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Machine learning is providing the needed algorithms, applications, and frameworks to bring greater predictive accuracy and value to enterprises’ data, leading to diverse company-wide strategies succeeding faster and more profitably than before.

Industries where machine learning is making an impact  

The good news for businesses is that all the data they have been saving for years can now be turned into a competitive advantage and lead to strategic goals being accomplished. Revenue teams are using machine learning to optimise promotions, compensation and rebates to drive the desired behaviour across selling channels. Predicting propensity to buy across all channels, making personalised recommendations to customers, forecasting long-term customer loyalty and anticipating potential credit risks of suppliers and buyers are also key. Figure 1 provides an overview of machine learning applications by industry.

Source: Tata Consultancy Services, Using Big Data for Machine Learning Analytics in Manufacturing – TCS

How machine learning is revolutionising sales and marketing  

Unlike advanced analytics techniques that seek out causality first, machine learning techniques are designed to seek out opportunities to optimise decisions based on the predictive value of large-scale data sets. And increasingly, data sets are comprised of structured and unstructured data, with the global proliferation of social networks fueling the growth of the latter type of data.  

Machine learning is proving to be efficient at handling predictive tasks including defining which behaviours have the highest propensity to drive desired sales and marketing outcomes. Businesses eager to compete and win more customers are applying machine learning to sales and marketing challenges first.  In the MIT Sloan Management Review article, Sales Gets a Machine-Learning Makeover the Accenture Institute for High Performance shared the results of a recent survey of enterprises with at least $500M in sales that are targeting higher sales growth with machine learning. Key takeaways from their study results include the following:

• 76% say they are targeting higher sales growth with machine learning. Gaining greater predictive accuracy by creating and optimising propensity models to guide up-sell and cross-sell is where machine learning is making contributions to omnichannel selling strategies today.

• 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 in sales and marketing.

• 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.

• Several European banks are increasing new product sales by 10% while reducing churn 20%. 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.

Why machine learning adoption is accelerating

Machine learning’s ability to scale across the broad spectrum of contract management, customer service, finance, legal, sales, quote-to-cash, quality, pricing and production challenges enterprises face is attributable to its ability to continually learn and improve. Machine learning algorithms are iterative in nature, continually learning and seeking to optimise outcomes.  Every time a miscalculation is made, machine learning algorithms correct the error and begin another iteration of the data analysis. These calculations happen in milliseconds which makes machine learning exceptionally efficient at optimising decisions and predicting outcomes.

The economics of cloud computing, cloud storage, the proliferation of sensors driving Internet of Things (IoT) connected devices growth, pervasive use of mobile devices that consume gigabytes of data in minutes are a few of the several factors accelerating machine learning adoption. Add to these the many challenges of creating context in search engines and the complicated problems companies face in optimizing operations while predicting most likely outcomes, and the perfect conditions exist for machine learning to proliferate.
The following are the key factors enabling machine learning growth today:

• Exponential data growth with unstructured data being over 80% of the data an enterprise relies on to make decisions daily. Demand forecasts, CRM and ERP transaction data, transportation costs, barcode and inventory management data, historical pricing, service and support costs and accounting standard costing are just a few of the many sources of structured data enterprises make decisions with today.   The exponential growth of unstructured data that includes social media, e-mail records, call logs, customer service and support records, Internet of Things sensing data, competitor and partner pricing and supply chain tracking data frequently has predictive patterns enterprises are completely missing out on today. Enterprises looking to become competitive leaders are going after the insights in these unstructured data sources and turning them into a competitive advantage with machine learning.

• The Internet of Things (IoT) networks, embedded systems and devices are generating real-time data that is ideal for further optimising supply chain networks and increasing demand forecast predictive As IoT platforms, systems, applications and sensors permeate value chains of businesses globally, there is an exponential growth of data generated. The availability and intrinsic value of these large-scale datasets are an impetus further driving machine learning adoption.

• Generating massive data sets through synthetic means including extrapolation and projection of existing historical data to create realistic simulated data. From weather forecasting to optimising a supply chain network using advanced simulation techniques that generate terabytes of data, the ability to fine-tune forecasts and attain greater optimizing is also driving machine learning adoption. Simulated data sets of product launch and selling strategies is a nascent application today and one that shows promise in developing propensity models that predict purchase levels.

• The economics of digital storage and cloud computing are combining to put infrastructure costs into freefall, making machine learning more affordable for all businesses. Online storage and public cloud instances can be purchased literally in minutes online with a credit card. Migrating legacy data off of databases where their accessibility is limited compared to cloud platforms is becoming more commonplace as greatest trust in secure cloud storage increases. For many small businesses who lack IT departments, the Cloud provides a scalable, secure platform for managing their data across diverse geographic locations.