For the first time, it was possible that I wouldn't have to manually transcribe the open-ended comments and painstakingly comb through them and analyze them separately from the data automatically collected from the surveys. Qualitative analysis was finally possible using SPSS, though it did still require manual set up and extensive coding for the words you wanted to analyze.

When I was in college, being a qualitative researcher was not as hardcore as being a quantitative researcher. While there may have been fewer statistics involved, it required much more work.

The time spent recording interviews with subjects, transcribing the text of the discussion and then having to assign keywords into categories so you could adequately summarize whether a respondent’s tone was more negative or positive based on the words used was laborious. The hardcore quantitative researchers could just run their data sets through SPSS and apply the parameters needed.

cartoon.jpgFortunately, text analytics software has improved incredibly since I began developing surveys. Additionally, you no longer have to have a degree in statistics to use the software. Now, text analytics is integrated into CRMs so that all the conversations generated by your fans and followers can be analyzed to provide you with insights needed to strategize and evolve the customer experience.

What is Text Analytics?

At its most basic level, text analytics refers to the process of deriving high-quality information from text. The quality of the information is derived from patterns and trends, or some combination of relevance, novelty and interestingness.

Remember when IBM’s Watson competed on Jeopardy? It used Natural Language Processing (NLP), a component of IBM's text analytics offerings, to help figure out the context of the answers. 

It's likely that you are already employing elements of text analytics. For example, you may have deployed sentiment analysis tools to help you predict trends based on the attitudes of users expressed on social media. However, the sentiment analysis process is not completely accurate because it doesn't account for the subtleties of sarcasm or body language.

Sentiment needs text analytics — especially if you want to go beyond simple brand management like mentions of a company’s name. To dive deeper into how products and services are being received, you’ll need to apply advanced analytical rules in text analysis.

Just as predictive models analyze past performance to assess how likely a customer is to exhibit a specific behavior in the future in order to improve marketing effectiveness, text analytics can help companies infer similar insights from analyzing subtle text patterns to answer questions about customer performance.

CRM + Text Analytics

A recent Temkin Group report examined voice of the customer (VoC) programs within large companies and found an increase in the use of analytics, with nearly three-quarters of large companies with VoC programs using or considering text analytics.

 

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