Could text analytics be the unsung hero of big data?
Text analytics mines reams of often ephemeral, unstructured data — from a voice recording of a customer call to emails or a Tweet — for meaningful insights that can inform business decisions, be it a branding strategy or a product launch.
And while retailers have hailed big data as the key to everything from delivering shoppers personalized merchandise offers to real-time metrics on product performance, the industry is mostly scratching its head on how to monetize all the data that’s being generated in the digital era.
One point of departure: Over 80% of all information comes in text format, Tom H.C. Anderson, CEO of OdinText, which markets its text analytics software to clients such as Coca-Cola KO +0.00% told Forbes.
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So if retailers, for one, “aren’t using text analytics in their customer listening, whether they know it or not, they’re not doing too much listening at all,” he said.
Anderson and chief technology officer Chris Lehew shared how OdinText’s clients are leveraging what the startup dubs its “next generation” text analytics software, while also putting a few big-data myths to rest.
Myth: Big Data Survey Scores Reign Supreme
“A lot of people think that common structured data, especially survey data, is the gold standard, especially if it comes with a large sample size,” Anderson said. “Many retailers and other businesses glommed onto something called the ‘Net Promoter Score’ about a decade ago when the term was first coined by Bain & Co consulting, and frequently touted as linked to business growth. It was believed this one survey question, ‘how likely are you to recommend our business/service to a friend or colleague?’ asked on an 11-point scale, was the only customer satisfaction number you needed.”
Client Problem: “Shell Oil’s Jiffy Lube International, with over 2,000 stores in North America, began text mining their NPS data and discovered that neither NPS nor any other structured survey metric was correlated to revenue,” Anderson said.
Text mining first helped explain why this was the case. As a general proposition, customer satisfaction survey responses shed little light on why consumers do the things they do, he said. “In other words, if they have a good experience and say they are likely to recommend your business, they also seem to think they didn’t wait in line long, that your employees are smart, friendly and well trained, etc. Conversely, those few who have a bad experience also tend to ding you on every single question, giving you very little reliable data to improve on. They either claim to love everything, or hate everything equally.”
Solution: “Using OdinText’s text analytics, Shell was able to analyze the text comments left by customers,” Anderson said. “These top-of-mind, unaided answers explained what things were most important to the customers, and how Jiffy Lube could affect positive change and have a real measurable impact on not just satisfaction, but also on actual revenue,” he said. “Jiffy Lube was further able to predict exactly what would happen when they addressed specific issues mentioned by their customers, from ‘coupons’ to ‘easiness of visit.’”
Myth: Bigger Social Media Data Analysis Is Better
“Many believe that simply because of the sheer volume of tweets, blog posts and public Facebook posts, social media data must be valuable, and that bigger is always better,” Lehew said. But in reality, “listening to larger amounts of noise is just more noise.”
Client Problem: “Coca-Cola’s social media hub had been analyzing social chatter for quite some time, but was uneasy about the kinds of insights they were getting,” Anderson said. The company wondered, “‘who were these comments really from?’ ‘What could be learned from the average 140-character comment?’”
By: Barbara Thau, Business Journalist, Forbes
Originally published atwww.forbes.com
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Yes. Even if text analytics WAS the unsung hero of big data – it’s not anymore.
Text analytics IS ALREADY the HERO of big data.
As you may know Oracle already structures unstructured data:
1. Oracle the first time ever automatically obtains statistics on queries and data from the data itself, internally.
3. Oracle automatically gets 100% patterns from data.
4. The first time ever Oracle uses synonyms searching.
5. The first time ever Oracle automatically indexes data by common dictionary.
6. Oracle killed SQL, there SQL either does not use statistics at all or uses manually assigned one. (Please see Oracle ATG Search.)
All of this Oracle can – I guess – do because it structures texts.
Why Oracle does not claim its priority?
1. Oracle is the SQL company: declaring it structures data Oracle kills its business.
2. Oracle could not develop ATG Search yet to the degree it is finished: Oracle still cannot finish its key component – dictionary.
The New Era came: no more unstructured data unless it was not processed yet
– and
There is no Big Data but data.
From my point of view, text analytics will ultimately be the unsung hero of big data. wordle unlimited
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