We recently partnered with Eli Finkelshteyn and Dan McCormick to help them build Constructor.io, a business providing machine learning-powered site-search. It was clear from our first meeting with them that Constructor and Zetta speak the same language when it comes to building competitive advantage through data and machine learning. Their early successes illustrate the power of the virtuous loop — they already count several large retailers as customers and helped those customers grow revenue.
Eli trained in computational linguistics, speaks 5 languages and spent his career leading data science teams. Dan knows how to grow companies as the former CTO of Shutterstock, a pioneer in digital stock photos.
Eli and Dan encountered the site search opportunity when they worked together at Shutterstock. There, customers searched for stock photos using vague terms like ‘business’ and often come away disappointed with the search results. Eli and Dan realized that by automatically suggesting related modifiers such as ‘business place’ or ‘business meeting’ the customer was more likely to find the stock photo she was looking for, and therefore more likely to make a purchase. They significantly increased the site’s revenue by simply applying autocomplete to Shutterstock’s search..
The two of them soon realized that search was something every website needed to improve its user experience. Google search worked well for finding information across many different sites but its site search API was difficult to use, and later deprecated. Many websites instead hired their own search teams to build search from open source tools — an expensive and imperfect solution. This same problem was being solved over and over again across the Internet and, as the shift from desktop to mobile continued, search needed to become more precise for these smaller screens. After all, sifting through a list of 200 results to find the right pair of running shoes is already frustrating on a desktop; on mobile, the website has less real estate and fewer opportunities to surface the right result before the customer gives up.
Furthermore, few of these site search tools were learning from successful searches to optimize their system Despite over two decades of Google dominance in web search, many site search companies are still taking the Altavista approach. The Constructor team realized that by feeding user engagement data into a learning algorithm it could tune its search suggestions to optimize conversion, making users more likely to make a purchase, view a video, or read an article — all increasing revenue. The impact of this insight was significant: Constructor helped its e-commerce customers achieve up to 23% revenue growth; for some of its larger enterprise-level clients, this represents eight figures of revenue.
Constructor effectively searches less common terms (such as specialized hardware parts) by anonymizing and pooling search feedback across their customer base. The performance of its search will grow better as the company brings on more websites.
Our ties to the team date back to its early days. Ivy met the Constructor team when they were raising their angel round almost two years ago. Even though they were just a two-person team, they were already winning contracts in side-by-side comparisons against earlier entrants to the search-as-a-service category: it was clear that the competitive advantage of the data-driven virtuous loop was powerful from the beginning. Ivy made a small investment in Constructor and brought Constructor in to Zetta just before she joined our team.
Beyond search, Constructor is well positioned as a customer insights product, enabling its customers to directly see what visitors to their site want — instead of having to rely on external information like Google search keywords or consumer surveys from research firms like Nielsen. This presents many exciting opportunities for Constructor to expand far beyond site as a service, and Eli and Dan already have some interesting ideas for where to go next. We looking forward to helping Eli and Dan bring that vision to life.
- AI-First Companies: Flipped
- The AI-first startup playbook
- AI adoption is limited by incurred risk, not potential benefit
- AI-First Companies
- Data rights are the new IP rights
- The Intelligent Enterprise Stack
- Beating Behemoths
- Don't sell your data
- Framework to grasp industrial analytics opportunities
- Beyond systems of record
- Positioning a machine learning company
- The intelligence era and the virtuous loop
- Vertical beats horizontal in machine learning
- Zetta Bytes AMA: Questions to ask about pricing
- Zetta Bytes AMA: Hiring a CTO
- AI Entrepreneurs: 3 things to check before you pursue a customer
- There are more data scientists than you think
- Stages of funding in the intelligence era
- Could data costs kill your AI startup?
- Measuring AI startups by the right yardstick
- Finding the Goldilocks zone for applied AI
- Data is not the new oil
- Machine Learning in the Deployment Age
- Innovations of the Next Decade
- Zetta Bytes: Privacy Preserving Machine Learning
- GDPR panic may spur data and AI innovation
- Computing like a human
- New opportunities for hardware acceleration in data analytics
- Hardware acceleration in data analytics
- Skan: Visualizing the Future (of Work)
- Announcing Zetta Fund III, a $180M fund for AI-first companies
- Aptology: The Science of Fit
- Verusen (The intelligent supply chain)
- Opsani: What's Next? Continuous Optimization
- Promethium: Starting a Fire
- Lilt: Translation for all