Voice Search and Big Data: Defining Technologies for 2017
April 20, 2017
I read “Voice Search and Data: The Two Trends That Will Shape Online Marketing in 2017.” If the story is accurate, figuring out what people say and making sense of data (lots of data) will create new opportunities for innovators.
The article states:
Advancements in voice search and artificial intelligence (AI) will drive rich answers that will help marketers understand the customer intent behind I-want-to-go, I-want-to-know, I-want-to-buy and I-want-to-do micro-moments. Google has developed algorithms to cater directly to the search intent of the customers behind these queries, enabling customers to find the right answers quickly.
My view is that the article is correct in its assessment.
Where the article and I differ boils down to search engine optimization. The idea that voice search and Big Data will make fooling the relevance algorithms of Bing, Google, and Yandex a windfall for search engine optimization experts is partially true. Marketing whiz kids will do and say many things to deliver results that do not answer my query or meet my expectation of a “correct” answer.
My view is that the proliferation of systems which purport to understand human utterances in text,and voice-to-text conversions will discover that the the error rates of 60 to 75 percent are not good enough. Errors can be buried in long lists of results. They can be sidestepped if a voice enabled system works from a set of rules confined to a narrow topic domain.
Open the door to natural language parsing, and the error rates which once were okay become a liability. In my opinion, this will set off a scramble among companies struggling to get their smart software to provide information that customers accept and use repeatedly. Fail and customer turnover can be a fatal knife wound to the heart of an organization. The cost of replacing a paying customer is high. Companies need to keep the customers they have with technology that helps keep paying customers smiling.
What companies are able to provide higher accuracy linguistic functions? There are dozens of companies which assert that their systems can extract entities, figure out semantic relationships, and manipulate content in a handful of languages.
The problem with most of these systems is that certain, very widely used methods collapse when high accuracy is required for large volumes of text. The short cut is to use numerical tricks, and some of those tricks create disconnects between the information the user requests or queries and the results the system displays. Examples range from the difficulties of tuning the Autonomy Digital Reasoning Engine to figuring out how in the heck Google Home arrived at a particular song when the user wanted something else entirely.
Our suggestion is that instead of emailing IBM to sign a deal for that companies language technology, you might have a more productive result if you contact Bitext. This is a company which has been on my mind. I interviewed the founder and CEO (an IBM alum as I learned) and met with some of the remarkable Bitext team.
I am unable to disclose Bitext’s clients. I can suggest that if you fancy a certain German sports car or use one of the world’s most popular online services, you will be bumping into Bitext’s Digital Linguistic Analysis platform. For more information, navigate to Bitext.com.
The data I reviewed suggested that Bitext’s linguistic platform delivers accuracy significantly better than some of the other systems’ outputs I have reviewed. How accurate? Good enough to get an A in my high school math class.
Stephen E Arnold, April 20, 2017