Machine learning: How to put it into practice with customers


Some might think that machine learning is the buzzword of the year –  and with good reason. A June 2017 McKinsey report estimates the total annual external investment in Artificial Intelligence (AI) was between $8 to $12 billion last year, and machine learning attracted almost 60 percent of that investment. The applications seem to have no bounds too – from healthcare and security to translation and smart cars. From where I sit, I see machine learning as an innovative asset to transform the way organizations engage with their customers.

Sales, service, marketing, and commerce teams can all benefit from automation, intelligent data, and more informed decisions. Chatbots are a quintessential example. Most of us have either heard of or interacted personally with a chatbot. It ultimately serves as a smart assistant that puts the customer in control and helps eliminate back-and-forth wait times. Through a conversational interface and more familiar and humanlike approach, customers can create, manage, and escalate their own service requests.

AI and ML solutions

Our SAP Hybris Labs team has developed a prototype they call Charly the Chatbot. Charly can find products, recommend items, and remind users of certain products at a specific time. He makes it easy to create a shopping list, add products to a shopping cart, and complete a checkout. Users can even take a photo of a product’s barcode and ask him to find it. This is all enabled via Facebook Messenger, using natural language.

With Charly as an example, it’s easy to see how wide-reaching Machine Learning can be when it comes to customer engagement – and why traditional CRM practices are long gone. Beyond using bots through Facebook or a website, what about using this type of intelligence for in-store shopping?

We’ve seen instances of this from some of the world’s largest retailers – tapping robots to enhance the in-store experience. Our Labs team was sure to test this scenario out too with Pepper Instore Assistance, which understands what customers are interested in and can point them to the right products.

It’s clear that while machine learning has been buzzing in all our ears in 2017, it will continue to in 2018 – and beyond. There are real use cases to be explored, especially when it comes to transforming customer engagement. At SAP Hybris, we have unique access to the most important customer and product data across the front office. Armed with this robust customer insight, we’re able to offer an application-led approach for intelligent scenarios.

Here are just some of the use cases we’re offering customers today: Advanced Personalization; Contextual Merchandizing; Customer Experience; Shopping Assistant Bot; Best Product; Best Offer; Best Channel; Best Sending Time; Best Audience; Affinity Scoring; Sentiment Analysis; Imaging Intelligence; Service Ticket Text Analysis; Customer Service Bot. Several customers have come aboard our new SAP Hybris Machine Learning co-innovation program as well, tapping ML capabilities from the SAP Hybris Sales and Service Cloud.

AI’s potential is huge and while there’s a lot of hype right now and in some cases inflated expectations, the technology is available and there are a lot of strong uses cases. Organizations can maximize the value if they complement human interaction and decision making with intelligent automation – instead of relying entirely on the promise of full automation.

Most importantly, the focus should always be on creating a better customer experience and not just cutting costs through automation. To be successful, it’s important to understand what the use case is, what you’re trying to achieve, and how much you want to automate. Start there – and the possibilities are endless.

Dr. Volker Hildebrand
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October 16, 2017
Dr. Volker Hildebrand

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