by Garret Robertson - Senior Analyst & Author
The future of technology is in machine learning. Talk of virtual assistants, neural networks and deep learning is proliferating across the Internet at a rapid pace. According to a recent CB Insights update, deal flow in this space is accelerating rapidly with current estimates of the industry size exceeding $100 billion with compounded annual growth estimated at over 50%. Despite the proliferation of this technology, it is misunderstood. Dreams of androids, self-driving cars and Skynet abound in the conversations of executives, the general population and everyone in between.
Machine learning tools can more accurately described as powerful tools that sort through terabytes of data in order to optimize relationships. These tools find solutions for minimizing fraud, maximizing sales revenue, maximizing lead generation, or minimizing errors in image recognition. What makes these algorithms truly special is their ability to take complex structured and/or unstructured data and find meaningful relationships. Some examples of these data sources can include text heavy sources such as emails or web sites, images, audio files, and/or data points.
Despite lack of awareness, these tools are already finding places in consumers’ lives. When consumers log into Netflix at night and pick a show from the recommended play list or when they choose to add a recommended product to their basket on Amazon, machine-learning algorithms are at the heart of those lists. It is not just limited to product recommendation though, when consumers’ credit cards deactivate over suspicious transactions, there was machine learning. When Social media presents ads to users, there again was machine learning. Additionally these powerful algorithms drive other services like the virtual assistants Siri, Cortana or Alexa. While these examples may be visible to consumers, Machine Learning is rapidly proliferating into many less visible markets like CRM, healthcare and government services and banking.
The valuation of machine learning service companies can best be described by its synergies with cloud service providers and businesses. Businesses create systems that gather data as they conduct business. These systems could include, as an example, systems for tracking customer receipts like an accounting ledger or a customer-profiling tool like a rewards program. Data science showed businesses how to combine these two data sets to better understand customer preferences. When the data moved to cloud services, machine-learning tools were then able to sift through much more complicated information like images, articles, or other unstructured sources and automate the search for interesting relationships.
As the outputs became better, the businesses rebuilt systems to integrate more data necessitating more data storage. Now the systems could create profiles, link them to purchasing trends and compare it to even more complex demographic information creating more powerful business insights. The outputs from the 3-way cycle thus reinforce themselves making it more and more efficient and increasing value to all parties.
These synergies define how the industry has been growing. Because the synergies are so strong, most capital investments in this industry occur as partnerships between businesses, cloud service providers and machine learning companies. These strategic investment partners provide two critical pieces to the growth round. First, they validate the effectiveness of the machine-learning product. Second, these partnerships provide access to data from interesting industries such as fraud, healthcare, product recommendation or sales analytics allowing opportunity for the systems to become even more effective.
Below is a sampling of some capital raises for machine learning companies where at least one of the investors was not a capital player but a business with a strategic interest and/or a cloud service provider.
The application of this technology is expanding every day. Nearly 70% of all investment into this space is driven by Seed and Series A funding. Additionally more than 40% of all companies that exist in this space are less than 3 years old. Additionally, with the power these solutions have to offer, the industry is expanding rapidly with total year over year transaction and investment volume increasing.
Due to the synergies in this industry, a few companies have been able to lead the charge. Some of these companies include Amazon, Microsoft, Google, IBM and Apple. This makes sense because the effectiveness of the algorithms grows as the access to relevant data grows. Companies with access to large quantities of data find more value than those with less.
Despite the power of machine learning, there remain two important hurdles for the typical company in adopting these technologies. First, company leadership needs to be aware of how these systems can help them. Understanding how data can be used to redefine and refine existing strategies is crucial in transforming the organization’s systems. General misunderstanding of machine learning has prevented many companies from adopting it.
Second, if companies want to pursue implementation of these systems, they need to understand how. This involves not only utilizing tools to gather the data, but also knowing what kinds of solutions are already available.
There are many machine learning companies such as BigML, Amazon, IBM, Microsoft, Google or others that have out-of-the-box solutions available to a wide range of industries. Increasingly, machine learning is moving from the world of PhD’s and large teams of data scientists to tools that anyone can implement.
Despite the newness of this technology to businesses, many industries have already found interesting and powerful solutions. A summary of some industries that have been impacted by machine learning as well as some specific examples in selected industries follows.
