I hate dealing with customer service for large corporations, and it has little to do with outsourcing. I hate it because in the past few months, I have sent out maybe three or four emails to customer service peeps, at places like BofA, Chase, Comcast, Ebay, etc. Having worked in a form of customer service previously (I worked at the computer services help desk as an undergrad at CMU to earn some extra money), I completely understand what's going on. But "understand" does not imply "accept." I post this here not as a rant, but because I think there are some interesting NLP problems under the hood.
So what's the problem? What has happened in all these cases is that I have some problem that I want to solve, can't find information about it in the FAQ or help pages on the web site, and so I email customer service with a question. As an example, I wanted to contest an Ebay charge but was two days past the 60 day cutoff (this was over Thanksgiving). So I asked customer service if, given the holiday, they could waive the cutoff. As a reply I get a form email, clearly copied directly out of the FAQ page, saying that there is a 60 day cutoff for filing contests to charges. Well no shit.
So here's my experience from working at the help desk. When we got emails, we had the option of either replying by crafting an email, or replying by selecting a prewritten document from a database. This database was pretty huge -- many thousands of problems, neatly categorized and searchable. For emails for which the answer existed in the database, it took maybe 10 seconds to send the reply out.
What seems to be happening nowadays is that this is being taken to the extreme. A prewritten form letter is always used, regardless of whether it is appropriate or not. If it is a person doing this bad routing, that's a waste of 10 seconds of person time (probably more for these large companies). If it's a machine, it's no big deal from there perspective, but it makes me immediately hate the company with my whole heart.
But this seems to be a really interesting text categorization/routing problem. Basically, you have lots of normal classes (the prewritten letters) plus a "needs human attention" class. There's a natural precision/recall/$$$ trade-off, which is somewhat different and more complex than is standardly considered. But it's clearly an NLP/text categorization problem, and clearly one that should be worked on. I know from my friends at AT&T that they have something similar for routing calls, but my understanding is that this is quite different. Their routing happens based on short bits of information into a small number of categories. The customer service routing problem would presumably be based on lots of information in a large number of categories.
You could argue that this is no different from providing a "help search" option on the Ebay web page. But I think it's different, if for no other reason that how it appears to the user. If the user thinks he is writing an email to a person, he will write a good email with full sentences and lots of information. If he's just "searching" then he'll only write a few keywords.
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3 comments:
Kana may interest you. They sell software to make handling customer email more efficient.
From their description of their Kana Response product: "Based on configurable rules, KANA Response interprets message content to determine customer intent and uses templates to automatically send personalized acknowledgements and replies. Intelligent queuing and routing distribute messages to the appropriate departmental, priority, language and skill-based queues where automatically suggested, fully scripted answers reduce the time agents need to review and reply to inquiries. ...
Agent email response efficiency and effectiveness accelerate with a broad range of productivity tools including desktop population with most likely solutions, pre-filled replies that include context-specific customer data aggregated from back-end systems, canned phrase selection, user-defined hotkeys and a universal history of interactions that maintains context across channels."
And from their description of their Kana IQ product: "The advice-driven desktop leverages expert reasoning to automate best-practice diagnostic processes for interpreting the customer’s intent and mapping intent to the right answer. Expert reasoning combines multiple information access methodologies that help agents rapidly find the correct solution. These include clarifying questions that turn Natural Language Queries into diagnostic conversations; guided interviews that eliminate answers until the best one is found; expert modeling which leverages specialists’ advice to present answers in order of relevance; and dynamic learning that polls solution history and usage to present solutions in order of popularity."
This is an interesting and a challenging research problem. IBM research, for example, does work on routing email messages at their service centers.
To complicate matters, customer messages need not be grammatical. For example, consider bug report for a software product. If the interface is plain text then customers are free to write it any way they want!
(Most customers like plain text interfaces instead of restrictive forms)
Plain classification might not work again. Consider a complaint mentioning multiple parts of a device. Where should the email be routed? It is tempting to apply ideas from WSD here.
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