AiThority.com Interview with Henning Smith, CTO at Luminoso

Please tell us about your role and the team / technology you handle at Luminoso.

I’ve been with Luminoso for the last year, and was recently promoted to CTO. In this role, I’m overseeing the development and delivery of the next generation of Luminoso’s text analytics applications. This involves leading the product, design, engineering and infrastructure teams at Luminoso as well as managing IT for the company. We have a team of brilliant engineers working/developing in Python and React.

Prior to joining Luminoso, I led multiple engineering and infrastructure teams for Fortune 500 companies, including Cisco and CA Technologies.

You are constantly innovating Text Analytics offerings with AI and Machine Learning. How do you convince your customers about the benefits of using AI-based text analytics for Marketing and Sales?

It’s well-documented that increased customer satisfaction leads to increased revenue. If you’re able to more quickly understand what customers like and don’t like about your product, this will give your team the opportunity to make adjustments that improve satisfaction and directly improve your company’s bottom line.

For financial impact, our research estimates that every one point increase in a company’s Net Promoter Score equals a 0.143% increase in revenue.

Tell us more about “Search Enhancement” and how it improves Content Discovery and experience.

Sure! Search Enhancement with Luminoso brings the flexibility and nuance of AI-powered natural language understanding to your search engine by understanding synonymous and conceptual relationships in context.

When an organization implements Search Enhancement, its customers can surface the right search results, when they’re most needed, with the help of the only unsupervised, generalized text analytics solution on the market.

Search Enhancement integrates with pre-existing search engines, and does a fantastic job of expanding search results regardless of industry-specific words, misspellings, or slang. For instance, if you type something like “leef blower” into search engine that has an expanded search index through Search Enhancement, it will show you results for leaf blowers.

What kind of techniques are we talking about when we discuss terms like automated chatbot transcripts, NLU based Survey responses, and text data analytics?

At the heart of Luminoso’s applications is QuickLearn® technology.

First introduced in 2016, QuickLearn is a natural language modeling system that automatically learns domain-specific terminology without any training, setup, or ontology-building — enabling organizations to quickly discover insights from text-based data such as surveys, product reviews, and call center transcripts.

We also recently released Concept Level Sentiment in Daylight that applies deep learning to the concepts identified in the text-based data, delivering highly accurate sentiment identification for each individual concept.

Hear it from the pro: What changes have you adopted to make your product more user-friendly meeting customer demands during the COVID-19 crisis?

We always strive to make our product as user-friendly as possible. When COVID-19 hit, we found that some of our clients had a greater need to run and analyze employee surveys. The release of Concept Level Sentiment greatly improved our ability to correctly and impactfully identify nuanced sentiment workers had around the pandemic and return-to-work protocols, as well as being able to give clear examples of the feedback that led to that identification.

Tag a person from the industry whose answers you would like to see here:

Machine Learning Engineer Vanya Cohen

Thank you, Henning! That was fun and we hope to see you back on AiThority.com soon.