This is one area where the use of machine learning is most visible to consumers. When customers buy products online, they leave behind with the business a treasure trove of information. Some of this information includes what products are typically bought together, how much the average consumer spends in a given purchase, what sorts of products and brands people like and much more. While individual tickets report single transaction information, registered users create entire shopping profiles over multiple purchases that can be analyzed.
With this kind of information, it is no wonder that Amazon reported shortly after rolling out its product recommendation platform that sales increased by nearly 30%. In fact this is not an uncommon story. With companies better able to identify the needs and wants of users, they are better able to put products consumers want, into their hands.
In addition to product recommendation, chatbots are taking over customer service. More than 11,000 bots have been added to Facebook Messenger since its launch, allowing brands and companies to use AI to connect with customers through virtual concierge services. These bots are replacing employees in physical stores, allowing companies to build long-term relationships with customers while saving labor costs.
Spring Bot is one example of many of these services that acts as a point of contact even after purchases are made and has a wide range of customers, including Givenchy and Lanvin, brands that do not have an established e-commerce platform. An automated interaction generally costs $0.25, while a live agent interaction costs anywhere from $6 to $20. The automated interactions are also faster than normal live interactions. While the natural language processing in these systems is not perfect, the overall results speak for themselves.
Increasingly machine learning tools are being used to enhance sales and CRM. Traditionally, sales data has been stored and analyzed manually. In addition to the time and money spent in performing these tasks, significant capital has been spent training sales teams to track the right data and how to effectively analyze it.
Machine learning has provided a way to collect data automatically and provide the analysis so sales agents can more effectively find, target and convert prospective clients into sales. InsideSales reports that some of its customers have increased their sales pipeline by 30% increase to sales and a 250% increase in leads. Costs associated with training implementation and data entry are reduced for users in addition to these strong revenue increases.
Increasingly, financial institutions are using automated financial advisors and planners. These tools monitor events and stock and bond price trends and compare them to the user’s financial goals. The machine will be able to compare the user’s portfolio and make recommendations on what stocks to buy or sell. There will be no need to pay an expensive human advisor to make decisions for customers. The machine-learning tool will now be able to make decisions based on data that is coming in real time.
In addition to automated financial advisors, algorithmic trading is a means to increase profitability and decrease risk in investment portfolios. Algorithmic trading systems are systems that process data on a very large scale to identify risks in investment portfolios and rebalance them in order to minimize risk. As these systems gain more data, they are better able to optimize portfolios and mitigate risk.
It is estimated that these algorithmic trading systems handle 75% of the volume of the global trades worldwide. These numbers get larger when looking at specific types of trading.
Algorithmic trading systems were responsible for nearly 80 per cent of foreign exchange futures trading volume, 67 per cent of interest rate futures volume, 62 per cent of equity futures volume, 47 per cent of metals and energy futures volume, and 38 per cent of agricultural product futures volume between October 2012 and October 2014.
Clinical variation management is an area in healthcare ripe for disruption by ML systems. Clinical variation is when clinicians deviate from recommended care pathways in the delivery of care to similar patients. It is estimated that there could be as much as 30% waste in healthcare, but this waste is hard to identify due to the complexity of healthcare and the great degree of variation in the way patients receive treatments.
A recent article by HealthCatalyst indicates the problem. Clinical variation is complicated by two main factors. The first is that studies indicate that only 20% of the care delivery is driven by scientific research. About 80% of the care delivery is determined by subjective clinical care pathway decisions. Second, Doctors must read hundreds of pages of primary literature every day in order to stay fully current. The process needs education, but the education is next to impossible to get and train through normal means. Until then, clinicians deliver care without much consistency driving waste and impeding process development.
Machine learning provides a means to monitor care pathways to ensure clinical variation is minimized. It also provides a means to monitor care pathways to determine areas to improve and optimize them with current methods in mind.
Traditional tools such as control charts, regressions and manually examined data are not robust enough to optimize the system. Machine learning tools are well positioned to do the work individual data scientists and analysts cannot do. Those machine-learning companies focused on healthcare like Ayasdi are well positioned to disrupt this space.
- Machine learning as an industry is still in its infancy.
- These examples represent only a few of the hundreds of companies that are emerging to solve next generation business problems.
- A new industrial revolution is coming in the form of computer code and automated data science.
- Companies that are not thinking about data and machine learning will soon find themselves unprepared.
- The companies who have adopted these technologies already enjoy significant advantages over those who have not implemented it yet. **
*special note of thanks to Naiss' contributors: