"Sometimes those things go out of control limits, [there'll be] some threshold that suggests we have a quality problem.
"If we start to see something coming out of the control [limits], the quality person who makes these decisions, we might recommend... 10 items today to go investigate for pervasive quality issues."
Listening to how a product is performing, or how customers are interacting with it out in the field, is another main application of text mining at Lenovo, Volpe said.
"People say 'I wish this had more USB ports', 'I wish the screen was brighter'," he noted.
He also includes competitor names when mining text data, finding out which features most customers focus on when comparing Lenovo to others. Some examples of what he found include customers saying 'I like my friend's Dell screen so much better', 'my friend's screen is easy to clean, mine is always dirty', and so on.
This can also lead to new feature or product ideas, said Volpe. "There are comments that say 'why can't I build my PC like I can use Moto Maker to build my phone?'"
One discovery Volpe and his team made was that a large volume of comments made on a particular laptop was in relation to gaming. Though the laptop was not designed intentionally for gamers, with text mining he discovered people were finding the laptop specifications ideal for gaming.
"So we actually figured out that if we marketed that machine to gamers, we would win. That's a huge outcome of just listening. And we don't just listen, we think about what we are listening to."
Volpe said when carrying out text mining for product performance, he places comments into certain categories.
"We have four different people responsible for performance; there are different aspects of performance. So we are asking, which of these 25 million [comments] point to a performance issue that we attribute to vice president 1's initiatives? Which ones go into vice president 2's initiatives?"
Volpe said tapping into unstructured data is an "uncomfortable" area for a lot people, mainly because there aren't lot of case studies or examples of how companies pull it off successfully. Skills and building capability is another issue.
"On my team I have a lot of mathematicians, statisticians, I have a physicist or two, some industrial engineers. They have never done text mining."
Furthermore, he said, there is no book of instructions or guide you can turn to for all the answers.
"I can't go to 'operations research' textbook, chapter one, section six and read 'here's the formulation to optimise our inventory position', or go to the metrics textbook and understand 'here's the type of forecast model I should have for lumpy demand'."
Source: CIO Australia
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