Enterprises are striving to find greater meaning in the massive amounts of data they generate and save every day. Machine learning is providing the needed algorithms, applications, and frameworks to bring greater predictive accuracy and value to enterprises’ data sets and contributing to diverse strategies succeeding.
Apple’s Siri automated assistant, pre-approved credit card offers, saving and investment offers from your bank, suggestions on Amazon, Expedia or Netflix are all examples of machine learning in action. What all these uses have in common is that each looks to create the highest quality prediction possible of future behavior based on history. Machine learning excels at solving complex problems that are predicated on creating accurate predictions.
Machine Learning’s Strategic Role
Unlike advanced analytics 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.
Machine learning is proving to be effective at handling predictive tasks including defining which behaviors have the highest propensity to drive desired outcomes, which companies like Apttus use to drive business decisions like discounting or automated approvals. Enterprises eager to compete and win more customers are the applying machine learning to sales and marketing challenges first.
The Accenture Institute for High Performance recently completed a study that found the following key takeaways:
• 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 2 or more while another 41% created improvements by a factor of 5 or more.
• 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 omnichannel selling strategies today.
• 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.
Four Factors Accelerating Machine Learning Adoption
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.
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 optimizing decisions and predicting outcomes.
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.
The Internet of Things (IoT) networks, embedded systems and devices are generating real-time data that is ideal for further optimizing supply chain networks and increasing demand forecast predictive accuracy.
As IoT platforms, systems, applications and sensors permeate value chains of businesses globally, there is an exponential increase of data generated. The availability and innate 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 optimizing 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.
(About the author: Louis Columbus is a technology consultant and enterprise software strategist at Apttus).
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