“Once burnt, twice shy” is a cliché that rings true when software development teams fail to build what their target user really wants. “Too many developers and companies know that pain of a user dropping off and never returning because the value was not there,” said Damion Heredia, IBM vice-president for Cloud Platform Services Product Management. Unfortunately, eliciting users’ true requirements often works against delivering applications as quickly as users demand. Speeding up the requirements phase of development with automated “personalization” tools is one approach to making both practices compatible.
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Heredia talks about how developers can use the IBM Watson Personality Insights service in this interview. Personality Insights is available on Bluemix, IBM’s Cloud Foundry-based open cloud development platform. The IBM Personality Insights (PI) cloud service uses linguistic analytics to determine potential software users’ needs, usage patterns and other information.
What are some ways that developers try to speed through the user requirements phase of content, service and software creation?
Heredia: The kitchen sink approach comes to mind; just throwing in lots of features to give the impression of value. On the user end, there’s a frustration point at which they’re getting a lot of content served up to them that’s not relevant, that turns them off very quickly to the product.
How would software developers use Watson Personality Insights?
Heredia: We’re making available for developers an API that can help them access personality data about their users. There’s a lot of documentation on the API and also a sandbox environment that is completely free to use.
So, when building recommendation engines around a particular set of data, a developer can embed our service into their application via the API. They’ll take that data, and they’ll actually map it to the behavioral data that they are collecting through their app. For example, they could see a certain demographic in a geographical location and tie that data to personality data.
Could you give an example of how that user data is gathered?
Heredia: The information is gathered through the Watson visual personality assessment tool. For example, say, we show a user an image with a caption associated with it, and the user has the choice between saying ‘me’ or ‘not me’ to that image, based upon their reaction to it. Behind the scenes, Watson is scoring that user’s responses as related to personality traits. We measure on over 100 personality traits, and then return to the developers the score for that user on each of those 100 plus traits. They can use that for recommendations, personalization, targeted marketing. There are other requirements aspects to explore, of course, but PI gives a view of the user that previously was unavailable so quickly.
How much impact has the rising number of mobile applications user had on the demand for tailored features?
Heredia: It’s been a huge factor, because mobile devices have become the predominant way that people access the Internet, content and applications. Mobile has disrupted the traditional text-based delivery system because mobile is a predominately visual medium. Even this (IBM PI) service is an example, because we had to design it to be used on mobile devices. Try to do a traditional personality assessment, like the Meyers-Briggs survey, on your smart phone. Sure, you could, but you would be pulling your hair out, it would be so frustrating. So, we had to find a way for the developer to collect data in a way that reduces that friction, and makes it fun and enjoyable for the user so everyone wins in the end.