What Machine Learning Means for the Customer Experience

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In this special guest feature, Elliott Yama, Chief Data Analyst at Apttus discusses the ways that machine learning is impacting the B2B customer experience – where the evolution mirrors the initial advancement of B2C E-Commerce. Elliott leads the team responsible for the company’s artificial intelligence and machine learning work.

Machine learning is a hot term right now – and deservedly so. Over the last few years it has significantly advanced how sales and revenue operations function. However, it’s often used as a guiding tool for salespeople – helping them to determine correct pricing, bundling and more in the name of progressing their deals. What kind of impact does machine learning have when customers are making the purchasers themselves? Welcome to the next stage of B2B E-Commerce.

Make no mistake: E-Commerce isn’t just a consumer-facing operation anymore. Many of the features and suggestions that you might be more familiar with as an Amazon.com shopper are now being applied to self-service enterprise deals. For example, an equipment manufacturer, offering thousands of different kinds of engine parts, could place those options into an E-Commerce portal accessible specifically through its customers. Those same customers, in the exact way they might purchase a book online in their personal lives, are then free to shop for the exact parts needed to complete their own offerings, such as planes, trains or automobiles. It’s an intuitive process and one that’s become commonplace in today’s business environment. After all, the customers know what they need, don’t they?

Not always, and here is where machine learning enters the picture. Once more, the evolution mirrors the initial advancement of B2C E-Commerce: add a book to your Amazon.com cart and what happens? You’re given insight into additional books that might pique your interest. You’re treated to a glimpse of what other customers, who selected similar offerings, have purchased.

What’s the B2B equivalent? Smart product recommendations are a great starting point, because they draw from existing data to offer extra value to your customers. Dynamic pricing, discounts, and bundles based on historical or similar orders are the next step: making orders more cost-effective and convenient based on what their needs might be. The advancement doesn’t stop there: building account-profiles to better judge the profile of customers, analyzing contextual data and behavior to predict their future needs – these are not a part of some sci-fi future, they are already live and being used by today’s business leaders.

What’s even more impressive about these functions is not the depths of the data they employ, or even the (in retrospect) obvious need to make large-scale sales more efficient, even in an E-Commerce portal. Instead, the gradual transition into what could be called ‘invisible processes’ is the most impressive part of this transformation. These data dives, recommendations and other related equations are occurring in the background: massive amounts of processing power are being condensed into a simple, flexible presentation.

The very nature of this process makes it difficult to notice sometimes, but the effect is already clear – it has made business faster, more efficient, and more effective for everyone involved. In the B2B world, where the value of the average order lies a little closer to the 7-figure range than your typical book transaction, that difference matters a great deal. It’s an exciting time: practical, valuable implementation of machine learning is the perfect realization of business needs colliding with modern capability, and we’re only scaling up from here.


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