Find the video introduction to this blog series here.
Efficiency in business takes many forms. It can mean doing the same job in less time or requiring fewer resources to accomplish a particular task. Technology is an enabler, helping companies maximize efficiency across numerous areas through automation and finding more ‘intelligent’ ways to sell more products, increase market share, and reduce costs. Today, many B2B E-Commerce firms are turning to machine learning for such intelligence.
Before we discuss how machine learning contributes to success in B2B E-Commerce, let’s first be clear on what it is. “Machine learning,” according to TechTarget, is “a type of artificial intelligence that provides computers with the ability to learn without being explicitly programmed”. For example, a system capable of machine learning could automatically analyze thousands of proposals a company sent to prospects. It could then determine which of these proposals ended in closed business and identify patterns (e.g., percentage discount offered and product type) common to successful proposals. Each new data point allows the system to enhance its model and, eventually, assess proposals before they are submitted, making recommendations where appropriate. B2B firms using machine learning in this way on their E-Commerce platforms can continuously improve and iterate their performance based on these insights.
Why Machine Learning is Important to You
Why should companies pay close attention to developments in this field and consider enriching their technology toolbox with solutions capable of machine learning? One important reason is the opportunity they offer to optimize omni-channel programs. Omni-channel interactions present challenges for B2B firms when it comes to maintaining consistency of pricing, brand, and messaging across all channels of customer interaction. Machine learning capabilities allow companies to automatically analyze multiple interactions with customers and coordinate communication accordingly. For example, a buyer might have received a special quote through live chat. If that buyer were to subsequently speak with another employee of the firm, a system capable of machine learning could direct said employee to offer this buyer the same price.
Machine learning capabilities can also optimize online ordering of diverse products (hard goods, services and digital goods), selections from extensive catalogs, Configure Price Quote (CPQ) functions for complex solutions and reordering. The key to effective online ordering and CPQ is configuring products or services in a way that caters to the precise needs of the buyer while also generating a price that is profitable, appropriate to the market, and in line with the buyer’s budget requirements. Use of machine learning helps B2B E-Commerce organizations automate this process. Once the company has captured the relevant data, machine learning insights can support the seller by automatically generating a quote that is a most likely fit with the expectations of that buyer, at a price point where the prospect is most likely to purchase.
Solutions capable of machine learning are versatile insofar as they will learn what the user of the solution wants them to learn. For example, if a company wants the solution to analyze customer conversations and identify which phrases during a phone call are most closely linked with customer churn, the application will do so. Similarly, the company might want the solution to determine which product images are most likely to generate a sale. The solution can then track countless web interactions, correlating images and outcomes, and begin making or testing image recommendations.
Overall, the opportunities provided by machine learning are virtually endless. To capitalize on these opportunities, all B2B firms need is the vision to know where to employ this capability. Once implemented and programmed properly, machine learning becomes the gift that keeps on giving, making B2B E-Commerce truly intelligent.