August 29 by Kingman Tang
1950 marked a watershed year for Artificial Intelligence (AI) as Alan Turing, some say 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.
Since 1950, the concept of AI has ballooned to include machine learning, computer vision, natural language processing, robotics and more. While all are incredibly compelling, this 4 part blog series specifically focuses around machine learning applications and what it means to the B2B enterprise.
Machine Learning Applications Are All Around Us
Enterprises are struggling to find value in the massive amounts of data they generate and save every day. Machine learning, the field of computational science centered on pattern recognition, is providing the needed insights that bring greater understanding, predictive accuracy, and prescriptive intelligence to enterprises’ data sets, as well as contribute to diverse strategic outcomes.
Machine learning applications are increasingly playing a role in our daily lives. Whether it is Apple’s Siri, or Microsoft’s Cortana automated assistants, pre-approved credit card offers, saving and investment offers from your bank, suggestions on Amazon, Expedia or Netflix, each is an example of machine learning in action. What they all have in common is that each looks to create the highest quality of predictive intelligence on future behavior, largely based on historic data. In summation, machine learning excels at solving the most complex problems by creating accurate predictions without explicit computer programming.
Machine Learning’s Strategic Role in the Enterprise
Unlike advanced analytic techniques that seek out causality first, machine learning techniques are designed to seek out opportunities to optimize decisions based on the predictive value of large-scale data sets. Data sets are comprised of both structured, i.e. highly organized data like that in databases, and unstructured data, i.e. less organized data like text in a sales contract or web traffic. The global proliferation of social networks is fueling the growth in the latter type of data, making it increasingly important for companies to effectively leverage their unstructured data.
In enterprise businesses, machine learning is proving to be effective at handling predictive and prescriptive tasks, allowing these companies to define which behaviors have the highest propensity to drive desired outcomes. Enterprises eager to compete and win more customers are applying machine learning to both sales and marketing challenges.
The Accenture Institute for High Performance recently completed a study that found the following key takeaways:
- 1. At least 40% of companies surveyed are already using machine learning to improve sales and marketing performance. Two out of five companies have already implemented machine learning based intelligence in sales and marketing.
- 2. 38% credited machine learning for improvements in sales performance metrics. Metrics the study tracked include new leads, upsells, and sales cycle times by a factor of two or more while another 41% created improvements by a factor of five or more.
- 3. 76% say they are targeting higher sales growth with machine learning. Gaining greater predictive accuracy by creating and optimizing propensity models to guide up-sell and cross-sell is where machine learning is making contributions to omni-channel selling strategies today.
- 4. Several European banks are increasing new product sales by 10% while reducing churn 20% using machine learning intelligence. A recent McKinsey study found that a dozen European banks are replacing statistical modeling techniques with machine learning. The banks are also increasing customer satisfaction scores and customer lifetime value as well.
 Sales Gets A Machine-Learning Makeover. MIT Sloan Management Review, May 17, 2016. H. James Wilson, Narendra Mulani, Allan Alter. https://sloanreview.mit.edu/article/sales-gets-a-machine-learning-makeover/
Why Machine Learning Adoption is Accelerating in the Enterprise
For enterprise businesses, machine learning has the ability to scale across a broad spectrum of business processes. Those directly related to revenue-making, often called Quote-to-Cash, are among the highest value applications and include sales, contract management, customer service, finance, legal, quality, pricing and order fulfillment.
The economics of cloud computing, cloud storage, the proliferation of sensors driving the Internet of Things (IoT) connected devices growth, and pervasive use of mobile devices that consume gigabytes of data in minutes are a few of the numerous factors accelerating machine learning adoption today. Add to this list the many challenges of creating context inside of search engines, as well as the complex problems companies face optimizing operations while predicting most likely outcomes, and the perfect environment is set for machine learning to proliferate dramatically.
Where Machine Learning is Delivering Business Outcomes Today
The good news for enterprises is that all the data they have been saving for years can now be turned into a competitive advantage and lead to the accomplishment of strategic goals. Revenue and senior management teams are concentrating on how they can capitalize on machine learnings’ core strengths to transform the strategic vision of their businesses into a reality. These teams are focusing on business outcomes first and are looking for machine learning to accelerate and simplify, determining which factors most influence buying behavior and lead to goals being accomplished. My colleague, Elliot Yama, recently wrote about why it is necessary to leverage machine learning to drive business outcomes.
Sales, marketing, and channel management teams are using machine learning to optimize promotions, while compensation and rebates drive the desired behavior across
selling channels. Predicting propensity to buy across channels, making personalized recommendations to customers, forecasting long-term customer loyalty, and anticipating potential revenue and credit risks of buyers are some specific applications of machine learning right now.
These are exciting times for machine learning in the enterprise. While 1950 was the dawn of machine learning and artificial intelligence, 2016 is a watershed moment for the application of machine learning to enterprise sales, marketing and related business processes.