Digital technology is revolutionizing supply chain management, driving significant innovation. Machine learning is a concept that has been around for decades, but lately has taken on new meaning as the industry has seen massive advancements in computing power and memory. This blog series will examine how machine learning is being applied to solve some of supply chain’s toughest challenges. Today, we’re looking at intelligent customer and market segmentation.
As any manufacturer, distributor, or retailer knows, no two customers are the same. Customers’ opinions on the value of a product or service, not to mention how much they are willing to pay for it, run the gamut. On one end of the spectrum, you have value-conscious customers, who always looking for a deal. On the other end, you have customers that will only buy premium goods and services. This is true for virtually every product category available in the market, from laptops to cereal.
Companies have long recognized the importance of designing offerings to serve the needs of different markets and customers, but accomplishing this can be challenging, especially given how quickly customers’ behaviors and expectations change. Because of this volatility, many companies struggle to segment their customers and markets intelligently and provide tailored products and services to those segments on an ongoing basis. This phenomenon is affecting both business-to-consumer (B2C) companies, which cater to end consumers, and business-to-business (B2B) companies. More and more, B2B customers are expecting a B2C-like experience when interacting with other businesses.
A tough problem to solve
Some years ago, intelligent segmentation was difficult because there was too little data available for reliable analysis. With the advent of social media and the Internet of Things, this is no longer true. Instead, companies now have access to too much data – so much that they simply are unable to analyze and derive insights in a timely manner. Plus, with how quickly consumer behavior is changing, humans cannot keep pace with the analysis required to identify new correlations that are forming all the time.
Unlocking the value of intelligent segmentation
Companies obsessed with customer-centricity have a simple business reason for doing so. By identifying patterns in customer behavior (getting to the why customers buy what they buy), and then meeting those needs through an intelligent segmentation approach, companies are destined to gain market share and profitable growth. This involves creating intelligent customer-product-channel segments and then developing supply chain policies to deliver differentiated offerings to these segments (all while balancing the cost to serve each segment against the value that segment brings to the business). With machine learning, this can be done more easily and with much success.
New technology ushers in new opportunities
Today, decision and data science algorithms can sift through big data and look for customer-centric insights that are not immediately obvious to a casual observer. Techniques such as unsupervised learning, where algorithms systematically examine data looking for patterns — what a customer buys, why she buys — are powering these insights. Additionally, cloud technology frees companies from physical infrastructure limitations, providing greater CPU and memory elasticity, if needed.
Technology for creating intelligent segments based on purchasing preferences has been commercialized and is available today. In fact, consumer product companies have been leading the way in creating intelligent customer, market, and product segments and are designing supply chain policies to service the needs of those segments differently.
Leveraging machine learning for intelligent segmentation is just one way leading manufacturers, distributors, and retailers are plotting their digital supply chain journeys. Are you?
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