How Businesses Can Use Machine Learning to Improve Customer Engagement

by   |   September 13, 2017 4:56 pm   |   0 Comments

Jamie Anderson

Jamie Anderson, Senior Vice President & CMO, SAP Hybris

The hype surrounding machine learning has led most, if not all, businesses to seriously consider the benefits of incorporating the technology into their core business functions. While the thought of making the leap to implement machine learning can be daunting, the good news is that many companies have already set up internal systems to track and collect customer data, they just need to find a way to make the existing data actionable and use it to their advantage.

Through machine learning technology, businesses can more easily and effectively make use of existing customer data in ways that humans alone cannot. Granular, comprehensive data is the key to designing successful, personalized engagements with customers that appeal at an individual level and move the business forward. The ability to sift through and analyze years of customer data to pinpoint trends and tailor actions is something we’ve been building towards for a while and now are finally ready to execute on.

Machine learning shifts traditional, rules-based processes to intelligent ones that can help businesses discover new patterns in large data sets as well as make strategic predictions about customer needs. While this is unfamiliar terrain for most businesses, there are a few specific ways that companies can work to incorporate machine learning into their core functions to eventually create an overall leaner business.

Tangible Implementations of Machine Learning    

The first way businesses can incorporate machine learning into their strategies is by taking advantage of digital assistants and bots. Advances in AI technology suggest that self-learning algorithms will soon be able to come to their own conclusions and develop context-sensitive behavior. These assistants are already beginning to interact with customers, schedule meetings or follow-ups, translate documents, understand customer inquiries and provide customer support. Implementing this self-learning technology into the back-end of a company website will not only save customer-service reps time, but over a certain period, they will also become essential in anticipating customer service needs before they become challenges.

Additionally, machine learning allows businesses to gather, analyze, and respond to customer inquiries with more speed than ever before. With this technology, organizations can efficiently gather intel from inbound chat, social media posts, emails, and other channels to automatically determine classifications as well as the appropriate response. By aggregating customer data in one place, businesses can better understand a customer’s needs and cater to those desires through predictive analytics. Today’s infrastructure must be working in real-time as a live transactional system by pulling through insights in the moment so that brands can react and adjust strategy accordingly.

Lastly, organizations should consider utilizing machine learning to assist with their sales and marketing functions. Businesses can get valuable insight into customers’ transactional behavior using machine learning to mine, predict, and capture lead conversion indictors for qualified individuals. With this insight, sales representatives can recommend sales content or personalized offers through more specific segmenting and targeting of customers. Eventually, this process will lead to increased loyalty and retention among an organizations’ customer-base.

Aggregate Data to Solve Real Business Challenges

One IT company comes to mind when I think about machine learning-enabled marketing automation. This specific company went from one digital marketing application to over 25 applications that communicate with each other in various ways and are integrated into their CRM systems. These disparate data sources made it difficult to bring the data together to tell a true story about the customer. Through implementing cloud and machine learning software, they’re now able to aggregate the data into a single repository that allows them to segment off ERP data, CRM data, social feed data, third-party data and more.

When the company aggregated their disparate data, they were able to easily feed it into their predictive analytics engine, providing a more complete view of the customer and giving the sales and marketing departments a better understanding of which clients have a propensity to buy certain applications, hardware, software, and other solutions. A 360-degree view of the customer allows this company to more deeply target the messages they send to clients. Ultimately, they will shift from targeting 60,000 clients with an umbrella message to targeting 6,000 clients with a more distinctive message that resonates with their particular circumstance.

Machine learning has the potential to solve real business problems that will have a large impact over time. The ultimate goal for any business should be to build machine learning technology into all business software, across industries. Keep in mind, though, that machine learning alone cannot get the job done. Instead, automating certain marketing functions should be approached as a strategy to save the marketer’s time on some of the more manually intensive tasks, such as data mining. This gives employees the freedom to focus their efforts on work that builds a more personal experience for their customers. Eventually, businesses should aim to align both types of communication to create synergy between man and machine.

Jamie Anderson, SAP Hybris, bio

In his current role as Senior Vice President and CMO of SAP Hybris, Jamie is responsible for leading solution marketing, industry marketing, demand generation, field/regional marketing, partner marketing, press, analyst, and influencer relations across the SAP Hybris solutions portfolio.

Jamie is a customer experience and CRM veteran, with nearly 20 years of experience in the customer-facing solutions space. He has worked with many brands in defining, building, and implementing engaging customer experiences. In addition to CRM and other front-office solutions, he has deep expertise in e-commerce, web content management, and social business applications.

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