Chatbots are everywhere. In a few months, these little entities of artificial intelligence have smartly conquered Internet. No wonder: putting a welcome end to interminable waiting songs and dead-end chats with overwhelmed contact centers, they are always available, answer in a matter of seconds, can process several conversations at a time and never lose their temper. And they are actually very easy to create. As a matter of fact, anyone could train a chatbot (as a living proof, I designed have my own chatbot, and it was super fun), with one of many online systems dedicated to that extent. Creating a chatbot is not about development or programming: it’s all about training. And as any training process, there are some guidelines. Here is a few things I learned about chatbot UX, from training mine, and benchmarking a few others.
A few years ago, I published a step-by-step tutorial to create a sticky version of a navigation menu in Axure, appearing once the user scrolls down. This is a great functionality for e-commerce (allowing to always display links to the cart, account, wishlist) and also content-heavy websites (like newspapers, for permanent access to search, share, back to top link, etc.) I now use a slightly different method to prototype a sticky version of a menu in Axure.
One of my clients recently came to me with a big table of raw data. He was a bit challenged by the fact that all the right data was properly collected and presented in this table, though users did not seem to be able to read the data. Even more challenging was the fact that the data actually came from them initially, but they had trouble even understanding the table itself, let alone make the decisions it was supposed to help them with. What was the problem there? When it comes to data visualization, tables are more often than not underestimated, to the benefit of good-looking graphs. But the choice between tables and graphs is not always that automatic.
Applying best practices and standards is not sufficient for e-commerce any longer. Successful websites have to focus on customer experience throughout the whole process, and offer the best possible service. In this era governed by pictures, with stars like Instagram or Youtube, product visual is getting more and more important for e-shoppers. If a thumbnail can potentially still be sufficient for everyday groceries, the power of a high-quality, detailed and in-situation picture has never been stronger. And technology can support working harder and going further on product visuals, including 360° view, cinemagraph and augmented reality.
Did you know online sales represented 6% of the global luxury market for personal goods in 2014? Luxury e-commerce has grown in the past years, by 27% between 2009 and 2014. Selling on the Internet is a big challenge for renowned luxury maisons, and one big strategic step, that more and more of them are taking.
Have you met Watson? He is IBM supercomputer, combining artificial intelligence and sophisticated analytical software that allows it to analyze lots of data and make complex decisions and predictions. Machine learning is everywhere nowadays, and just as any buzzword, its use far outreaches its true meaning. During Crea first innovation meetup in Geneva last week, François Rodriguez, from Crea, and IMB Jérôme de Nomazy introduced me to Watson, and at the same occasion helped extract the truth from the myth.