Lenovo's chief corporate analytics officer, Anthony Volpe
Social media, user forums, review sites, call centres and many other sources can offer a rich source of data to extract insights on what customers want and need from your product and service.
The problem is it's unstructured, big and often messy. But that doesn't mean it's not manageable.
During his visit to Sydney this week to speak at a SAS event, Lenovo's chief corporate analytics officer, Anthony Volpe, discussed how he and his team mined text for early detection on product quality issues, and to help shape their products based on customer wants and needs.
"When we do text mining, it's not about whether people like us or not. It's mining for specific insights. If it's not for anything and everything, so for specific things, text mining is a much more manageable problem," he said.
Lenovo's product quality teams have many items they work on daily, but which ones are a priority, and what are the most common customer complaints? This is where Volpe found a strong case to build up his team's skills and capability to carry out text mining to listen to the millions of customers voicing their problems around the world.
The team built a Lenovo Early Detection (LED) system to help product quality staff decide which issues to focus on first before they manifest into major problems, resulting in higher customer dissatisfaction rates, and possibly some brand damage.
"Before LED, we would sell customers our software and if customer satisfaction was bad, people would get mad at us, we would incur lots of costs - both real in terms of fixing them but also in terms of our brand.
"These things finally trickled through to the warranty claims system where somebody looks at warranty claims and says 'wow, we must have a problem with G580 keyboards' months too late."
Volpe and his team applied a statistical process control (SPC) model on the data coming mostly from user forums and call centres where people provide greater detail on their problem.
"We know there's randomness and there's systemic migration from a desired need. SPC separates randomness from a systemic variation - that's where we want to be. We said 'that sounds like our problem'," he said.
"Who has ever done SPC on social media data?" he asked the audience. "My guess is nobody. But we did it."
Volpe and his team looked at specific key words or products in comments -'G580 keyboard' instead of 'Lenovo keyboards', for example - along with the volume of comments. They then subset those down to comments related to quality. Thresholds were used to filter out ones that indicate an issue.
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