3 ways to improve customer experience using A.I.

Today, software-as-a-service (SaaS) companies can choose from several cloud computing providers, dozens of monitoring providers and hundreds of different apps to increase their efficiency and help bring their solutions to market. While great marketing and brand awareness efforts can make it seem like some companies are more favored in the marketplace, sustained customer growth only occurs with a great product experience. This is especially true given that most cloud solutions are available on a freemium basis, which further inspires prospective customers to try before they buy. As a result, SaaS companies are taking advantage of user-collected data to provide customized experiences, intelligent functions and improved product support. These product improvements are easily deployed thanks to APIs and solutions that make use of artificial intelligence (A.I.).

Here are three A.I.-based capabilities that can be used to deliver high customer value and build product loyalty. With the growing popularity of A.I. for enterprise use, there will be more avenues for companies to improve their product experience. Have more suggestions? Leave a comment at the end of this post!

1. Use natural language processing to generate user documentation and updates

Natural language processing (NLP) technology can both parse text from a knowledge base and produce text for consumption within the same repository. Elements of support documentation can be generated automatically, served up to a knowledge worker for further editing and then placed in new articles. If the article is simple (less than 100 words), then you may not even need a human to edit the document. For example, Narrative Science provides solutions for companies wishing to provide a better way to generate documentation with heavy technical data (like compliance reports). In fact, any strings of text that have values within a database source can be generated using A.I. This approach can provide great value for a company that needs to produce required text for compliance reasons or to help establish a community around its offerings.

2. Use visual categorization to organize images

Many companies leverage visual recognition to identify objects within an image to either catalog them for further analysis or correlate them to user behaviors. For example, an e-commerce platform may want to allow customers to list an item for sale without having to tag information about the item itself. A visual recognition API powered by A.I. algorithms could sort through the picture and understand the objects within the scene intuitively. The scale of cloud computing supports the computational resources required to analyze complex scenes. Another popular, albeit more complex, use case can be found in cars with autonomous driving capabilities. The incredibly powerful cameras on cars must analyze many dynamic changes in a scene, so companies invest heavily in A.I.-based models to rapidly understand an environment even at high speeds. For interactive API examples, check out IBM’s visual recognition API or Clarifai. 

3. Leverage user patterns to create a unique customer experience

User patterns, analyzed from consumer metric data, provide a unique window into the service you provide. These patterns can be used to create personal experiences for clients who use a SaaS platform. First, companies must gather this information via an analytics tool. Development teams can send user behavior data from Mixpanel through an API. Once information has been gathered, the data can be fed to an algorithm to discern patterns based on time-of-day, rate-of-rise/decline, or how the data correlates with other behaviors on the platform. The output of the algorithms can also loop back into how pages are presented to users. To continue with the e-commerce example, a company could improve the shopping experience by understanding what actions users take just before they decide to delete items from their carts. Such a company may analyze web analytics, changes to users’ carts, and users’ buying histories to suggest alternatives within the experience. Amazon.com does this to some extent with suggestions related to your buying history or similar products.