I have a confession. For more than a decade, SLI has been far too modest about our e-commerce innovations. Perhaps our engineering team has garnered so many patents that they haven’t stopped to reflect. It hit me when we learned of the latest SLI patent related to our auto complete algorithm. We began applying for site search technology patents back when Amazon’s home page was about as intuitive as a bibliography and relevancy wasn’t even a thought.
The fact is, SLI is a global team of e-commerce innovators and expert consultants with unparalleled experience. Since 2001, our team has pioneered apps designed to generate more traffic, convert shoppers into buyers, and maximize orders for B2B and B2C e-commerce leaders worldwide – whether selling 500 or 500,000 products, whether in English, Arabic or 18 other languages, or whether specializing in high fashion or high tech.
At the epicenter of SLI’s flexible and powerful e-commerce suite is machine learning technology, which – until today – remained nameless. The SLI Buyer Engine™ is a cloud-based, machine-learning platform that powers all SLI apps and answers the single most important question: “What is your shopper most likely to buy right now?”
This means our customers’ bespoke search, navigation, recommendations, mobile experience, merchandising tools and user-generated SEO apps are driven by the same patented technology that makes really smart use of big data in real time.
Machine Learning in Action – A Virtuous Cycle
Our Buyer Engine™ continuously learns to improve the performance of all SLI apps and make their results more relevant, whether a shopper is a new user or frequent customer. It can consume in real time a vast array of data streams today, as well as those to come tomorrow.
- Behavioral data (what someone is clicking on and not clicking on)
- Transactional data (what someone is buying)
- Product data
- Non-product data (blogs, reviews, articles, videos, etc.)
- Social data (including the shoppers own social graph, and what they have been up to)
- Contextual data (where are they, local weather, device type being used)
- Third-party data (e.g. FICO scores)
This is where big data meets personalization. The SLI Buyer Engine drives personalized shopping experiences for first-time visitors and loyal customers. It continuously learns from user behavior, improving the performance of all SLI apps over time by making their results more relevant. By accurately predicting which products to present shoppers through their buying journey, the Engine shortens the path to purchase and makes it easy to delight customers and increase revenues.
Gartner’s 2016 Hype Cycle for Emerging Technologies puts machine learning at the cycle peak, noting that “This Hype Cycle specifically focuses on the set of technologies that is showing promise in delivering a high degree of competitive advantage over the next five to 10 years.”
A recent InformationWeek article explains that machine learning is often used to make predictions through improving search results, anticipating movie or product selections, anticipating customer purchasing behavior, or even predicting new types of hacking techniques.
SLI provides clients with competitive advantage and clear results by addressing most of these use cases today. Search is indeed high-value. Our customers see 2.7x greater conversion rate when site visitors use search and those using the SLI Learning Recommendations™ app, which provides contextually relevant product suggestions while increasing upsell and cross-sell opportunities, see 5-15% average uplift in conversions.
So while the automotive industry awaits self-driving cars, companies in the retail sector that are using SLI machine learning technology are already seeing business impact.
Performance-driven commerce will remain the mission for SLI Systems as we strive to continuously deliver the most relevant solutions for customers, and the most relevant results to their buyers. We will continue to invest in machine learning and big data in order to support other emerging technologies that may be applied to e-commerce, such as conversational commerce, augmented or virtual reality, etc. This will help ensure clients are able to provide the best possible and most relevant experience within new shopping paradigms as well. And at the heart of incrementally better e-commerce performance will be the SLI Buyer Engine.
To learn more about results that B2B and B2C e-commerce companies have achieved with the SLI Buyer Engine, download the report detailing how 40 Retailers Have Transformed Their Online Business Using SLI.