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Will Hayes, CEO of Lucidworks – Interview Series

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Will Hayes is the CEO of Lucidworks, the leader in AI-powered search. Hayes has over 20 years of experience in Silicon Valley leading product, marketing, and business development initiatives. Prior to Lucidworks, Hayes was head of technical business development for Splunk. He also created and led the company’s global partner program. Earlier in his career, Hayes served as a software engineer at Genentech, where he built solutions that supported the sales and drug development teams. For the past three years, Hayes has served as a member of YPO, a global leadership community of chief executives dedicated to improving lives, businesses, and the world. He has also been a partner to 10,000 Degrees, an organization that provides financial, emotional, and educational resources to Bay Area kids. He currently lives with his family in San Francisco, California, where Lucidworks is headquartered.

Could you briefly explain how Lucidworks personalizes search and discovery for customers and employees?

Lucidworks takes mission-critical business problems and solves them with search. For employees, those challenges look like getting answers to questions and finding information or being far away from customer data and unable to curate the best experience. For shoppers, it’s about making them feel like they’re one in a million, with recommendations that anticipate their needs and show them products they didn’t even know they wanted.

Lucidworks connects experiences throughout the entire user journey to meet customer and employee intent in the moment. True, real-time personalization requires you to understand your user’s intent—that goes way beyond a basic user profile. We make it easy for our customers to capture invaluable signals from their users and create personalized search, browse, and discovery experiences that drive top and bottom lines.

What are examples of the different types of customer interaction data that can be monitored and captured?

Customer data and behavioral data such as clickstreams and transactions from all your channels can be used to drive personalized experiences across all channels. Customer interaction data includes information collected directly from the user in the form of signals, such as a view or click, a purchase, a filter or search, a support request, a cart abandonment, an email open—all of these are common events that can generate signals.

There are two types of signals: explicit and implicit. Explicit signals can include ratings or reviews, purchases, social posts, and returns. Implicit signals don’t require any effort from the user, and they can also be inferences based on behavior. Signals that capture intent such as search, browse, and result views are powerful insights you can get from most analytics platforms. At Lucidworks, we take those signals and apply machine learning to learn from behavior and optimize the experience.

Could you provide some details on the different types of machine learning applications that are used, and how important deep learning is to Lucidworks?

Lucidworks offers products and applications for commerce, customer service, and the workplace that use AI and machine learning to solve search. Fusion, our flagship product, uses AI extensively through every stage of enriching data—during ingest and at query time, for understanding user intent, and personalizing results that match that intent. Fusion utilizes one of the broadest combinations of ML techniques in the industry, including natural language processing, named entity recognition, deep-learning sentiment analysis, self-learning, and relevancy tuning. This allows us to deliver extremely relevant results to our users and ensure the system continuously improves based on usage.

We also offer a cloud-service called Never Null to keep shoppers from ending up on a “No Results” page. A product search with no results is a deathtrap for the customer-retailer relationship. Never Null is a deep-learning encoder that learns from customers’ behavior to associate queries and products with similar meanings, so customers don’t end up stranded on the dreaded null results page. Plus, advanced machine learning relieves merchandising teams from spending valuable time analyzing results and manually fixing rules so they can execute more strategic tactics.

Lucidworks offers a solution to enhance the productivity of chatbots and virtual assistants to connect users to correct answers by understanding contextual intent, could you discuss this solution?

Lucidworks Smart Answers is an add-on that enhances chatbots and virtual assistants. Traditional chatbots aren’t smart enough to move beyond questions in a company’s FAQ doc. Smart Answers uses machine learning to enhance chatbots so they can answer questions that haven’t even been asked yet. It uses natural language processing and behavioral analysis to understand the user’s intent and pulls from multiple data sources to provide relevant answers.

What’s your vision for the future of connecting different recommendation experiences across different applications?

 Our vision for the Connected Experience Cloud (CXC) depends on using behavioral signals to personalize user experiences. These signals are invaluable insights into the digital experience of customers, employees, and contact center agents. By optimizing the digital experience in real-time for the entire ecosystem of users, CXC ensures that customers receive relevant content and offers, and employees receive the insights needed to do their jobs. This creates compounding value across the entire organization and builds connections between data streams and people that are typically siloed.

Could you share a success story with a client using the Lucidworks solution?

Lenovo, one of the top PC market shareholders, became a Lucidworks customer about four years ago. Lenovo wanted to capture and aggregate all of the valuable data customers put into the search bar to produce actionable insights into their behavior that would drive better digital experiences. In their first year of using Lucidworks Fusion, Lenovo saw a 73% increase in search engagement, 93% increase in clickthrough rate, and 35% conversion rate. All of which has led to a 34% increase in customer satisfaction. In parallel, customers who interact with search are now spending more money on Lenovo.com than those who only use browse. Search revenue growth has outpaced browse growth by 23%, an indication that search is more rapidly and more successfully providing the results that allow customers to achieve their goals.

Is there anything else that you would like to share about Lucidworks?

Lucidworks has established our reputation as a leader in next-generation search solutions and we have an exciting roadmap of cloud products coming in the near future. We are building on an impressive wave of momentum. In the past year, the number of customers using Lucidworks’ cloud-ready Fusions grew by almost 200% and the average recurring revenue (ARR) attributed to cloud-based solutions tripled. Retailers, financial service firms, healthcare companies, grocers, and manufacturers rely on Lucidworks’ cloud-based solutions to create connected experiences across their organizations. This move to the cloud is industry agnostic and we’re excited to answer the call with the next evolution of Lucidworks solutions for our customers and the market.

Thank you for the great interview, readers who wish to learn more should visit Lucidworks.

Antoine Tardif is a Futurist who is passionate about the future of AI and robotics. He is the CEO of BlockVentures.com, and has invested in over 50 AI & blockchain projects. He is the Co-Founder of Securities.io a news website focusing on digital assets, digital securities and investing. He is a founding partner of unite.AI & a member of the Forbes Technology Council.