Machine Learning: Powering the Next Wave of SaaS Solutions

 

Machine Learning: Powering the Next Wave of SaaS Solutions1950 marked a watershed year for Artificial Intelligence (AI) as Alan Turing,    the father of modern computer science, developed the Turing Test while at the    University of Manchester. In his seminal paper, Computing Machinery and    Intelligence, Turing considers the question, “Can machines think?” Simply put,  the Turing Test is a criterion to determine whether a computer has human-like  intelligence. Six and a half decades later enterprise software is about to undergo  radical transformation – a substantial change that will make the shift to  software as a service (SaaS) look inevitable. Unsurprisingly, this transformation  is being powered by machine learning.

 With machine learning, computers can process and mine data in real time to  automatically discover insights and generate predictive models. Companies can find patterns and foresee what will happen in the future based on real-time analysis of their data. The possibilities fuelled by machine learning are endless. SaaS solutions are typically exposed to enormous amounts of data that reside in the Internet ecosystem, much of which is widely available for all manner of analytics. As such, several categories of SaaS solutions are potential candidates to apply both machine learning and cognitive applications to leverage the “sea of data” available via the internet.

Machine learning applications are all around us. For entrepreneurs and investors, this is an exciting time to innovate and place new bets in the enterprise software market. Below are some of the SaaS solutions where machine learning is playing a strategic role.

Product Search

When the user searches for a product, how do we find the best results for the user? One factor used in product ranking is user click-through rates or product sell-through rates. In addition, user behavioural data gives the link from a query, to a product page view, all the way to the purchase event. Through large-scale data analysis of query logs, we can create graphs between queries and products, and between different products.

We can also mine data to understand user query intent. When a user searches for “Toyota Prius”, are they searching for a new car, or just repair parts of the car? Query intent detection comes from understanding the user, other users’ searches, and the semantics of query terms.

Documentation Search

SaaS based company ContractWorks has innovated in the field of document search for enterprises.  By providing a central repository for securely storing all sorts of contractual data and, using advanced tag-based search engine algorithms to retrieve data, they have made contract management, once a complex and, tedious task, into a simple one click solution. In the past, Google and Microsoft have pioneered Hadoop map reduce application with their search algorithms.

Product Recommendation and Promotions

Typical recommendation systems are built upon the principle of “collaborative filtering”, where the aggregated choices of similar, past users can be used to provide insights for the current user. Predictive analytics makes this challenge easier by using machine learning to understand a consumer’s behaviour, including the purchase history of that consumer and the performance of different products on the site to determine relevant recommendations that have a higher probability of generating a sale. Collaborative filtering has the same functionality with promotions, and is able to identify what has worked in the past, and then offer the best promotions in real-time based on the consumer’s browsing patterns.

Anticipatory Purchases

The popular audio-recognition app Shazam now has an always-on feature that listens for audio all day long, tags songs that don’t exist in your library, and makes them available for your perusal. This saves you the hassle of unlocking your phone, loading the app and nearly causing an accident in your car.

With machine learning, Shazam and products like it could intelligently filter your auto-tags to what you’d most likely be interested in based on your existing library. It can also use deep neural networks to be able to decide what songs to add to your library, even making a purchase on your behalf.  Big Data has also led to the success of similar music streaming companies like Spotify, Songza, MusicMetric, and Rhythm. Without it, these companies wouldn’t exist. Spotify produces an estimated 1.5 terabytes of compressed data daily. It also has one of the largest Hadoop clusters in Europe, with close to 700 heterogeneous nodes running approximately 7,000 jobs per day.

Amazon recently filed a patent for “anticipatory shipping,” which ships and order before it’s even placed. Amazon’s wealth of order history, search, wish list and click stream data may one day be leveraged this way.

Big Opportunity Ahead

Machine learning adoption is accelerating in enterprise. For entrepreneurs and investors, this is an exciting time to innovate and place new bets in enterprise software. BCC Research predicts the machine learning market will reach $15.3 billion by 2019, with an average annual growth rate of 19.7 percent. One of the early growth categories is predictive analytics software, which according to Transparency Market Research is expected to reach $6.5 billion worldwide in 2019, up from $2 billion in 2012,

These are exciting times for machine learning adoption in enterprise. While 1950 was the dawn of machine learning and AI, 2017 will be a watershed moment for the application of machine learning to enterprise sales, marketing and related business processes. As we look forward, machine learning will be the defining characteristic that distinguishes “legacy” from “modern” enterprise applications.

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